Contents

The following course cover the following content:

• Convert climate seasonal forecasting into model forcing datasets

• Translate climate forecasting into hydrological forecasting

3. Translate climate forecasting into hydrological forecasting

1. Preparing tercile forecasting

• Runing multiple simulaiton

• Understanding seasonal hydrological forecasting

WARNING!. Before you start the analyis, please make sure that you directories have the following structure. All required files are already located on the server.

print("Local_directory\n|__training\n      |___forecast\n      |      |__regional\n      |      |     |__datasets\n      |      |     |    |__csv\n      |      |     |    |__shp\n      |      |     |__model\n      |      |     |    |__inputs\n      |      |     |__outputs\n      |      |     |__postpp\n      |      |          |__csv\n      |      |          |__netcdf\n      |      |          |__raster\n      |      |          |__fig\n      |            |__basin\n      |___historical\n             |__regional\n             |     |__datasets\n             |     |__model\n             |     |__outputs\n             |     |__postpp\n                    |__basin\n")

Local_directory
|__training
      |___forecast
      |      |__regional
      |      |     |__datasets
      |      |     |    |__csv
      |      |     |    |__shp
      |      |     |__model
      |      |     |    |__inputs
      |      |     |__outputs
      |      |     |__postpp
      |      |          |__csv
      |      |          |__netcdf
      |      |          |__raster
      |      |          |__fig
      |      |__basin
      |___historical
             |__regional
             |     |__datasets
             |     |__model
             |     |__outputs
             |     |__postpp
             |__basin

3.1 Forcast: Preparation

The first stage of the forecasting analysis consist of preprocesing the historical simulations (“hindcast”). It includes preprocessing, temporal aggregation, and estimation of historical thresholds for all analysed variables.

All model simulations for both historical and forecasting outputs are located in the corresponding directories.

In order to quickly access all model files during the entire historical analysis, a “config_hindcast.py” file has been created. This file contain all paths required to store variables as well as additional parameters used in the analysis. A similar file was created for the forecasting, “config_forecasting.py”, which will be used for all files and directories during the forecasting analysis.

Additional tools for post-processing and ploting model and forecasting outputs is also provided. This component also has a config file, “config_plot_cuwalid.py”, however, not all component are connected in order to allow this component to run separately.

Some scripts make take hours to run, so make sure you allocate enough time to run the process. Also, scripts are namem using numerical labels, this indicates the files need to be run sequentially. Files with the same number can be run at the same time.

Please, make sure that the following list of files are available on your system.

"00_cuwalid_HAD_get_csv_TS_files_from_multi_CSV.py" "01a_cuwalid_HAD_get_TWSA_from_mult_files.py" "01b_cuwalid_HAD_get_additional_variables_multi_netcdf.py" "02a_cuwalid_HAD_get_percentiles_multi_files.py" "02c_cuwalid_HAD_get_extremes_quantiles_multi_netcdf.py" "02d_cuwalid_HAD_get_average_multi_netcdf.py" "03_cuwalid_HAD_get_anomalies_multi_netcdf.py" "04a_cuwalid_HAD_get_tercile_hindcast_fluxes.py" "04b_cuwalid_HAD_get_tercile_hindcast_extreme_values.py" "05_cuwalid_HAD_extract_forecasting_variable.py" "06_cuwalid_HAD_get_areas_terciles.py" "07_cuwalid_HAD_get_update_TWSA.py" "08_cuwalid_HAD_get_updated_TWSA_ensamble.py" "09_cuwalid_HAD_get_ensamble_forecasting.py" "10a_cuwalid_HAD_get_probabilistic_tercile_forecast_ensamble.py" "10b_cuwalid_HAD_get_deterministic_forecast_ensamble.py" "11a_cuwalid_HAD_plot_tercile_probability_forecast.py" "11b_cuwalid_HAD_plot_deterministic_forecast.py"

config_hindcast.py "02a_cuwalid_HAD_get_percentiles_multi_files.py" "02d_cuwalid_HAD_get_average_multi_netcdf.py" config_forecasting.py "09_cuwalid_HAD_get_ensamble_forecasting.py" "10a_cuwalid_HAD_get_probabilistic_tercile_forecast_ensamble.py" "10b_cuwalid_HAD_get_deterministic_forecast_ensamble.py"

