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
ax.set_title(reg_names[reg])
ctl.adjust_ax_scale(axes)
ctl.custom_legend(fig, coloks, modoks, ncol=4)
fig.suptitle(
'Change of avg. regime residence time in 2050-2100 wrt 1964-2014')
fig.savefig(cart_out + 'WR_av_restime_dtr_{}_{}_{}_{}.pdf'.format(*ke))
# regime frequency change for subseasons: ND, DJF, FM
allseas = ['ND', 'DJF', 'FM']
for sea in allseas:
for mod in modoks:
lab_seas, dates_seas = ctl.sel_season(
results_ssp585[mod]['labels'], results_ssp585[mod]['dates'],
sea)
results_ssp585[mod]['freq_clus_' + sea] = ctl.calc_clus_freq(
lab_seas, numclus)
lab_seas, dates_seas = ctl.sel_season(results_hist[mod]['labels'],
results_hist[mod]['dates'],
sea)
results_hist[mod]['freq_clus_' + sea] = ctl.calc_clus_freq(
lab_seas, numclus)
fig = plt.figure(figsize=(16, 12))
axes = []
for reg in range(4):
ax = fig.add_subplot(2, 2, reg + 1)
axes.append(ax)
figure_file_cross = cart_out+'hfc_crosssections_ERAref.pdf'
figures_cross = []
fluxes = dict()
fluxnames = ['mshf', 'mpef', 'mlhf']
fluxlongnames = ['Sensible Heat', 'Potential Energy', 'Latent Heat']
factors = [cp/g, 1., L/g]
vars = dict()
varnames = ['hus', 'ta', 'va', 'zg']
fils = ['lcs0_day_1988_{}.nc'.format(varna) for varna in varnames]
for varna, fi in zip(varnames, fils):
var, level, lat, lon, dates, time_units, var_units, time_cal = ctl.read4Dncfield(cart_in+fi)
var, okda = ctl.sel_season(var, dates, 'Feb')
vars[varna] = var
press0, latdad, londsad, datespress, time_units, var_units = ctl.read3Dncfield(cart_in+'lcs0_day_1988_ps.nc')
press0, _ = ctl.sel_season(press0, datespress, 'Feb')
press0 = np.mean(press0, axis = 0)
mshf = factors[0]*vars['va']*vars['ta']
mpef = vars['va']*vars['zg']
mlhf = factors[2]*vars['va']*vars['hus']
mshf = np.mean(mshf, axis = 0)
mpef = np.mean(mpef, axis = 0)
mlhf = np.mean(mlhf, axis = 0)
mshfint = np.zeros(mshf.shape[1:])
cart_out = '/home/fabiano/Research/lavori/WeatherRegimes/taspr_composites_ERA/'
if not os.path.exists(cart_out): os.mkdir(cart_out)
#file_in = '/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc'
file_in = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/zg500/ERAInt_daily_1979-2018_129_zg500_remap25_meters.nc'
print(ctl.datestamp())
# lat = np.arange(-90, 91, 2.5)
# lon = np.arange(0., 360, 2.5)
var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var_season, dates_season = ctl.sel_season(var, dates, season)
all_years = np.arange(dates[0].year, dates[-1].year + 1)
kwar = dict()
kwar['numclus'] = 4
kwar['run_significance_calc'] = False
kwar['numpcs'] = 4
kwar['detrended_eof_calculation'] = False
kwar['detrended_anom_for_clustering'] = False
kwar['nrsamp_sig'] = 500
results_ref = cd.WRtool_core(var_season,
lat,
lon,
dates_season,
area,
vars[list(var2.keys())[0]] = L * var2[list(var2.keys())[0]]
era_zonal_factor = 2 * np.pi * Rearth * np.cos(np.deg2rad(lat))
era_fluxes_maps = dict()
era_fluxes_zonal = dict()
seasons = ['Feb', 'DJF', 'JJA']
fluxnames = ['tot', 'SH', 'PE', 'LH']
