def saveGPM():
chirpsbox = [-81,-68,-18.5,0] # peru daily
chirpsall_list = glob.glob('/media/ck/Elements/SouthAmerica/GPM/daily/*.nc4')
for ids, ch in enumerate(chirpsall_list):
chirpsall = xr.open_dataset(ch)
#ipdb.set_trace()
if ids ==0:
date = '2016-01-13'
dt = pd.to_datetime(date)
era5 = xr.open_dataset('/media/ck/Elements/SouthAmerica/ERA5/hourly/uv_15UTC/uv_15UTC_1985_peru.nc')
u200 = era5['u'].isel(time=0).sel(level=250).squeeze().load()
u200 = uda.flip_lat(u200)
chirps = chirpsall['HQprecipitation'].T.squeeze()
#ipdb.set_trace()
ch_on_e, lut = u200.salem.lookup_transform(chirps, return_lut=True)
else:
ch_on_e = u200.salem.lookup_transform(chirpsall['HQprecipitation'].T.squeeze(), lut=lut)
ch_on_e.name = 'precip'
comp = dict(zlib=True, complevel=5)
encoding = {'precip': comp}
fname = os.path.basename(ch)
fname = fname.replace('.V06.nc4.SUB.nc4', '_onERA.nc')
#ipdb.set_trace()
ch_on_e.to_netcdf('/media/ck/Elements/SouthAmerica/GPM/daily_onERA/'+fname+'.nc', mode='w', encoding=encoding, format='NETCDF4')
def readERA():
u200orig = xr.open_dataset('/media/ck/Elements/SouthAmerica/ERA5/hourly/u_15UTC_1981-2019_peru_big.nc')
u200orig = uda.flip_lat(u200orig)
datetimes = pd.to_datetime(u200orig.time.values)
newtimes = []
for t in datetimes:
newtimes.append(t.replace(hour=0))
u200orig['time'] = ('time', newtimes)
return u200orig
def saveERA5():
#chirpsbox = [-81, -68, -18.5, 0] # peru daily
era5 = xr.open_mfdataset(cnst.ERA5_HOURLY_PL_HU + '/ERA5_*_pl.nc', concat_dim='time', combine='nested')
u200 = era5['u'].sel(level=850, time=(era5[
'time.hour'] == 9)) # .load() #longitude=slice(bigbox[0], bigbox[1]), latitude=slice(bigbox[3], bigbox[2]),
u200 = uda.flip_lat(u200)
u200 = u200.sel(time=((u200['time.year'] > 1980) & (u200['time.year'] < 2019)))
u200.name = 'u'
comp = dict(zlib=True, complevel=5)
encoding = {'u': comp}
u200.to_netcdf('/media/ck/Elements/SouthAmerica/ERA5/hourly/pressure_levels/u850_09UTC_1981-2018_peru.nc', mode='w',
encoding=encoding, format='NETCDF4')
def saveCHIRPS():
chirpsbox = [-81,-68,-18.5,0] # peru daily
chirpsall = xr.open_dataset(cnst.elements_drive + 'SouthAmerica/CHIRPS/chirps-v2.0.daily.peru.nc').chunk({'time':365})
#chirpsm = xr.open_dataset(cnst.elements_drive + 'SouthAmerica/CHIRPS/chirps-v2.0.monthly.nc')
chirpsall = chirpsall['precip']
date = '2016-01-13'
dt = pd.to_datetime(date)
era5 = xr.open_dataset(cnst.ERA5_HOURLY_PL_HU+'/ERA5_'+str(dt.year)+'_'+str(dt.month).zfill(2)+'_'+str(dt.day).zfill(2)+'_pl.nc')
u200 = era5['u'].sel(longitude=slice(chirpsbox[0], chirpsbox[1]), latitude=slice(chirpsbox[3], chirpsbox[2]), level=200, time=(era5['time.hour']==15)).squeeze().load()
u200 = uda.flip_lat(u200)
chirps = chirpsall.sel(time=date).load().squeeze()
ch_on_e, lut = u200.salem.lookup_transform(chirps, return_lut=True)
ch_on_e_all = u200.salem.lookup_transform(chirpsall, lut=lut)
comp = dict(zlib=True, complevel=5)
encoding = {'precip': comp}
ch_on_e_all.to_netcdf('/media/ck/Elements/SouthAmerica/CHIRPS/CHIRPS_peru_onERA5.nc', mode='w', encoding=encoding, format='NETCDF4')
def saveGRIDSAT():
chirpsbox = [-81,-68,-18.5,0] # peru daily
chirpsall_list = glob.glob(cnst.GRIDSAT_PERU + '/daily_-15ALLkm2_UTC_DAY/*.nc')
for ids, ch in enumerate(chirpsall_list):
fname = os.path.basename(ch)
fname = fname.replace('UTCDay', 'UTCDay_onERA')
outpath = cnst.GRIDSAT_PERU + '/daily_-15ALLkm2_UTC_DAY_onBIGERA/' + fname + '.nc'
if os.path.isfile(outpath):
print('File exists, continue')
continue
chirpsall = xr.open_dataset(ch)
if ids ==0:
date = '2016-01-13'
dt = pd.to_datetime(date)
era5 = xr.open_dataset('/media/ck/Elements/SouthAmerica/ERA5/hourly/uv_15UTC/uv_15UTC_1985_peru.nc')
u200 = era5['u'].sel( level=250, time=(era5['time.hour']==15)).squeeze().load()
u200 = uda.flip_lat(u200)
chirps = chirpsall.isel(time=0).squeeze()
#ipdb.set_trace()
ch_on_e, lut = u200.salem.lookup_transform(chirps, return_lut=True)
else:
ch_on_e = u200.salem.lookup_transform(chirpsall, lut=lut)
#ch_on_e.name = 'tir'
comp = dict(zlib=True, complevel=5)
encoding = {'tir': comp}
fname = os.path.basename(ch)
fname = fname.replace('UTCDay', 'UTCDay_onERA')
#ipdb.set_trace()
ch_on_e.to_netcdf(outpath, mode='w', encoding=encoding, format='NETCDF4')
def rewrite_AMSR2(file):
out = file.replace('raw', 'nc')
#out = out.replace('.nc4', '.nc')
out = out.replace('LPRM-AMSR2', 'AMSR2')
if '_A_' in file:
day = True
else:
day = False
if day:
hour = 13
else:
hour = 1
cut = os.path.basename(out)
path = os.path.dirname(out)
path = path.replace(path[-6::], '')
pieces = cut.split('_')
time = (pieces[5])[0:8]
out = path + os.sep + pieces[0] + '_' + pieces[1] + '_LPRMv05_' + pieces[
2] + '_' + time + '.nc'
#ipdb.set_trace()
if os.path.isfile(out):
return
yr = int(time[0:4])
mon = int(time[4:6])
day = int(time[6:8])
date = [pd.datetime(yr, mon, day, hour, 0)]
ds = xr.open_dataset(file)
ds = u_darrays.flip_lat(ds)
da = xr.DataArray((ds['soil_moisture_c1']).values.T[None, ...],
coords={
'time': date,
'lat': ds.Latitude.values,
'lon': ds.Longitude.values
},
dims=['time', 'lat', 'lon']) # .isel(time=0)
da.values[da.values < -1] = np.nan
if np.sum(da.values) == np.nan:
return
ds = xr.Dataset({'SM': da})
ds = ds.sel(lon=slice(-18, 30),
lat=slice(0, 27)) #lon=slice(-20,55), lat=slice(-40,40) AFRICA
try:
comp = dict(zlib=True, complevel=5)
encoding = {var: comp for var in ds.data_vars}
ds.to_netcdf(path=out, mode='w', encoding=encoding, format='NETCDF4')
except OSError:
print('Did not find ' + out)
print('Out directory not found')
print('Wrote ' + out)
return ds
def trend_all():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mean_mcs = 'aggs/gridsat_WA_-65_monthly_count_-40base_15-21UTC_1000km2.nc'
extreme_mcs = 'aggs/gridsat_WA_-65_monthly_count_-40base_15-21UTC_1000km2.nc'
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-65_monthly_count_-40base_15-21UTC_1000km2.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
box = [5, 55, -36, 0] # [-18,40,0,25] #
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
press = da2['sp']
press = press[press['time.hour'] == 12]
press.values = press.values * 1000
low_press = 850
up_press = 550
q = da['q'].sel(level=slice(low_press - 100, low_press)).mean('level')
q = q[q['time.hour'] == 12]
t2d = da2['t2m'] #['t2m']
#t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour'] == 12]
u600 = da['u'].sel(level=slice(up_press - 100, up_press)).mean('level')
u600 = u600[u600['time.hour'] == 12]
v600 = da['v'].sel(level=slice(up_press - 100, up_press)).mean('level')
v600 = v600[v600['time.hour'] == 12]
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=slice(low_press - 100, low_press)).mean('level')
u800 = u800[u800['time.hour'] == 12]
v800 = da['v'].sel(level=slice(low_press - 100, low_press)).mean('level')
v800 = v800[v800['time.hour'] == 12]
shear_u = u600 - u800 #u600-
shear_v = v600 - v800 # v600-
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
u6 = u600
v6 = v600
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = t2d # grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
grid = grid.to_dataset()
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
months = [
(11, 1), 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
] #[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'mk'
if type(m) == int:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir,
m,
method=method,
sig=sig,
wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
shear_mean = shearmean.mean(axis=0)
u6trend, u6mean = calc_trend(u6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
u6_mean = u6mean.mean(axis=0)
v6trend, v6mean = calc_trend(v6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
v6_mean = v6mean.mean(axis=0)
t2trend_unstacked = t2trend * 10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
u6trend_unstacked = u6trend * 10
v6trend_unstacked = v6trend * 10
tirtrend_unstacked = (
(tirtrend.values) * 10. / tirm_mean.values) * 100.
tirtrend_out = xr.DataArray(tirtrend_unstacked,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
tirmean_out = xr.DataArray(tirm_mean,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirmean_out
t_da = t2trend_unstacked
q_da = qtrend_unstacked
s_da = sheartrend_unstacked
ti_da = tirtrend_unstacked
if len(m) == 1:
fp = fpath + 'trend_synop_SA_-40base1000_-65Ctrend_' + str(
m[0]).zfill(2) + '.png'
else:
fp = fpath + 'trend_synop_SA_-40base1000_-65Ctrend_' + str(
m[0]).zfill(2) + '-' + str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(15, 8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#Quiver only every 7th grid point
u = u6trend_unstacked.values[1::2, 1::2]
v = v6trend_unstacked.values[1::2, 1::2]
#Quiver only every 7th grid point
uu = u6_mean.values[1::2, 1::2]
vv = v6_mean.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
ax1 = f.add_subplot(221)
map.set_data(t_da.values, interp='linear') # interp='linear'
map.set_contour((t2_mean.values - 273.15).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5,
levels=[20, 23, 26, 29, 32, 35])
map.set_plot_params(levels=[
-0.5, -0.4, -0.3, -0.2, -0.1, -0.05, -0.02, 0.02, 0.05, 0.1, 0.2,
0.3, 0.4, 0.5
],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax1,
title='2m temperature trend | contours: mean T',
cbar_title='K decade-1')
qu = ax1.quiver(xx, yy, uu, vv, scale=80, width=0.002)
qk = plt.quiverkey(qu,
0.4,
0.03,
4,
'4 m s$^{-1}$',
labelpos='E',
coordinates='figure')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(222)
map.set_data(q_da.values, interp='linear') # interp='linear'
map.set_contour((q_mean.values).astype(np.float64),
interp='linear',
colors='k',
levels=[6, 8, 10, 12, 14, 16],
linewidths=0.5)
map.set_plot_params(levels=[
-0.4, -0.3, -0.2, -0.1, -0.05, -0.02, 0.02, 0.05, 0.1, 0.2, 0.3,
0.4
],
cmap='RdBu',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(
ax=ax2,
title='800hPa Spec. humidity trend | contours: mean q',
cbar_title='g kg-1 decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(s_da.values, interp='linear') # interp='linear'
map.set_contour(s_da.values,
interp='linear',
levels=np.arange(-7, 7, 8),
cmap='Blues')
map.set_plot_params(levels=[
-0.5, -0.4, -0.3, -0.2, -0.1, -0.05, -0.02, 0.02, 0.05, 0.1, 0.2,
0.3, 0.4, 0.5
],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(
ax=ax3,
title='800-500hPa wind shear trend, mean 500hPa wind vectors',
cbar_title='m s-1 decade-1')
qu = ax3.quiver(xx, yy, uu, vv, scale=80, width=0.002)
qk = plt.quiverkey(qu,
0.4,
0.03,
4,
'4 m s$^{-1}$',
labelpos='E',
coordinates='figure')
ax4 = f.add_subplot(224)
map.set_contour(tirm_mean.values,
interp='linear',
levels=[0.1, 0.5, 1, 2.5],
colors='k',
linewidths=0.5)
ti_da[ti_da == 0] = np.nan
map.set_data(ti_da) #
coord = [18, 25, -28, -20]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
#map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
map.set_plot_params(
cmap='viridis', extend='both', levels=np.arange(
10, 41,
10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
dic = map.visualize(ax=ax4,
title='-65C cloud cover change | >1000km2 -40C',
cbar_title='$\%$ decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
pkl.dump(
dicm,
open(
cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_SA.p',
'wb'))
pkl.dump(
dicmean,
open(
cnst.network_data +
'data/CLOVER/saves/storm_frac_mean_synop12UTC_SA.p', 'wb'))
def trend_all():
srfc = cnst.ERA5_MONTHLY_SRFC_SYNOP #cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA5_MONTHLY_PL_SYNOP #cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/ERA5_WA/'
box=[-18,30,0,25]# [-18,40,0,25] #
da = xr.open_dataset(pl) #xr.open_dataset(pl)
#da = xr.decode_cf(da)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da2 = xr.open_dataset(srfc) #xr.open_dataset(srfc)
#da2 = xr.decode_cf(da2)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da3 = xr.open_dataarray(mcs)*100
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2],box[3]))
lons = da.longitude
lats = da.latitude
press = da2['tcwv']
press = press[press['time.hour'] == 12]
#press.values = press.values#*1000
low_press = 925
up_press = 650
mid_press = 700
q = da['q'].sel(level=slice(low_press-20, low_press)).mean('level')
q = q[q['time.hour']==12]
t2d = da2['sshf']#['t2m']
#t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour']==12]
# theta_low = u_met.theta_e(da.level.values, da['t'].sel(level=low_press), da['q'].sel(level=low_press))
# theta_high = u_met.theta_e(da.level.values, da['t'].sel(level=mid_press), da['q'].sel(level=mid_press))
#
# theta_e = theta_low - theta_high
u600 = da['u'].sel(level=slice(up_press-20, up_press)).mean('level')
u600 = u600[u600['time.hour']==12]
v600 = da['v'].sel(level=slice(up_press-20, up_press)).mean('level')
v600 = v600[v600['time.hour']==12]
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=slice(low_press-20, low_press)).mean('level')
u800 = u800[u800['time.hour']==12]
v800 = da['v'].sel(level=slice(low_press-20, low_press)).mean('level')
v800 = v800[v800['time.hour']==12]
shear_u = u600-u800 #u600-
shear_v = v600-v800 # v600-
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
u6 = shear_u#u800
v6 = shear_v#v800
q.values = q.values*1000
grid = t2d.salem.grid.regrid(factor=1)
t2 = t2d # grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3) #t2d.salem.lookup_transform(da3['tir']) #
grid = grid.to_dataset()
tir = xr.DataArray(tir, coords=[da3['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
months= [4, (3,5), (6,8), (9,11)]#[3,4,5,6,9,10,11]#,4,5,6,9,10,11#,4,5,6,9,10,11,(3,5), (9,11)]#, 10,5,9]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'mk'
if type(m)==int:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir, m, method=method, sig=sig, wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
shear_mean = shearmean.mean(axis=0)
presstrend, pressmean = calc_trend(press, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
press_mean = pressmean.mean(axis=0)
u6trend, u6mean = calc_trend(u6, m, method=method, sig=sig, hour=12,wilks=False) #hour=12,
u6_mean = u6mean.mean(axis=0)
v6trend, v6mean = calc_trend(v6, m, method=method, sig=sig, hour=12,wilks=False) #hour=12,
v6_mean = v6mean.mean(axis=0)
# thetatrend, thetamean = calc_trend(theta_e, m, method=method, sig=sig, hour=12,wilks=False) #hour=12,
# theta_mean = thetamean.mean(axis=0)
t2trend_unstacked = t2trend*10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
u6trend_unstacked = u6trend * 10
v6trend_unstacked = v6trend * 10
presstrend_unstacked = presstrend * 10
# thetatrend_unstacked = thetatrend * 10
tirtrend_unstacked = ((tirtrend.values)*10. / tirm_mean.values) * 100.