Prepare Historical simulation

Preprocessing historical values

Understanding and modifying config hindcast file

#config_hindcast.py

• In the config_hindcast.py file the variable postpp_path should be as follows:

postpp_path = “/home//training/historical/regional/postpp/”

Concatenate *.csv files from yearly simulation files

#%00_cuwalid_HAD_get_csv_TS_files_from_multi_CSV.py

Calculate total water storage anomalies (TWSA) from storage (TWSC)

#%run 01a_cuwalid_HAD_get_TWSA_from_mult_files.py

Calculate Water Requirement Satisfaction Index (WRSI) and Calculate total Actual evapotranspirationt (aet) by adding actual evapotranspiration (aet) and evaporation from capillary rise (egw)

#%run 01b_cuwalid_HAD_get_additional_variables_multi_netcdf.py

Calculate average values from historical simulations. Depending on the number of variables that has been selected, this script may take a couple of hours to run.

#%02d_cuwalid_HAD_get_average_multi_netcdf.py

Calculate thresholds from historical simulations, terciales approach. This is only required once since these thresholds are constant over time. These threshold should be updated when the historical period increases(i.e. once a year).

Calculate percentiles 1/3 and 2/3

#%02a_cuwalid_HAD_get_percentiles_multi_files.py

Forecast analysis

Understanding and modifying the config forecasting file

#config_forecasting.py

• In the config_forecasting.py file the variable model_name should be as follows:

model_name= ‘MAM_2022_realization_test’

• In the config_forecasting.py file the variable postpp_path should be as follows:

postpp_path = “/home//training/forecast/regional/postpp/”

• In the config_forecasting.py file the variable threshold_path should be as follows:

threshold_path = “/home//training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_extremes_quantiles.nc”

Post-processing hydrological variables of the ensamble simulation (e.g. calculate TWSA and WRSI)

#08_cuwalid_HAD_get_updated_TWSA_ensamble.py

Generate ensamble of model simulations for each variable from the forecasting

#09_cuwalid_HAD_get_ensamble_forecasting.py

Caclulate tercile probabilistic forecasting from the ensamble simulations

#10a_cuwalid_HAD_get_probabilistic_tercile_forecast_ensamble.py

Calculate the deterministic forecast from the ensamble of simulations.

#%10b_cuwalid_HAD_get_deterministic_forecast_ensamble.py

3.2 Understanding seasonal hydrological forecast

# Import general libraries
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator

# Import libraries from local repository
import sys
#sys.path.append('C:/Users/Edisson/Documents/GitHub/DRYPv2.0.1')
sys.path.append("/home/cuwalid/CUWALID/DRYP/")
import tools.DRYP_pptools as pptools
import xarray as xr

Working with historical total averages

WARNING! This section needs to donwload files from the server, so before move to the following section, download all csv files from the forecastin outputs

• Assesing the temporal variability of water balance components

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import datetime
import rasterio

def aggregate_slice_csv(fname, agg_step='M', mean=False, date_start='2000-01-01', date_end='2023-01-01', timefield='Date'):
    df = read_csv(fname, timefield, date_start, date_end)
    return aggregate_dataframe(df, agg_step, mean).reset_index()
    #return df

def get_seasson_csv(fname, months, mean=False, reset_index=True, date_start='2000-01-01', date_end='2023-01-01', timefield='Date', aggregate=True):
    df = read_csv(fname, timefield, date_start, date_end)
    #print(df)
    df = aggregate_dataframe(df, 'M', mean)
    # Use boolean indexing to select the specified months
    df = df[df.index.month.isin(months)]
    if aggregate is True:
        df = aggregate_dataframe(df, 'Y', mean)
    if reset_index is True:
        df = df.reset_index()
    return df

def read_csv(fname, timefield, date_start, date_end):
    df = pd.read_csv(fname)
    df[timefield] = pd.to_datetime(df[timefield])
    df = df[df[timefield].between(date_start, date_end)]
    df.index = pd.DatetimeIndex(df[timefield])
    #print(df)# = df.drop([timefield])
    return df

def aggregate_dataframe(df, agg_step, mean):
    #print(df)
    if mean is True:
            df = df.resample(agg_step).mean(numeric_only=True)#.reset_index()
    else:
            df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
    return df

