def compute(ifile):
## PRECOMPUTE
print(ifile)
print('Running precompute\n')
var, level, lat, lon, dates, time_units, var_units, time_cal = ctl.read4Dncfield(
ifile, extract_level=50000.)
var_season, dates_season = ctl.sel_season(var, dates, season)
climate_mean, dates_climat, climat_std = ctl.daily_climatology(
var_season, dates_season, wnd)
var_anom = ctl.anomalies_daily(var_season,
dates_season,
climate_mean=climate_mean,
dates_climate_mean=dates_climat)
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_anom, area)
print(var_area.shape)
print('Running compute\n')
#### EOF COMPUTATION
eof_solver = ctl.eof_computation(var_area, lat_area)
PCs = eof_solver.pcs()[:, :numpcs]
print('Running clustering\n')
#### CLUSTERING
centroids, labels = ctl.Kmeans_clustering(PCs,
numclus,
algorithm='molteni')
cluspattern = ctl.compute_clusterpatterns(var_anom, labels)
cluspatt_area = []
for clu in cluspattern:
cluarea, _, _ = ctl.sel_area(lat, lon, clu, area)
cluspatt_area.append(cluarea)
cluspatt_area = np.stack(cluspatt_area)
varopt = ctl.calc_varopt_molt(PCs, centroids, labels)
print('varopt: {:8.4f}\n'.format(varopt))
freq_clus = ctl.calc_clus_freq(labels)
print('Running clus sig\n')
significance = ctl.clusters_sig(PCs,
centroids,
labels,
dates_season,
nrsamp=5000)
# significance_2 = ctl.clusters_sig(PCs, centroids, labels, dates_season, nrsamp = 5000)
# print('Significances: {:7.3f} vs {:7.3f}\n'.format(significance, significance_2))
return lat, lon, var_anom, eof_solver, centroids, labels, cluspattern, cluspatt_area, freq_clus, significance
def dothing(wind, coords, lanc=lanc20):
area = [-60., 0., 20., 70.]
lat = coords['lat']
lon = coords['lon']
wind_area, latsel, lonsel = ctl.sel_area(lat, lon, wind, area)
wind_low = np.zeros(wind_area.shape)
for ila, la in enumerate(latsel):
for ilo, lo in enumerate(lonsel):
wind_low[:, ila, ilo] = np.convolve(lanc20,
wind_area[:, ila, ilo],
mode='same')
#wind_low = ctl.running_mean(wind_area, 10)
wind_low_djf, dates = ctl.sel_season(wind_low, coords['dates'], 'DJF')
return wind_low_djf, dates
results_ref = cd.WRtool_core(var_season,
lat,
lon,
dates_season,
area,
heavy_output=True,
**kwar)
# OK. Now I have the regimes. Read the temp/prec.
file_temp_era = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/ERAInt_daily_1979-2018_167.nc'
temp, coords_temp, aux_info_temp = ctl.read_iris_nc(file_temp_era)
seas_temp_mean, seas_temp_std = ctl.seasonal_climatology(
temp, coords_temp['dates'], season)
temp_seas, datetemp = ctl.sel_season(temp, coords_temp['dates'], season)
temp_seas_NH, lat, lon = ctl.sel_area(coords_temp['lat'], coords_temp['lon'],
temp_seas, 'NH')
del temp, temp_seas
coords_temp['lat'] = lat
coords_temp['lon'] = lon
temp_anoms = ctl.anomalies_daily_detrended(temp_seas_NH, datetemp)
file_temp_ref = iris.load(
'/data-hobbes/fabiano/OBS/ERA/ERAInterim/ERAInt_daily_1979-2018_167.nc')[0]
# file_prec_era = '/data-hobbes/fabiano/OBS/GPCC/daily/gpcc_daily_EU_1982-2016.nc'
# prec, coords_prec, aux_info_prec = ctl.read_iris_nc(file_prec_era, select_var = 'gpcc full data daily product version 2018 precipitation per grid')
file_prec_era = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/ERAInt_daily_1979-2018_228_pr_daysum_ok.nc'