#ipdb.set_trace()
tirtrend_out = xr.DataArray(tirtrend_unstacked, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
tirtrend_out.name = 'tir'
#tirmean_out = xr.DataArray(tirm_mean, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirm_mean
t_da = t2trend_unstacked
q_da = qtrend_unstacked
s_da = sheartrend_unstacked
ti_da = tirtrend_out
tcwv_da = presstrend_unstacked
# theta_da = thetatrend_unstacked
if len(m) == 1:
fp = fpath + 'use/ERA5_trend_synop_WA_sig_poly_tcwv_1991_skt_'+str(m[0]).zfill(2)+'.png'
else:
fp = fpath + 'use/ERA5_trend_synop_WA_sig_poly_tcwv_1991_skt_' + str(m[0]).zfill(2) +'-'+ str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map()
ti_da = t2d.salem.transform(ti_da)
f = plt.figure(figsize=(15,8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values, shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#Quiver only every 7th grid point
u = u6trend_unstacked.values[1::2, 1::2]
v = v6trend_unstacked.values[1::2, 1::2]
#Quiver only every 7th grid point
uu = u6_mean.values[1::2, 1::2]
vv = v6_mean.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
pdic = {
'tlin' : (t2_mean.values-273.15).astype(np.float64),
'tmean' : (t2_mean.values-273.15).astype(np.float64),
'qmean' : (q_mean.values).astype(np.float64),
'qlin' : q_da.values,
'shearlin' : s_da.values,
'u' : u,
'v' : v,
'xx' : xx,
'yy' : yy,
'tirmean' : tirm_mean,
}
pkl.dump(dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_WA.p',
'wb'))
ax1 = f.add_subplot(221)
map.set_data(t_da.values, interp='linear') # interp='linear'
map.set_contour((t2_mean.values-273.15).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=[20,23,26,29,32,35])
map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax1, title='2m temperature trend | contours: mean T', cbar_title='K decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(222)
map.set_data(tcwv_da.values,interp='linear') # interp='linear'
map.set_contour((press_mean.values).astype(np.float64),interp='linear', colors='k', levels=[20,30,40,50,60], linewidths=0.5) #[6,8,10,12,14,16]
map.set_plot_params(levels=[-0.8,-0.6,-0.4,-0.2,0.2,0.4, 0.6,0.8], cmap='RdBu', extend='both') # levels=np.arange(-0.5,0.51,0.1), [-0.6,-0.4,-0.2,0.2,0.4,0.6]
dic = map.visualize(ax=ax2, title='925hPa Spec. humidity trend | contours: mean q', cbar_title='g kg-1 decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(s_da.values, interp='linear') # interp='linear'
map.set_contour(s_da.values, interp='linear', levels=np.arange(-15,-8,8), colors='k')
map.set_plot_params(levels=[-0.8,-0.6,-0.4,-0.2,0.2,0.4, 0.6,0.8], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='925-650hPa wind shear trend, mean 650hPa wind vectors', cbar_title='m s-1 decade-1')
qu = ax3.quiver(xx, yy, u, v, scale=60, width=0.002)
qk = plt.quiverkey(qu, 0.4, 0.03, 1, '1 m s$^{-1}$',
labelpos='E', coordinates='figure')
ax4 = f.add_subplot(224)
map.set_contour((tirm_mean), interp='linear', levels=[0.1,0.5,1,2.5], colors='k', linewidths=0.5)
#.values).astype(np.float64)
ti_da.values[ti_da.values==0] = np.nan
map.set_data(ti_da) #
coord = [18, 25, -28, -20]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
#map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,51,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
dic = map.visualize(ax=ax4, title='-70C cloud cover change | >5000km2', cbar_title='$\%$ decade-1', addcbar=True)
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
pkl.dump(dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_WA.p',
'wb'))
pkl.dump(dicmean,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC_WA.p',
'wb'))
def trend_all():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-60_monthly_count_-50base.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
box = [-18, 55, -35, 35] # [-18,40,0,25] #
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800, 850)).mean('level')
q = q[q['time.hour'] == 12]
t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour'] == 12]
u925 = da['u'].sel(level=slice(800, 850)).mean('level')
u925 = u925[u925['time.hour'] == 12]
u600 = da['u'].sel(level=slice(600, 650)).mean('level')
u600 = u600[u600['time.hour'] == 12]
shear = u600 - u925
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
q = grid.lookup_transform(q)
shear = grid.lookup_transform(shear)
# tir = t2d.salem.lookup_transform(da3['tir'])
# t2 = t2d
# #tir = da3['tir']
# q = q
# shear = shear
grid = grid.to_dataset()
t2 = xr.DataArray(t2,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
q = xr.DataArray(q,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
shear = xr.DataArray(shear,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
dicm = {}
dicmean = {}
for m in months:
method = 'mk'
if len([m]) == 1:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2,
m,
hour=12,
method=method,
sig=sig,
wilks=False)
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir,
m,
method=method,
sig=sig,
wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q,
m,
hour=12,
method=method,
sig=sig,
wilks=False)
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear,
m,
hour=12,
method=method,
sig=sig,
wilks=False)
shear_mean = shearmean.mean(axis=0)
t2trend_unstacked = t2trend * 10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
tirtrend_unstacked = (
(tirtrend.values) * 10. / tirm_mean.values) * 100.
tirtrend_out = xr.DataArray(tirtrend_unstacked,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
tirmean_out = xr.DataArray(tirm_mean,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirmean_out
t_da = t2trend_unstacked
q_da = qtrend_unstacked
s_da = sheartrend_unstacked
ti_da = tirtrend_unstacked
fp = fpath + 'trend_mk_-60C_synop_-50base' + str(
m[0]).zfill(2) + '_sig.png'
map = shear.salem.get_map()
# f = plt.figure(figsize=(8, 5), dpi=300)
# ax1 = f.add_subplot(221)
#
# # map.set_shapefile(rivers=True)
# map.set_plot_params()
#
# map.set_data(t_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu_r', extend='both')
# map.visualize(ax=ax1, title='t2')
#
# ax2 = f.add_subplot(222)
# map.set_data(q_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu', extend='both')
# map.visualize(ax=ax2, title='q')
#
# ax3 = f.add_subplot(223)
# map.set_data(s_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-1,1.1,10), cmap='RdBu_r', extend='both')
# map.visualize(ax=ax3, title='u-shear')
#
# ax4 = f.add_subplot(224)
# map.set_data(ti_da)
# map.set_plot_params(cmap='Blues', extend='both', levels=np.arange(20,101,20)) #levels=np.arange(20,101,20)
# map.visualize(ax=ax4, title='-70C frequency')
#
# plt.tight_layout()
# plt.savefig(fp)
# plt.close('all')
f = plt.figure(figsize=(13, 7), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(t_da, interp='linear') # interp='linear'
contours = map.set_contour(t2_mean - 273.15,
interp='linear',
levels=np.arange(24, 37, 4),
cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(levels=np.linspace(-0.5, 0.5, 10),
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='925hP temperature')
ax2 = f.add_subplot(222)
map.set_data(q_da, interp='linear') # interp='linear'
map.set_contour(q_mean,
interp='linear',
levels=np.arange(5, 19, 3),
cmap='inferno')
map.set_plot_params(levels=np.linspace(-0.5, 0.5, 10),
cmap='RdBu',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='925hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(s_da, interp='linear') # interp='linear'
map.set_contour(shear_mean,
interp='linear',
levels=np.arange(-10, 1, 3),
cmap='inferno')
map.set_plot_params(levels=np.linspace(-1, 1, 10),
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='600-925hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(tirm_mean,
interp='linear',
levels=[0.1, 0.5, 1, 2.5],
cmap='inferno')
ti_da[ti_da == 0] = np.nan
map.set_data(ti_da) #
map.set_plot_params(
cmap='viridis', extend='both', levels=np.arange(
10, 51,
10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4,
title='-70C cloud cover change',
cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
pkl.dump(
dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC.p',
'wb'))
pkl.dump(
dicmean,
open(
cnst.network_data +
'data/CLOVER/saves/storm_frac_mean_synop12UTC.p', 'wb'))
def corr_all():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_count.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(-18, 40),
latitude=slice(0, 25)) #latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(-18, 40),
latitude=slice(0, 25)) #latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(-18, 40), lat=slice(0, 25))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(850, 925)).mean(dim='level')
#q = da2['tcwv']
t2 = da['t'].sel(level=925)
u925 = da['u'].sel(level=slice(850, 925)).mean(dim='level')
u600 = da['u'].sel(level=slice(600, 650)).mean(dim='level')
shear = u600 - u925
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
#months=[6,7]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour)
& (data['time.year'] >= 1983) &
(data['time.year'] <= 2013) &
(data['time.year'] != 2003)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983)
& (data['time.year'] <= 2013) &
(data['time.year'] != 2003)]
data_years = data.groupby('time.year').mean(axis=0)
diff = xr.DataArray(
data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
coords=[data_years.year[1::], data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_years
months = [9, 10]
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12)
qdiff, qyear = array_juggling(q, m, hour=12)
shdiff, sheyear = array_juggling(shear, m, hour=12)
tirdiff, tiryear = array_juggling(tir, m)
def corr(a, b):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
bb = b.sel(latitude=lat, longitude=lon)
r, p = stats.pearsonr(aa.values, bb.values)
if np.nansum(aa.values == 0) >= 10:
p = np.nan
r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
return ds
qcorr = corr(tirdiff, qdiff)
shearcorr = corr(tirdiff, shdiff)
tcorr = corr(tirdiff, t2diff)
#pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
#print(pthresh)
pthresh = 0.05
qcorr['r'].values[qcorr['pval'].values > pthresh] = np.nan
#pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
#print(pthresh)
shearcorr['r'].values[shearcorr['pval'].values > pthresh] = np.nan
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
#print(pthresh)
tcorr['r'].values[tcorr['pval'].values > pthresh] = np.nan
fp = fpath + 'corr_synop_' + str(m).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(8, 5), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
map.set_plot_params()
map.set_data(tcorr['r']) # interp='linear'
map.set_contour(t2year.mean('year') - 273.15)
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu',
extend='both')
map.visualize(ax=ax1, title='t2')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r']) # interp='linear'
map.set_contour(qyear.mean('year'))
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu',
extend='both')
map.visualize(ax=ax2, title='q')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r']) # interp='linear'
map.set_contour(sheyear.mean('year'))
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu',
extend='both')
map.visualize(ax=ax3, title='u-shear')
ax4 = f.add_subplot(224)
map.set_data(shearcorr['r']) # interp='linear'
map.set_plot_params(cmap='Blues',
extend='both',
levels=np.arange(20, 101,
20)) #levels=np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C frequency')
plt.savefig(fp)
plt.close('all')
def trend_all():
pl = cnst.ERA5_MONTHLY_PL_SYNOP_HU #cnst.ERA_MONTHLY_PL_SYNOP
#mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc'
fpath = cnst.network_data + 'figs/HUARAZ/monthly/'
box = [-82, -40, -28, 4] # [-18,40,0,25] #
topo = xr.open_dataset(
'/media/ck/Elements/SouthAmerica/ERA5/monthly/ERA5_static_synop_0.7deg.nc'
)
topo = u_darrays.flip_lat(topo)
z = topo['z'].isel(number=0, time=0)
z = z.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],
box[3])).values
da = xr.open_mfdataset(pl + '/*.nc') #xr.open_dataset(pl)
#da = xr.decode_cf(da)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]),
time=(da['time.hour'] == 12)).load()
#ipdb.set_trace()
#da3 = xr.open_dataarray(mcs)*100
#da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2],box[3]))
lons = da.longitude
lats = da.latitude
#ipdb.set_trace()
low_press = 850
up_press = 200
mid_press = 550
gp = da['z'].mean('time') / 9.81
gp_high = da['z'].sel(level=up_press) / 9.81
w_mid = da['w'].sel(level=mid_press)
low_z = gp.sel(level=low_press) > 1400
mid_z = gp.sel(level=mid_press) > 5500
#ipdb.set_trace()
tlow = da['t'].sel(level=low_press).where(low_z) - 273.15
qlow = da['q'].sel(level=low_press).where(low_z) * 1000
tmid = da['t'].sel(level=mid_press) - 273.15 #.where(mid_z)-273.15
qmid = da['q'].sel(level=mid_press) * 1000 #.where(mid_z)*1000
theta_low = u_met.theta_e(low_press, tlow, qlow)
theta_high = u_met.theta_e(mid_press, tmid, qmid)
theta_e = theta_low - theta_high
u600 = da['u'].sel(level=up_press) #.where(mid_z)
v600 = da['v'].sel(level=up_press) #.where(mid_z)
u600.name = 'u200'
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=mid_press) # 200-500 shear
v800 = da['v'].sel(level=mid_press)
shear_u = u600 - u800
shear_v = v600 - v800
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = u600.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = u600.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
vars = [
't600', 'q600', 'shear', 'q550', 'u200', 'v200', 'u550', 'v550', 'gp',
'w550'
]
data = [tmid, qmid, shear, qmid, u600, v600, u800, v800, gp_high, w_mid]
months = [
11
] #,6]#,2,3,4,5,6,7,8,9,10,11,12]#[3,4,5,6,9,10,11]#,4,5,6,9,10,11#,4,5,6,9,10,11,(3,5), (9,11)]#, 10,5,9]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
#ipdb.set_trace()
for m in months:
dic = {}
for v in vars:
dic[v] = 0
if type(m) == int:
m = [m]
for v, dat in zip(vars, data):
print('Doing ', v)
dic[v] = get_trend(dat, m, sig=True, wilks=False, method='mk')
if len(m) == 1:
fp = fpath + 'low_ERA5_trend_synop_HU_poly_quatro_' + str(
m[0]).zfill(2) + '.png'
else:
fp = fpath + 'low_ERA5_trend_synop_HU_poly_quatro_' + str(
m[0]).zfill(2) + '-' + str(m[1]).zfill(2) + '.png'
#ipdb.set_trace()
map = shear.salem.get_map()
# ti_da = t2d.salem.transform(ti_da)
f = plt.figure(figsize=(12, 5), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xo, yo = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xH, yH = map.grid.transform(-77.52, -9.52, crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xo, yo)
ss = 3
#Quiver only every 7th grid point
u = (dic['u200'][0]).values[1::ss, 1::ss] # 200hpa
v = (dic['v200'][0]).values[1::ss, 1::ss]
#Quiver only every 7th grid point
uu = (dic['u200'][1]).values[1::ss, 1::ss] # 200mean
vv = (dic['v200'][1]).values[1::ss, 1::ss]
u500 = (dic['u550'][0]).values[1::ss, 1::ss]
v500 = (dic['v550'][0]).values[1::ss, 1::ss]
#Quiver only every 7th grid point
uu500 = (dic['u550'][1]).values[1::ss, 1::ss]
vv500 = (dic['v550'][1]).values[1::ss, 1::ss]
xx = xx[1::ss, 1::ss]
yy = yy[1::ss, 1::ss]
ax1 = f.add_subplot(121)
map.set_data((dic['t600'][0]).values,
interp='linear') # interp='linear'
map.set_contour(((dic['t600'][1]).values).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5)
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.5, -0.4, -0.3, -0.2, 0.2, 0.3, 0.4, 0.5]
) #levels=[-0.5,-0.4,-0.3,-0.2,0.2,0.3,0.4,0.5], , levels=np.arange(-0.5,0.51,0.1),
ax1.plot(xH, yH, 'ko', markersize=7)
ax1.plot(xH, yH, 'wo', markersize=5)
ax1.text(xH, yH + 10, 'Huaraz', fontsize=11)
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
cc = ['b', 'r', 'k']
tags = ['1985-1994', '1995-2004', '2005-2014']
for ids, pp in enumerate((dic['gp'][2])):
xh, yh = map.grid.transform(pp[0],
pp[1],
crs=shear.salem.grid.proj)
if ids < 2:
ax1.plot(xh - 80,
yh - 130,
color=cc[ids],
marker='x',
markersize=10,
label=tags[ids])
else:
ax1.plot(xh,
yh,
color=cc[ids],
marker='x',
markersize=10,
label=tags[ids])
#ax1.text(xh + 10, yh, tags[ids], fontsize=9)
plt.legend(loc='upper left')
pdic = map.visualize(
ax=ax1,
title='550hpa T trend (shading) and mean (contour)',
cbar_title='K decade-1')
contours = pdic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(122)
map.set_data((dic['u200'][0]).values,
interp='linear') # interp='linear'