def plot_anomalies_bar(time, var, threshold=0, bar_width = 300, ax=None):
    """Create a bar plot of anomalies"""
    # create plot
    if ax is None:
        fig, ax = plt.subplots()
    x_values = np.arange(len(time))+time[0].year
    mask_avobe = var < threshold
    mask_below = var >= threshold
    #ax.bar(x_values[mask_avobe], var[mask_avobe], width=bar_width, color='r')#, alpha=1.00)
    #ax.bar(x_values[mask_below], var[mask_below], width=bar_width, color='b')#, alpha=1.00)
    ax.bar(time[mask_avobe], var[mask_avobe], width=bar_width, color='r')#, alpha=1.00)
    ax.bar(time[mask_below], var[mask_below], width=bar_width, color='b')#, alpha=1.00)

def plot_anomalies_line(time, var, threshold=0, ax=None):
    """Create a bar plot of anomalies"""
    # create plot
    if ax is None:
        fig, ax = plt.subplots()
    ax.fill_between(time, var, threshold, where=(var > threshold), interpolate=True, color='blue')#, alpha=.25)
    ax.fill_between(time, var, threshold, where=(var <= threshold), interpolate=True, color='red')#,  alpha=.25)

• Create yearly anomalies of water balance components

Specify the name of the model results files for ttime series

#fname_csv = basin_path + "output/Tana_IMb_sim_avg.csv"
#fname_csv = "D:/HAD/postpp/csv/HAD_IMERGba_sim0_avg.csv"
fname_csv = "/home/cuwalid/training/historical/regional/outputs/HAD_IMERGba_sim0_avg.csv"

Calculate yearly and seasonal anomalies

# Calculate yearly anomalies
# read dataset, slice and temporaly aggregate
dfavg = aggregate_slice_csv(fname_csv, agg_step='Y')
#calculate mean
mean_avg = dfavg.mean()

/tmp/ipykernel_2039448/249471917.py:31: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

ifield = 'pre_0'
fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(9, 2.5)
plot_anomalies_bar(dfavg['Date'], dfavg[ifield].values-mean_avg[ifield], ax=ax)
plt.xlabel('Year')
plt.axhline(0, color='k', alpha=0.5)
plt.ylabel('Precipitation\nAnomalies [mm/year]')
plt.title("precipitation anomalies")

Text(0.5, 1.0, 'precipitation anomalies')

• Create seasonal anomalies for March-May (MAM)

# calculate seasonal anomalies
# select seasons
data_MAM = get_seasson_csv(fname_csv, [3, 4, 5]).reset_index()
# get mean values from season
mean_MAM = data_MAM.mean()
# get anomales of historical values
#anomaly_MAM = data_MAM - mean_MAM

/tmp/ipykernel_2039448/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2039448/249471917.py:31: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

#data_MAM.head(3)

Plot precipitation seasonal anomalies

ifield = 'pre_0'
fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(9, 2.5)
#plot_anomalies_bar(dfavg['Date'], dfavg[ifield].values-mean_avg[ifield], ax=ax)
plot_anomalies_bar(data_MAM['Date'], data_MAM[ifield].values-mean_MAM[ifield], ax=ax)
plt.xlabel('Year')
plt.axhline(0, color='k', alpha=0.5)
plt.ylabel('Precipitation\nAnomalies [mm/year]')
plt.title("MAM precipitation anomalies")

Text(0.5, 1.0, 'MAM precipitation anomalies')

Plot actual evapotranspiration historical anomalies

ifield = 'aet_0'
fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(9, 2.5)
plot_anomalies_line(data_MAM['Date'], data_MAM[ifield].values-mean_MAM[ifield], ax=ax)
#plot_anomalies_line(dfavg['Date'], dfavg[ifield].values-mean_avg[ifield], ax=ax)
plt.xlabel('Year')
plt.ylabel('Actual Evapotranspiration\n[mm/year]')

Text(0, 0.5, 'Actual Evapotranspiration\n[mm/year]')

TASK: Plot anomalies for soil moisture and groundwater recharge

Plot soil moisture histrical anomalies

ifield = 'tht_0'
fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(9, 2.5)
plot_anomalies_line(data_MAM['Date'], data_MAM[ifield].values-mean_MAM[ifield], ax=ax)
#plot_anomalies_line(dfavg['Date'], dfavg[ifield].values-mean_avg[ifield], ax=ax)
plt.xlabel('Year')
plt.ylabel('Actual Evapotranspiration\n[mm/year]')

Text(0, 0.5, 'Actual Evapotranspiration\n[mm/year]')

