def t_trend():
#file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/localscratch/wllf030/cornkle/ERA-I/monthly/monthly_1979-2017_srfc.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
dam = xr.open_dataset(file)
dam = dam['t2m']
months = np.arange(1,13)
for m in months:
da = dam[(dam['time.month']==m)]
da = da.sel(longitude=slice(-18,51), latitude=slice(36, -37))
da = da.groupby('time.year').mean(axis=0)
lons = da.longitude
lats = np.flip(da.latitude.values, axis=0)
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['latitude','longitude'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(u_darrays.linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
trend_unstacked = trend_unstacked*10. # warming over decade
da2 = xr.DataArray(trend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_WA'+str(m).zfill(2)+'.png'
up.quick_map_salem(da2, levels=np.arange(-0.5,0.5,0.1), cmap='RdBu_r', save=fp) #
plt.close('all')
def t_trend_slice():
#file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/localscratch/wllf030/cornkle/ERA-I/monthly/old/ERA-Int-Monthly-2mTemp.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
dam = xr.open_dataarray(file)
lower = 9
higher = 11
da = dam[(dam['time.month'] >= lower) & (dam['time.month'] <= higher)]
da = da.sel(longitude=slice(-18, 51), latitude=slice(36, -37))
da = da.groupby('time.year').mean(axis=0)
lons = da.longitude
lats = np.flip(da.latitude.values, axis=0)
# define a function to compute a linear trend of a timeseries
def linear_trend(x):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),
x.squeeze().values,
eps=0.001,
alpha=0.01,
Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if ind == 1:
issig = slope
else:
issig = np.nan
return xr.DataArray(issig, )
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['latitude', 'longitude'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
trend_unstacked = trend_unstacked * 10. # warming over decade
da2 = xr.DataArray(trend_unstacked,
coords=[lats, lons],
dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_' + str(lower).zfill(2) + '-' + str(higher).zfill(
2) + '.png'
up.quick_map_salem(da2, vmin=-0.4, vmax=0.4, cmap='RdBu_r', save=fp) #
plt.close('all')
def t_trend_slice():
#file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/localscratch/wllf030/cornkle/ERA-I/monthly/old/ERA-Int-Monthly-2mTemp.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
dam = xr.open_dataarray(file)
lower = 9
higher = 11
da = dam[(dam['time.month']>=lower) & (dam['time.month']<=higher)]
da = da.sel(longitude=slice(-18,51), latitude=slice(36, -37))
da = da.groupby('time.year').mean(axis=0)
lons = da.longitude
lats = np.flip(da.latitude.values, axis=0)
# define a function to compute a linear trend of a timeseries
def linear_trend(x):
#pf = np.polyfit(np.arange(len(x)), x, 1)
pf, slope, int, p, ind = mk.test(np.arange(len(x)),x.squeeze().values, eps=0.001, alpha=0.01, Ha='upordown')
# we need to return a dataarray or else xarray's groupby won't be happy
if ind == 1:
issig = slope
else:
issig = np.nan
return xr.DataArray(issig, )
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['latitude','longitude'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
trend_unstacked = trend_unstacked*10. # warming over decade
da2 = xr.DataArray(trend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_'+str(lower).zfill(2)+'-'+str(higher).zfill(2)+'.png'
up.quick_map_salem(da2, vmin=-0.4, vmax=0.4, cmap='RdBu_r', save=fp) #
plt.close('all')
def t_mean():
# file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/users/global/cornkle/data/ERA-I monthly/ERA-Int-Monthly-2mTemp.nc'
fpath = '/users/global/cornkle/figs/gap_filling_Tgrad/months/'
dam = xr.open_dataarray(file)
months = np.arange(1, 13)
for m in months:
da = dam[(dam['time.month'] == m)]
da = da.sel(longitude=slice(-18, 51), latitude=slice(36, -37))
da = da.mean(axis=0)-273.15
fp = fpath + 'tmean_' + str(m).zfill(2) + '.png'