#map.set_contour(u6_mean.values, interp='linear', colors='k')
map.set_contour()
ax3.plot(xH, yH, 'ko', markersize=7)
ax3.plot(xH, yH, 'wo', markersize=5)
map.set_plot_params(
levels=[-3, -2.5, -2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2, 2.5, 3],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3,
title='200hPa wind trend (shading) and mean (vectors)',
cbar_title='m s-1 decade-1')
qu = ax3.quiver(xx, yy, uu, vv, scale=100, width=0.002)
cc = ['b', 'r', 'k']
tags = ['1985-1994', '1995-2004', '2005-2014']
for ii in range(3):
use = (dic['u200'][2]).values[ii, :]
for id, xpos in enumerate(xo):
#ipdb.set_trace()
ax3.plot(xpos,
yo[use[id]],
color=cc[ii],
marker='o',
markersize=4,
label=tags[ii])
# if id == 15:
# ax3.text(xpos + 10, yo[use[id]], tags[ii], fontsize=9)
#plt.legend(loc='upper right')
qk = plt.quiverkey(qu,
0.9,
0.03,
4,
'4 m s$^{-1}$',
labelpos='E',
coordinates='figure')
ax1.text(14, -60,
'(x) "Bolivian High centre" (max. 200hPa geopotential)')
ax3.text(14, -60, '(o) Area of easterlies')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP # _SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-65_monthly_count_-40base_1000km2.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/'
box = [-10, 55, -35, 0] #[-18,55,-35,35]#[-10,55,-35,0]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800)).mean('level')
#q = q[q['time.hour']==12]
t2 = da2['t2m'] #.sel(level=slice(800)).mean('level')
#t2 = t2[t2['time.hour']==12]
u925 = da['u'].sel(level=slice(800)).mean('level')
#u925 = u925[u925['time.hour']==12]
u600 = da['u'].sel(level=slice(500, 550)).mean('level')
v600 = da['v'].sel(level=slice(500, 550)).mean('level')
shear = u925 # u600-
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
months = [1, 12]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour)
& (data['time.year'] >= 1983) &
(data['time.year'] <= 2017)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983)
& (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(
data_years.values,
coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
#corr_box = [-10,11,4.5,8]
corr_box = [16, 24, -24, -18] #West: [13,25,-23,-10]
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]),
longitude=slice(
corr_box[0],
corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
pf = np.polyfit(aa.values, bb, 1)
else:
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
# if (np.nansum(aa.values == 0) >= 10):
# p = np.nan
# r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12) #
qdiff, qyear = array_juggling(q, m, hour=12) #, hour=12
shdiff, sheyear = array_juggling(shear, m, hour=12) #, hour=12
vdiff, vyear = array_juggling(v600, m, hour=12) # , hour=12
udiff, uyear = array_juggling(u600, m, hour=12) # , hour=12
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
mcs_month = mcs_temp[mcs_temp['time.month'] ==
m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs)
tcorr = corr(t2diff, tirdiff, bsingle=bs)
# pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
# print(pthresh)
pthresh = 0.05
#cloud['slope'].values[cloud['pval'].values > pthresh] = np.nan
#qcorr['r'].values[qcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#shearcorr['r'].values[shearcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#tcorr['r'].values[tcorr['pval'].values > pthresh] = 0
dicm[m].values[dicm[m].values == 0] = np.nan
print('plot')
fp = fpath + 'corr_box_SYNOP_SAWest_-50base_' + str(m).zfill(
2) + '.png'
map = shear.salem.get_map()
#xx, yy = map.grid.xy_coordinates
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#ipdb.set_trace()
f = plt.figure(figsize=(15, 8), dpi=200)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(tcorr['r'], interp='linear') # interp='linear'
contours = map.set_contour(t2year - 273.15,
interp='linear',
levels=np.arange(24, 37, 4),
cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6,
0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='2m temperature')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'], interp='linear') # interp='linear'
map.set_contour(qyear,
interp='linear',
levels=np.arange(5, 19, 3),
cmap='inferno')
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6,
0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='800hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6, 0.7])
map.set_contour(sheyear,
interp='linear',
levels=np.arange(-10, 1, 6),
cmap='inferno')
cs = ax3.contour(sheyear,
interp='linear',
levels=np.arange(-10, 1, 6),
cmap='inferno')
plt.clabel(cs, inline=1, fontsize=10)
# Quiver only every 7th grid point
u = uyear[4::7, 4::7]
v = vyear[4::7, 4::7]
xx = xx[4::7, 4::7]
yy = yy[4::7, 4::7]
qu = ax3.quiver(xx, yy, u.values, v.values, scale=50)
# levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='500-800hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m],
interp='linear',
levels=[0.1, 0.5, 1, 2.5],
cmap='inferno')
map.set_data(dicm[m]) #
#ax4.axhspan(-26,18) #[15,25,-26,-18]
coord = [16, 24, -24, -18]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
map.set_geometry(geom,
zorder=99,
color='darkorange',
linewidth=3,
linestyle='--',
alpha=0.3)
# ax4.axvline(x=25, ymin=-26, ymax=-18)
# ax4.axhline(y=-26, xmin=15, xmax=25)
# ax4.axhline(y=-18, xmin=15, xmax=25)
map.set_plot_params(
cmap='viridis', extend='both', levels=np.arange(
10, 51,
10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4,
title='-65C cloud cover change',
cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def tgrad_shear_trend():
srfc = cnst.ERA_MONTHLY_SRFC
pl = cnst.ERA_MONTHLY_PL
mcs = cnst.GRIDSAT + 'aggs/box_13W-13E-4-8N_meanT-50_from5000km2.nc' #box_25-33E-28-10S_meanT-50_from5000km2.nc'#box_13W-13E-4-8N_meanT-50_from5000km2.nc'
out = cnst.network_data + 'figs/CLOVER/timeseries/'
#
box = [-10,10,5.5,8]
tpick = [-10,10,6,25]
Tlons = [-10,10]
# box = [18,30,-22,-10]
# tpick = [18,30,-30,-20]
# Tlons = [18,30]
dam = xr.open_dataset(srfc)
dam = u_darrays.flip_lat(dam)
dam = dam['t2m']
tgrad = dam.sel(longitude=slice(tpick[0], tpick[1]), latitude=slice(tpick[2],tpick[3])).mean('longitude').groupby('time.month').mean('time')
Tgrad_lat = []
for tgrad_ts in tgrad:
ttgrad = np.argmax(tgrad_ts.values[2::] - tgrad_ts.values[0:-2])
lat_pos = tgrad.latitude[ttgrad+1]
Tgrad_lat.append(float(lat_pos))
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))#latitude=slice(36, -37))
u925 = da['u'].sel(level=925)#(level=slice(850, 925)).mean(dim='level')#).mean(dim='level') #slice(850
u600 = da['u'].sel(level=700)#(level=slice(550, 650)).mean(dim='level')
qq925 = da['q'].sel(level=slice(850,925)).mean(dim='level')
qq600 = da['q'].sel(level=slice(550, 650)).mean(dim='level')
mcs_temp = xr.open_dataset(mcs)
mcs_temp = mcs_temp['tir']
months = [1,2,3,4,5,6,7,8,9,10,11,12]
mnames = {1:'January',2:'February', 3 : 'March', 4 : 'April', 5 : 'May', 6:'June', 7:'July', 8:'August', 9 : 'September', 10 : 'October', 11:'November', 12:'December'}
mcs_change = []
shear_change = []
tgrad_change = []
for m in months:
tsouthlice = slice(Tgrad_lat[m-1]-1.25, Tgrad_lat[m-1]-0.25)
tnorthslice = slice(Tgrad_lat[m-1]+0.25, Tgrad_lat[m-1]+1.25)
tsouth = dam.sel(longitude=slice(Tlons[0], Tlons[1]), latitude=slice(5,8))
tnorth = dam.sel(longitude=slice(Tlons[0], Tlons[1]), latitude=slice(11,18))
south = tsouth[(tsouth['time.month']==m)]
north = tnorth[(tnorth['time.month']==m)]
ulow = u925[(u925['time.month']==m)]
uhigh = u600[(u600['time.month']==m)]
mcs_month = mcs_temp[mcs_temp['time.month']==m]#*(-1)
qlow = qq925[(qq925['time.month']==m)]
qmid = qq600[(qq600['time.month'] == m)]
##da = da.sel(longitude=slice(-18,51), latitude=slice(36, -37))
south_peryear = south.groupby('time.year').mean('longitude').min('latitude')
north_peryear = north.groupby('time.year').mean('longitude').max('latitude')
u925_peryear = ulow.groupby('time.year').mean('longitude').max('latitude') #ulow.groupby('time.year').mean()
u600_peryear = uhigh.groupby('time.year').mean('longitude').min('latitude')#.mean() # ('latitude').min()
qlow_peryear = qlow.groupby('time.year').mean('longitude').max('latitude')#.mean() # ('latitude').min()
qmid_peryear = qmid.groupby('time.year').mean('longitude').max('latitude')
tgrad = ((north_peryear-south_peryear)[4::])
shear = (u600_peryear-u925_peryear)[4::] # -q_peryear[4::]#
q = qlow_peryear[4::]*1000
r = stats.pearsonr(shear.values[1::]-shear.values[0:-1],mcs_month.values[1::]-mcs_month.values[0:-1])
rq = stats.pearsonr(q.values[1::]-q.values[0:-1],mcs_month.values[1::]-mcs_month.values[0:-1])
tshear_cor = stats.pearsonr(shear.values[1::]-shear.values[0:-1],tgrad.values[1::]-tgrad.values[0:-1])
#sslope, sint = ustats.linear_trend(shear)
sslope, sint = ustats.linear_trend(shear)
sslope, sint, sr, sp, sstd_err = stats.linregress(np.arange(len(shear)), shear)
try:
#mslope, mint = ustats.linear_trend(mcs_month)
mslope, mint, mrr, mp, mstd_err = stats.linregress(np.arange(len(mcs_month)), mcs_month)
except:
continue
mr = stats.pearsonr(np.arange(len(mcs_month)), mcs_month)
print(m, mr, mrr, mslope)
sig = 'SIG'
if mr[1]>0.05:
#continue
sig = 'not_sig'
mcs_change.append(mcs_month[-5::].mean()-mcs_month[0:5].mean())
shear_change.append(shear[-5::].mean()-shear[0:5].mean())
tgrad_change.append(tgrad[-5::].mean()-tgrad[0:5].mean())
x = np.arange(0,len(shear))
rr = r[0]
f=plt.figure(figsize=(6,3))
ax = f.add_subplot(111)
ax.plot(tgrad.year, shear, 'x-', label='Zonal wind shear 600-925hPa', color='k')
ax.plot(tgrad.year, sint + x*sslope, '--', color='k')
ax.text(0.5,0.8, 'MCS: '+ sig +'_' + str(np.round(mslope,decimals=2)*10) + ' p-val: ' + str(np.round(mp, decimals=2)), transform=ax.transAxes)
ax.set_ylim(-8,0)
#ax.set_ylim(9.8, 10)
ax1 = ax.twinx()
ax1.plot(mcs_month['time.year'],mcs_month, 'o-', label='Mean MCS temp.', color='r')
ax1.plot(tgrad.year, mint + x*mslope, '--', color='r')
mcsline = lines.Line2D([],[], color='r', label='Mean MCS temp.', linestyle='solid', marker='o')
shearline = lines.Line2D([],[], color='k', label='Zonal wind shear 600-925hPa', linestyle='solid', marker='x', markersize=5)
ax1.set_ylabel('degC')
ax.set_ylabel('m s-1')
ax.set_title(mnames[m]+' | Corr.:'+ str(np.round(rr, decimals=2)) +'|' + str(np.round(rq[0], decimals=2)) + '| Tgrad/Shear corr: ' + str(np.round(tshear_cor[0], decimals=2)))
if m==3:
ax.legend(handles=[mcsline, shearline], loc='lower left')
f.savefig(out + 'timeseries_WA'+str(m)+'.png')
plt.close('all')
def trend_all():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-65_monthly_count_-40base_15-21UTC_1000km2.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
box=[5,55,-36,0]# [-18,40,0,25] #
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2],box[3]))
lons = da.longitude
lats = da.latitude
press = da2['sp']
press = press[press['time.hour'] == 12]
press.values = press.values*1000
low_press = 850
up_press = 550
q = da['q'].sel(level=slice(low_press-100, low_press)).mean('level')
q = q[q['time.hour']==12]
t2d = da2['t2m']#['t2m']
#t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour']==12]
u600 = da['u'].sel(level=slice(up_press-100, up_press)).mean('level')
u600 = u600[u600['time.hour']==12]
v600 = da['v'].sel(level=slice(up_press-100, up_press)).mean('level')
v600 = v600[v600['time.hour']==12]
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=slice(low_press-100, low_press)).mean('level')
u800 = u800[u800['time.hour']==12]
v800 = da['v'].sel(level=slice(low_press-100, low_press)).mean('level')
v800 = v800[v800['time.hour']==12]
shear_u = u600-u800 #u600-
shear_v = v600-v800 # v600-
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
u6 = u600
v6 = v600
q.values = q.values*1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = t2d # grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
grid = grid.to_dataset()
tir = xr.DataArray(tir, coords=[da3['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
months= [(11,1), 2,3,10]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'mk'
if type(m)==int:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir, m, method=method, sig=sig, wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear, m, method=method, sig=sig,hour=12, wilks=False) #hour=12,
shear_mean = shearmean.mean(axis=0)
u6trend, u6mean = calc_trend(u6, m, method=method, sig=sig, hour=12,wilks=False) #hour=12,
u6_mean = u6mean.mean(axis=0)
v6trend, v6mean = calc_trend(v6, m, method=method, sig=sig, hour=12,wilks=False) #hour=12,
v6_mean = v6mean.mean(axis=0)
t2trend_unstacked = t2trend*10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
u6trend_unstacked = u6trend * 10
v6trend_unstacked = v6trend * 10
tirtrend_unstacked = ((tirtrend.values)*10. / tirm_mean.values) * 100.
tirtrend_out = xr.DataArray(tirtrend_unstacked, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
tirmean_out = xr.DataArray(tirm_mean, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirmean_out
t_da = t2trend_unstacked
q_da = qtrend_unstacked
s_da = sheartrend_unstacked
ti_da = tirtrend_unstacked
if len(m) == 1:
fp = fpath + 'trend_synop_'+str(m[0]).zfill(2)+'.png'
else:
fp = fpath + 'trend_synop_' + str(m[0]).zfill(2) +'-'+ str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(15,8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values, shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#Quiver only every 7th grid point
u = u6trend_unstacked.values[1::2, 1::2]
v = v6trend_unstacked.values[1::2, 1::2]
#Quiver only every 7th grid point
uu = u6_mean.values[1::2, 1::2]
vv = v6_mean.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
ax1 = f.add_subplot(221)
map.set_data(t_da.values, interp='linear') # interp='linear'
map.set_contour((t2_mean.values-273.15).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=[20,23,26,29,32,35])
map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax1, title='2m temperature trend | contours: mean T', cbar_title='K decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(222)
map.set_data(q_da.values,interp='linear') # interp='linear'
map.set_contour((q_mean.values).astype(np.float64),interp='linear', colors='k', levels=[6,8,10,12,14,16], linewidths=0.5)
map.set_plot_params(levels=[-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4], cmap='RdBu', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax2, title='800hPa Spec. humidity trend | contours: mean q', cbar_title='g kg-1 decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(s_da.values, interp='linear') # interp='linear'
map.set_contour(s_da.values, interp='linear', levels=np.arange(-7,7,8), cmap='Blues')
map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='800-500hPa wind shear trend, mean 500hPa wind vectors', cbar_title='m s-1 decade-1')
qu = ax3.quiver(xx, yy, uu, vv, scale=80, width=0.002)
qk = plt.quiverkey(qu, 0.4, 0.03, 4, '4 m s$^{-1}$',
labelpos='E', coordinates='figure')
ax4 = f.add_subplot(224)
map.set_contour(tirm_mean.values, interp='linear', levels=[0.1,0.5,1,2.5], colors='k', linewidths=0.5)
ti_da[ti_da==0] = np.nan
map.set_data(ti_da) #
coord = [18, 25, -28, -20]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
#map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,41,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
dic = map.visualize(ax=ax4, title='-65C cloud cover change | >1000km2 -40C', cbar_title='$\%$ decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
pkl.dump(dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_SA.p',
'wb'))
pkl.dump(dicmean,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC_SA.p',
'wb'))
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP # _SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-65_monthly_count_-40base_1000km2.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/'
box=[-10,55,-35,0]#[-18,55,-35,35]#[-10,55,-35,0]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800)).mean('level')
#q = q[q['time.hour']==12]
t2 = da2['t2m']#.sel(level=slice(800)).mean('level')
#t2 = t2[t2['time.hour']==12]
u925 = da['u'].sel(level=slice(800)).mean('level')
#u925 = u925[u925['time.hour']==12]
u600 = da['u'].sel(level=slice(500,550)).mean('level')
v600 = da['v'].sel(level=slice(500,550)).mean('level')
shear = u925 # u600-
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
