def Tb_evaluation():
result_file = r'D:\work\LDAS\2018-06_rmse_uncertainty\Tb_evaluation\validation.csv'
DA_const_err = LDAS_io('ObsFcstAna', 'US_M36_SMOS40_DA_cal_scaled')
DA_varia_err = LDAS_io('ObsFcstAna', 'US_M36_SMOS40_DA_cal_scl_errfile')
ismn = ISMN_io(col_offs=DA_const_err.grid.tilegrids.loc['domain',
'i_offg'],
row_offs=DA_const_err.grid.tilegrids.loc['domain',
'j_offg'])
for i, (meta, ts_insitu) in enumerate(ismn.iter_stations()):
logging.info('%i/%i' % (i, len(ismn.list)))
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
for io, mode in zip([DA_const_err, DA_varia_err],
['const_err', 'varia_err']):
ubRMSD = np.sqrt(
(((io.timeseries['obs_obs'][:, row, col, :] -
io.timeseries['obs_obs'][:, row, col, :].mean()) -
(io.timeseries['obs_fcst'][:, row, col, :] -
io.timeseries['obs_fcst'][:, row, col, :].mean()))**2
).mean().values)
ensstd = np.sqrt(io.timeseries['obs_anavar'][:, row,
col, :].mean()).values
res['ubrmsd_' + mode] = ubRMSD
res['ensstd_' + mode] = ensstd
if (os.path.isfile(result_file) == False):
res.to_csv(result_file, float_format='%0.4f')
else:
res.to_csv(result_file,
float_format='%0.4f',
mode='a',
header=False)
def plot_cat_timeseries():
outpath = r'D:\work\LDAS\2018-02_scaling\_new\ismn_eval\timeseries'
fname = r"D:\work\LDAS\2018-02_scaling\_new\ismn_eval\validation.csv"
res = pd.read_csv(fname)
diff_srf = res['corr_DA_cal_pent_ma_sm_surface'] - res[
'corr_DA_uncal_pent_ma_sm_surface']
diff_rz = res['corr_DA_cal_pent_ma_sm_rootzone'] - res[
'corr_DA_uncal_pent_ma_sm_rootzone']
diff_prof = res['corr_DA_cal_pent_ma_sm_profile'] - res[
'corr_DA_uncal_pent_ma_sm_profile']
ind = (diff_srf > 0.2) | (diff_rz > 0.2) | (diff_prof > 0.2)
res = res.loc[ind, ['network', 'station', 'lat', 'lon']]
ismn = ISMN_io()
cal = LDAS_io('xhourly', 'US_M36_SMOS_DA_calibrated_scaled')
uncal = LDAS_io('xhourly', 'US_M36_SMOS_DA_nocal_scaled_pentadal')
variables = ['sm_surface', 'sm_rootzone', 'sm_profile']
for idx, stat in res.iterrows():
fname = os.path.join(outpath,
stat.network + '_' + stat.station + '.png')
ts_ismn = ismn.read(stat.network, stat.station)
lat = stat.lat
lon = stat.lon
plt.figure(figsize=(17, 9))
for i, var in enumerate(variables):
ax = plt.subplot(3, 1, i + 1)
ts_cal = calc_anomaly(cal.read_ts(var, lon, lat), method='ma')
ts_cal.index += pd.to_timedelta('2 hours')
ts_uncal = calc_anomaly(uncal.read_ts(var, lon, lat), method='ma')
ts_uncal.index += pd.to_timedelta('2 hours')
df = pd.DataFrame({
'cal': ts_cal,
'uncal': ts_uncal,
'insitu': calc_anomaly(ts_ismn[var], method='ma')
}).dropna()
if len(df) > 0:
df.plot(ax=ax)
else:
continue
title = 'R(ismn - cal) = %.2f , R(ismn - uncal) = %.2f' % (
df.corr().loc['insitu', 'cal'], df.corr().loc['insitu',
'uncal'])
ax.set_title(title, fontsize=12)
ax.set_xlim('2010-01-01', '2016-01-01')
ax.set_ylim(-0.3, 0.3)
ax.set_xlabel('')
plt.tight_layout()
plt.savefig(fname, dpi=150)
plt.close()
def run(part):
parts = 6
result_file = r'D:\work\ESA_CCI_SM\validation_%i.csv' % part
cci = CCISM_io()
ismn = ISMN_io()
# ismn.list = ismn.list.iloc[100:120]
# Split station list in 4 parts for parallelization
subs = (np.arange(parts + 1) * len(ismn.list) / parts).astype('int')
subs[-1] = len(ismn.list)
start = subs[part - 1]
end = subs[part]
ismn.list = ismn.list.iloc[start:end, :]
periods = {
'p1': ['2007-10-01', '2010-01-14'],
'p2': ['2010-01-15', '2011-10-04'],
'p3': ['2011-10-05', '2012-06-30'],
'p4': ['2012-07-01', '2014-12-31']
}
freq = ['abs', 'anom']
corr_tags = [
'corr_' + m + '_' + v + '_' + p + '_' + f for m in cci.modes
for v in cci.versions for p in periods.keys() for f in freq
]
p_tags = [
'p_' + m + '_' + v + '_' + p + '_' + f for m in cci.modes
for v in cci.versions for p in periods.keys() for f in freq
]
n_tags = [
'n_' + m + '_' + v + '_' + p + '_' + f for m in cci.modes
for v in cci.versions for p in periods.keys() for f in freq