Plot recharge historical anomalies

ifield = 'rch_0'
fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(9, 2.5)
plot_anomalies_line(data_MAM['Date'], data_MAM[ifield].values-mean_MAM[ifield], ax=ax)
#plot_anomalies_line(dfavg['Date'], dfavg[ifield].values-mean_avg[ifield], ax=ax)
plt.xlabel('Year')
plt.ylabel('Actual Evapotranspiration\n[mm/year]')

Text(0, 0.5, 'Actual Evapotranspiration\n[mm/year]')

Working with historical grided datasets

Plot mean of historical values

fname_nc = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_mean.nc"

data = xr.open_dataset(fname_nc)['pre']
data.plot(x="lon", y="lat", robust=False, cmap='gist_ncar_r', levels=11, figsize=(4,3.5), vmin=0, vmax=300)

<matplotlib.collections.QuadMesh at 0x7f0dffb84e10>

data = xr.open_dataset(fname_nc)['pet']
data.plot(x="lon", y="lat", robust=False, cmap='gist_ncar_r', levels=11, figsize=(4,3.5), vmin=300, vmax=600)

<matplotlib.collections.QuadMesh at 0x7f0dff9d1c50>

data = xr.open_dataset(fname_nc)['aet']
data.plot(x="lon", y="lat", robust=False, cmap='gist_ncar_r', levels=11, figsize=(4,3.5), vmin=0, vmax=300)

<matplotlib.collections.QuadMesh at 0x7f0e01121e90>

#data = xr.open_dataset(fname_nc)['rch']
#data.plot(x="lon", y="lat", robust=False, cmap='gist_ncar_r', levels=11, figsize=(4,3.5), vmin=0, vmax=300)

Plot terciles values from historical values

fname_nc = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_quantiles.nc"

data = xr.open_dataset(fname_nc)['pre']
data.plot(x="lon", y="lat", col="time", robust=False, cmap='gist_ncar_r', levels=11, figsize=(9,3.5), vmin=0, vmax=400)

<xarray.plot.facetgrid.FacetGrid at 0x7f0dfffe8c50>

#data

<xarray.DataArray 'pre' (time: 3, lat: 2614, lon: 2313)> Size: 145MB
[18138546 values with dtype=float64]
Coordinates:
  * lat       (lat) float32 10kB -1.17e+06 -1.17e+06 ... 1.231e+06 1.232e+06
  * lon       (lon) float32 9kB 1.32e+06 1.32e+06 ... 3.444e+06 3.445e+06
    quantile  (time) float64 24B ...
Dimensions without coordinates: time

xarray.DataArray

'pre'

• time: 3
• lat: 2614
• lon: 2313

• ...

  [18138546 values with dtype=float64]

• Coordinates: (3)
  • lat
    (lat)
    float32
    -1.17e+06 -1.17e+06 ... 1.232e+06

    units :

    meters

    array([-1170429.4, -1169510.1, -1168590.9, ...,  1229711.2,  1230630.5,
            1231549.8], dtype=float32)

  • lon
    (lon)
    float32
    1.32e+06 1.32e+06 ... 3.445e+06

    units :

    meters

    array([1319567.2, 1320486.5, 1321405.8, ..., 3443016. , 3443935.2, 3444854.5],
          dtype=float32)

  • quantile
    (time)
    float64
    ...

    [3 values with dtype=float64]

• Indexes: (2)
  • lat
    PandasIndex

    PandasIndex(Index([-1170429.375, -1169510.125, -1168590.875, -1167671.625, -1166752.375,
           -1165833.125, -1164913.875, -1163994.625, -1163075.375, -1162156.125,
           ...
            1223276.625,   1224195.75,    1225115.0,   1226034.25,    1226953.5,
             1227872.75,    1228792.0,   1229711.25,    1230630.5,   1231549.75],
          dtype='float32', name='lat', length=2614))

  • lon
    PandasIndex

    PandasIndex(Index([1319567.25,  1320486.5, 1321405.75,  1322325.0, 1323244.25,  1324163.5,
           1325082.75,  1326002.0, 1326921.25,  1327840.5,
           ...
            3436581.5, 3437500.75,  3438420.0,  3439339.0, 3440258.25,  3441177.5,
           3442096.75,  3443016.0, 3443935.25,  3444854.5],
          dtype='float32', name='lon', length=2313))