eraname = {'tot': 'p76.162', 'SH': 'p70.162', 'PE': 'p74.162', 'LH': 'p72.162'}
for flun in fluxnames:
for seas in seasons:
era_fluxes_maps[(flun,
seas)] = np.mean(ctl.sel_season(vars[eraname[flun]],
dates,
seas,
cut=False)[0],
axis=0)
era_fluxes_maps[(flun, 'year')] = np.mean(vars[eraname[flun]], axis=0)
for fu in era_fluxes_maps:
era_fluxes_zonal[fu] = np.mean(era_fluxes_maps[fu],
axis=1) * era_zonal_factor
######
print('Is it the first level???\n')
tag = 'ERAwith1000'
file_list = cart_in + 'all_vtgq_1988_6hrs.nc'
factors = {'SH': cp / g, 'PE': 1., 'LH': L / g}
#factors['PE'] = 1./g
file_in = '/data-hobbes/fabiano/OBS/ERA/ERA40+Int_daily_1957-2018_zg500_remap25_meters.nc' var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa=500) lat = coords['lat'] lon = coords['lon'] dates = coords['dates'] var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1979, 2014)) mean_field, _ = ctl.seasonal_climatology(var, dates, season) var_anoms = ctl.anomalies_daily_detrended(var, dates) # LOW FREQ VARIABILITY #var_low = ctl.running_mean(var_anoms, 5) var_low = ctl.lowpass_lanczos(var_anoms, 6) var_low_DJF, dates_DJF = ctl.sel_season(var_low, dates, season) lowfr_variab = np.std(var_low_DJF, axis=0) lowfr_variab_zonal = ctl.zonal_mean(lowfr_variab) # High freq var_high = var_anoms - var_low var_high_DJF, dates_DJF = ctl.sel_season(var_high, dates, season) highfr_variab = np.std(var_high_DJF, axis=0) highfr_variab_zonal = ctl.zonal_mean(highfr_variab) # Stationary eddy zonal_mean = ctl.zonal_mean(mean_field) stat_eddy = np.empty_like(mean_field) for i in range(stat_eddy.shape[0]):
zonal_margins['tot'] = (-3.5e16, 4.5e16)
zonal_margins['mshf'] = (-3.5e16, 4.5e16)
zonal_margins['mpef'] = (-3.5e16, 4.5e16)
zonal_margins['mlhf'] = (-7e15, 9.e15)
map_margins = dict()
map_margins['mshf'] = (-1.5e10, 1.5e10)
map_margins['mpef'] = (-4.e9, 4.e9)
map_margins['mlhf'] = (-7.e8, 7.e8)
map_margins['tot'] = (-2.e10, 2.e10)
# Loading reference pressure file
pressurefile = '/data-hobbes/fabiano/SPHINX/heat_flux/1988_daily/lcs0_day_1988_ps.nc'
press0row, lat, lon, datespress, time_units, var_units = ctl.read3Dncfield(
pressurefile)
press0 = dict()
press0['DJF'] = np.mean(ctl.sel_season(press0row, datespress, 'DJF',
cut=False)[0],
axis=0)
press0['JJA'] = np.mean(ctl.sel_season(press0row, datespress, 'JJA',
cut=False)[0],
axis=0)
press0['year'] = np.mean(press0row, axis=0)
# Loading ERA reference
cart_era = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/'
era_fi = 'prova_heatflux_1988_MM.nc'
cpc = nc.Dataset(cart_era + era_fi)
era_lat = cpc.variables['latitude'][:]
era_lon = cpc.variables['longitude'][:]
era_zonal_factor = 2 * np.pi * Rearth * np.cos(np.deg2rad(era_lat))
era_fluxes_maps = dict()
kwar['numclus'] = 4
kwar['run_significance_calc'] = False
kwar['numpcs'] = 4
kwar['detrended_eof_calculation'] = False # detrendo io all'inizio
kwar['detrended_anom_for_clustering'] = False
kwar['nrsamp_sig'] = 500
var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa=500)
lat = coords['lat']
lon = coords['lon']