]
res = ismn.list.copy()
res.drop(['ease_col', 'ease_row'], axis='columns', inplace=True)
for col in corr_tags + p_tags:
res[col] = np.nan
for col in n_tags:
res[col] = 0
for i, (meta, ts_insitu) in enumerate(ismn.iter_stations()):
print('%i/%i (Proc %i)' % (i, len(ismn.list), part))
if ts_insitu is None:
print('No in situ data for ' + meta.network + ' / ' + meta.station)
continue
ts_insitu = ts_insitu[periods['p1'][0]:periods['p4'][1]]
if len(ts_insitu) < 10:
print('No in situ data for ' + meta.network + ' / ' + meta.station)
continue
df_insitu = pd.DataFrame(ts_insitu).dropna()
df_insitu_anom = pd.DataFrame(calc_anomaly(ts_insitu)).dropna()
for m in cci.modes:
df_cci = cci.read(meta.lon, meta.lat, mode=m).dropna()
if len(df_cci) < 10:
print('No CCI ' + m + ' data for ' + meta.network + ' / ' +
meta.station)
continue
for f in freq:
if f == 'abs':
matched = df_match(df_cci, df_insitu, window=0.5)
else:
for v in cci.versions:
df_cci.loc[:, m + '_' + v] = calc_anomaly(
df_cci[m + '_' + v])
df_cci.dropna(inplace=True)
if (len(df_cci) < 10) | (len(df_insitu_anom) < 10):
print('No in situ or CCI ' + m + ' anomaly data for ' +
meta.network + ' / ' + meta.station)
continue
matched = df_match(df_cci, df_insitu_anom, window=0.5)
data = df_cci.join(matched['insitu']).dropna()
for p in periods.keys():
vals = data[periods[p][0]:periods[p][1]].values
n_matches = vals.shape[0]
if n_matches < 10:
continue
for k, v in enumerate(cci.versions):
corr, p_value = pearsonr(vals[:, k], vals[:, -1])
res.loc[meta.name, 'corr_' + m + '_' + v + '_' + p +
'_' + f] = corr
res.loc[meta.name, 'p_' + m + '_' + v + '_' + p + '_' +
f] = p_value
res.loc[meta.name, 'n_' + m + '_' + v + '_' + p + '_' +
f] = n_matches
res.to_csv(result_file, float_format='%0.4f')
def EC_ascat_smap_ismn_ldas():
result_file = Path('/Users/u0116961/Documents/work/extended_collocation/ec_ascat_smap_ismn_ldas.csv')
names = ['insitu', 'ascat', 'smap', 'ol', 'da']
combs = list(combinations(names, 2))
ds_ol = LDAS_io('xhourly', 'US_M36_SMAP_TB_OL_noScl').timeseries
ds_da = LDAS_io('xhourly', 'US_M36_SMAP_TB_MadKF_DA_it11').timeseries
ds_da_ana = LDAS_io('ObsFcstAna', 'US_M36_SMAP_TB_MadKF_DA_it11').timeseries['obs_ana']
tg = LDAS_io().grid.tilegrids
modes = ['absolute','longterm','shortterm']
ismn = ISMN_io()
ismn.list = ismn.list.iloc[70::]
ascat = HSAF_io()
smap = SMAP_io()
lut = pd.read_csv(Paths().lut, index_col=0)
i = 0
for meta, ts_insitu in ismn.iter_stations(surface_only=True):
i += 1
logging.info('%i/%i' % (i, len(ismn.list)))
try:
if len(ts_insitu := ts_insitu['2015-04-01':'2020-04-01'].resample('1d').mean().dropna()) < 25:
continue
except:
continue
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
colg = col + tg.loc['domain', 'i_offg'] # col / lon
rowg = row + tg.loc['domain', 'j_offg'] # row / lat
tmp_lut = lut[(lut.ease2_col == colg) & (lut.ease2_row == rowg)]
if len(tmp_lut) == 0:
continue
gpi_smap = tmp_lut.index.values[0]
gpi_ascat = tmp_lut.ascat_gpi.values[0]
try:
ts_ascat = ascat.read(gpi_ascat, resample_time=False).resample('1d').mean().dropna()
ts_ascat = ts_ascat[~ts_ascat.index.duplicated(keep='first')]
ts_ascat.name = 'ASCAT'
except:
continue
ts_smap = smap.read(gpi_smap)
if (ts_ascat is None) | (ts_smap is None):
continue
ind = (ds_ol['snow_mass'][:, row, col].values == 0)&(ds_ol['soil_temp_layer1'][:, row, col].values > 277.15)
ts_ol = ds_ol['sm_surface'][:, row, col].to_series().loc[ind].dropna()
ts_ol.index += pd.to_timedelta('2 hours')
ind = (ds_da['snow_mass'][:, row, col].values == 0)&(ds_da['soil_temp_layer1'][:, row, col].values > 277.15)
ts_da = ds_da['sm_surface'][:, row, col].to_series().loc[ind].dropna()
ts_da.index += pd.to_timedelta('2 hours')
for mode in modes:
if mode == 'absolute':
ts_ins = ts_insitu.copy()
ts_asc = ts_ascat.copy()
ts_smp = ts_smap.copy()
ts_ol = ts_ol.copy()