• Attributes: (0)

Visualizing anomalies

fname_nc = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_pre_anomalies.nc"

data = xr.open_dataset(fname_nc)['pre']
data.plot(x="lon", y="lat", col="time", robust=False, cmap='bwr_r', levels=11, figsize=(12, 7), vmin=-300, vmax=300, col_wrap=6)

<xarray.plot.facetgrid.FacetGrid at 0x7facd2f05750>

fname_nc = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_aet_anomalies.nc"

data = xr.open_dataset(fname_nc)['aet']
data.plot(x="lon", y="lat", col="time", robust=False, cmap='bwr_r', levels=11, figsize=(12, 7), vmin=-300, vmax=300, col_wrap=6)

<xarray.plot.facetgrid.FacetGrid at 0x7facd8499f10>

fname_nc = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_tht_anomalies.nc"

data = xr.open_dataset(fname_nc)['tht']
data.plot(x="lon", y="lat", col="time", robust=False, cmap='bwr_r', levels=11, figsize=(12, 7), vmin=-0.2, vmax=0.2, col_wrap=6)

<xarray.plot.facetgrid.FacetGrid at 0x7facd7ea7c10>

fname_fig = "/home/aquichimbo/training/forecast/regional/postpp/fig/example.png"
plt.savefig(fname_fig, dpi=300)

<Figure size 640x480 with 0 Axes>

Calculate long term seasonal average for the selected variables of the historical simulation

dfseason = get_seasson_csv(fname_csv, [3, 4, 5], reset_index=False, aggregate=False)['pre_0']

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

# get month number
dfseason_mean = dfseason.groupby(dfseason.index.month).mean()
#dfseason.groupby(pd.TimeGrouper(freq='M')).mean()

dfseason_mean.index

Index([3, 4, 5], dtype='int32', name='Date')

• Get uncertainty of stochastic simulations

Create ensamble od stochastic simulations

# create an ensamble of model simulations
def create_ensamble(flist, field, agg_step='M', timefield='Date'):
    first_read = True
    for i, ifname in enumerate(flist):
        if first_read is True:
            df = aggregate_slice_csv(ifname, agg_step=agg_step, timefield=timefield)[[timefield, field]]
            # Rename the 'A' column to 'X'
            df.rename(columns={field: 'sim'+str(i)}, inplace=True)
            first_read = False
        else:
            df['sim'+str(i)] = aggregate_slice_csv(ifname, agg_step=agg_step, timefield=timefield)[field].values
    return df

def create_dataframe_stats(data):
    """This fuction create a dataframe with statistical values of
    q05, q33, q50, mean, q66, q95 from ensamble dataframe"""
    dfinterval = data[['Date']].copy()
    dfinterval['q5'] = data.quantile(q=0.05, axis=1, numeric_only=True).values
    dfinterval['q33'] = data.quantile(q=0.33, axis=1, numeric_only=True).values
    dfinterval['mean'] = data.mean(axis=1, numeric_only=True).values
    dfinterval['q50'] = data.quantile(q=0.50, axis=1, numeric_only=True).values
    dfinterval['q66'] = data.quantile(q=0.66, axis=1, numeric_only=True).values
    dfinterval['q95'] = data.quantile(q=0.95, axis=1, numeric_only=True).values
    dfinterval['Date'] = pd.to_datetime(dfinterval['Date'])
    dfinterval['month'] = np.array(dfinterval['Date'].dt.month, int)
    return dfinterval

#fname_list = [basin_path + "output/forecast/Tana_IMa_sto"+str(i)+"_avg.csv" for i in range(30)]
#fname_list = ["D:/HAD/postpp/csv/MAM_2022_realization_"+str(i)+"_avg.csv" for i in range(30)]
fname_list = ["/shared/training/forecast/regional/outputs/MAM_2022_realization_test_"+str(i)+"_avg.csv" for i in range(1,30)]

data = create_ensamble(fname_list, 'pre_0')
data

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

	Date	sim0	sim1	sim2	sim3	sim4	sim5	sim6	sim7	sim8	...	sim19	sim20	sim21	sim22	sim23	sim24	sim25	sim26	sim27	sim28
0	2022-03-31	53.355518	51.936824	36.456681	49.997749	27.982686	53.355518	51.936824	50.858848	23.573927	...	30.592740	32.159934	32.159934	51.780181	46.285535	51.936824	39.408169	23.615421	46.224174	23.573927
1	2022-04-30	93.501302	101.809235	89.453359	114.073488	96.719799	93.501302	101.809235	95.639110	93.402017	...	75.706013	70.259699	70.259699	87.245345	121.517597	101.809235	90.256752	94.722533	90.400389	93.402017
2	2022-05-31	81.468582	62.955899	70.121010	48.696139	68.913471	81.468582	62.955899	72.905591	58.542892	...	87.492903	86.508770	86.508770	72.757934	59.297001	62.955899	65.941157	78.093968	63.401270	58.542892