months = [1,12]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour) & (data['time.year'] >= 1983)& (
data['time.year'] <= 2017)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(data_years.values, coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
#corr_box = [-10,11,4.5,8]
corr_box = [16,24,-24,-18]#West: [13,25,-23,-10]
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]), longitude=slice(corr_box[0], corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
pf = np.polyfit(aa.values, bb, 1)
else:
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
# if (np.nansum(aa.values == 0) >= 10):
# p = np.nan
# r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12) #
qdiff, qyear = array_juggling(q, m, hour=12) #, hour=12
shdiff, sheyear = array_juggling(shear, m, hour=12) #, hour=12
vdiff, vyear = array_juggling(v600, m, hour=12) # , hour=12
udiff, uyear = array_juggling(u600, m, hour=12) # , hour=12
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
mcs_month = mcs_temp[mcs_temp['time.month'] == m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs)
tcorr = corr(t2diff, tirdiff, bsingle=bs)
# pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
# print(pthresh)
pthresh = 0.05
#cloud['slope'].values[cloud['pval'].values > pthresh] = np.nan
#qcorr['r'].values[qcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#shearcorr['r'].values[shearcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#tcorr['r'].values[tcorr['pval'].values > pthresh] = 0
dicm[m].values[dicm[m].values==0] = np.nan
print('plot')
fp = fpath + 'corr_box_SYNOP_SAWest_-50base_' + str(m).zfill(2) + '.png'
map = shear.salem.get_map()
#xx, yy = map.grid.xy_coordinates
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values, shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx,yy)
#ipdb.set_trace()
f = plt.figure(figsize=(15,8), dpi=200)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(tcorr['r'], interp='linear') # interp='linear'
contours = map.set_contour(t2year-273.15, interp='linear', levels=np.arange(24,37,4), cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='2m temperature')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'],interp='linear') # interp='linear'
map.set_contour(qyear,interp='linear', levels=np.arange(5,19,3), cmap='inferno')
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='800hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
map.set_plot_params(cmap='RdBu_r', extend='both', levels=[-0.7,-0.6,-0.5,-0.4, 0.4,0.5,0.6,0.7])
map.set_contour(sheyear, interp='linear', levels=np.arange(-10,1,6), cmap='inferno')
cs = ax3.contour(sheyear, interp='linear', levels=np.arange(-10,1,6), cmap='inferno')
plt.clabel(cs, inline=1, fontsize=10)
# Quiver only every 7th grid point
u = uyear[4::7, 4::7]
v = vyear[4::7, 4::7]
xx = xx[4::7, 4::7]
yy = yy[4::7, 4::7]
qu = ax3.quiver(xx, yy, u.values, v.values, scale=50)
# levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='500-800hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m], interp='linear', levels=[0.1,0.5,1,2.5], cmap='inferno')
map.set_data(dicm[m]) #
#ax4.axhspan(-26,18) #[15,25,-26,-18]
coord = [16,24,-24,-18]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
# ax4.axvline(x=25, ymin=-26, ymax=-18)
# ax4.axhline(y=-26, xmin=15, xmax=25)
# ax4.axhline(y=-18, xmin=15, xmax=25)
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,51,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4, title='-65C cloud cover change', cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP # _SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-60_monthly_count_-50base.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/'
dicm = pkl.load(
open(
cnst.network_data +
'data/CLOVER/saves/storm_frac_synop12UTC_test.p', 'rb'))
dicmean = pkl.load(
open(
cnst.network_data +
'data/CLOVER/saves/storm_frac_mean_synop12UTC_test.p', 'rb'))
mcsbox = cnst.GRIDSAT + 'aggs/box_12-25E-23-10S_meanT-50_from5000km2_SA.nc' # box_13W-13E-4-8N_meanT-50_from5000km2.nc'
mcs_temp = xr.open_dataset(mcsbox)
mcs_temp = mcs_temp['tir']
box = [-18, 55, -35, 35]
#box = [-18, 40, 0, 25]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800, 850)).mean('level')
#q = q[q['time.hour']==12]
t2 = da['t'].sel(level=slice(800, 850)).mean('level')
#t2 = t2[t2['time.hour']==12]
u925 = da['u'].sel(level=slice(800, 850)).mean('level')
#u925 = u925[u925['time.hour']==12]
u600 = da['u'].sel(level=slice(600, 650)).mean('level')
#u600 = u600[u600['time.hour']==12]
shear = u600 - u925 # u600-
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour)
& (data['time.year'] >= 1983) &
(data['time.year'] <= 2017)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983)
& (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(
data_years.values,
coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
#corr_box = [-10,11,4.5,8]
corr_box = [13, 25, -23, -10]
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]),
longitude=slice(
corr_box[0],
corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
pf = np.polyfit(aa.values, bb, 1)
else:
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
# if (np.nansum(aa.values == 0) >= 10):
# p = np.nan
# r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12) #
qdiff, qyear = array_juggling(q, m, hour=12) #, hour=12
shdiff, sheyear = array_juggling(shear, m, hour=12) #, hour=12
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
mcs_month = mcs_temp[mcs_temp['time.month'] ==
m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs)
tcorr = corr(t2diff, tirdiff, bsingle=bs)
# pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
# print(pthresh)
pthresh = 0.05
#cloud['slope'].values[cloud['pval'].values > pthresh] = np.nan
#qcorr['r'].values[qcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#shearcorr['r'].values[shearcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#tcorr['r'].values[tcorr['pval'].values > pthresh] = 0
dicm[m].values[dicm[m].values == 0] = np.nan
print('plot')
fp = fpath + 'corr_box_SYNOP_SAWest_-50base' + str(m).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(13, 7), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(tcorr['r'], interp='linear') # interp='linear'
contours = map.set_contour(t2year - 273.15,
interp='linear',
levels=np.arange(24, 37, 4),
cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6,
0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='925hP temperature')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'], interp='linear') # interp='linear'
map.set_contour(qyear,
interp='linear',
levels=np.arange(5, 19, 3),
cmap='inferno')
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6,
0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='925hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
map.set_plot_params(
cmap='RdBu_r',
extend='both',
levels=[-0.7, -0.6, -0.5, -0.4, 0.4, 0.5, 0.6, 0.7])
map.set_contour(sheyear,
interp='linear',
levels=np.arange(-10, 1, 3),
cmap='inferno')
# levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='600-925hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m],
interp='linear',
levels=[0.1, 0.5, 1, 2.5],
cmap='inferno')
map.set_data(dicm[m]) #
map.set_plot_params(
cmap='viridis', extend='both', levels=np.arange(
10, 51,
10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4,
title='-70C cloud cover change',
cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-65_monthly_count_-40base_15-21UTC_1000km2.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/'
dicm = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_SA.p', 'rb'))
dicmean = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC_SA.p', 'rb'))
# mcsbox = cnst.GRIDSAT + 'aggs/SAboxWest_meanT-40_1000km2.nc' # box_13W-13E-4-8N_meanT-50_from5000km2.nc'
# mcs_temp = xr.open_dataset(mcsbox)
# mcs_temp = mcs_temp['tir']
box=[5,55,-36,0]#[-18,55,-35,35]#[-10,55,-35,0]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
press = da2['sp']
press = press[press['time.hour'] == 12]
press.values = press.values*1000
low_press = 850
up_press = 550
q = da['q'].sel(level=slice(low_press-50, low_press)).mean('level')
q = q[q['time.hour']==12]
t2d = da2['t2m']#['t2m']
#t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour']==12]
u600 = da['u'].sel(level=slice(up_press-50, up_press)).mean('level')
u600 = u600[u600['time.hour']==12]
v600 = da['v'].sel(level=slice(up_press-50, up_press)).mean('level')
v600 = v600[v600['time.hour']==12]
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=slice(low_press-50, low_press)).mean('level')
u800 = u800[u800['time.hour']==12]
v800 = da['v'].sel(level=slice(low_press-50, low_press)).mean('level')
v800 = v800[v800['time.hour']==12]
shear_u = u600-u800 #u600-
shear_v = v600-v800 # v600-
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
q.values = q.values * 1000
tir = da3['tir']
tir = t2d.salem.lookup_transform(tir)
def array_juggling(data, month, hour=None):
m = month
if hour is not None:
if len(month) > 1:
data = data[((data['time.month'] >= month[0]) | (data['time.month'] <= month[1])) & (
data['time.hour'] == hour) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
else:
data = data[
(data['time.month'] == month[0]) & (data['time.hour'] == hour) & (data['time.year'] >= 1983) & (
data['time.year'] <= 2017)]
else:
if len(month) > 1:
data = data[((data['time.month'] >= month[0]) | (data['time.month'] <= month[1])) & (
data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
else:
data = data[
(data['time.month'] == month[0]) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(data_years.values, coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None, c_box=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
corr_box = c_box
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]), longitude=slice(corr_box[0], corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
pf = np.polyfit(aa.values, bb, 1)
else:
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
if (np.nansum(aa.values == 0) >= 10):
p = np.nan
r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
box_dic = {
1 : [16,30,-25,-20],
2 : [12,28,-10,3],
3: [16,25,-23,-18],
4: [16,25,-23,-18],
10 : [14,28,-15,-5],
11 : [16,30,-20,-10],
12 : [16,30,-22,-12],
(11,1) : [18,30,-23,-18]
}
months = [1,2,3,4,10,11,12]
months = [10]
months = [(11,1),2,3,10]
for m in months:
c_box = box_dic[m]
if type(m)==int:
m = [m]
t2diff, t2year = array_juggling(t2d, [10]) #
qdiff, qyear = array_juggling(q, m) #, hour=12
shdiff, sheyear = array_juggling(shear, m) #, hour=12
vdiff, vyear = array_juggling(v600, m) # , hour=12
udiff, uyear = array_juggling(u600, m) # , hour=12
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
#mcs_month = mcs_temp[mcs_temp['time.month'] == m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs, c_box=c_box)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs, c_box=c_box)
tcorr = corr(t2diff, tirdiff, bsingle=bs, c_box=c_box)
dicm[m[0]].values[dicm[m[0]].values==0] = np.nan
print('plot')
if len(m) == 1:
fp = fpath + 'corr_mid_-70C_synop_-50base_linear_OCT_'+str(m[0]).zfill(2)+'.png'
else:
fp = fpath + 'corr_mid_-70C_synop_-50base_linear_OCT_' + str(m[0]).zfill(2) +'-'+ str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map()
xx, yy = map.grid.transform(shear.longitude.values, shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
# Quiver only every 7th grid point
u = uyear.values[1::2, 1::2]
v = vyear.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
#ipdb.set_trace()
f = plt.figure(figsize=(15,8), dpi=350)
ax1 = f.add_subplot(221)
map.set_data(tcorr['r'], interp='linear') # interp='linear'
map.set_contour(t2year-273.15, interp='linear', levels=np.arange(24,37,4),colors='k', linewidths=0.5)
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,-0.3,0.3,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax1, title='2m temperature corr. | contours: mean T', cbar_title='K decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'],interp='linear') # interp='linear'
map.set_contour(qyear,interp='linear', levels=np.arange(5,19,3), colors='k', linewidths=0.5)
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,-0.3,0.3,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax2, title='800hPa Spec. humidity corr. | contours: mean q', cbar_title='g kg-1 decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
map.set_plot_params(cmap='RdBu_r', extend='both', levels=[-0.7,-0.6,-0.5,-0.4, -0.3,0.3,0.4,0.5,0.6,0.7])
#map.set_contour(sheyear, interp='linear', levels=np.arange(-10,1,6), colors='k', linewidths=0.5)
qu = ax3.quiver(xx, yy, u, v, scale=50, width=0.002)
qk = plt.quiverkey(qu, 0.4, 0.03, 4, '4 m s$^{-1}$',
labelpos='E', coordinates='figure')
# levels=np.arange(-0.5,0.51,0.1)
dic = map.visualize(ax=ax3, title='800-500hPa wind shear corr., mean 500hPa wind vectors', cbar_title='m s-1 decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m[0]], interp='linear', levels=[0.1,0.5,1,2.5], colors='k', linewidths=0.5)
map.set_data(dicm[m[0]]) #
#ax4.axhspan(-26,18) #[15,25,-26,-18]
coord = c_box#[17, 25, -28, -22]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
# ax4.axvline(x=25, ymin=-26, ymax=-18)
# ax4.axhline(y=-26, xmin=15, xmax=25)
# ax4.axhline(y=-18, xmin=15, xmax=25)
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,41,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
dic = map.visualize(ax=ax4, title='-65C cloud cover change | >1000km2 -40C', cbar_title='$\%$ decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def trend_all():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-60_monthly_count_-50base.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
box=[-18,55,-35,35]# [-18,40,0,25] #
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],box[3]))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2],box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800,850)).mean('level')
q = q[q['time.hour']==12]
t2d = da['t'].sel(level=slice(800,850)).mean('level')
t2d = t2d[t2d['time.hour']==12]
u925 = da['u'].sel(level=slice(800,850)).mean('level')
u925 = u925[u925['time.hour']==12]
u600 = da['u'].sel(level=slice(600,650)).mean('level')
u600 = u600[u600['time.hour']==12]
shear = u600-u925
q.values = q.values*1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
q = grid.lookup_transform(q)
shear = grid.lookup_transform(shear)
# tir = t2d.salem.lookup_transform(da3['tir'])
# t2 = t2d
# #tir = da3['tir']
# q = q
# shear = shear
grid = grid.to_dataset()
t2 = xr.DataArray(t2, coords=[t2d['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
q = xr.DataArray(q, coords=[t2d['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
tir = xr.DataArray(tir, coords=[da3['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
shear = xr.DataArray(shear, coords=[t2d['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
months=[1,2,3,4,5,6,7,8,9, 10,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'mk'
if len([m])==1:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2, m, hour=12, method=method, sig=sig, wilks=False)
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir, m, method=method, sig=sig, wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q, m, hour=12, method=method, sig=sig, wilks=False)
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear, m, hour=12, method=method, sig=sig, wilks=False)
shear_mean = shearmean.mean(axis=0)
t2trend_unstacked = t2trend*10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
tirtrend_unstacked = ((tirtrend.values)*10. / tirm_mean.values) * 100.
tirtrend_out = xr.DataArray(tirtrend_unstacked, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
tirmean_out = xr.DataArray(tirm_mean, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirmean_out
t_da = t2trend_unstacked
q_da = qtrend_unstacked
s_da = sheartrend_unstacked
ti_da = tirtrend_unstacked
fp = fpath + 'trend_mk_-60C_synop_-50base'+str(m[0]).zfill(2)+'_sig.png'
map = shear.salem.get_map()