ts_da = ts_da.copy()
else:
ts_ins = calc_anom(ts_ins.copy(), longterm=(mode=='longterm')).dropna()
ts_asc = calc_anom(ts_asc.copy(), longterm=(mode == 'longterm')).dropna()
ts_smp = calc_anom(ts_smp.copy(), longterm=(mode == 'longterm')).dropna()
ts_ol = calc_anom(ts_ol.copy(), longterm=(mode == 'longterm')).dropna()
ts_da = calc_anom(ts_da.copy(), longterm=(mode == 'longterm')).dropna()
tmp = pd.DataFrame(dict(zip(names, [ts_ins, ts_asc, ts_smp, ts_ol, ts_da]))).dropna()
corr = tmp.corr()
ec_res = ecol(tmp[['insitu', 'ascat', 'smap', 'ol', 'da']], correlated=[['smap', 'ol'], ['smap', 'da'], ['ol', 'da']])
res[f'len_{mode}'] = len(tmp)
for c in combs:
res[f'corr_{"_".join(c)}'] = corr.loc[c]
res[f'err_corr_smap_ol_{mode}'] = ec_res['err_corr_smap_ol']
res[f'err_corr_smap_da_{mode}'] = ec_res['err_corr_smap_da']
res[f'err_corr_ol_da_{mode}'] = ec_res['err_corr_ol_da']
if not result_file.exists():
res.to_csv(result_file, float_format='%0.4f')
else:
res.to_csv(result_file, float_format='%0.4f', mode='a', header=False)
def TCA_insitu_evaluation():
result_file = r'D:\work\LDAS\2018-06_rmse_uncertainty\TCA_evaluation\validation.csv'
noDA = LDAS_io('xhourly', 'US_M36_SMOS40_noDA_cal_scaled')
DA_const_err = LDAS_io('xhourly', 'US_M36_SMOS40_DA_cal_scaled')
DA_varia_err = LDAS_io('xhourly', 'US_M36_SMOS40_DA_cal_scl_errfile')
t_ana = pd.DatetimeIndex(
LDAS_io('ObsFcstAna', 'US_M36_SMOS40_DA_cal_scaled').timeseries.time.
values).sort_values()
ascat = HSAF_io()
gpi_list = pd.read_csv(
r"D:\data_sets\ASCAT\warp5_grid\pointlist_warp_conus.csv", index_col=0)
ismn = ISMN_io(col_offs=noDA.grid.tilegrids.loc['domain', 'i_offg'],
row_offs=noDA.grid.tilegrids.loc['domain', 'j_offg'])
runs = ['noDA', 'DA_const_err', 'DA_varia_err']
tss = [noDA.timeseries, DA_const_err.timeseries, DA_varia_err.timeseries]
variables = [
'sm_surface',
]
modes = [
'absolute',
]
for i, (meta, ts_insitu) in enumerate(ismn.iter_stations()):
logging.info('%i/%i' % (i, len(ismn.list)))
try:
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
gpi = lonlat2gpi(meta.lon, meta.lat, gpi_list)
ts_asc = ascat.read(gpi, resample_time=False)
if ts_asc is None:
continue
ts_asc.name = 'ascat'
ts_asc = pd.DataFrame(ts_asc)
for var in variables:
for mode in modes:
ts_ins = ts_insitu[var].dropna()
ts_ins.name = 'insitu'
ts_ins = pd.DataFrame(ts_ins)
for run, ts_model in zip(runs, tss):
ind = (ts_model['snow_mass'][row, col].values == 0) & (
ts_model['soil_temp_layer1'][row,
col].values > 277.15)
ts_mod = ts_model[var][row, col].to_series().loc[ind]
ts_mod.index += pd.to_timedelta('2 hours')
ts_mod = ts_mod.loc[t_ana].dropna()
ts_mod.name = 'model'
ts_mod = pd.DataFrame(ts_mod)
matched = df_match(ts_mod, ts_asc, ts_ins, window=0.5)
data = ts_mod.join(matched[0][[
'ascat',
]]).join(matched[1][[
'insitu',
]]).dropna()
tc_res = TCA(data['model'].values,
data['ascat'].values,
data['insitu'].values)
res['RMSE_model_' + run + '_' + mode + '_' +
var] = tc_res[1][0]
res['RMSE_ascat_' + run + '_' + mode + '_' +
var] = tc_res[1][1]
res['RMSE_insitu_' + run + '_' + mode + '_' +
var] = tc_res[1][2]
res['beta_ascat_' + run + '_' + mode + '_' +
var] = tc_res[2][1]
res['beta_insitu_' + run + '_' + mode + '_' +
var] = tc_res[2][2]
res['len_' + mode + '_' + var] = len(data)
if (os.path.isfile(result_file) == False):
res.to_csv(result_file, float_format='%0.4f')
else:
res.to_csv(result_file,
float_format='%0.4f',
mode='a',
header=False)
except:
continue
def insitu_evaluation():
result_file = r'D:\work\LDAS\2018-06_rmse_uncertainty\insitu_evaluation\validation.csv'
noDA = LDAS_io('xhourly', 'US_M36_SMOS40_noDA_cal_scaled')
DA_const_err = LDAS_io('xhourly', 'US_M36_SMOS40_DA_cal_scaled')
DA_varia_err = LDAS_io('xhourly', 'US_M36_SMOS40_DA_cal_scl_errfile')
t_ana = pd.DatetimeIndex(
LDAS_io('ObsFcstAna', 'US_M36_SMOS40_DA_cal_scaled').timeseries.time.