up.quick_map_salem(da, levels=np.arange(20,41,2), cmap='jet', save=fp)
def t_mean():
# file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/users/global/cornkle/data/ERA-I monthly/ERA-Int-Monthly-2mTemp.nc'
fpath = '/users/global/cornkle/figs/gap_filling_Tgrad/months/'
dam = xr.open_dataarray(file)
months = np.arange(1, 13)
for m in months:
da = dam[(dam['time.month'] == m)]
da = da.sel(longitude=slice(-18, 51), latitude=slice(36, -37))
da = da.mean(axis=0) - 273.15
fp = fpath + 'tmean_' + str(m).zfill(2) + '.png'
up.quick_map_salem(da,
levels=np.arange(20, 41, 2),
cmap='jet',
save=fp)
def t_trend():
#file = '/users/global/cornkle/data/ERA-I monthly/ERA-WA-Monthly-2mTemp.nc'
file = '/localscratch/wllf030/cornkle/ERA-I/monthly/monthly_1979-2017_srfc.nc'
fpath = '/users/global/cornkle/figs/CLOVER/months/'
dam = xr.open_dataset(file)
dam = dam['t2m']
months = np.arange(1, 13)
for m in months:
da = dam[(dam['time.month'] == m)]
da = da.sel(longitude=slice(-18, 51), latitude=slice(36, -37))
da = da.groupby('time.year').mean(axis=0)
lons = da.longitude
lats = np.flip(da.latitude.values, axis=0)
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['latitude', 'longitude'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(u_darrays.linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
trend_unstacked = trend_unstacked * 10. # warming over decade
da2 = xr.DataArray(trend_unstacked,
coords=[lats, lons],
dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_WA' + str(m).zfill(2) + '.png'
up.quick_map_salem(da2,
levels=np.arange(-0.5, 0.5, 0.1),
cmap='RdBu_r',
save=fp) #
plt.close('all')
def trend_map():
def linear_trend(x):
pf = np.polyfit(np.arange(len(x)), x, 1)
# we need to return a dataarray or else xarray's groupby won't be happy
return xr.DataArray(pf[0])
msg_folder = '/users/global/cornkle/data/OBS/gridsat/gridsat_netcdf/'
fpath = '/users/global/cornkle/figs/gap_filling_Tgrad/gridsat/'
fname = 'gridsat_WA_-70_monthly_count.nc'
dam = xr.open_dataarray(msg_folder + fname)
dam = dam.sel(lon=slice(-18, 51), lat=slice(-37, 36))
lons = dam.lon
lats = dam.lat
months = np.arange(1, 13)
#da[da == 0] = np.nan
for m in months:
da = dam[(dam['time.month'] == m) & (dam['time.year'] > 1982)]
da = da.groupby('time.day').sum(axis=0)
da = da.groupby('time.year').mean(
axis=0) # average daily frequency per year
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['lat', 'lon'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
trend_unstacked = trend_unstacked * 10.
da2 = xr.DataArray(trend_unstacked,
coords=[lats, lons],
dims=['latitude', 'longitude'])
fp = fpath + 'ttrend_' + str(m).zfill(2) + '.png'
up.quick_map_salem(da2, vmin=-0.4, vmax=0.4, cmap='RdBu_r', save=fp)
def trend_map():
def linear_trend(x):
pf = np.polyfit(np.arange(len(x)), x, 1)
# we need to return a dataarray or else xarray's groupby won't be happy
return xr.DataArray(pf[0])
msg_folder = '/users/global/cornkle/data/OBS/gridsat/gridsat_netcdf/z18_panAfrica/'
fpath = '/users/global/cornkle/figs/gap_filling_Tgrad/gridsat/'
fname = 'gridsat_monthly_18UTC.nc'
dam = xr.open_dataarray(msg_folder + fname)
dam = dam.sel(lon=slice(-18,51), lat=slice(-37, 36))
lons = dam.lon
lats = dam.lat
months = np.arange(1, 13)
#da[da == 0] = np.nan
for m in months[0:1]:
da = dam[(dam['time.month']==m) & (dam['time.year']>1982)]
#da = da.groupby('time.year').mean(axis=0) # average daily frequency per year
# stack lat and lon into a single dimension called allpoints
stacked = da.stack(allpoints=['lat', 'lon'])
# apply the function over allpoints to calculate the trend at each point
trend = stacked.groupby('allpoints').apply(linear_trend)
# unstack back to lat lon coordinates
trend_unstacked = trend.unstack('allpoints')
#trend_unstacked = trend_unstacked * 10.
da2 = xr.DataArray(trend_unstacked, coords=[lats, lons], dims=['latitude', 'longitude'])
fp = fpath + 'mcstrend_' + str(m).zfill(2) + '.png'
up.quick_map_salem(da2, vmin=-1, vmax=1, cmap='RdBu_r', save=fp)
