A generalized approach to generate synthetic short-to-medium range hydro-meteorological forecasts

Forecast informed operations hold great promise as a soft pathway to improve water resources system performance. Generating synthetic forecasts of hydro-meteorological variables is crucial for robust validation of this approach, as advanced numerical weather prediction hindcasts have a limited timespan (10-40 years) that is insufficient for assessing risk related to forecast-informed operations during extreme events. We develop a generalized error model for synthetic forecast generation that is applicable to a range of forecasted variables used in water resources management. The approach samples from the distribution of forecast errors over the available hindcast period and adds them to long records of observed data to generate synthetic forecasts. 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. An empirical copula is used to capture covariance between variables and forecast lead times and across space. We demonstrate the method for medium-range forecasts across Northern California in two case studies for 1) streamflow and 2) temperature and precipitation, which are based on hindcasts from operational CONUS hydrologic and meteorological forecast models. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at 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 are appropriate for the design and testing of forecast informed policy or characterization of forecast uncertainty for water resources risk analysis.