values).sort_values()
ismn = ISMN_io(col_offs=noDA.grid.tilegrids.loc['domain', 'i_offg'],
row_offs=noDA.grid.tilegrids.loc['domain', 'j_offg'])
runs = ['noDA', 'DA_const_err', 'DA_varia_err']
tss = [noDA.timeseries, DA_const_err.timeseries, DA_varia_err.timeseries]
variables = ['sm_surface', 'sm_rootzone', 'sm_profile']
# modes = ['absolute','longterm','shortterm']
modes = [
'absolute',
]
# ismn.list = ismn.list.iloc[101::]
i = 0
for meta, ts_insitu in ismn.iter_stations():
i += 1
logging.info('%i/%i' % (i, len(ismn.list)))
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
for var in variables:
for mode in modes:
if mode == 'absolute':
ts_ref = ts_insitu[var].dropna()
elif mode == 'mean':
ts_ref = calc_anomaly(ts_insitu[var], mode).dropna()
else:
ts_ref = calc_anomaly(
ts_insitu[var],
method='moving_average',
longterm=(mode == 'longterm')).dropna()
for run, ts_model in zip(runs, tss):
ind = (ts_model['snow_mass'][row, col].values == 0) & (
ts_model['soil_temp_layer1'][row, col].values > 277.15)
ts_mod = ts_model[var][row, col].to_series().loc[ind]
ts_mod.index += pd.to_timedelta('2 hours')
# TODO: Make sure that time of netcdf file is correct!!
if mode == 'absolute':
ts_mod = ts_mod.dropna()
else:
ts_mod = calc_anomaly(
ts_mod,
method='moving_average',
longterm=mode == 'longterm').dropna()
tmp = pd.DataFrame({
1: ts_ref,
2: ts_mod
}).loc[t_ana, :].dropna()
res['len_' + mode + '_' + var] = len(tmp)
r, p = pearsonr(tmp[1], tmp[2])
res['corr_' + run + '_' + mode + '_' +
var] = r if (r > 0) & (p < 0.01) else np.nan
res['rmsd_' + run + '_' + mode + '_' + var] = np.sqrt(
((tmp[1] - tmp[2])**2).mean())
res['ubrmsd_' + run + '_' + mode + '_' + var] = np.sqrt(
(((tmp[1] - tmp[1].mean()) -
(tmp[2] - tmp[2].mean()))**2).mean())
if (os.path.isfile(result_file) == False):
res.to_csv(result_file, float_format='%0.4f')
else:
res.to_csv(result_file,
float_format='%0.4f',
mode='a',
header=False)
def run_ismn_eval():
experiments = [['SMOSSMAP', 'short']]
names = ['open_loop'] + ['MadKF_SMOS40'
] + ['_'.join(exp) for exp in experiments]
runs = ['US_M36_SMAP_TB_OL_noScl'] + [
'US_M36_SMOS40_TB_MadKF_DA_it613'
] + [f'US_M36_SMAP_TB_DA_scl_{name}' for name in names[2::]]
dss = [LDAS_io('xhourly', run).timeseries for run in runs]
result_file = Path(
'/Users/u0116961/Documents/work/LDAS/2020-03_scaling/validation/ismn_eval.csv'
)
t_ana = pd.DatetimeIndex(
LDAS_io('ObsFcstAna', runs[0]).timeseries.time.values).sort_values()
variables = ['sm_surface', 'sm_rootzone', 'sm_profile']
modes = ['absolute', 'longterm', 'shortterm']
ismn = ISMN_io()
ismn.list = ismn.list.iloc[70::]
i = 0
for meta, ts_insitu in ismn.iter_stations(surface_only=False):
i += 1
logging.info('%i/%i' % (i, len(ismn.list)))
if len(ts_insitu := ts_insitu['2015-04-01':'2020-04-01']) < 50:
continue
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
for var in variables:
for mode in modes:
if mode == 'absolute':
ts_ref = ts_insitu[var].dropna()
else:
ts_ref = calc_anom(ts_insitu[var],
longterm=(mode == 'longterm')).dropna()
for run, ts_model in zip(names, dss):
ind = (ts_model['snow_mass'][:, row, col].values == 0) & (
ts_model['soil_temp_layer1'][:, row,
col].values > 277.15)
ts_mod = ts_model[var][:, row, col].to_series().loc[ind]
ts_mod.index += pd.to_timedelta('2 hours')
if mode == 'absolute':
