A Multivariate Approach to Generate Synthetic Short‐To‐Medium Range Hydro‐Meteorological Forecasts Across Locations, Variables, and Lead Times
A Multivariate Approach to Generate Synthetic Short‐To‐Medium Range Hydro‐Meteorological Forecasts Across Locations, Variables, and Lead Times
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DOI：
10.1029/2020wr029453
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发表时间：
2021-06
影响因子：
5.4
通讯作者：
Z. Brodeur;S. Steinschneider
中科院分区：
文献类型：
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作者：
Z. Brodeur;S. Steinschneider
在水资源管理中使用水文气象预报作为改善系统性能的软途径具有很大的前景。生成水文气象变量综合预报的方法对于预报使用的可靠性验证至关重要，因为数值天气预报后报仅适用于相对较短的时期（10-40年），不足以评估极端事件期间与预测知情决策相关的风险。我们开发了一个广义误差模型的综合预测生成，适用于一系列的预测变量用于水资源管理。该方法从可用的后报期间的预测误差分布中采样，并将其添加到观测数据的长记录中以生成合成预测。该方法利用偏斜广义误差分布（SGED）来模拟预测误差的边缘分布，这些分布可能表现出异方差，自相关和非高斯行为。经验copula用于捕获变量之间的协方差，预测提前期和跨空间。我们在两个案例研究中展示了北方加州中期预报的方法，（1）径流和（2）温度和降水，这是基于后报从NOAA/NWS水文环境预报系统（HEFS）和NCEP GEFS/R V2气候模式，分别。案例研究突出了该模型的灵活性及其在水资源管理关键尺度上模拟预测时空结构的能力。所提出的方法是可推广到其他地点和计算效率，使快速生成的长合成预测合奏，适合风险分析。
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.