prec, coords_prec, aux_info_prec = ctl.read_iris_nc(
file_prec_era, convert_units_to='mm', regrid_to_reference=file_temp_ref
) #, select_var = 'gpcc full data daily product version 2018 precipitation per grid')
var, dates = ctl.sel_time_range(var, dates,
ctl.range_years(yearange[0], yearange[1]))
var_set, dates_set = ctl.seasonal_set(var, dates, 'DJF', seasonal_average=True)
years = np.array([da.year for da in dates_set])
############## PLOT GLOBAL TRENDS ######################
fig, ax = plt.subplots()
glob_mea = ctl.global_mean(var_set, lat)
g0 = glob_mea[0]
m, c = ctl.linear_regre(years, glob_mea)
ax.scatter(years, glob_mea - g0, label='Global', color='blue')
ax.plot(years, c + m * years - g0, color='blue')
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_set, 'EAT')
eat_mea = ctl.global_mean(var_area, lat_area)
g0 = eat_mea[0]
m, c = ctl.linear_regre(years, eat_mea)
ax.scatter(years, eat_mea - g0, label='EAT', color='green')
ax.plot(years, c + m * years - g0, color='green')
var_area, lat_area, lon_area = ctl.sel_area(lat, lon, var_set, 'NH')
eat_mea = ctl.global_mean(var_area, lat_area)
g0 = eat_mea[0]
m, c = ctl.linear_regre(years, eat_mea)
ax.scatter(years, eat_mea - g0, label='NH', color='orange')
ax.plot(years, c + m * years - g0, color='orange')
ax.legend()
ax.set_title('Trend in the average zg500 during DJF')
plot_anomalies=False,
filename=cart_out_maps + 'map_full_ERA.pdf')
def func_sum(blok_ind, reg_ind):
alsums = []
for reg in range(4):
blokok = blok_ind[reg_ind == reg]
okmap = np.mean(blokok, axis=0)
alsums.append(np.mean(okmap))
return np.array(alsums)
blok_anom = blok - ERA_map_full
blok_anom_area, _, _ = ctl.sel_area(lat, lon, blok_anom, 'EAT')
res_boot_ERA = ctl.bootstrap(blok_anom_area,
datcom,
'DJF',
y=wri,
apply_func=func_sum,
n_choice=30,
n_bootstrap=500)
EAT_mean = dict()
ERA_map_area, _, _ = ctl.sel_area(lat, lon, ERA_map_full, 'EAT')
EAT_mean['ERA_0'] = np.mean(ERA_map_area)
# allmaps[('ERA', 'full')], lato, lono = ctl.sel_area(lat, lon, ERA_map_full, 'EAT')
#
# ERA_allmaps = []
#wind_low = ctl.running_mean(wind_area, 10)
wind_low_djf, dates = ctl.sel_season(wind_low, coords['dates'], 'DJF')
return wind_low_djf, dates
area = [-60., 0., 20., 70.]
cart_out = '/home/fabiano/Research/lavori/Jet_latitude/'
#orogfi = '/data-hobbes/reference/ERAInterim/geopot_vegcover_25.nc'
orogfi = '/data-hobbes/reference/ERAInterim/geopot_vegcover.nc'
orog, coords, aux_info = ctl.readxDncfield(orogfi, select_var='z')
#orog = orog/9.80665
orogmask = orog > 1300.0
orogarea, _, _ = ctl.sel_area(coords['lat'], coords['lon'], orogmask, area)
orogarea = orogarea[0]
#ctl.plot_map_contour(orogmask, plot_type = 'pcolormesh')
cart_in = '/nas/reference/ERA40/daily/u/'
file_in = 'u_Aday_ERA40_{}01_{}12.nc'
yea = 1957
file_in_57 = 'u_Aday_ERA40_195709_195712.nc'
winds = []
alldates = []
wind, coords, aux_info = ctl.readxDncfield(cart_in + file_in_57,
extract_level=850)
wind_lo, dates = dothing(wind, coords)
winds.append(wind_lo)
varunits = dict()
varunits['temp'] = 'K'
varunits['prec'] = 'mm/day'
limitz = dict()
limitz['temp'] = (0.4, 2.)