ts_mod = ts_mod.dropna()
else:
ts_mod = calc_anom(
ts_mod, longterm=mode == 'longterm').dropna()
tmp = pd.DataFrame({1: ts_ref, 2: ts_mod}).dropna()
res['len_' + mode + '_' + var] = len(tmp)
r, p = pearsonr(
tmp[1], tmp[2]) if len(tmp) > 10 else (np.nan, np.nan)
res['r_' + run + '_' + mode + '_' + var] = r
# res['p_' + run +'_' + mode + '_' + var] = p
# res['rmsd_' + run +'_' + mode + '_' + var] = np.sqrt(((tmp[1]-tmp[2])**2).mean())
res['ubrmsd_' + run + '_' + mode + '_' + var] = np.sqrt(
(((tmp[1] - tmp[1].mean()) -
(tmp[2] - tmp[2].mean()))**2).mean())
tmp = pd.DataFrame({
1: ts_ref,
2: ts_mod
}).reindex(t_ana).dropna()
res['ana_len_' + mode + '_' + var] = len(tmp)
r, p = pearsonr(
tmp[1], tmp[2]) if len(tmp) > 10 else (np.nan, np.nan)
res['ana_r_' + run + '_' + mode + '_' + var] = r
# res['ana_p_' + run + '_' + mode + '_' + var] = p
# res['ana_rmsd_' + run +'_' + mode + '_' + var] = np.sqrt(((tmp[1]-tmp[2])**2).mean())
res['ana_ubrmsd_' + run + '_' + mode + '_' +
var] = np.sqrt((((tmp[1] - tmp[1].mean()) -
(tmp[2] - tmp[2].mean()))**2).mean())
if not result_file.exists():
res.to_csv(result_file, float_format='%0.4f')
else:
res.to_csv(result_file,
float_format='%0.4f',
mode='a',
header=False)
def run(part):
parts = 15
smos = SMOS_io()
ismn = ISMN_io()
ascat = HSAF_io(ext=None)
mswep = MSWEP_io()
# Median Q from MadKF API/CONUS run.
Q_avg = 12.
R_avg = 74.
# Select only SCAN and USCRN
ismn.list = ismn.list[(ismn.list.network == 'SCAN') |
(ismn.list.network == 'USCRN')]
ismn.list.index = np.arange(len(ismn.list))
# Split station list in 4 parts for parallelization
subs = (np.arange(parts + 1) * len(ismn.list) / parts).astype('int')
subs[-1] = len(ismn.list)
start = subs[part - 1]
end = subs[part]
ismn.list = ismn.list.iloc[start:end, :]
if platform.system() == 'Windows':
result_file = os.path.join('D:', 'work', 'MadKF', 'CONUS', 'ismn_eval',
'result_part%i.csv' % part)
elif platform.system() == 'Linux':
result_file = os.path.join('/', 'scratch', 'leuven', '320', 'vsc32046',
'output', 'MadKF', 'CONUS', 'ismn_eval',
'result_part%i.csv' % part)
else:
result_file = os.path.join('/', 'work', 'MadKF', 'CONUS', 'ismn_eval',
'parts2', 'result_part%i.csv' % part)
dt = ['2010-01-01', '2015-12-31']
for cnt, (station,
insitu) in enumerate(ismn.iter_stations(surf_depth=0.1)):
# station = ismn.list.loc[978,:]
# insitu = ismn.read_first_surface_layer('SCAN','Los_Lunas_Pmc')
print('%i / %i' % (cnt, len(ismn.list)))
# if True:
try:
gpi = lonlat2gpi(station.lon, station.lat, mswep.grid)
mswep_idx = mswep.grid.index[mswep.grid.dgg_gpi == gpi][0]
smos_gpi = mswep.grid.loc[mswep_idx, 'smos_gpi']
precip = mswep.read(mswep_idx)
sm_ascat = ascat.read(gpi)
sm_smos = smos.read(smos_gpi) * 100.
if (precip is None) | (sm_ascat is None) | (sm_smos is None) | (
insitu is None):
continue
precip = calc_anomaly(precip[dt[0]:dt[1]],
method='moving_average',
longterm=False)
sm_ascat = calc_anomaly(sm_ascat[dt[0]:dt[1]],
method='moving_average',
longterm=False)
sm_smos = calc_anomaly(sm_smos[dt[0]:dt[1]],
method='moving_average',
longterm=False)
insitu = calc_anomaly(insitu[dt[0]:dt[1]].resample('1d').first(),
method='moving_average',
longterm=False).tz_localize(None)
df = pd.DataFrame({
1: precip,
2: sm_ascat,
3: sm_smos,
4: insitu
},
index=pd.date_range(dt[0], dt[1]))
df.loc[np.isnan(df[1]), 1] = 0.
n = len(df)
if len(df.dropna()) < 50:
continue
gamma = mswep.grid.loc[mswep_idx, 'gamma']
api = API(gamma=gamma)
# --- OL run ---
x_OL = np.full(n, np.nan)
model = deepcopy(api)
for t, f in enumerate(precip.values):
x = model.step(f)
x_OL[t] = x
# ----- Calculate uncertainties -----
# convert (static) forcing to model uncertainty
P_avg = Q_avg / (1 - gamma**2)
# calculate TCA based uncertainty and scaling coefficients
tmp_df = pd.DataFrame({
1: x_OL,
2: sm_ascat,
3: sm_smos
},
index=pd.date_range(dt[0], dt[1])).dropna()
snr, r_tc, err, beta = tc(tmp_df)
P_TC = err[0]**2
Q_TC = P_TC * (1 - gamma**2)
R_TC = (err[1] / beta[1])**2
H_TC = beta[1]
# Calculate RMSD based uncertainty
R_rmsd = (np.nanmean(
(tmp_df[1].values - H_TC * tmp_df[2].values)**2) - P_avg)
if R_rmsd < 0:
R_rmsd *= -1
# -----------------------------------
# ----- Run KF using TCA-based uncertainties -----
api_kf = API(gamma=gamma, Q=Q_TC)
x_kf, P, R_innov_kf, checkvar_kf, K_kf = \
KF(api_kf, df[1].values.copy(), df[2].values.copy(), R_TC, H=H_TC)
# ----- Run EnKF using static uncertainties -----
forc_pert = ['normal', 'additive', Q_avg]
obs_pert = ['normal', 'additive', R_avg]
x_avg, P, R_innov_avg, checkvar_avg, K_avg = \
EnKF(api, df[1].values.copy(), df[2].values.copy(), forc_pert, obs_pert, H=H_TC, n_ens=50)
# ----- Run EnKF using RMSD-based uncertainties (corrected for model uncertainty) -----
# forc_pert = ['normal', 'additive', Q_avg]
# obs_pert = ['normal', 'additive', R_rmsd]
# x_rmsd, P, R_innov_rmsd, checkvar_rmsd, K_rmsd = \
# EnKF(api, df[1].values.copy(), df[2].values.copy(), forc_pert, obs_pert, H=H_TC, n_ens=50)
# ----- Run MadKF -----
cnt = 0
checkvar_madkf = 9999.
while ((checkvar_madkf < 0.95) |
(checkvar_madkf > 1.05)) & (cnt < 5):
cnt += 1
tmp_x_madkf, P_madkf, R_madkf, Q_madkf, H_madkf, R_innov_madkf, tmp_checkvar_madkf, K_madkf = \
MadKF(api, df[1].values.copy(), df[2].values.copy(), n_ens=100, n_iter=20)
if abs(1 - tmp_checkvar_madkf) < abs(1 - checkvar_madkf):
checkvar_madkf = tmp_checkvar_madkf
x_madkf = tmp_x_madkf
df['x_ol'] = x_OL
df['x_kf'] = x_kf
df['x_avg'] = x_avg
# df['x_rmsd'] = x_rmsd
df['x_madkf'] = x_madkf
# tc_ol = tc(df[[4,3,'x_ol']])
# tc_kf = tc(df[[4,3,'x_kf']])
# tc_avg = tc(df[[4,3,'x_avg']])
# tc_rmsd = tc(df[[4,3,'x_rmsd']])
# tc_madkf = tc(df[[4,3,'x_madkf']])
ci_l_ol, ci_m_ol, ci_u_ol = bootstrap_tc(df[[4, 3, 'x_ol']])
ci_l_kf, ci_m_kf, ci_u_kf = bootstrap_tc(df[[4, 3, 'x_kf']])
ci_l_avg, ci_m_avg, ci_u_avg = bootstrap_tc(df[[4, 3, 'x_avg']])
# ci_l_rmsd, ci_m_rmsd, ci_u_rmsd = bootstrap_tc(df[[4,3,'x_rmsd']])
ci_l_madkf, ci_m_madkf, ci_u_madkf = bootstrap_tc(
df[[4, 3, 'x_madkf']])
corr = df.dropna().corr()
n_all = len(df.dropna())
result = pd.DataFrame(
{
'lon': station.lon,
'lat': station.lat,
'network': station.network,
'station': station.station,
'gpi': gpi,
'n_all': n_all,
'Q_est_madkf': Q_madkf,
'R_est_madkf': R_madkf,
'corr_ol': corr[4]['x_ol'],
'corr_kf': corr[4]['x_kf'],
'corr_avg': corr[4]['x_avg'],
# 'corr_rmsd': corr[4]['x_rmsd'],
'corr_madkf': corr[4]['x_madkf'],
# 'snr_ol': tc_ol[0][2],
# 'snr_kf': tc_kf[0][2],
# 'snr_avg': tc_avg[0][2],
# 'snr_rmsd': tc_rmsd[0][2],
# 'snr_madkf': tc_madkf[0][2],
# 'r_ol': tc_ol[1][2],
# 'r_kf': tc_kf[1][2],
# 'r_avg': tc_avg[1][2],
# 'r_rmsd': tc_rmsd[1][2],
# 'r_madkf': tc_madkf[1][2],
# 'rmse_kf': tc_kf[2][2],
# 'rmse_avg': tc_avg[2][2],
# 'rmse_rmsd': tc_rmsd[2][2],
# 'rmse_madkf': tc_madkf[2][2],
# 'rmse_ol': tc_ol[2][2],
'r_ol_l': ci_l_ol,
'r_ol_m': ci_m_ol,
'r_ol_u': ci_u_ol,
'r_kf_l': ci_l_kf,
'r_kf_m': ci_m_kf,
'r_kf_u': ci_u_kf,
'r_avg_l': ci_l_avg,
'r_avg_m': ci_m_avg,
'r_avg_u': ci_u_avg,
# 'r_rmsd_l': ci_l_rmsd,
# 'r_rmsd_m': ci_m_rmsd,
# 'r_rmsd_u': ci_u_rmsd,
'r_madkf_l': ci_l_madkf,
'r_madkf_m': ci_m_madkf,
'r_madkf_u': ci_u_madkf,
'checkvar_kf': checkvar_kf,
'checkvar_avg': checkvar_avg,
# 'checkvar_rmsd': checkvar_rmsd,
'checkvar_madkf': checkvar_madkf,
'R_innov_kf': R_innov_kf,
'R_innov_avg': R_innov_avg,
# 'R_innov_rmsd': R_innov_rmsd,
'R_innov_madkf': R_innov_madkf
},
index=(station.name, ))
if (os.path.isfile(result_file) == False):
result.to_csv(result_file, float_format='%0.4f')
else:
result.to_csv(result_file,
float_format='%0.4f',
mode='a',
header=False)
except:
print('GPI failed.')
continue
ascat.close()
mswep.close()
def plot_suspicious_stations(root):
statlist = pd.read_csv('/Users/u0116961/Documents/work/MadKF/CLSM/suspicious_stations/station_list_r_diff.csv', index_col=0)
rmsd_root = 'US_M36_SMAP_TB_DA_SM_PROXY_'
rmsd_exps = list(np.sort([x.name.split(rmsd_root)[1] for x in Path('/Users/u0116961/data_sets/LDASsa_runs').glob('*SM_PROXY*')]))
ds_ol = LDAS_io('xhourly', 'US_M36_SMAP_TB_OL_scaled_4K_obserr').timeseries
ds_da = LDAS_io('xhourly', 'US_M36_SMAP_TB_DA_scaled_4K_obserr').timeseries
ts_ana = LDAS_io('ObsFcstAna', 'US_M36_SMAP_TB_DA_scaled_4K_obserr').timeseries['obs_obs']
t_ana = pd.DatetimeIndex(ts_ana.time.values).sort_values()
ascat = HSAF_io()
gpi_list = pd.read_csv(ascat.root / 'warp5_grid' / 'pointlist_warp_conus.csv', index_col=0)
ismn = ISMN_io()
variables = ['sm_surface', 'sm_rootzone']
modes = ['absolute', 'longterm', 'shortterm']
ismn.list.index = ismn.list.network + '_' + ismn.list.station
ismn.list.reindex(statlist.index)
ismn.list = ismn.list.reindex(statlist.index)
for i, (meta, ts_insitu) in enumerate(ismn.iter_stations(surface_only=False)):
if 'tmp_res' in locals():
if (meta.network in tmp_res) & (meta.station in tmp_res):
print(f'Skipping {i}')
continue
try:
res = pd.DataFrame(meta.copy()).transpose()
col = meta.ease_col
row = meta.ease_row
gpi = lonlat2gpi(meta.lon, meta.lat, gpi_list)
ts_ascat = ascat.read(gpi) / 100 * 0.6
if ts_ascat is None:
continue
for mode in modes:
for var in variables:
tmp = statlist[(statlist.network==meta.network)&(statlist.station==meta.station)]
dpr = tmp[f'diff_pearsonr2_{mode}_{var}'].values[0]
dtr = tmp[f'diff_tcar2_{mode}_{var}'].values[0]
if not ((dtr < 0) & (dpr > 0)):
continue
if mode == 'absolute':
ts_asc = ts_ascat.dropna()
else:
ts_asc = calc_anom(ts_ascat, longterm=(mode == 'longterm')).dropna()
ts_asc.name = 'ascat'
ts_asc = pd.DataFrame(ts_asc)
if mode == 'absolute':
ts_ins = ts_insitu[var].dropna()
else:
ts_ins = calc_anom(ts_insitu[var], longterm=(mode == 'longterm')).dropna()
ts_ins.name = 'insitu'
ts_ins = pd.DataFrame(ts_ins)
ind = (ds_ol['snow_mass'].isel(lat=row, lon=col).values == 0) & \
(ds_ol['soil_temp_layer1'].isel(lat=row, lon=col).values > 277.15)
ts_ol = ds_ol[var].isel(lat=row, lon=col).to_series().loc[ind]
ts_ol.index += pd.to_timedelta('2 hours')
ind_obs = np.bitwise_or.reduce(~np.isnan(ts_ana[:, :, row, col].values), 1)
if mode == 'absolute':
ts_ol = ts_ol.reindex(t_ana[ind_obs]).dropna()
else:
ts_ol = calc_anom(ts_ol.reindex(t_ana[ind_obs]), longterm=(mode == 'longterm')).dropna()
ts_ol.name = 'open_loop'
ts_ol = pd.DataFrame(ts_ol)
ind = (ds_da['snow_mass'].isel(lat=row, lon=col).values == 0) & \
(ds_da['soil_temp_layer1'].isel(lat=row, lon=col).values > 277.15)
ts_da = ds_da[var].isel(lat=row, lon=col).to_series().loc[ind]
ts_da.index += pd.to_timedelta('2 hours')
ind_obs = np.bitwise_or.reduce(~np.isnan(ts_ana[:, :, row, col].values), 1)
if mode == 'absolute':
ts_da = ts_da.reindex(t_ana[ind_obs]).dropna()
else:
ts_da = calc_anom(ts_da.reindex(t_ana[ind_obs]), longterm=(mode == 'longterm')).dropna()
ts_da.name = 'DA_4K'
ts_da = pd.DataFrame(ts_da)
matched = df_match(ts_ol, ts_da, ts_asc, ts_ins, window=0.5)
data = ts_ol.join(matched[0]['DA_4K']).join(matched[1]['ascat']).join(matched[2]['insitu']).dropna()
dpr_triplets = data.corr()['DA_4K']['insitu'] - data.corr()['open_loop']['insitu']
if dpr_triplets < 0:
continue
f = plt.figure(figsize=(15, 5))
sns.lineplot(data=data[['open_loop', 'DA_4K', 'insitu']], dashes=False, linewidth=1.5, axes=plt.gca())
plt.title(f'{meta.network} / {meta.station} ({var}): d(Pearson R2) = {dpr_triplets:.3f} , d(TCA R2) = {dtr:.3f}')
fbase = Path('/Users/u0116961/Documents/work/MadKF/CLSM/suspicious_stations/timeseries')
fname = fbase / f'{mode}_{var}_{meta.network}_{meta.station}.png'
f.savefig(fname, dpi=300, bbox_inches='tight')
plt.close()
except:
continue
def run(part):
parts = 6
result_file = r'D:\work\ESA_CCI_SM\ismn_r2\ismn_r2_part%i.csv' % part
cci = CCISM_io()
ismn = ISMN_io()
# ismn.list = ismn.list.iloc[100:120]
# Split station list in 4 parts for parallelization
subs = (np.arange(parts + 1) * len(ismn.list) / parts).astype('int')
subs[-1] = len(ismn.list)
start = subs[part - 1]
end = subs[part]
ismn.list = ismn.list.iloc[start:end, :]
freq = ['abs', 'anom']
res = ismn.list.copy()
res.drop(['ease_col', 'ease_row'], axis='columns', inplace=True)
res['r_abs'] = np.nan
res['r_anom'] = np.nan
for i, (meta, ts_insitu) in enumerate(ismn.iter_stations()):
print('%i/%i (Proc %i)' % (i, len(ismn.list), part))
if ts_insitu is None:
print('No in situ data for ' + meta.network + ' / ' + meta.station)
continue
ts_insitu = ts_insitu['2007-10-01':'2014-12-31']
if len(ts_insitu) < 10:
print('No in situ data for ' + meta.network + ' / ' + meta.station)
continue
df_insitu = pd.DataFrame(ts_insitu).dropna()
df_insitu_anom = pd.DataFrame(calc_anomaly(ts_insitu)).dropna()
df_cci = cci.read(meta.lon,
meta.lat,
version='v04.4',
mode=['ACTIVE', 'PASSIVE']).dropna()
if len(df_cci) < 10:
print('No CCI data for ' + meta.network + ' / ' + meta.station)
continue
for f in freq:
if f == 'abs':
matched = df_match(df_cci, df_insitu, window=0.5)
else:
df_cci.loc[:, 'ACTIVE_v04.4'] = calc_anomaly(
df_cci['ACTIVE_v04.4'])
df_cci.loc[:, 'PASSIVE_v04.4'] = calc_anomaly(
df_cci['PASSIVE_v04.4'])
df_cci.dropna(inplace=True)
if (len(df_cci) < 10) | (len(df_insitu_anom) < 10):
print('No in situ or CCI anomaly data for ' +
meta.network + ' / ' + meta.station)
continue
matched = df_match(df_cci, df_insitu_anom, window=0.5)
data = df_cci.join(matched['insitu']).dropna()
if len(data) < 100:
continue
vals = data[['insitu', 'ACTIVE_v04.4']].values
c1, p1 = pearsonr(vals[:, 0], vals[:, 1])
vals = data[['insitu', 'PASSIVE_v04.4']].values
c2, p2 = pearsonr(vals[:, 0], vals[:, 1])
vals = data[['ACTIVE_v04.4', 'PASSIVE_v04.4']].values
c3, p3 = pearsonr(vals[:, 0], vals[:, 1])
if (c1 < 0) | (c2 < 0) | (c3 < 0) | (p1 > 0.05) | (p2 > 0.05) | (
p3 > 0.05):
continue
res.loc[meta.name, 'r_' + f] = np.sqrt(tc(data)[1][2])
res.to_csv(result_file, float_format='%0.4f')