A generalized, copula-based error model for synthetic generation of medium-range hydro-meteorological forecasts

Abstract:

Forecast informed reservoir operations holds great promise as a soft pathway to improve water resources system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of this approach, as numerical weather prediction hindcasts are only available for a relatively short period (20-30 years) that is insufficient for assessing risk related to forecast-informed operations during extreme events. We develop a generalized, copula-based error model for synthetic forecast generation applicable to a range of forecasted variables used in water resources management. The approach utilizes the flexible Skew Generalized Error Distribution (SGED) to model marginal distributions of forecast errors that can exhibit heteroskedastic, auto-correlated, and non-Gaussian behavior. The error model uses copulas to capture important covariance properties between variables, forecast lead times, and across spatial domains. We demonstrate the analysis for medium-range forecasts (0-14 days) across Northern California for streamflow, temperature, and precipitation using widely available global reforecasts (NCEP GEFS/R V2), a global historical reanalysis (NOAA CIRES 20^th Century V3), and the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS). The case study highlights the model’s flexibility and ability to emulate key covariance structures at time scales critical for flood management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that capture state-of-the-art weather and hydrologic forecast skill and are appropriate for water resources risk analysis.