limitz['prec'] = (0.6, 0.8)
area = 'EAT'
area_compos = dict()
for var in ['temp', 'prec']:
for k in all_compos:
if type(all_compos[k][var]) == list:
all_compos[k][var] = np.stack(all_compos[k][var])
area_compos[(var, k)], lat_area, lon_area = ctl.sel_area(lat, lon, all_compos[k][var], area)#'Eu')
ncar = np.sqrt(area_compos[(var, k)][0, ...].size)
val_compare = dict()
for cos in ['base', 'stoc']:
for var in ['temp', 'prec']:
et, patcor = ctl.calc_RMS_and_patcor(area_compos[(var, cos)], area_compos[(var, 'ERA')])
val_compare[('RMS', var, cos)] = et/ncar
val_compare[('patcor', var, cos)] = patcor
et2, patcor2 = ctl.calc_RMS_and_patcor(area_compos[(var, cos)]+area_compos[(var, cos+'_std')], area_compos[(var, 'ERA')])
et3, patcor3 = ctl.calc_RMS_and_patcor(area_compos[(var, cos)]-area_compos[(var, cos+'_std')], area_compos[(var, 'ERA')])
etd = np.array([max([et[i], et2[i], et3[i]])-min([et[i], et2[i], et3[i]]) for i in range(4)])/(2*ncar)
patd = np.array([max([patcor[i], patcor2[i], patcor3[i]])-min([patcor[i], patcor2[i], patcor3[i]]) for i in range(4)])/2.
val_compare[('RMS', var, cos+'_std')] = etd
for aaa in ['NML', 'EAT', 'EAText']:
# Taylor semplice
cart_out = cart + aaa + '/'
ctl.mkdir(cart_out)
for tip in ['tot LWA', 'trans LWA', 'Montg streamf']:
patt_ref = resu['ERA5'][tip]
olat = resu['ERA5']['lat']
olon = resu['ERA5']['lon']
fig = plt.figure(figsize=(16, 12))
for num, patt in enumerate(patnames):
ax = plt.subplot(2, 2, num + 1, polar=True)
obs = patt_ref[num]
obs, lat_area, lon_area = ctl.sel_area(olat, olon, obs, areas[aaa])
modpats = [
ctl.sel_area(resu[mod]['lat'], resu[mod]['lon'],
resu[mod][tip][num], areas[aaa])[0]
for mod in mods_all
]
ctl.Taylor_plot(modpats,
obs,
latitude=lat_area,
ax=ax,
colors=cols,
markers=marks,
only_first_quarter=True,
plot_ellipse=False,
ellipse_color=colors[0],
allnums = [5, 10, 20]
for ifile, name in zip(listafils, model_names):
print(name)
var, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(
ifile, extract_level=500)
# var, coords, aux_info = ctl.read_iris_nc(ifile)
# lat = coords['lat']
# lon = coords['lon']
# dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1957, 2014))
for num in allnums:
clm, dtclm, _ = ctl.daily_climatology(var, dates, num)
clmarea, lat_area, lon_area = ctl.sel_area(lat, lon, clm, 'EAT')
clm_fullperiod[(name, num)] = clmarea
dtclmpd = pd.to_datetime(dtclm)
datesall[name] = dtclmpd
climate_mean, dates_climate_mean = ctl.trend_daily_climat(var,
dates,
window_days=20,
window_years=nya)
difftot, lat_area, lon_area = ctl.sel_area(
lat, lon, climate_mean[-1] - climate_mean[0], 'EAT')
climate_mean_dict[name] = difftot
lat_sect = [(16, None), (8, 16), (0, 8)]
lon_sect = [(0, 16), (16, 32), (32, None)]
cart_sst = '/data-woodstock/PRIMAVERA/stream1/merged/tos/'
filsst = 'tos-{}-1950-2014-{}-remap.nc'