# f = plt.figure(figsize=(8, 5), dpi=300)
# ax1 = f.add_subplot(221)
#
# # map.set_shapefile(rivers=True)
# map.set_plot_params()
#
# map.set_data(t_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu_r', extend='both')
# map.visualize(ax=ax1, title='t2')
#
# ax2 = f.add_subplot(222)
# map.set_data(q_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu', extend='both')
# map.visualize(ax=ax2, title='q')
#
# ax3 = f.add_subplot(223)
# map.set_data(s_da, interp='linear')
# map.set_plot_params(levels=np.linspace(-1,1.1,10), cmap='RdBu_r', extend='both')
# map.visualize(ax=ax3, title='u-shear')
#
# ax4 = f.add_subplot(224)
# map.set_data(ti_da)
# map.set_plot_params(cmap='Blues', extend='both', levels=np.arange(20,101,20)) #levels=np.arange(20,101,20)
# map.visualize(ax=ax4, title='-70C frequency')
#
# plt.tight_layout()
# plt.savefig(fp)
# plt.close('all')
f = plt.figure(figsize=(13,7), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(t_da, interp='linear') # interp='linear'
contours = map.set_contour(t2_mean-273.15, interp='linear', levels=np.arange(24,37,4), cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='925hP temperature')
ax2 = f.add_subplot(222)
map.set_data(q_da,interp='linear') # interp='linear'
map.set_contour(q_mean,interp='linear', levels=np.arange(5,19,3), cmap='inferno')
map.set_plot_params(levels=np.linspace(-0.5,0.5,10), cmap='RdBu', extend='both') # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='925hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(s_da, interp='linear') # interp='linear'
map.set_contour(shear_mean, interp='linear', levels=np.arange(-10,1,3), cmap='inferno')
map.set_plot_params(levels=np.linspace(-1,1,10), cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='600-925hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(tirm_mean, interp='linear', levels=[0.1,0.5,1,2.5], cmap='inferno')
ti_da[ti_da==0] = np.nan
map.set_data(ti_da) #
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,51,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C cloud cover change', cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
pkl.dump(dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC.p',
'wb'))
pkl.dump(dicmean,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC.p',
'wb'))
def tgrad_shear_trend():
srfc = cnst.ERA_MONTHLY_SRFC
pl = cnst.ERA_MONTHLY_PL
mcs = cnst.GRIDSAT + 'aggs/box_13W-13E-4-8N_meanT-50_from5000km2.nc' #box_25-33E-28-10S_meanT-50_from5000km2.nc'#box_13W-13E-4-8N_meanT-50_from5000km2.nc'
out = cnst.network_data + 'figs/CLOVER/timeseries/'
#
box = [-10, 10, 5.5, 8]
tpick = [-10, 10, 6, 25]
Tlons = [-10, 10]
# box = [18,30,-22,-10]
# tpick = [18,30,-30,-20]
# Tlons = [18,30]
dam = xr.open_dataset(srfc)
dam = u_darrays.flip_lat(dam)
dam = dam['t2m']
tgrad = dam.sel(
longitude=slice(tpick[0], tpick[1]),
latitude=slice(
tpick[2],
tpick[3])).mean('longitude').groupby('time.month').mean('time')
Tgrad_lat = []
for tgrad_ts in tgrad:
ttgrad = np.argmax(tgrad_ts.values[2::] - tgrad_ts.values[0:-2])
lat_pos = tgrad.latitude[ttgrad + 1]
Tgrad_lat.append(float(lat_pos))
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
u925 = da['u'].sel(
level=925
) #(level=slice(850, 925)).mean(dim='level')#).mean(dim='level') #slice(850
u600 = da['u'].sel(level=700) #(level=slice(550, 650)).mean(dim='level')
qq925 = da['q'].sel(level=slice(850, 925)).mean(dim='level')
qq600 = da['q'].sel(level=slice(550, 650)).mean(dim='level')
mcs_temp = xr.open_dataset(mcs)
mcs_temp = mcs_temp['tir']
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
mnames = {
1: 'January',
2: 'February',
3: 'March',
4: 'April',
5: 'May',
6: 'June',
7: 'July',
8: 'August',
9: 'September',
10: 'October',
11: 'November',
12: 'December'
}
mcs_change = []
shear_change = []
tgrad_change = []
for m in months:
tsouthlice = slice(Tgrad_lat[m - 1] - 1.25, Tgrad_lat[m - 1] - 0.25)
tnorthslice = slice(Tgrad_lat[m - 1] + 0.25, Tgrad_lat[m - 1] + 1.25)
tsouth = dam.sel(longitude=slice(Tlons[0], Tlons[1]),
latitude=slice(5, 8))
tnorth = dam.sel(longitude=slice(Tlons[0], Tlons[1]),
latitude=slice(11, 18))
south = tsouth[(tsouth['time.month'] == m)]
north = tnorth[(tnorth['time.month'] == m)]
ulow = u925[(u925['time.month'] == m)]
uhigh = u600[(u600['time.month'] == m)]
mcs_month = mcs_temp[mcs_temp['time.month'] == m] #*(-1)
qlow = qq925[(qq925['time.month'] == m)]
qmid = qq600[(qq600['time.month'] == m)]
##da = da.sel(longitude=slice(-18,51), latitude=slice(36, -37))
south_peryear = south.groupby('time.year').mean('longitude').min(
'latitude')
north_peryear = north.groupby('time.year').mean('longitude').max(
'latitude')
u925_peryear = ulow.groupby('time.year').mean('longitude').max(
'latitude') #ulow.groupby('time.year').mean()
u600_peryear = uhigh.groupby('time.year').mean('longitude').min(
'latitude') #.mean() # ('latitude').min()
qlow_peryear = qlow.groupby('time.year').mean('longitude').max(
'latitude') #.mean() # ('latitude').min()
qmid_peryear = qmid.groupby('time.year').mean('longitude').max(
'latitude')
tgrad = ((north_peryear - south_peryear)[4::])
shear = (u600_peryear - u925_peryear)[4::] # -q_peryear[4::]#
q = qlow_peryear[4::] * 1000
r = stats.pearsonr(shear.values[1::] - shear.values[0:-1],
mcs_month.values[1::] - mcs_month.values[0:-1])
rq = stats.pearsonr(q.values[1::] - q.values[0:-1],
mcs_month.values[1::] - mcs_month.values[0:-1])
tshear_cor = stats.pearsonr(shear.values[1::] - shear.values[0:-1],
tgrad.values[1::] - tgrad.values[0:-1])
#sslope, sint = ustats.linear_trend(shear)
sslope, sint = ustats.linear_trend(shear)
sslope, sint, sr, sp, sstd_err = stats.linregress(
np.arange(len(shear)), shear)
try:
#mslope, mint = ustats.linear_trend(mcs_month)
mslope, mint, mrr, mp, mstd_err = stats.linregress(
np.arange(len(mcs_month)), mcs_month)
except:
continue
mr = stats.pearsonr(np.arange(len(mcs_month)), mcs_month)
print(m, mr, mrr, mslope)
sig = 'SIG'
if mr[1] > 0.05:
#continue
sig = 'not_sig'
mcs_change.append(mcs_month[-5::].mean() - mcs_month[0:5].mean())
shear_change.append(shear[-5::].mean() - shear[0:5].mean())
tgrad_change.append(tgrad[-5::].mean() - tgrad[0:5].mean())
x = np.arange(0, len(shear))
rr = r[0]
f = plt.figure(figsize=(6, 3))
ax = f.add_subplot(111)
ax.plot(tgrad.year,
shear,
'x-',
label='Zonal wind shear 600-925hPa',
color='k')
ax.plot(tgrad.year, sint + x * sslope, '--', color='k')
ax.text(0.5,
0.8,
'MCS: ' + sig + '_' + str(np.round(mslope, decimals=2) * 10) +
' p-val: ' + str(np.round(mp, decimals=2)),
transform=ax.transAxes)
ax.set_ylim(-8, 0)
#ax.set_ylim(9.8, 10)
ax1 = ax.twinx()
ax1.plot(mcs_month['time.year'],
mcs_month,
'o-',
label='Mean MCS temp.',
color='r')
ax1.plot(tgrad.year, mint + x * mslope, '--', color='r')
mcsline = lines.Line2D([], [],
color='r',
label='Mean MCS temp.',
linestyle='solid',
marker='o')
shearline = lines.Line2D([], [],
color='k',
label='Zonal wind shear 600-925hPa',
linestyle='solid',
marker='x',
markersize=5)
ax1.set_ylabel('degC')
ax.set_ylabel('m s-1')
ax.set_title(mnames[m] + ' | Corr.:' + str(np.round(rr, decimals=2)) +
'|' + str(np.round(rq[0], decimals=2)) +
'| Tgrad/Shear corr: ' +
str(np.round(tshear_cor[0], decimals=2)))
if m == 3:
ax.legend(handles=[mcsline, shearline], loc='lower left')
f.savefig(out + 'timeseries_WA' + str(m) + '.png')
plt.close('all')
def get_ERA5(inputs):
date = inputs[0]
indic = inputs[1]
# era_pl = inputs[2]
# era_srfc = inputs[3]
ids = inputs[2]
clim = inputs[3]
dic = dictionary_storm()
if clim:
time = str(date.month).zfill(2) + '-12'
stormtime = str(date.month).zfill(2) + '-18'
pl_str = 'ERA5/monthly/synop_selfmade/CLIM_2000-2014/pressure_levels/ERA5_2000-2014_CLIM_'
srfc_str = 'ERA5/monthly/synop_selfmade/CLIM_2000-2014/surface/ERA5_2000-2014_CLIM_'
try:
print('Open '+ cnst.local_data + pl_str + time + '_pl.nc')
era_day_pl = xr.open_dataset(
cnst.local_data + pl_str + time + '_pl.nc')
except (TypeError, IndexError, KeyError):
print('Era missing:', date)
# for k in dic.keys():
# dic[k].append(np.nan)
return
era_day_sf = xr.open_dataset(
cnst.local_data + srfc_str + time + '_srfc.nc')
try:
print('Open ' + cnst.local_data + srfc_str + stormtime + '_srfc.nc')
era_day_sft = xr.open_dataset(
cnst.local_data + srfc_str + stormtime + '_srfc.nc')
except IndexError:
return
era_day_plt = xr.open_dataset(
cnst.local_data + pl_str + stormtime + '_pl.nc')
else:
era_pl = xr.open_dataset(cnst.local_data + 'ERA5/hourly/pressure_levels/ERA5_' +str(date.year) + '_' + str(date.month).zfill(2) + '_pl.nc')
era_srfc = xr.open_dataset(
cnst.local_data + 'ERA5/hourly/surface/ERA5_' + str(date.year) + '_' + str(date.month).zfill(2) + '_srfc.nc')
era_pl = era_pl.rename({'longitude' : 'lon', 'latitude' : 'lat'})
era_srfc = era_srfc.rename({'longitude' : 'lon', 'latitude' : 'lat'})
time = str(date.year) + str(date.month).zfill(2) + str(date.day).zfill(2) + 'T12'
stormtime = str(date.year) + str(date.month).zfill(2) + str(date.day).zfill(2) + 'T18'
try:
era_day_pl = era_pl.sel(time=time).isel(time=0)
except (TypeError, IndexError, KeyError):
print('Era missing:', date)
# for k in dic.keys():
# dic[k].append(np.nan)
return
era_day_sf = era_srfc.sel(time=time).isel(time=0)
try:
era_day_sft = era_srfc.sel(time=stormtime).isel(time=0)
except IndexError:
return
era_day_plt = era_pl.sel(time=stormtime).isel(time=0)
era_day_sf = uda.flip_lat(era_day_sf)
era_day_pl = uda.flip_lat(era_day_pl)
for id in ids:
print('Doing', date)
# elat = indic.clat[id]
# elon = indic.clon[id]
elat = indic.clat[id]
elon = indic.minlon[id]
# dic['dates'].append(date)
# dic['lat'].append(elat)
# dic['lon'].append(elon)
# ipdb.set_trace()
point = era_day_pl.sel(lat=elat, lon=elon, method='nearest')
posx = int(np.where(era_day_sf.lon == point.lon)[0])
posy = int(np.where(era_day_sf.lat == point.lat)[0])
posxx = int(np.where(era_day_pl.lon == point.lon)[0])
posyy = int(np.where(era_day_pl.lat == point.lat)[0])
dist = 18 # ca 200km i.e. 45 *4.4km
ds_pl = era_day_pl.apply(uda.cut_box, xpos=posxx, ypos=posyy, dist=dist)
ds_srfc = era_day_sf.apply(uda.cut_box, xpos=posx, ypos=posy, dist=dist)
ds_plt = era_day_plt.apply(uda.cut_box, xpos=posxx, ypos=posyy, dist=dist)
ds_srfct = era_day_sft.apply(uda.cut_box, xpos=posx, ypos=posy, dist=dist)
try:
#ipdb.set_trace()
dic['u925'].append(xr.DataArray(ds_pl['u'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id], 'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
except ValueError:
print('Value problem')
continue
# ipdb.set_trace()
dic['u650'].append(xr.DataArray(ds_pl['u'].sel(level=650).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id], 'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['v925'].append(xr.DataArray(ds_pl['v'].sel(level=925).values,
coords={'tmean': indic.tmean[id], 'tmin': indic.tmin[id],
'area': indic.area[id], 'time': date, 'lat': ds_pl['y'].values,
'lon': ds_pl['x'].values}, dims=['lat', 'lon']))
dic['q925'].append(xr.DataArray(ds_pl['q'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['q700'].append(xr.DataArray(ds_pl['q'].sel(level=700).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['d925'].append(xr.DataArray(ds_pl['d'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['CAPE'].append(xr.DataArray(ds_srfc['cape'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['tcwv'].append(xr.DataArray(ds_srfc['tcwv'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['t2'].append(xr.DataArray(ds_srfc['t2m'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['divMoist'].append(xr.DataArray(ds_srfc['p84.162'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['slp'].append(xr.DataArray(ds_srfc['msl'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['u10'].append(xr.DataArray(ds_srfc['u10'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['v10'].append(xr.DataArray(ds_srfc['v10'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['u925_s'].append(
xr.DataArray(ds_plt['u'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['v925_s'].append(
xr.DataArray(ds_plt['v'].sel(level=925).values,
coords={'tmean': indic.tmean[id], 'tmin': indic.tmin[id], 'area': indic.area[id], 'time': date,
'lat': ds_pl['y'].values, 'lon': ds_pl['x'].values}, dims=['lat', 'lon']))
dic['u650_s'].append(
xr.DataArray(ds_plt['u'].sel(level=650).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['q925_s'].append(
xr.DataArray(ds_plt['q'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['q700_s'].append(
xr.DataArray(ds_plt['q'].sel(level=700).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['d925_s'].append(
xr.DataArray(ds_plt['d'].sel(level=925).values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['CAPE_s'].append(xr.DataArray(ds_srfct['cape'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['tcwv_s'].append(xr.DataArray(ds_srfct['tcwv'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['t2_s'].append(xr.DataArray(ds_srfct['t2m'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['divMoist_s'].append(xr.DataArray(ds_srfct['p84.162'].values, coords={'tmean':indic.tmean[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['slp_s'].append(xr.DataArray(ds_srfct['msl'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['u10_s'].append(xr.DataArray(ds_srfct['u10'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
dic['v10_s'].append(xr.DataArray(ds_srfct['v10'].values, coords={'tmean':indic.tmean[id],'tmin':indic.tmin[id],'area':indic.area[id],'time': date, 'lat': ds_pl['y'].values, 'lon':ds_pl['x'].values}, dims=['lat', 'lon']))
print('DID', date)
ds = xr.Dataset()
try:
for k in dic.keys():
print('doing', k)
ds[k] = xr.concat(dic[k], 'id')
except ValueError:
return
return ds
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP # _SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-60_monthly_count_-50base.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/'
dicm = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_test.p', 'rb'))
dicmean = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC_test.p', 'rb'))
mcsbox = cnst.GRIDSAT + 'aggs/box_12-25E-23-10S_meanT-50_from5000km2_SA.nc' # box_13W-13E-4-8N_meanT-50_from5000km2.nc'
mcs_temp = xr.open_dataset(mcsbox)
mcs_temp = mcs_temp['tir']
box=[-18,55,-35,35]
#box = [-18, 40, 0, 25]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(800,850)).mean('level')
#q = q[q['time.hour']==12]
t2 = da['t'].sel(level=slice(800,850)).mean('level')
#t2 = t2[t2['time.hour']==12]
u925 = da['u'].sel(level=slice(800,850)).mean('level')
#u925 = u925[u925['time.hour']==12]
u600 = da['u'].sel(level=slice(600,650)).mean('level')
#u600 = u600[u600['time.hour']==12]
shear = u600-u925 # u600-
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
months = [1,2,3,4,5,6,7,8,9, 10,11,12]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour) & (data['time.year'] >= 1983)& (
data['time.year'] <= 2017)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(data_years.values, coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
#corr_box = [-10,11,4.5,8]
corr_box = [13,25,-23,-10]
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]), longitude=slice(corr_box[0], corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
pf = np.polyfit(aa.values, bb, 1)
else:
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
# if (np.nansum(aa.values == 0) >= 10):
# p = np.nan
# r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12) #
qdiff, qyear = array_juggling(q, m, hour=12) #, hour=12
shdiff, sheyear = array_juggling(shear, m, hour=12) #, hour=12
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
mcs_month = mcs_temp[mcs_temp['time.month'] == m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs)
tcorr = corr(t2diff, tirdiff, bsingle=bs)
# pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
# print(pthresh)
pthresh = 0.05
#cloud['slope'].values[cloud['pval'].values > pthresh] = np.nan
#qcorr['r'].values[qcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#shearcorr['r'].values[shearcorr['pval'].values > pthresh] = 0
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
# print(pthresh)
#tcorr['r'].values[tcorr['pval'].values > pthresh] = 0
dicm[m].values[dicm[m].values==0] = np.nan
print('plot')
fp = fpath + 'corr_box_SYNOP_SAWest_-50base' + str(m).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(13,7), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(tcorr['r'], interp='linear') # interp='linear'
contours = map.set_contour(t2year-273.15, interp='linear', levels=np.arange(24,37,4), cmap='inferno')
#plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='925hP temperature')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'],interp='linear') # interp='linear'
map.set_contour(qyear,interp='linear', levels=np.arange(5,19,3), cmap='inferno')
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='925hPa Spec. humidity')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
map.set_plot_params(cmap='RdBu_r', extend='both', levels=[-0.7,-0.6,-0.5,-0.4,0.4,0.5,0.6,0.7])
map.set_contour(sheyear, interp='linear', levels=np.arange(-10,1,3), cmap='inferno')
# levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='600-925hPa Zonal wind shear')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m], interp='linear', levels=[0.1,0.5,1,2.5], cmap='inferno')
map.set_data(dicm[m]) #
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,51,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C cloud cover change', cbar_title='$\%$ decade-1')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def corr_box():
srfc = cnst.ERA5_MONTHLY_SRFC_SYNOP
pl = cnst.ERA5_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc' # -70count
fpath = cnst.network_data + 'figs/CLOVER/months/ERA5_WA/use/'
dicm = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_WA.p', 'rb'))
dicmean = pkl.load(open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean_synop12UTC_WA.p', 'rb'))
# mcsbox = cnst.GRIDSAT + 'aggs/SAboxWest_meanT-40_1000km2.nc' # box_13W-13E-4-8N_meanT-50_from5000km2.nc'
# mcs_temp = xr.open_dataset(mcsbox)
# mcs_temp = mcs_temp['tir']
box=[-18,30,0,25]#[-18,55,-35,35]#[-10,55,-35,0]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2], box[3])) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
press = da2['sp']
press = press[press['time.hour'] == 12]
press.values = press.values*1000
low_press = 925
up_press = 650
q = da['q'].sel(level=slice(low_press-50, low_press)).mean('level')
q = q[q['time.hour']==12]
t2d = da2['t2m']#['t2m']
#t2d = da['t'].sel(level=slice(800, 850)).mean('level')
t2d = t2d[t2d['time.hour']==12]
u600 = da['u'].sel(level=slice(up_press-50, up_press)).mean('level')
u600 = u600[u600['time.hour']==12]
v600 = da['v'].sel(level=slice(up_press-50, up_press)).mean('level')
v600 = v600[v600['time.hour']==12]
u925 = da['u'].sel(level=slice(low_press-50, low_press)).mean('level')
u925 = u925[u925['time.hour']==12]
v925 = da['v'].sel(level=slice(low_press-50, low_press)).mean('level')
v925 = v925[v925['time.hour']==12]
#
# u925_mean = u925.mean(axis=0)
# v925_mean = v925.mean(axis=0)
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=slice(low_press-50, low_press)).mean('level')
u800 = u800[u800['time.hour']==12]
v800 = da['v'].sel(level=slice(low_press-50, low_press)).mean('level')
v800 = v800[v800['time.hour']==12]
shear_u = u600-u800 #u600-
shear_v = v600-v800 # v600-
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
q.values = q.values * 1000
tir = da3['tir']
tir = t2d.salem.lookup_transform(tir)
def array_juggling(data, month, hour=None):
m = month
if hour is not None:
if len(month) > 1:
data = data[((data['time.month'] >= month[0]) | (data['time.month'] <= month[1])) & (
data['time.hour'] == hour) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
else:
data = data[
(data['time.month'] == month[0]) & (data['time.hour'] == hour) & (data['time.year'] >= 1983) & (
data['time.year'] <= 2017)]
else:
if len(month) > 1:
data = data[((data['time.month'] >= month[0]) | (data['time.month'] <= month[1])) & (
data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
else:
data = data[
(data['time.month'] == month[0]) & (data['time.year'] >= 1983) & (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :],
# coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(data_years.values, coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, bsingle=None, c_box=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
corr_box = c_box
if bsingle:
bb = b
else:
bb = b.sel(latitude=slice(corr_box[2], corr_box[3]), longitude=slice(corr_box[0], corr_box[1])).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
aa = a.sel(latitude=lat, longitude=lon)
if bsingle:
r, p = stats.pearsonr(aa.values, bb)
#pf = np.polyfit(aa.values, bb, 1)
pf, intercept, r, p, std_err = stats.linregress(aa.values, bb)
else:
# r, p = stats.pearsonr(aa.values, bb.values)
# pf = np.polyfit(aa.values, bb.values, 1)
pf, intercept, r, p, std_err = stats.linregress(aa.values, bb.values)
slope = pf#[0]
if (np.nansum(aa.values == 0) >= 10):
p = np.nan
r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
ds['intercept'].loc[{'latitude': lat, 'longitude': lon}] = intercept
return ds
box_dic = {
1 : [16,30,-25,-20],
2 : [12,28,-10,3],
3: [16,25,-23,-18],
4: [16,25,-23,-18],
10 : [14,28,-15,-5],
11 : [16,30,-20,-10],
12 : [16,30,-22,-12],
(11,1) : [18,30,-23,-18]
}
months = [1,2,3,4,10,11,12]
months = [3,4,10,5,9,11] #(3,5), (9,11),
months = [3,10]
for m in months:
c_box = [-12,12,4.5,8.5] # [-12,12,4.5,8.5]#box_dic[m]
if type(m)==int:
m = [m]
tirdiff, tiryear = array_juggling(tir, m) # average frequency change
t2diff, t2year = array_juggling(t2d, m) #
qdiff, qyear = array_juggling(q, m) #, hour=12
shdiff, sheyear = array_juggling(shear, m) #, hour=12
vdiff, vyear = array_juggling(v925, m) # , hour=12
udiff, uyear = array_juggling(u925, m) # , hour=12
vdiff6, vyear6 = array_juggling(u600, m) # , hour=12
#mcs_month = mcs_temp[mcs_temp['time.month'] == m] # meanT box average change
#tirdiff = mcs_month.values[1::]-mcs_month.values[0:-1]
bs = False
try:
qcorr = corr(qdiff, tirdiff, bsingle=bs, c_box=c_box)
except:
continue
shearcorr = corr(shdiff, tirdiff, bsingle=bs, c_box=c_box)
tcorr = corr(t2diff, tirdiff, bsingle=bs, c_box=c_box)
vcorr = corr(vdiff6,tirdiff, bsingle=bs, c_box=c_box)
dicm[m[0]].values[dicm[m[0]].values==0] = np.nan #.values
print('plot')
if len(m) == 1:
fp = fpath + 'ERA5_corr_WA_-70C_synop_linear_SLOPE_'+str(m[0]).zfill(2)+'.png'
else:
fp = fpath + 'ERA5_corr_WA_-70C_synop_linear_SLOPE_' + str(m[0]).zfill(2) +'-'+ str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map()
xx, yy = map.grid.transform(shear.longitude.values, shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
# Quiver only every 7th grid point
u = uyear.values[1::2, 1::2]
v = vyear.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
#ipdb.set_trace()
f = plt.figure(figsize=(15,8), dpi=350)
ax1 = f.add_subplot(221)
ipdb.set_trace()
map.set_data(tcorr['intercept']+tcorr['slope'], interp='linear') # interp='linear'
map.set_contour(t2year-273.15, interp='linear', levels=np.arange(24,37,4),colors='k', linewidths=0.5)
map.set_plot_params(cmap='RdBu_r', extend='both',levels=np.arange(-0.5,0.51,0.1)) # , levels=[-0.7,-0.6,-0.5,-0.4,-0.3,0.3,0.4,0.5,0.6,0.7])
dic = map.visualize(ax=ax1, title='2m temperature corr. | contours: mean T', cbar_title='')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(222)
map.set_data(qcorr['slope']*10,interp='linear') # interp='linear'
map.set_contour(qyear,interp='linear', levels=np.arange(5,19,3), colors='k', linewidths=0.5)
map.set_plot_params(cmap='RdBu_r', extend='both',levels=[-0.7,-0.6,-0.5,-0.4,-0.3,0.3,0.4,0.5,0.6,0.7]) # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(ax=ax2, title='925hPa Spec. humidity corr. | contours: mean q', cbar_title='')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['slope']*10, interp='linear') # interp='linear'
map.set_plot_params(cmap='RdBu_r', extend='both', levels=[-0.7,-0.6,-0.5,-0.4, -0.3,0.3,0.4,0.5,0.6,0.7])
#map.set_contour(sheyear, interp='linear', levels=np.arange(-10,1,6), colors='k', linewidths=0.5)
qu = ax3.quiver(xx, yy, u, v, scale=70, width=0.002)
qk = plt.quiverkey(qu, 0.4, 0.03, 4, '4 m s$^{-1}$',
labelpos='E', coordinates='figure')
# levels=np.arange(-0.5,0.51,0.1)
dic = map.visualize(ax=ax3, title='650-925hPa wind shear corr., mean 925hPa wind vectors', cbar_title='')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax4 = f.add_subplot(224)
map.set_contour(dicmean[m[0]], interp='linear', levels=[0.1,0.5,1,2.5], colors='k', linewidths=0.5)
map.set_data(dicm[m[0]]) #
#ax4.axhspan(-26,18) #[15,25,-26,-18]
coord = c_box#[17, 25, -28, -22]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.1)
# ax4.axvline(x=25, ymin=-26, ymax=-18)
# ax4.axhline(y=-26, xmin=15, xmax=25)
# ax4.axhline(y=-18, xmin=15, xmax=25)
map.set_plot_params(cmap='viridis', extend='both', levels=np.arange(10,41,10)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
dic = map.visualize(ax=ax4, title='-70C cloud cover change | >5000km2', cbar_title='$\%$ decade-1')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def trend_all():
srfc = cnst.ERA5_MONTHLY_SRFC_SYNOP #cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA5_MONTHLY_PL_SYNOP #cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc' #gridsat_WA_-70_monthly_mean_5000km2.nc' #gridsat_WA_-50_monthly_count_-50base.nc' #gridsat_WA_-70_monthly_mean_5000km2.nc' gridsat_WA_-50_monthly_count
fpath = cnst.network_data + 'figs/CLOVER/months/ERA5_WA/'
box = [-18, 30, 0, 25] #[-18,30,0,25]# [-18,40,0,25] #
da = xr.open_dataset(pl) #xr.open_dataset(pl)
#da = xr.decode_cf(da)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da2 = xr.open_dataset(srfc) #xr.open_dataset(srfc)
#da2 = xr.decode_cf(da2)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]))
da3 = xr.open_dataarray(mcs) * 100 #/30*100
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
#ipdb.set_trace()
da = da.isel(time=(da['time.hour'] == 12))
da2 = da2.isel(time=(da2['time.hour'] == 12))
lons = da.longitude
lats = da.latitude
press = da2['tcwv']
#press = press[press['time.hour'] == 12]
#press.values = press.values#*1000
low_press = 950
up_press = 650
mid_press = 700
q = da['q'].sel(level=slice(low_press - 30, low_press)).mean('level')
t2d = da2['t2m']
theta_low = u_met.theta_e(
low_press,
da['t'].sel(level=slice(low_press -
30, low_press)).mean('level').values - 273.15,
da['q'].sel(level=slice(low_press -
30, low_press)).mean('level').values)
theta_high = u_met.theta_e(
mid_press,
da['t'].sel(level=slice(up_press, mid_press)).mean('level').values -
273.15,
da['q'].sel(level=slice(up_press, mid_press)).mean('level').values)
theta_high_d = u_met.theta(
mid_press,
da['t'].sel(level=slice(up_press, mid_press)).mean('level').values -
273.15)
theta_low_d = u_met.theta(
low_press,
da['t'].sel(level=slice(low_press -
30, low_press)).mean('level').values - 273.15)
# punit = units.Quantity(mid_press, 'hPa')
# tunit = units.Quantity(da['t'].sel(level=slice(mid_press-30, mid_press)).mean('level').values, 'K')
# theta_high_d = calc.saturation_equivalent_potential_temperature(punit,tunit)
#
# punit = units.Quantity(low_press, 'hPa')
# tunit = units.Quantity(da['t'].sel(level=slice(low_press-30, low_press)).mean('level').values, 'K')
# theta_low_d = calc.saturation_equivalent_potential_temperature(punit, tunit)
theta_diff = (theta_high /
theta_low) * 100 #(np.array(theta_high)-273.15) #theta_low -
theta_diff_d = da2[
'cape'] ##np.array(theta_low_d) - np.array(theta_high_d)
#
theta_e = t2d.copy(deep=True)
theta_e.name = 'theta'
theta_e.values = theta_diff
theta_e = da['r'].sel(level=slice(mid_press - 30, mid_press)).mean(
'level') #da2['cape']
theta_d = t2d.copy(deep=True)
theta_d.name = 'theta'
theta_d.values = theta_diff_d
u600 = da['u'].sel(level=slice(up_press, mid_press)).mean('level')
v600 = da['v'].sel(level=slice(up_press, mid_press)).mean('level')
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=925)
v800 = da['v'].sel(level=925)
shear_u = u600 - u800
shear_v = v600 - v800
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = t2d.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = t2d.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
u6 = shear_u
v6 = shear_v
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=1)
t2 = t2d # grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3) #t2d.salem.lookup_transform(da3['tir']) #
grid = grid.to_dataset()
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
months = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
] #[3,4,5,6,9,10,11]#,4,5,6,9,10,11#,4,5,6,9,10,11,(3,5), (9,11)]#, 10,5,9]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
dicm = {}
dicmean = {}
for m in months:
method = 'polyfit'
if type(m) == int:
m = [m]
sig = True
t2trend, t2mean = calc_trend(t2,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
t2_mean = t2mean.mean(axis=0)
tirtrend, tirmean = calc_trend(tir,
m,
method=method,
sig=True,
wilks=False)
tirm_mean = tirmean.mean(axis=0)
qtrend, qmean = calc_trend(q,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
q_mean = qmean.mean(axis=0)
sheartrend, shearmean = calc_trend(shear,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
shear_mean = shearmean.mean(axis=0)
presstrend, pressmean = calc_trend(press,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
press_mean = pressmean.mean(axis=0)
u6trend, u6mean = calc_trend(u6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
u6_mean = u6mean.mean(axis=0)
v6trend, v6mean = calc_trend(v6,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
v6_mean = v6mean.mean(axis=0)
u8trend, u8mean = calc_trend(u800,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
u8_mean = u8mean.mean(axis=0)
v8trend, v8mean = calc_trend(v800,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
v8_mean = v8mean.mean(axis=0)
aej = np.argmin(u6_mean, axis=0)
itd = np.argmin(np.abs(v8_mean.values), axis=0)
thetatrend, thetamean = calc_trend(theta_e,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
theta_mean = thetamean.mean(axis=0)
thetatrend_d, thetamean_d = calc_trend(theta_d,
m,
method=method,
sig=sig,
hour=12,
wilks=False) #hour=12,
thetad_mean = thetamean_d.mean(axis=0)
t_da = t2trend * 10. # warming over decade
q_da = qtrend * 10. # warming over decade
s_da = sheartrend * 10. # warming over decade
u6trend = u6trend * 10
v6trend = v6trend * 10
tcwv_da = presstrend * 10
theta_da = thetatrend * 10
thetad_da = thetatrend_d * 10
u8trend = u8trend * 10
v8trend = v8trend * 10
tdata = (tirtrend.values * 10. / tirm_mean.values) * 100.
#ipdb.set_trace()
tirtrend_out = xr.DataArray(tdata,
coords=[grid['y'], grid['x']],
dims=['latitude', 'longitude'])
tirtrend_out.name = 'tir'
#tirmean_out = xr.DataArray(tirm_mean, coords=[grid['y'], grid['x']], dims=['latitude','longitude'])
dicm[m[0]] = tirtrend_out
dicmean[m[0]] = tirm_mean
if len(m) == 1:
fp = fpath + 'use/ERA5_-70_use_nosig_2003_' + str(
m[0]).zfill(2) + '.png'
else:
fp = fpath + 'use/ERA5_-70_use_nosig_2003_' + str(
m[0]).zfill(2) + '-' + str(m[1]).zfill(2) + '.png'
map = shear.salem.get_map(countries=False)
# Change the country borders
map.set_shapefile(countries=True, color='grey', linewidths=0.5)
#map.set_lonlat_contours(interval=0)
# Change the lon-lat countour setting
map.set_lonlat_contours(add_ytick_labels=True,
interval=5,
linewidths=0.01,
linestyles='-',
colors='white')
ti_da = t2d.salem.transform(tirtrend_out)
f = plt.figure(figsize=(15, 8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xx, yy = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xaej, yaej = map.grid.transform(u6_mean.longitude.values,
u6_mean.latitude.values[aej.values],
crs=shear.salem.grid.proj)
xitd, yitd = map.grid.transform(v8_mean.longitude.values,
v8_mean.latitude.values[itd],
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xx, yy)
#ipdb.set_trace()
#Quiver only every 7th grid point
u = u6trend.values[1::2, 1::2]
v = v6trend.values[1::2, 1::2]
#Quiver only every 7th grid point
uu = u8trend.values[1::2, 1::2]
vv = v8trend.values[1::2, 1::2]
#Quiver only every 7th grid point
um = u8_mean.values[1::2, 1::2]
vm = v8_mean.values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
# pdic = {
# 'tlin' : (t2_mean.values-273.15).astype(np.float64),
# 'tmean' : (t2_mean.values-273.15).astype(np.float64),
# 'qmean' : (q_mean.values).astype(np.float64),
# 'qlin' : q_da.values,
# 'shearlin' : s_da.values,
# 'u' : u,
# 'v' : v,
# 'xx' : xx,
# 'yy' : yy,
# 'tirmean' : tirm_mean,
#
#
# }
# pkl.dump(dicm,
# open(cnst.network_data + 'data/CLOVER/saves/storm_frac_synop12UTC_WA.p',
# 'wb'))
map.set_shapefile(countries=True, linewidths=1.2, color='grey')
ax1 = f.add_subplot(221)
