OpenForecast results in 2020-2021

OpenForecast is the first national-scale operational runoff forecasting system in Russia. The presented data supports a research article on a long-term assessment of OpenForecast performance in 2020-2021.

File listing:

• calibration_vs_hindcast.npy -- Python dictionary that provides results of efficiency assessment for calibration and evaluation (hindcast) periods in terms of NSE and KGE metrics for individual hydrological models (GR4J_NSE, GR4J_KGE, HBV_NSE, HBV_KGE).

• hindcast_vs_forecast.npy -- Python dictionary that provides results of efficiency assessment for hindcast, pre-operational hindcast, and forecast periods in terms of NSE and KGE metrics for individual hydrological models (GR4J_NSE, GR4J_KGE, HBV_NSE, HBV_KGE), as well as their ensemble mean (ENS).

• meteo_forecast.npy -- Python dictionary that reports correlation coefficients between ICON and ERA5 reanalysis for air temperature and precipitation forecasts.

• users.csv -- daily numbers of OpenForecast users.

Sample code for data access:

import numpy as np
import pandas as pd

calibration_hindcast = np.load("calibration_vs_hindcast.npy", allow_pickle=True).item()

hindcast_forecast = np.load("hindcast_vs_forecast.npy", allow_pickle=True).item()

meteo_forecasts = np.load("meteo_forecast.npy", allow_pickle=True).item()

users = pd.read_csv("users.csv", index_col=0, parse_dates=True, dayfirst=True)

% pandas dataframe for the GR4J_KGE model efficiency in terms of NSE for calibration and hindcast periods
calibration_hindcast["GR4J_KGE"]["NSE"]

% pandas dataframe for the ensemble mean efficiency in terms of NSE for hindcast and forecast periods
hindcast_forecast["ENS"]["NSE"]

% pandas dataframe for correlation coefficients between ICON and ERA5 for precipitation forecasts
meteo_forecasts["P"]["Correlation"]

% available keys of Python dictionaries could be checked as follows
calibration_hindcast.keys()