dates = coords['dates']
var, dates = ctl.sel_time_range(var, dates, ctl.range_years(1957, 2014))
var_anoms = ctl.anomalies_daily_detrended(var, dates)
var_season, dates_season = ctl.sel_season(var_anoms, dates, season)
all_years = np.arange(dates[0].year, dates[-1].year + 1)
results_ref = cd.WRtool_core(var_season,
lat,
lon,
dates_season,
area,
heavy_output=True,
**kwar)
kwar['ref_solver'] = results_ref['solver']
kwar['ref_patterns_area'] = results_ref['cluspattern_area']
all_results = dict()
for i in range(100):
print(i)
zg_zon = np.mean(zg, axis=-1)
zg_eddy = zg - zg_zon[..., np.newaxis]
ua_climate_mean, dates_climate_mean, _ = ctl.daily_climatology(ua,
coords['dates'],
window=20)
zg_climate_mean, dates_climate_mean, _ = ctl.daily_climatology(zg,
coords['dates'],
window=20)
zg_climate_mean_eddy, dates_climate_mean, _ = ctl.daily_climatology(
zg_eddy, coords['dates'], window=20)
zg_climate_mean_zon, dates_climate_mean, _ = ctl.daily_climatology(
zg_zon, coords['dates'], window=20)
ua_low = ctl.butter_filter(ua, 10)
ua_low_djfm, dates_djfm = ctl.sel_season(ua_low, coords['dates'], 'DJFM')
zg_djfm, dates_djfm = ctl.sel_season(zg, coords['dates'], 'DJFM')
zg_eddy_djfm, dates_djfm = ctl.sel_season(zg_eddy, coords['dates'], 'DJFM')
# zg_low = ctl.butter_filter(zg, 10)
# zg_low_djfm, dates_djfm = ctl.sel_season(zg_low, coords['dates'], 'DJFM')
#
# zg_low_eddy = ctl.butter_filter(zg_eddy, 10)
# zg_low_eddy_djfm, dates_djfm = ctl.sel_season(zg_low_eddy, coords['dates'], 'DJFM')
### Test jli.
figs = []
# jli, jspeed, jdates = cd.jetlatindex(ua, coords['lat'], coords['lon'], coords['dates'], filter = 'butter')
# figs.append(cd.plot_jli_w_speed(jli, jspeed, jdates, title = 'butterworth w 0.22'))
# jli, jspeed, jdates = cd.jetlatindex(ua, coords['lat'], coords['lon'], coords['dates'], filter = 'butter')
cart_out = '/home/fabiano/Research/lavori/WeatherRegimes/taspr_composites_ERA/' if not os.path.exists(cart_out): os.mkdir(cart_out) file_in = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/zg500/ERAInt_daily_1979-2018_129_zg500_remap25_meters.nc' print(ctl.datestamp()) # lat = np.arange(-90, 91, 2.5) # lon = np.arange(0., 360, 2.5) var, coords, aux_info = ctl.read_iris_nc(file_in, extract_level_hPa = 500) lat = coords['lat'] lon = coords['lon'] dates = coords['dates'] print(dates[0], dates[-1], var.shape) var_season, dates_season = ctl.sel_season(var, dates, season) all_years = np.arange(dates[0].year, dates[-1].year+1) kwar = dict() kwar['numclus'] = 4 kwar['run_significance_calc'] = False kwar['numpcs'] = 4 kwar['detrended_eof_calculation'] = False kwar['detrended_anom_for_clustering'] = False kwar['nrsamp_sig'] = 500 results_ref = cd.WRtool_core(var_season, lat, lon, dates_season, area, heavy_output = True, **kwar) sys.exit() kwar['ref_solver'] = results_ref['solver']
#######################################
cart_in = '/data-hobbes/fabiano/SPHINX/zg_daily/'
cart_out = '/home/fabiano/Research/lavori/SPHINX_for_lisboa/WRtool/test_trend_traj_lcb0/'
if not os.path.exists(cart_out): os.mkdir(cart_out)