map.set_data(t_da.values, interp='linear') # interp='linear'
map.set_contour(s_da.values,
interp='linear',
levels=[0.4, 0.6, 0.8],
colors='k',
linewidths=1.8)
map.set_plot_params(
levels=[-0.5, -0.4, -0.3, -0.2, 0.2, 0.3, 0.4, 0.5],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
qu = ax1.quiver(xx, yy, u, v, scale=30, width=0.002, headwidth=4)
# qk = plt.quiverkey(qu, 0.4, 0.03, 1, '1 m s$^{-1}$decade$^{-1}$',
# labelpos='E', coordinates='figure')
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
dic = map.visualize(
ax=ax1,
title=
'2m temperature | 925-600hPa wind shear | 650hPa wind vectors',
cbar_title=r'K decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
qk = plt.quiverkey(qu,
0.45,
0.52,
1,
'1 m s$^{-1}$decade$^{-1}$',
labelpos='E',
coordinates='figure')
ax2 = f.add_subplot(222)
map.set_data(theta_da.values - 0.2, interp='linear') # interp='linear'
map.set_contour(
(q_da.values).astype(np.float64),
interp='linear',
colors='k',
linewidths=1.8,
levels=[
-0.6, -0.4, -0.2, 0.2, 0.4, 0.6
]) #[6,8,10,12,14,16] #levels=[-0.6,-0.4,-0.2,0.2,0.4, 0.6],
map.set_plot_params(
levels=np.array([-0.4, -0.3, -0.2, -0.1, 0.1, 0.2, 0.3, 0.4]) * 10,
cmap='RdBu',
extend='both'
) # levels=np.arange(-0.5,0.51,0.1), [-0.6,-0.4,-0.2,0.2,0.4,0.6]
qu = ax2.quiver(xx, yy, um, vm, scale=100, width=0.002, headwidth=4)
qk = plt.quiverkey(qu,
0.94,
0.52,
3,
'3 m s$^{-1}$',
labelpos='E',
coordinates='figure')
dic = map.visualize(
ax=ax2,
title=r'650hPa RH | 925hPa q | 925hPa wind vectors',
cbar_title=r'% decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
ax3 = f.add_subplot(223)
map.set_data(tcwv_da.values - 0.05, interp='linear') # interp='linear'
map.set_contour(thetad_da.values,
interp='linear',
levels=np.array([-2, -1.5, -1, -0.5, 0.5, 1, 1.5, 2]) *
100,
colors='k',
linewidths=1.8)
map.set_plot_params(levels=[
-1.5, -1, -0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8, 1, 1.5
],
cmap='RdBu',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
qu = ax3.quiver(xx, yy, uu, vv, scale=30, width=0.002, headwidth=4)
qk = plt.quiverkey(qu,
0.45,
0.03,
1,
'1 m s$^{-1}$decade$^{-1}$',
labelpos='E',
coordinates='figure')
dic = map.visualize(ax=ax3,
title=r'TCWV | CAPE | 925hPa wind vectors',
cbar_title=r'kg m$^{-2}$ decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.0f')
ax4 = f.add_subplot(224)
map.set_contour((tirm_mean),
interp='linear',
levels=[0.1, 1, 2, 4],
colors='k',
linewidths=1.5)
ti_da.values[ti_da.values == 0] = np.nan
map.set_data(ti_da) #
coord = [18, 25, -28, -20]
geom = shpg.box(coord[0], coord[2], coord[1], coord[3])
#map.set_geometry(geom, zorder=99, color='darkorange', linewidth=3, linestyle='--', alpha=0.3)
map.set_plot_params(cmap='viridis',
extend='both',
levels=np.arange(10, 41,
10)) # levels=np.arange(10,51,10)
ax4.scatter(xaej, yaej, color='r', s=50, edgecolors='r', linewidths=1)
#ax4.scatter(xitd, yitd, color='r', s=50, edgecolors='k', linewidths=1)
dic = map.visualize(ax=ax4,
title='-70$^{\circ}$C cloud cover change ',
cbar_title='$\%$ decade$^{-1}$')
contours = dic['contour'][0]
plt.clabel(contours, inline=True, fontsize=9, fmt='%1.1f')
plt.tight_layout()
plt.annotate('a)',
xy=(0.02, 0.96),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('b)',
xy=(0.49, 0.96),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('c)',
xy=(0.02, 0.48),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.annotate('d)',
xy=(0.49, 0.48),
xytext=(0, 4),
size=13,
xycoords=('figure fraction', 'figure fraction'),
textcoords='offset points')
plt.savefig(fp)
plt.close('all')
def trend_all_polyfit():
srfc = cnst.ERA_MONTHLY_SRFC
pl = cnst.ERA_MONTHLY_PL
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_count.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
box = [-18, 40, 0, 30]
#box = [-8, -4, 5.5, 7.5]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=925)
qdry = da['q'].sel(level=650)
#q = da2['tcwv']
t2d = da['t'].sel(level=925)
u925 = da['u'].sel(level=925) #).mean(dim='level') #slice(850
u600 = da['u'].sel(level=650)
shear = u600 - u925
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
q = grid.lookup_transform(q)
qdry = grid.lookup_transform(q)
shear = grid.lookup_transform(shear)
# tir = t2d.salem.lookup_transform(da3['tir'])
#
# t2 = t2d
# #tir = da3['tir']
# q = q
# shear = shear
grid = grid.to_dataset()
t2 = xr.DataArray(t2,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
q = xr.DataArray(q,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
shear = xr.DataArray(shear,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
months = np.arange(1, 13)
#months=[10]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m)
& (data['time.hour'] == hour)]
else:
data = data[(data['time.month'] == m)]
data_years = data.groupby('time.year').mean(axis=0)
# stack lat and lon into a single dimension called allpoints
datastacked = data_years.stack(allpoints=['latitude', 'longitude'])
# apply the function over allpoints to calculate the trend at each point
print('Entering trend calc')
dtrend = datastacked.groupby('allpoints').apply(
u_darrays.linear_trend) #_trend_mk, alpha=0.05, eps=0.0001)
#dtrend = datastacked.groupby('allpoints').apply(u_darrays.linear_trend)
ddtrend = dtrend['slope']
# unstack back to lat lon coordinates
return ddtrend.unstack('allpoints'), data_years
for m in months:
tirtrend, dyears = array_juggling(tir, m)
tirm_mean = dyears.mean(axis=0)
t2trend, dummy = array_juggling(t2, m) #, hour=12
qtrend, dummy = array_juggling(q, m) #, hour=12
sheartrend, dummy = array_juggling(shear, m) #, hour=12
t2trend_unstacked = t2trend * 10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
tirtrend_unstacked = (
(tirtrend.values) * 10.) * 100. / tirm_mean.values
t_da = t2trend_unstacked #xr.DataArray(t2trend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
q_da = qtrend_unstacked #xr.DataArray(qtrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
s_da = sheartrend_unstacked #xr.DataArray(sheartrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
ti_da = tirtrend_unstacked #xr.DataArray(tirtrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_linear_-70C_coarse_u_srfc' + str(m).zfill(
2) + '.png' #'MCS_trend_10_TEST_mk.png'#
map = shear.salem.get_map()
f = plt.figure(figsize=(8, 5), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
map.set_plot_params()
map.set_data(t_da) # interp='linear'
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu_r',
extend='both')
map.visualize(ax=ax1, title='t2')
ax2 = f.add_subplot(222)
map.set_data(q_da) # interp='linear'
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu',
extend='both')
map.visualize(ax=ax2, title='q')
ax3 = f.add_subplot(223)
map.set_data(s_da) # interp='linear'
map.set_plot_params(levels=np.arange(-1, 1.1, 0.2),
cmap='RdBu',
extend='both')
map.visualize(ax=ax3, title='u-shear')
ax4 = f.add_subplot(224)
map.set_data(ti_da) # interp='linear'
map.set_plot_params(cmap='Blues',
extend='both') #levels=np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C frequency')
plt.savefig(fp)
plt.close('all')
def file_loop(f):
print('Doing file: ' + f)
dic = xr.open_dataset(f)
edate = pd.Timestamp(dic.time.values)
if np.nanmin(dic['tc_lag0'].values) > -53:
return
outt = dic['tc_lag0'].values
outp = dic['p'].values
tminpos = np.where(dic['tc_lag0'].values == np.nanmin(
dic['tc_lag0'].values)) # era position close to min temp
if len(tminpos[0]) > 1:
ptmax = np.nanmax((dic['p'].values)[tminpos])
if ptmax > 0:
prpos = np.where((dic['p'].values)[tminpos] == ptmax)
tminpos = ((tminpos[0])[prpos], (tminpos[1])[prpos])
else:
tminpos = ((tminpos[0])[0], (tminpos[1])[0])
elon = dic['lon'].values[tminpos]
elat = dic['lat'].values[tminpos]
out = dictionary()
out['lon'] = dic['lon'].values
out['lat'] = dic['lat'].values
out['hour'] = dic['time.hour'].item()
out['month'] = dic['time.month'].item()
out['year'] = dic['time.year'].item()
out['date'] = dic['time'].values
out['clat'] = np.min(out['lat']) + (
(np.max(out['lat']) - np.min(out['lat'])) * 0.5)
out['clon'] = np.min(out['lon']) + (
(np.max(out['lon']) - np.min(out['lon'])) * 0.5)
if (out['clat'] < 9) | (out['clon'] < -15) | (out['clon'] > 15):
print('MCS out of box')
return
# if (edate.hour < 16):
# return
try:
#era_pl = xr.open_dataset('/home/ck/DIR/mymachine/ERA5/monthly/synoptic/pl_2004-2019_monthlyCLIM_synop_07x07.nc')
era_pl = xr.open_dataset(
'/home/ck/DIR/mymachine/ERA5/monthly/synoptic/pl_1979-2019_monthly_synop_07x07.nc'
)
except:
print('ERA5 pl missing')
return
try:
#era_srfc = xr.open_dataset('/home/ck/DIR/mymachine/ERA5/monthly/synoptic/srfc_2004-2019_monthlyCLIM_synop_07x07.nc')
era_srfc = xr.open_dataset(
'/home/ck/DIR/mymachine/ERA5/monthly/synoptic/srfc_1979-2019_monthly_synop_07x07.nc'
)
except:
print('ERA5 srfc missing')
return
era_pl = uda.flip_lat(era_pl)
era_srfc = uda.flip_lat(era_srfc)
edate = edate.replace(hour=12, minute=0, day=1)
era_pl_day = era_pl.sel(time=edate,
longitude=slice(-16, 17),
latitude=slice(
4, 26)) #.groupby('time.month').mean('time')
era_srfc_day = era_srfc.sel(time=edate,
longitude=slice(-16, 17),
latitude=slice(
4,
26)) #.groupby('time.month').mean('time')
# try:
# era_day = era.isel(time=int(getera[0]))
# except TypeError:
# print('Era missing')
# return
res = []
try:
era_day = era_pl_day.sel(latitude=elat,
longitude=elon,
method='nearest',
tolerance=0.7) # take point of minimum T
except:
return
era_day_srfc = era_srfc_day.sel(latitude=elat,
longitude=elon,
method='nearest',
tolerance=0.7) # take point of minimum T
del era_srfc_day
e925 = era_day.sel(level=925).mean()
e850 = era_pl_day['t'].sel(level=850)
elow = era_day.sel(level=slice(925, 850)).mean('level').mean()
e650 = era_day.sel(level=650).mean()
emid = era_day.sel(level=slice(600, 700)).mean('level').mean()
srfc = era_day_srfc.mean()
t_thresh = -50 # -40C ~ 167 W m-2
mask = np.isfinite(outp) & (outt <= t_thresh) & np.isfinite(outt)
mask_area = (outt <= t_thresh) & np.isfinite(outt)
mask70 = (outt <= -70) & np.isfinite(outt)
if np.sum(mask) < 3:
return
print(
np.nanmax(outt[mask])
) # can be bigger than cutout threshold because of interpolation to 5km grid after cutout
out['area'] = np.sum(mask_area)
out['area70'] = np.sum(mask70)
out['clat'] = np.min(out['lat']) + (
(np.max(out['lat']) - np.min(out['lat'])) * 0.5)
out['clon'] = np.min(out['lon']) + (
(np.max(out['lon']) - np.min(out['lon'])) * 0.5)
out['tmin'] = np.min(outt[mask])
out['tmean'] = np.mean(outt[mask])
maxpos = np.unravel_index(np.nanargmax(outp), outp.shape)
out['pmax'] = np.nanmean(ua.cut_kernel(outp, maxpos[1], maxpos[0],
1)) #np.max(outp[mask])
out['pmean'] = np.mean(outp[mask])
dbox = e850.copy(deep=True)
minlon = era_pl_day.sel(latitude=8,
longitude=np.min(out['lon']),
method='nearest')
maxlon = era_pl_day.sel(latitude=8,
longitude=np.max(out['lon']),
method='nearest')
del era_pl_day
tgrad = dbox.sel(longitude=slice(
minlon.longitude.values, maxlon.longitude.values)).mean('longitude')
tmin = np.nanargmin(tgrad.values)
tmax = np.nanargmax(tgrad.values)
tgrad = tgrad.isel(latitude=slice(tmin, tmax))
lingress = uda.linear_trend_lingress(tgrad)
out['Tgrad'] = lingress['slope'].values
tgrad2 = dbox.sel(longitude=slice(np.min(out['lon']), np.max(out['lon'])), latitude=slice(10, 20)).mean(
['longitude', 'latitude']) - \
dbox.sel(longitude=slice(np.min(out['lon']), np.max(out['lon'])), latitude=slice(5, 7)).mean(['longitude', 'latitude'])
out['Tgradbox'] = tgrad2.values
try:
out['q925'] = float(e925['q'])
except TypeError:
return
out['q650'] = float(e650['q'])
out['v925'] = float(e925['v'])
out['v650'] = float(e925['v'])
out['u925'] = float(e925['u'])
out['u650'] = float(e650['u'])
out['w925'] = float(e925['w'])
out['w650'] = float(e650['w'])
out['rh925'] = float(e925['r'])
out['rh650'] = float(e650['r'])
out['t925'] = float(e925['t'])
out['t650'] = float(e650['t'])
# out['pv925'] = float(e925['pv'])
# out['pv650'] = float(e650['pv'])
out['div925'] = float(e925['d'])
out['div650'] = float(e650['d'])
out['q_low'] = float(elow['q'])
out['q_mid'] = float(emid['q'])
out['tcwv'] = float(srfc['tcwv'])
out['shear'] = float(e650['u'] - e925['u'])
theta_down = u_met.theta_e(925, e925['t'] - 273.15, e925['q'])
theta_up = u_met.theta_e(650, e650['t'] - 273.15, e650['q'])
out['dtheta'] = (theta_down - theta_up).values
out['thetaup'] = theta_up.values
out['thetadown'] = theta_down.values
out['pgt30'] = np.sum(outp[mask] >= 30)
out['isvalid'] = np.sum(mask)
out['pgt01'] = np.sum(outp[mask] >= 0.1)
#
out['p'] = outp[mask]
out['t'] = outt[mask]
#ipdb.set_trace()
dic.close()
del era_day
del era_day_srfc
del era_pl
del era_srfc
return out
def corr_box():
srfc = cnst.ERA_MONTHLY_SRFC_SYNOP
pl = cnst.ERA_MONTHLY_PL_SYNOP
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_count.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
dicm = pkl.load(
open(cnst.network_data + 'data/CLOVER/saves/storm_frac.p', 'rb'))
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(-18, 40),
latitude=slice(0, 25)) # latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(-18, 40),
latitude=slice(0, 25)) # latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(-18, 40), lat=slice(0, 25))
lons = da.longitude
lats = da.latitude
q = da['q'].sel(level=slice(850, 925)).mean(dim='level')
# q = da2['tcwv']
t2 = da['t'].sel(level=925)
u925 = da['v'].sel(level=925) #.mean(dim='level') c#slice(850,
u600 = da['u'].sel(level=slice(600, 650)).mean(dim='level')
shear = u925
q.values = q.values * 1000
tir = da3['tir']
tir = t2.salem.lookup_transform(tir)
months = np.arange(1, 13)
months = [3, 4, 5, 6, 10]
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m) & (data['time.hour'] == hour)
& (data['time.year'] >= 1983) &
(data['time.year'] <= 2017)]
else:
data = data[(data['time.month'] == m) & (data['time.year'] >= 1983)
& (data['time.year'] <= 2017)]
data_years = data.groupby('time.year').mean(axis=0)
data_mean = data.mean(axis=0)
# diff = xr.DataArray(data_years.values[1::, :, :] - data_years.values[0:-1, :, :], coords=[data_years.year[1::], data.latitude, data.longitude], dims=['year','latitude', 'longitude'] )
diff = xr.DataArray(
data_years.values,
coords=[data_years.year, data.latitude, data.longitude],
dims=['year', 'latitude', 'longitude'])
# unstack back to lat lon coordinates
return diff, data_mean
def corr(a, b, single=None):
ds = xr.Dataset()
ds['pval'] = a.copy(deep=True).sum('year') * np.nan
ds['r'] = a.copy(deep=True).sum('year') * np.nan
ds['slope'] = a.copy(deep=True).sum('year') * np.nan
aa = a.sel(latitude=slice(4, 8),
longitude=slice(-10,
11)).mean(dim=['latitude', 'longitude'])
for lat in a.latitude.values:
for lon in a.longitude.values:
if single:
bb = b
r, p = stats.pearsonr(bb, aa.values)
pf = np.polyfit(bb, aa.values, 1)
else:
bb = b.sel(latitude=lat, longitude=lon)
r, p = stats.pearsonr(aa.values, bb.values)
pf = np.polyfit(aa.values, bb.values, 1)
slope = pf[0]
# if (np.nansum(aa.values == 0) >= 10):
# p = np.nan
# r = np.nan
ds['r'].loc[{'latitude': lat, 'longitude': lon}] = r
ds['pval'].loc[{'latitude': lat, 'longitude': lon}] = p
ds['slope'].loc[{'latitude': lat, 'longitude': lon}] = slope
return ds
for m in months:
t2diff, t2year = array_juggling(t2, m, hour=12) #
qdiff, qyear = array_juggling(q, m, hour=12) #, hour=12
shdiff, sheyear = array_juggling(shear, m, hour=12) #, hour=12
tirdiff, tiryear = array_juggling(tir, m)
qcorr = corr(tirdiff, qdiff)
shearcorr = corr(tirdiff, shdiff)
tcorr = corr(tirdiff, t2diff)
cloud = corr(tirdiff, np.arange(len(tirdiff)), single=True)
# pthresh = us.fdr_threshold(qcorr['pval'].values[np.isfinite(qcorr['pval'].values)], alpha=0.05)
# print(pthresh)
pthresh = 0.01
#cloud['slope'].values[cloud['pval'].values > pthresh] = np.nan
qcorr['r'].values[qcorr['pval'].values > pthresh] = np.nan
# pthresh = us.fdr_threshold(shearcorr['pval'].values[np.isfinite(shearcorr['pval'].values)], alpha=0.05)
# print(pthresh)
shearcorr['r'].values[shearcorr['pval'].values > pthresh] = np.nan
# pthresh = us.fdr_threshold(tcorr['pval'].values[np.isfinite(tcorr['pval'].values)], alpha=0.05)
# print(pthresh)
tcorr['r'].values[tcorr['pval'].values > pthresh] = np.nan
tplot = ((cloud['slope'].values) * 10. / tiryear.values) * 100.