#cart_sst = '/nas/PRIMAVERA/Stream1/hist-1950/{}/{}/tos/'
filera = '/nas/reference/ERA40+Int/sst_Amon_ERA40_195701-201812_1deg.nc'
sstera, datacoords, _ = ctl.readxDncfield(filera)
datesera = datacoords['dates']
lat = datacoords['lat']
lon = datacoords['lon']
sstera = sstera - 273.15
sstera_mean, sstera_sd = ctl.seasonal_climatology(sstera, datesera, 'DJF', dates_range = ctl.range_years(1957, 2014))
area_box = (-80, 10, 20, 80)
sstera_mean_area, latsel, lonsel = ctl.sel_area(lat, lon, sstera_mean, area_box)
okpoera = (sstera_mean_area < -100) | (sstera_mean_area > 500)
allcose = dict()
allrms = dict()
allpatcor = dict()
#for mod, mem in zip(model_names, ens_mems):
for mod in model_names:
print(mod)
filmod_part = 'tos-{}-1950-2014-'.format(mod)
if 'AWI' in mod:
filmod_part = 'tos-{}-1950-2010-'.format(mod)
allfimod = [fi for fi in os.listdir(cart_sst) if filmod_part in fi and 'remap.nc' in fi]
print(allfimod)
for fi in allfimod:
n_color_levels=21,
draw_contour_lines=False,
n_lines=5,
color_percentiles=(0, 100),
bounding_lat=30,
plot_margins=area,
add_rectangles=None,
draw_grid=True,
plot_type='filled_contour',
verbose=False,
lw_contour=0.5)
figs.append(fig)
gigi = sspcoso - histcoso
gogo, _, _ = ctl.sel_area(lat, lon, gigi, area)
diff = ref_solver.projectField(gogo,
neofs=4,
eofscaling=0,
weighted=True)
stri = 4 * '{:7.2f}' + '\n'
cosi = [ctl.cosine(diff, cen) for cen in results_ref['centroids']]
print(stri.format(*cosi))
ctl.plot_pdfpages(cart_out_orig + 'map_rebase_diff_{}.pdf'.format(area),
figs)
bau = results_hist[mod]['var_dtr']
bauda = np.arange(1965, 2015)
gigi = results_ssp[mod]['var_dtr']
# zonme = ctl.zonal_mean(trend)
# stat_eddy_trend = np.empty_like(trend)
# for i in range(trend.shape[0]):
# stat_eddy_trend[i,:] = trend[i,:]-zonme[i]
stat_eddy_trend = cose[('se_trend', mod)].squeeze()
se_errtrend = cose[('se_errtrend', mod)].squeeze()
# trend_area, lat_area, lon_area = ctl.sel_area(lat, lon, trend, area)
# trend_anom = trend-np.mean(trend_area)
trendsanom.append(trend)
trendsstateddy.append(stat_eddy_trend)
zontrend.append(cose[('zontrend', mod)].squeeze())
var, lata, lona = ctl.sel_area(lat, lon, trend, 'EAT')
zeat = ctl.zonal_mean(var)
zontrend_EAT.append(zeat)
var, lata, lona = ctl.sel_area(lat, lon, trend, 'PNA')
zpna = ctl.zonal_mean(var)
zontrend_PNA.append(zpna)
hatchs.append(np.abs(trend) > 2 * errtrend)
hatchs_se.append(np.abs(stat_eddy_trend) > 2 * se_errtrend)
NSIG = int(0.8 *
len(okmods)) # number of models to consider response significant
trendsanom.append(np.mean(trendsanom, axis=0))
hatchs.append(
land_mask = land_mask[128:, :]
#lat_range = [37, 45]
#lon_range = [7, 18]
lat_range = [37, 45]
lon_range = [-79, -70]
plt.ion()
binzz = np.linspace(-20,25,50)
cart = '/home/fedefab/Scrivania/Research/Post-doc/SPHINX/Daily_temp/'
var_all = dict()