fil = cart_in + 'lcb0-1850-2100-NDJFM_zg500_NH_14473.nc'
var, level, lat, lon, dates, time_units, var_units, time_cal = ctl.read4Dncfield(
fil, extract_level=50000)
#climate_mean, dates_climate_mean = ctl.trend_daily_climat(var, dates)
#var_anom = ctl.anomalies_daily_detrended(var, dates, climate_mean = climate_mean, dates_climate_mean = dates_climate_mean)
var_season, dates_season = ctl.sel_season(var, dates, 'DJF')
erafile = '/data-hobbes/fabiano/OBS/ERA/ERAInterim/zg500/zg500_Aday_ERAInterim_2deg_1979-2014.nc'
ERA_ref_EAT = cd.WRtool_from_file(erafile,
'DJF',
'EAT',
extract_level_4D=50000.,
numclus=4,
heavy_output=True,
run_significance_calc=False)
area = 'EAT'
ref_solver = ERA_ref_EAT['solver']
ref_patterns_area = ERA_ref_EAT['cluspattern_area']
#resu_nodet = cd.WRtool_core(var_season, lat, lon, dates_season, area, run_significance_calc = False, ref_solver = ref_solver, ref_patterns_area = ref_patterns_area, detrended_eof_calculation = False, detrended_anom_for_clustering = False, heavy_output = True)
var2, lat, lon, dates, time_units, var_units, time_cal = ctl.readxDncfield(cart_in_ref+fi)
print(var2.keys())
vars[list(var2.keys())[0]] = L*var2[list(var2.keys())[0]]
era_zonal_factor = 2*np.pi*Rearth*np.cos(np.deg2rad(lat))
era_fluxes_maps = dict()
era_fluxes_zonal = dict()
seasons = ['Feb','DJF', 'JJA']
fluxnames = ['tot', 'SH', 'PE', 'LH']
eraname = {'tot': 'p76.162', 'SH': 'p70.162', 'PE': 'p74.162', 'LH': 'p72.162'}
for flun in fluxnames:
for seas in seasons:
era_fluxes_maps[(flun, seas)] = np.mean(ctl.sel_season(vars[eraname[flun]], dates, seas, cut = False)[0], axis = 0)
era_fluxes_maps[(flun, 'year')] = np.mean(vars[eraname[flun]], axis = 0)
for fu in era_fluxes_maps:
era_fluxes_zonal[fu] = np.mean(era_fluxes_maps[fu], axis = 1)*era_zonal_factor
###################################################################################
ann = np.arange(1950,2101,10)
annme = [(a1+a2)//2 for a1,a2 in zip(ann[:-1], ann[1:])]
print(annme)
cart_out_results = cart_out + 'out_flux_calc_NEW/'
if not os.path.exists(cart_out_results): os.mkdir(cart_out_results)
seasons = ['DJF', 'JJA']
cross3d = pickle.load(open(filos, 'rb'))
else:
cross3d = dict()
for ens in ensmem:
for seas in seasons + ['year']:
cross3d[(ens, seas)] = []
for yea in years:
filena = '{}_mon_{}_{}.nc'.format(ens, yea, varna)
var, level, lat, lon, dates, time_units, var_units, time_cal = ctl.read4Dncfield(
cart_in_3d + filena)
cli, datescli, _ = ctl.monthly_climatology(var, dates)
for seas in seasons:
coso = np.mean(ctl.sel_season(cli,
datescli,
seas,
cut=False)[0],
axis=0)
cross3d[(ens, seas)].append(np.mean(coso, axis=-1))
coso = np.mean(cli, axis=0)
cross3d[(ens, 'year')].append(np.mean(coso, axis=-1))
for seas in seasons + ['year']:
cross3d[(ens, seas)] = np.stack(cross3d[(ens, seas)])
for seas in seasons + ['year']:
cross3d[('base', seas)] = np.mean(
[cross3d[(ens, seas)] for ens in ensmem[:3]], axis=0)
cross3d[('stoc', seas)] = np.mean(
[cross3d[(ens, seas)] for ens in ensmem[3:]], axis=0)