fp = fpath + 'corr_synop_v_srfc' + str(m).zfill(2) + '.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(8, 5), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
# bla = ma.masked_invalid(tcorr['r'].values)
map.set_data(tcorr['r'].values, interp='linear') # interp='linear'
# map.set_contour(t2year.mean('year')-273.15)
map.set_plot_params(cmap='RdBu_r',
extend='both',
levels=np.arange(
-0.7, 0.71,
0.1)) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax1, title='t2')
ax2 = f.add_subplot(222)
map.set_data(qcorr['r'], interp='linear') # interp='linear'
# map.set_contour(qyear.mean('year'))
map.set_plot_params(cmap='RdBu_r',
extend='both',
levels=np.arange(
-0.7, 0.71,
0.1)) # levels=np.arange(-0.5,0.51,0.1),
map.visualize(ax=ax2, title='q')
ax3 = f.add_subplot(223)
map.set_data(shearcorr['r'], interp='linear') # interp='linear'
# map.set_contour(sheyear.mean('year'))
map.set_plot_params(cmap='RdBu_r',
extend='both',
levels=np.arange(
-0.7, 0.71,
0.1)) # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3, title='u-shear')
ax4 = f.add_subplot(224)
map.set_data(dicm[m]) # interp='linear'
map.set_plot_params(
cmap='Blues', extend='both', levels=np.arange(
0.1, 0.8,
0.1)) # levels=np.arange(20,101,20) #np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C frequency')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def tgrad_shear_trend():
srfc = cnst.ERA_MONTHLY_SRFC
pl = cnst.ERA_MONTHLY_PL
mcs = cnst.GRIDSAT + 'aggs/box_13W-13E-4-8N_meanT-50_from5000km2.nc'
out = cnst.network_data + 'figs/CLOVER/'
box = [-10, 10, 5.5, 9]
TNORTH = [-10, 10, 11, 18]
TSOUTH = [-10, 10, 5, 9]
dam = xr.open_dataset(srfc)
dam = u_darrays.flip_lat(dam)
dam = dam['t2m']
tsouth = dam.sel(longitude=slice(TSOUTH[0], TSOUTH[1]),
latitude=slice(TSOUTH[2], TSOUTH[3]))
tnorth = dam.sel(longitude=slice(TNORTH[0], TNORTH[1]),
latitude=slice(TNORTH[2], TNORTH[3]))
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
u925 = da['u'].sel(level=925) #).mean(dim='level') #slice(850
u600 = da['u'].sel(level=650)
qq = da['q'].sel(level=925)
mcs_temp = xr.open_dataset(mcs)
mcs_temp = mcs_temp['tir']
months = [3, 4, 5, 9, 10]
mnames = {3: 'March', 4: 'April', 5: 'May', 9: 'September', 10: 'October'}
for m in months:
south = tsouth[(tsouth['time.month'] == m)]
north = tnorth[(tnorth['time.month'] == m)]
ulow = u925[(u925['time.month'] == m)]
uhigh = u600[(u600['time.month'] == m)]
mcs_month = mcs_temp[mcs_temp['time.month'] == m]
qmonth = qq[(qq['time.month'] == m)]
##da = da.sel(longitude=slice(-18,51), latitude=slice(36, -37))
south_peryear = south.groupby('time.year').mean('longitude').min(
'latitude')
south_peryear5 = south_peryear.rolling(time=5, center=True).mean()
north_peryear = north.groupby('time.year').mean('longitude').max(
'latitude')
north_peryear5 = north_peryear.rolling(time=5, center=True).mean()
u925_peryear = ulow.groupby('time.year').mean('longitude').max(
'latitude') #ulow.groupby('time.year').mean()
u925_peryear5 = u925_peryear.rolling(time=5, center=True).mean()
u600_peryear = uhigh.groupby('time.year').mean('longitude').min(
'latitude') #.mean() # ('latitude').min()
u600_peryear5 = u600_peryear.rolling(time=5, center=True).mean()
q_peryear = qmonth.groupby('time.year').mean('longitude').max(
'latitude') #.mean() # ('latitude').min()
q_peryear5 = q_peryear.rolling(time=5, center=True).mean()
tgrad = ((north_peryear - south_peryear)[4::])
shear = (u600_peryear - u925_peryear)[4::] # -q_peryear[4::]#
r = stats.pearsonr(shear.values[1::] - shear.values[0:-1],
mcs_month.values[1::] - mcs_month.values[0:-1])
tshear_cor = stats.pearsonr(shear.values[1::] - shear.values[0:-1],
tgrad.values[1::] - tgrad.values[0:-1])
sslope, sint = ustats.linear_trend(shear)
mslope, mint = ustats.linear_trend(mcs_month)
x = np.arange(0, len(shear))
rr = r[0]
f = plt.figure(figsize=(6, 3))
ax = f.add_subplot(111)
ax.plot(tgrad.year,
shear,
'x-',
label='Zonal wind shear 600-925hPa',
color='k')
ax.plot(tgrad.year, sint + x * sslope, '--', color='k')
ax.set_ylim(-14, -7)
ax1 = ax.twinx()
ax1.plot(mcs_month['time.year'],
mcs_month,
'o-',
label='Mean MCS temp.',
color='r')
ax1.plot(tgrad.year, mint + x * mslope, '--', color='r')
mcsline = lines.Line2D([], [],
color='r',
label='Mean MCS temp.',
linestyle='solid',
marker='o')
shearline = lines.Line2D([], [],
color='k',
label='Zonal wind shear 600-925hPa',
linestyle='solid',
marker='x',
markersize=5)
ax1.set_ylabel('degC')
ax.set_ylabel('m s-1')
ax.set_title(mnames[m] + ' | Corr.:' + str(np.round(rr, decimals=2)) +
'| Tgrad/Shear corr: ' +
str(np.round(tshear_cor[0], decimals=2)))
if m == 3:
ax.legend(handles=[mcsline, shearline])
f.savefig(out + 'trend_timeseries_' + str(m) + '.png')
plt.close('all')
def trend_all():
pl = cnst.ERA5_MONTHLY_PL_SYNOP_HU #cnst.ERA_MONTHLY_PL_SYNOP
#mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_mean_5000km2.nc'
fpath = cnst.network_data + 'figs/HUARAZ/monthly/'
box = [-82, -40, -28, 4] # [-18,40,0,25] #
topo = xr.open_dataset(
'/media/ck/Elements/SouthAmerica/ERA5/monthly/ERA5_static_synop_0.7deg.nc'
)
topo = u_darrays.flip_lat(topo)
z = topo['z'].isel(number=0, time=0)
z = z.sel(longitude=slice(box[0], box[1]), latitude=slice(box[2],
box[3])).values
da = xr.open_mfdataset(pl + '/*.nc') #xr.open_dataset(pl)
#da = xr.decode_cf(da)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3]),
time=(da['time.hour'] == 12)).load()
#ipdb.set_trace()
#da3 = xr.open_dataarray(mcs)*100
#da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2],box[3]))
lons = da.longitude
lats = da.latitude
#ipdb.set_trace()
low_press = 925
up_press = 200
mid_press = 500
gp = da['z'].mean('time') / 9.81
gp_high = da['z'].sel(level=up_press) / 9.81
w_mid = da['w'].sel(level=mid_press)
low_z = gp.sel(level=low_press) > z
mid_z = gp.sel(level=mid_press) > z
tlow = da['t'].sel(level=low_press) - 273.15 # .where(low_z)-273.15
qlow = da['q'].sel(level=low_press) * 1000 #.where(low_z)*1000
tmid = da['t'].sel(level=mid_press) - 273.15 # .where(mid_z)-273.15
qmid = da['q'].sel(level=mid_press) * 1000 # .where(mid_z)*1000
theta_low = u_met.theta_e(low_press, tlow, qlow)
theta_high = u_met.theta_e(mid_press, tmid, qmid)
theta_e = theta_low - theta_high
u600 = da['u'].sel(level=up_press) #.where(mid_z)
v600 = da['v'].sel(level=up_press) #.where(mid_z)
u600.name = 'u200'
ws600 = u_met.u_v_to_ws_wd(u600, v600)
u800 = da['u'].sel(level=mid_press) # 200-500 shear
v800 = da['v'].sel(level=mid_press)
shear_u = u600 - u800
shear_v = v600 - v800
ws_shear = u_met.u_v_to_ws_wd(shear_u.values, shear_v.values)
ws_600 = u600.copy(deep=True)
ws_600.name = 'ws'
ws_600.values = ws600[0]
shear = u600.copy(deep=True)
shear.name = 'shear'
shear.values = ws_shear[0]
vars = [
't600', 'q600', 'shear', 'q500', 'u200', 'v200', 'u500', 'v500', 'gp',
'w500'
]
data = [tlow, qlow, shear, qmid, u600, v600, u800, v800, gp_high, w_mid]
#grid = u600.salem.grid.regrid(factor=0.5)
#tir = grid.lookup_transform(da3) #t2d.salem.lookup_transform(da3['tir']) #
#grid = grid.to_dataset()
#tir = xr.DataArray(tir, coords=[da3['time'], grid['y'], grid['x']], dims=['time', 'latitude','longitude'])
months = [
5, 6
] #,2,3,4,5,6,7,8,9,10,11,12]#[3,4,5,6,9,10,11]#,4,5,6,9,10,11#,4,5,6,9,10,11,(3,5), (9,11)]#, 10,5,9]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]#[(12,2)]#[1,2,3,4,5,6,7,8,9,10,11,12]# #,2,3,11,12]
#ipdb.set_trace()
for m in months:
dic = {}
for v in vars:
dic[v] = 0
if type(m) == int:
m = [m]
for v, dat in zip(vars, data):
print('Doing ', v)
dic[v] = get_trend(dat,
m,
sig=False,
wilks=False,
method='polyfit')
if len(m) == 1:
fp = fpath + 'low_ERA5_trend_synop_HU_poly_' + str(
m[0]).zfill(2) + '.png'
else:
fp = fpath + 'low_ERA5_trend_synop_HU_poly_' + str(
m[0]).zfill(2) + '-' + str(m[1]).zfill(2) + '.png'
#ipdb.set_trace()
map = shear.salem.get_map()
# ti_da = t2d.salem.transform(ti_da)
f = plt.figure(figsize=(15, 8), dpi=300)
# transform their coordinates to the map reference system and plot the arrows
xo, yo = map.grid.transform(shear.longitude.values,
shear.latitude.values,
crs=shear.salem.grid.proj)
xx, yy = np.meshgrid(xo, yo)
#Quiver only every 7th grid point
u = (dic['u200'][0]).values[1::2, 1::2] # 200hpa
v = (dic['v200'][0]).values[1::2, 1::2]
#Quiver only every 7th grid point
uu = (dic['u200'][1]).values[1::2, 1::2] # 200mean
vv = (dic['v200'][1]).values[1::2, 1::2]
u500 = (dic['u500'][0]).values[1::2, 1::2]
v500 = (dic['v500'][0]).values[1::2, 1::2]
#Quiver only every 7th grid point
uu500 = (dic['u500'][1]).values[1::2, 1::2]
vv500 = (dic['v500'][1]).values[1::2, 1::2]
xx = xx[1::2, 1::2]
yy = yy[1::2, 1::2]
ax1 = f.add_subplot(231)
map.set_data((dic['t600'][0]).values,
interp='linear') # interp='linear'
map.set_contour(((dic['t600'][1]).values).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5)
map.set_plot_params(
levels=[-0.5, -0.4, -0.3, -0.2, 0.2, 0.3, 0.4, 0.5],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
pdic = map.visualize(ax=ax1,
title='500hpa t trend | contours: mean t',
cbar_title='K decade-1')
contours = pdic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax2 = f.add_subplot(232)
map.set_data((dic['q600'][0]).values,
interp='linear') # interp='linear'
map.set_contour(((dic['q600'][1]).values).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5) #[6,8,10,12,14,16]
map.set_plot_params(
levels=[-0.2, -0.1, -0.05, -0.01, 0.01, 0.05, 0.1, 0.2],
cmap='RdBu',
extend='both'
) # levels=np.arange(-0.5,0.51,0.1), [-0.6,-0.4,-0.2,0.2,0.4,0.6]
pdic = map.visualize(
ax=ax2,
title='500hPa Spec. humidity trend | contours: mean q',
cbar_title='g kg-1 decade-1')
contours = pdic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax3 = f.add_subplot(233)
map.set_data((dic['u200'][0]).values,
interp='linear') # interp='linear'
#map.set_contour(u6_mean.values, interp='linear', colors='k')
map.set_plot_params(
levels=[-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(ax=ax3,
title='200hPa wind trend, mean 200hPa wind',
cbar_title='m s-1 decade-1')
qu = ax3.quiver(xx, yy, uu, vv, scale=70, width=0.002)
cc = ['b', 'r', 'k']
for ii in range(3):
use = (dic['u200'][2]).values[ii, :]
for id, xpos in enumerate(xo):
#ipdb.set_trace()
ax3.plot(xpos, yo[use[id]], color=cc[ii], marker='o')
qk = plt.quiverkey(qu,
0.4,
0.03,
1,
'1 m s$^{-1}$',
labelpos='E',
coordinates='figure')
ax4 = f.add_subplot(234)
map.set_data((dic['u500'][0]).values,
interp='linear') # interp='linear'
#map.set_contour(u8_mean.values, interp='linear', colors='k')
map.set_plot_params(
levels=[-0.8, -0.6, -0.4, -0.2, 0.2, 0.4, 0.6, 0.8],
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1)
map.visualize(
ax=ax4,
title='500hPa zonal wind trend, mean 500hpa wind vectors',
cbar_title='m s-1 decade-1')
qu = ax4.quiver(xx, yy, uu500, vv500, scale=70, width=0.002)
qk = plt.quiverkey(qu,
0.4,
0.03,
1,
'1 m s$^{-1}$',
labelpos='E',
coordinates='figure')
ax5 = f.add_subplot(235)
map.set_data((dic['gp'][0]).values / 1000,
interp='linear') # interp='linear'
map.set_contour(((dic['gp'][1]).values / 1000).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5)
map.set_plot_params(levels=np.linspace(-10, 10, 40),
cmap='RdBu_r',
extend='both') # levels=np.arange(-0.5,0.51,0.1),
cc = ['b', 'r', 'k']
for ids, pp in enumerate((dic['gp'][2])):
xh, yh = map.grid.transform(pp[0],
pp[1],
crs=shear.salem.grid.proj)
ax5.plot(xh, yh, color=cc[ids], marker='o')
#map.set_contour((t2_mean.values).astype(np.float64), interp='linear', colors='k', linewidths=0.5, levels=np.linspace(800,925,8))
#map.set_plot_params(levels=[-0.5,-0.4,-0.3,-0.2,-0.1,-0.05,-0.02, 0.02,0.05,0.1,0.2,0.3,0.4,0.5], cmap='RdBu_r', extend='both') # levels=np.arange(-0.5,0.51,0.1),
pdic = map.visualize(
ax=ax5,
title='200hpa geopotential trend | contours: mean geopotential',
cbar_title='m decade-1')
contours = pdic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
ax6 = f.add_subplot(236)
map.set_data((dic['w500'][0]).values * 10,
interp='linear') # interp='linear'
map.set_contour(((dic['w500'][1]).values * 10).astype(np.float64),
interp='linear',
colors='k',
linewidths=0.5) #[6,8,10,12,14,16]
map.set_plot_params(
levels=[-0.2, -0.1, -0.05, -0.01, 0.01, 0.05, 0.1, 0.2],
cmap='RdBu',
extend='both'
) # levels=np.arange(-0.5,0.51,0.1), [-0.6,-0.4,-0.2,0.2,0.4,0.6]
pdic = map.visualize(ax=ax6,
title='500hPa w mean | contours: mean w',
cbar_title='P s-1 decade-1')
contours = pdic['contour'][0]
plt.clabel(contours, inline=True, fontsize=7, fmt='%1.1f')
plt.tight_layout()
plt.savefig(fp)
plt.close('all')
def trend_all():
srfc = cnst.ERA_MONTHLY_SRFC
pl = cnst.ERA_MONTHLY_PL
mcs = cnst.GRIDSAT + 'aggs/gridsat_WA_-70_monthly_count.nc'
fpath = cnst.network_data + 'figs/CLOVER/months/'
box = [-18, 40, 0, 25]
#box=[-18,55,-45,45]
#box = [-8, -4, 5.5, 7.5]
da = xr.open_dataset(pl)
da = u_darrays.flip_lat(da)
da = da.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
da2 = xr.open_dataset(srfc)
da2 = u_darrays.flip_lat(da2)
da2 = da2.sel(longitude=slice(box[0], box[1]),
latitude=slice(box[2], box[3])) #latitude=slice(36, -37))
da3 = xr.open_dataset(mcs)
da3 = da3.sel(lon=slice(box[0], box[1]), lat=slice(box[2], box[3]))
lons = da.longitude
lats = da.latitude
#q = da['q'].sel(level=925)
q = da['q'].sel(level=650)
#q = da2['tcwv']
t2d = da['t'].sel(level=925)
u925 = da['v'].sel(level=925) #).mean(dim='level') #slice(850
u600 = da['u'].sel(level=slice(650, 700)).mean(dim='level')
shear = u600 - u925
q.values = q.values * 1000
grid = t2d.salem.grid.regrid(factor=0.5)
t2 = grid.lookup_transform(t2d)
tir = grid.lookup_transform(da3['tir'])
q = grid.lookup_transform(q)
shear = grid.lookup_transform(shear)
# tir = t2d.salem.lookup_transform(da3['tir'])
#
# t2 = t2d
# #tir = da3['tir']
# q = q
# shear = shear
grid = grid.to_dataset()
t2 = xr.DataArray(t2,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
q = xr.DataArray(q,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
tir = xr.DataArray(tir,
coords=[da3['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
shear = xr.DataArray(shear,
coords=[t2d['time'], grid['y'], grid['x']],
dims=['time', 'latitude', 'longitude'])
#months = np.arange(1,13)
months = [3, 10]
dicm = {}
dicmean = {}
def array_juggling(data, month, hour=None):
m = month
if hour != None:
data = data[(data['time.month'] == m)
& (data['time.hour'] == hour)]
else:
data = data[(data['time.month'] == m)]
data_years = data.groupby('time.year').mean(axis=0)
# stack lat and lon into a single dimension called allpoints
datastacked = data_years.stack(allpoints=['latitude', 'longitude'])
# apply the function over allpoints to calculate the trend at each point
print('Entering trend calc')
dtrend = datastacked.groupby('allpoints').apply(
u_darrays.linear_trend_mk, alpha=0.05, eps=0.0001)
ddtrend = dtrend['slope']
pthresh = 0.05
# try:
# pthresh = us.fdr_threshold(dtrend['pval'].values[np.isfinite(dtrend['pval'].values)], alpha=0.05)
# ddtrend.values[(dtrend['pval'].values > pthresh) | np.isnan(dtrend['pval'].values)] = np.nan
# except ValueError:
# ddtrend.values = ddtrend.values*np.nan
# pthresh = np.nan
ddtrend.values[(dtrend['pval'].values > pthresh)
| np.isnan(dtrend['pval'].values)] = np.nan
print('p value threshold', pthresh)
# unstack back to lat lon coordinates
return ddtrend.unstack('allpoints'), data_years
for m in months:
tirtrend, dyears = array_juggling(tir, m)
tirm_mean = dyears.mean(axis=0)
t2trend, dummy = array_juggling(t2, m) #, hour=12
qtrend, dummy = array_juggling(q, m) #, hour=12
sheartrend, dummy = array_juggling(shear, m) #, hour=12
t2trend_unstacked = t2trend * 10. # warming over decade
qtrend_unstacked = qtrend * 10. # warming over decade
sheartrend_unstacked = sheartrend * 10. # warming over decade
tirtrend_unstacked = (
(tirtrend.values) * 10. / tirm_mean.values) * 100.
dicm[m] = tirtrend_unstacked
dicmean[m] = tirm_mean
t_da = t2trend_unstacked #xr.DataArray(t2trend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
q_da = qtrend_unstacked #xr.DataArray(qtrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
s_da = sheartrend_unstacked #xr.DataArray(sheartrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
ti_da = tirtrend_unstacked #xr.DataArray(tirtrend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_mktrend_-70C_coarse_vsurface' + str(m).zfill(
2) + '.png' #'ttrend_synop_-70C_coarse_'+str(m).zfill(2)+'.png'
map = shear.salem.get_map()
f = plt.figure(figsize=(8, 5), dpi=300)
ax1 = f.add_subplot(221)
# map.set_shapefile(rivers=True)
map.set_plot_params()
map.set_data(t_da) # interp='linear'
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu_r',
extend='both')
map.visualize(ax=ax1, title='t2')
ax2 = f.add_subplot(222)
map.set_data(q_da) # interp='linear'
map.set_plot_params(levels=np.arange(-0.5, 0.51, 0.1),
cmap='RdBu',
extend='both')
map.visualize(ax=ax2, title='q')
ax3 = f.add_subplot(223)
map.set_data(s_da) # interp='linear'
map.set_plot_params(levels=np.arange(-1, 1.1, 0.2),
cmap='RdBu',
extend='both')
map.visualize(ax=ax3, title='u-shear')
ax4 = f.add_subplot(224)
map.set_data(ti_da) # interp='linear'
map.set_plot_params(cmap='Blues',
extend='both',
levels=np.arange(20, 101,
20)) #levels=np.arange(20,101,20)
map.visualize(ax=ax4, title='-70C frequency')
plt.savefig(fp)
plt.close('all')
pkl.dump(dicm,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac.p', 'wb'))
pkl.dump(
dicmean,
open(cnst.network_data + 'data/CLOVER/saves/storm_frac_mean.p', 'wb'))