for ran in [1950,2020,2090]:
fil = 'lcb0_tas_{}-{}.nc'.format(ran, str(ran+5)[-2:])
var, lat, lon, dates, time_units, var_units = ctl.read3Dncfield(cart+fil)
var = var - 273.15
var_all[ran] = var
var_ok, lat_area, lon_area = ctl.sel_area(lat, lon, var, lat_range+lon_range)
mask_ok, lat_area, lon_area = ctl.sel_area(lat, lon, land_mask, lat_range+lon_range)
listavals = []
for cos in var_ok:
cosok = np.min(cos[mask_ok])
#cosok = np.percentile(cos[mask_ok], 20)
listavals.append(cosok)
plt.hist(listavals, bins = binzz, label = str(ran), alpha = 0.5)
plt.legend()
plt.xlim(-20,None)
v, vcoords, _ = ctl.readxDncfield(filnam.format('v', 'v'))
ta, tacoords, _ = ctl.readxDncfield(filnam.format('ta', 'ta'))
zg, zgcoords, _ = ctl.readxDncfield(filnam.format('zg', 'zg'))
zg = 9.80665 * zg
pv, pvcoords, _ = ctl.readxDncfield(filnam.format('pv', 'pv'))
#u.shape (365, 5, 241, 480)
lat_orig = ucoords['lat']
lon_orig = ucoords['lon']
levs = np.array([850, 500, 200, 50, 30])
area = (-180, 180, 10, 85)
#for da in np.arange(365):
da = -3
u_, lat, lon = ctl.sel_area(lat_orig, lon_orig, u[da, ::-1, ...], area)
v_, lat, lon = ctl.sel_area(lat_orig, lon_orig, v[da, ::-1, ...], area)
ta_, lat, lon = ctl.sel_area(lat_orig, lon_orig, ta[da, ::-1, ...], area)
zg_, lat, lon = ctl.sel_area(lat_orig, lon_orig, zg[da, ::-1, ...], area)
pv_, lat, lon = ctl.sel_area(lat_orig, lon_orig, pv[da, ::-1, ...], area)
Om = 2 * np.pi / 86400.
f = 2 * Om * np.sin(np.deg2rad(lat))
f[f == 0.0] = 1.e-6
f = f[:, np.newaxis]
#f = np.reshape(np.repeat(f, len(lon)), zg[0,0].shape)
grad_zg = dict()
ug = dict()
vg = dict()
vortg = dict()
var, coords, aux_info = ctl.readxDncfield(cart_data + okfil)
except Exception as exp:
print('Unable to read data for {}, going on with next model..'.format(mod + '_' + mem))
print(exp)
continue
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1964, 2014))
zg_dtr, coeffs, var_regional, dates_seas = ctl.remove_global_polytrend(lat, lon, var, dates, 'NDJFM', deg = 1, area = 'NML', print_trend = True)
for area in ['EAT', 'PNA']:
zg_ok, _, _ = ctl.sel_area(lat, lon, zg_dtr, area)
cmean, dates_cm, _ = ctl.daily_climatology(zg_ok, dates_seas, window = 20)
climmeans[area].append(cmean)
#trendmat, errtrendmat, cmat, errcmat = ctl.local_lineartrend_climate(lat, lon, var, dates, 'NDJFM', print_trend = True, remove_global_trend = False, global_deg = 3, global_area = 'global')
#field_trends[(ssp, mod, 'NDJFM')] = trendmat
for area in ['EAT', 'PNA']:
climate_mean[(area, mod)] = np.mean(climmeans[area], axis = 0)
climate_std[(area, mod)] = np.std(climmeans[area], axis = 0)
climate_mean_dates[mod] = dates_cm
num_members[mod] = len(climmeans)
pickle.dump([climate_mean, climate_mean_dates, climate_std, num_members], open(cart_out + 'climate_mean_hist.p', 'wb'))
for area in ['EAT', 'PNA']: