def rain_runoff_calculate_here(year_N, dirname, mask_rg, area_in, year_ref):
ts_rain_test = np.zeros((year_N * 12))
ts_runoff_test = np.zeros((year_N * 12))
NN = 0
for NY in range(year_N):
for NM in range(12):
filename = 'P_R_' + str(year_ref + NY +
31) + '-' + str(NM + 1).zfill(2) + '.nc'
print(filename)
f = Dataset(dirname + filename, mode='r')
P_in = f.variables['RAIN'][:, :, :].data.squeeze(
) + f.variables['SNOW'][:, :, :].data.squeeze()
P_in[P_in > 1.e11] = np.nan
R_in = f.variables['QCHANR'][:, 176, 248].data.squeeze()
# R_in[R_in>1.e11] = np.nan
f.close()
ts_rain_test[NN] = np.nansum(
P_in[:, :] * mask_rg * area_in) / np.nansum(mask_rg * area_in)
# ts_runoff_test[NN] = np.nansum(R_in[:,:]*mask_rg*area_in)#/np.nansum(mask_rg*area_in)
ts_runoff_test[NN] = R_in
NN = NN + 1
ts_rmean = ts_rain_test.copy()
ts_rmean[int((N - 1) / 2):-int((N - 1) / 2)] = np.convolve(
ts_rain_test.copy(), np.ones((N, )) / N, mode='valid')
rain_monthly = ts_rmean.reshape((year_N, 12))
[rain_max_test, rain_min_test] = max_min_select(rain_monthly, year_N)
[rain_test_trend, rain_test_sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
np.linspace(1, year_N, year_N)[0:-2],
rain_max_test[1:-1] - rain_min_test[1:-1], sig_level)
ts_rmean = ts_runoff_test.copy()
ts_rmean[int((N - 1) / 2):-int((N - 1) / 2)] = np.convolve(
ts_runoff_test.copy(), np.ones((N, )) / N, mode='valid')
runoff_monthly = ts_rmean.reshape((year_N, 12))
[runoff_max_test, runoff_min_test] = max_min_select(runoff_monthly, year_N)
[runoff_test_trend, runoff_test_sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
np.linspace(1, year_N, year_N)[0:-2],
runoff_max_test[1:-1] - runoff_min_test[1:-1], sig_level)
return rain_test_trend, rain_test_sig, runoff_test_trend, runoff_test_sig, rain_max_test - rain_min_test
def sss_calculate_here(TLONG, TLAT, sss_test):
# Regrid
lon = np.linspace(0, 360, 360)
lat = np.linspace(-79, 89, 180)
[gridx_out, gridy_out] = np.meshgrid(lon, lat)
ny = 180
nx = 360
[gridx_in, gridy_in] = [TLONG, TLAT]
nt = sss_test.shape[0]
sss_test_interp = np.zeros((nt, ny, nx))
for NT in range(nt):
print('Ocean Salinity')
print(NT)
sss_test_interp[NT, :, :] = griddata(
(gridy_in.ravel(), gridx_in.ravel()),
np.squeeze(sss_test[NT, :, :]).ravel(), (gridy_out, gridx_out),
method='nearest')
# Select region
lon_amz1 = 295
lon_amz2 = 311
lat_amz1 = -3
lat_amz2 = 15
mask_sss = (sss_test_interp[11, :, :] / sss_test_interp[11, :, :]).copy()
[x1, x2, y1,
y2] = data_process_f.find_lon_lat_index(lon_amz1, lon_amz2, lat_amz1,
lat_amz2, lon, lat)
area_so = data_process_f.area_calculate_nonuniform(lon, lat)
ts_sss_test = np.zeros((nt))
for NT in range(nt):
ts_sss_test[NT] = np.nansum(
sss_test_interp[NT, y1:y2 + 1, x1:x2 + 1] *
area_so[y1:y2 + 1, x1:x2 + 1]) / np.nansum(
area_so[y1:y2 + 1, x1:x2 + 1] * mask_sss[y1:y2 + 1, x1:x2 + 1])
ts_rmean = ts_sss_test.copy()
ts_rmean[int((N - 1) / 2):-int((N - 1) / 2)] = np.convolve(
ts_sss_test.copy(), np.ones((N, )) / N, mode='valid')
sss_monthly = ts_rmean.reshape((int(nt / 12), 12))
[sss_max_test, sss_min_test] = max_min_select(sss_monthly, int(nt / 12))
[sss_test_trend, sss_test_sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
np.linspace(1, nt / 12, nt / 12)[0:-2],
sss_max_test[1:-1] - sss_min_test[1:-1], sig_level)
return sss_test_trend, sss_test_sig, pvalue
ts_test_rmean[int((N - 1) / 2):-int((N - 1) / 2)] = np.convolve(
ts_test, np.ones((N, )) / N, mode='valid')
# Arrange to monthly data
ts_monthly_soda = ts_test_rmean.reshape((year_N - 1, 12))
# Arrange max and min values
[sss_soda_max, sss_soda_min] = max_min_select(ts_monthly_soda,
year_N - 1)
print(sss_soda_max.shape)
print(year[1:-1])
print(sss_soda_max[1:-1])
ts_temp = sss_soda_max.copy()
[
reg, sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
year[2:-1], ts_temp[1:-1], sig_level)
print('SODA max', str(reg * 10), sig)
ts_temp = sss_soda_min.copy()
[
reg, sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
year[2:-1], ts_temp[1:-1], sig_level)
print('SODA min', str(reg * 10), sig)
ts_temp = (sss_soda_max - sss_soda_min).copy()
[
reg, sig, pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
year[2:-1], ts_temp[1:-1], sig_level)
print('SODA max-min', str(reg * 10), sig)
# ================================================================
run_trend_core2 = np.zeros((len(core2_case_txt), 58))
run_sig_core2 = np.zeros((len(core2_case_txt), 58))
# ================================================================
# Read Argo data
# ================================================================
for II in range(len(argo_case_txt)):
[ts_max, ts_min] = read_here1(dirname, argo_case_txt[II])
nt = len(ts_max)
# sss_argo_trends[II] = np.polyfit(np.linspace(1,nt,nt)[1:-1],ts_max[1:-1]-ts_min[1:-1],1)[0]
# sss_argo_sig[II] = stats.ttest_ind(np.linspace(1,nt,nt)[1:-1]*0.,ts_max[1:-1]-ts_min[1:-1])[1]
[
sss_argo_trends[II], sss_argo_sig[II], pvalue
] = statistical_f.linear_regression_1D_t_test_with_sample_size_adjusted(
np.linspace(1, nt, nt)[1:-1], ts_max[1:-1] - ts_min[1:-1],
sig_level)
if II == 0: ts_argo_ciso = ts_max - ts_min
if II == 1: ts_argo_iprc = ts_max - ts_min
if II == 2: ts_argo_isas = ts_max - ts_min
if II == 3: ts_argo_jamstec = ts_max - ts_min
if II == 4: ts_argo_scripps = ts_max - ts_min
sss_argo_trends[-1] = sss_argo_trends[:-1].mean()
for II in range(len(core2_case_txt)):
[ts_max, ts_min] = read_here1(dirname, core2_case_txt[II])
nt = len(ts_max[-31:])
# nt = len(ts_max[:])
[
sss_core2_trends[II], sss_core2_sig[II], pvalue