Data repository for: A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times

The use of hydro-meteorological forecasts in water resources management holds great promise as a soft pathway to improve system performance. Methods for generating synthetic forecasts of hydro-meteorological variables are crucial for robust validation of forecast use, as numerical weather prediction hindcasts are only available for a relatively short period (10-40 years) that is insufficient for assessing risk related to forecast-informed decision-making 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 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, 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 the NOAA/NWS Hydrologic Ensemble Forecast System (HEFS) and the NCEP GEFS/R V2 climate model, respectively. The case studies highlight the flexibility of the model and its ability to emulate space-time structures in forecasts at scales critical for water resources management. The proposed method is generalizable to other locations and computationally efficient, enabling fast generation of long synthetic forecast ensembles that are appropriate for risk analysis.

水文气象预报在水资源管理中的应用，作为提升系统效能的柔性路径，具备广阔应用前景。生成水文气象变量合成预报的方法，对实现预报应用的稳健验证至关重要——当前数值天气预报后报数据的可用时长仅为10至40年，相对较短，无法充分评估极端事件场景下与预报辅助决策相关的风险。本研究构建了一款适用于水资源管理中多类预报变量的合成预报生成广义误差模型：该方法从可用后报时段内的预报误差分布中采样，并将采样得到的误差叠加至长时序观测数据中，以此生成合成预报；同时采用偏斜广义误差分布（Skew Generalized Error Distribution, SGED）对预报误差的边缘分布进行建模，可适配具有异方差、自相关及非高斯特性的误差序列，并借助经验Copula函数刻画变量间、不同预报预见期间以及空间维度上的协方差关系。本研究以北加州全域的中期预报为对象，开展两项案例研究验证该方法：其一针对河道流量，其二针对气温与降水，两项案例分别采用美国国家海洋和大气管理局/国家气象局（NOAA/NWS）水文集合预报系统（HEFS）以及美国国家环境预报中心（NCEP）全球集合预报系统修订版2（GEFS/R V2）的后报数据构建。案例研究彰显了该模型的灵活性，以及其在水资源管理关键尺度上复刻预报时空结构的能力。所提方法具备跨区域推广性，且计算效率优异，可快速生成长时序合成预报集合，适用于风险分析场景。