3 rows × 30 columns

Calculate confidence interval

#q1, q2, q3 = gen.confidence_interval_TS(SMdata, weight, 0.95)
dfinterval = create_dataframe_stats(data)
dfinterval

	Date	q5	q33	mean	q50	q66	q95	month
0	2022-03-31	23.590524	36.070749	40.791789	39.408169	49.254780	54.237367	3
1	2022-04-30	72.438224	87.775269	92.198621	93.402017	96.157841	110.650738	4
2	2022-05-31	58.542892	63.645875	70.760086	68.913471	75.676142	87.099250	5

Create a figure representing the long term precipitaiton (LTA) for MAM and the forecasting.

#dfseason_mean.index.values
#dfseason_mean.values
#dfinterval['month']

fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(6, 2.5)
#plt.plot(dfinterval['month'].values, dfinterval['q50'].values)
plt.plot(dfinterval['month'].values, dfinterval['mean'].values)
plt.fill_between(dfinterval['month'], dfinterval['q5'], dfinterval['q95'], alpha=0.6, label='Forecasting')
plt.plot(dfseason_mean.index.values, dfseason_mean.values, 'k', label='LTA')
# ax.set_xticklabels([10,11,12])
plt.xlabel('month')
plt.ylabel('Precipitation [mm/month]')
plt.legend(frameon=False)
plt.title('MAM Precipitaiton')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

TASK: Plot uncertanties for soil moisture (“tht_0”), actual evapotranspiration (“aet_0”), and groundwater recharge (“rch_0”)

1. Create ensamble data for the selected variable.

2. Calculate quantiles for the selected variable.

3. Plot results (copy script for plotting)

Soil Moisture

dfseason = get_seasson_csv(fname_csv, [3, 4, 5], reset_index=False, aggregate=False)['tht_0']
dfseason_mean = dfseason.groupby(dfseason.index.month).mean()

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

data = create_ensamble(fname_list, 'tht_0')
dfinterval = create_dataframe_stats(data)

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(6, 2.5)
#plt.plot(dfinterval['month'].values, dfinterval['q50'].values)
plt.plot(dfinterval['month'].values, dfinterval['mean'].values)
plt.fill_between(dfinterval['month'], dfinterval['q5'], dfinterval['q95'], alpha=0.6, label='Forecasting')
plt.plot(dfseason_mean.index.values, dfseason_mean.values, 'k', label='LTA')
# ax.set_xticklabels([10,11,12])
plt.xlabel('month')
plt.ylabel('Precipitation [mm/month]')
plt.legend(frameon=False)
plt.title('MAM Soil moisture')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

dfseason = get_seasson_csv(fname_csv, [3, 4, 5], reset_index=False, aggregate=False)['aet_0']
dfseason_mean = dfseason.groupby(dfseason.index.month).mean()

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

data = create_ensamble(fname_list, 'aet_0')
dfinterval = create_dataframe_stats(data)

/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()
/tmp/ipykernel_2182569/249471917.py:31: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.
  df = df.resample(agg_step).sum(numeric_only=True)#.reset_index()

fig, ax = plt.subplots()#1, 1, sharex=True, sharey=True)
fig.set_size_inches(6, 2.5)
#plt.plot(dfinterval['month'].values, dfinterval['q50'].values)
plt.plot(dfinterval['month'].values, dfinterval['mean'].values)
plt.fill_between(dfinterval['month'], dfinterval['q5'], dfinterval['q95'], alpha=0.6, label='Forecasting')
plt.plot(dfseason_mean.index.values, dfseason_mean.values, 'k', label='LTA')
# ax.set_xticklabels([10,11,12])
plt.xlabel('month')
plt.ylabel('Actual Precipitation [mm/month]')
plt.legend(frameon=False)
plt.title('MAM Actual ET')
ax.xaxis.set_major_locator(MaxNLocator(integer=True))

Visualizing deterministic and probabilistic forecast

import sys
#sys.path.append("C:/Users/Edisson/Documents/GitHub/CUWALID_training/Forecasting/scripts/src")
sys.path.append("/home/cuwalid/CUWALID/CUWALID_training/Forecasting/scripts")
#sys.path.append("/home/aquichimbo/CUWALID/CUWALID_training/Forecasting/scripts/src/")

import CUWALID_forecast_tools as cuwalid
import CUWALID_view_tool as cuwalidplt
import xarray as xr

#path = "C:/Users/Edisson/OneDrive - Cardiff University/PhD/WS/LandLab/HAD/WS/"
path = "/home/cuwalid/training/historical/regional/model/input/"

path_mask = path + "HAD_mask_utm_m.asc"
path_river =  path + "HAD_riv_length_utm.asc"
#shapefile_county = "D:/HAD/data/gis/Horn_Africa/Horn_africa_contry.shp"
#shapefile_county = "/home/cuwalid/Datasets/data/shp/wgs84/Horn_africa_contry.shp"
shapefile_county = "/home/cuwalid/Datasets/data/shp/utm/HAD_country_bnd_utm.shp"

• Deterministic forecast

#fname = "D:/HAD/postpp/netcdf/MAM_2022_realization_pre_MAM_2022_deterministic_forecast.nc"
#fname = "/home/cuwalid/training/forecast/regional/postpp/netcdf/MAM_2022_realization_pet_MAM_2022_deterministic_forecast.nc"

#fname = "D:/HAD/postpp/netcdf/MAM_2022_realization_pre_MAM_2022_deterministic_forecast.nc"
#fname = "/home/cuwalid/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_pet_MAM_2022_deterministic_forecast.nc"
#fname = "/home/<username>/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_pet_MAM_2022_deterministic_forecast.nc"
fname = "/home/aquichimbo/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_pre_MAM_2022_deterministic_forecast.nc"
#fname = "/home/cuwalid/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_pet_MAM_2022_probabilistic_tercile_forecast.nc"

data = xr.open_dataset(fname)
im = cuwalidplt.plot_deterministic_forecast(data,
                    title="Deterministic Forecast\nPrecipitation",
                    reproject=False,
                    fshapefile=shapefile_county,
                    plot_anomaly=True,
                    fmask=path_mask
            )

TASK: Plot probabilistic forecasting of soil moisture (“tht_0”), actual evapotranspiration (“aet_0”), and groundwater recharge (“rch_0”)

1. Check if the ensamble has been created for the selected variable

2. Check if long term average values for the selected variable have been estimated.

3. Plot results (copy script for plotting)

• Probabilistic forecast

#fname = "D:/HAD/postpp/netcdf/MAM_2022_realization_pre_MAM_2022_probabilistic_tercile_forecast.nc"
#fname = "/home/cuwalid/training/forecast/regional/postpp/netcdf/MAM_2022_realization_pet_MAM_2022_probabilistic_tercile_forecast.nc"
#fname = "/home/cuwalid/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_aet_MAM_2022_probabilistic_tercile_forecast.nc"
#fname = "/home/<username>/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_aet_MAM_2022_probabilistic_tercile_forecast.nc"
fname = "/home/aquichimbo/training/forecast/regional/postpp/netcdf/MAM_2022_realization_test_pre_MAM_2022_probabilistic_tercile_forecast.nc"

data = xr.open_dataset(fname)
im = cuwalidplt.plot_probabilistic_tercile_forecast(data,
                    title="Probabilistic Forecasting\nPrecipitation",
                    reproject=False,
                    fshapefile=shapefile_county,
                    fmask=path_mask
            )

TASK: Plot probabilistic forecasting of soil moisture (“tht_0”), actual evapotranspiration (“aet_0”), and groundwater recharge (“rch_0”)

1. Check if the ensamble has been created for the selected variable

2. Check if quantiles of the selected variable have been estimated.

3. Plot results (copy script for plotting)

#fname = "/home/cuwalid/training/historical/regional/postpp/netcdf/HAD_IMERGba_sim0_MAM_mean.nc"

#data = xr.open_dataset(fname)

#data["pre"].plot(x="lon", y="lat", robust=False, cmap='gist_ncar_r', vmin=200, vmax=1600, levels=15)