Bias correction of ensemble precipitation forecasts in the improvement of summer streamflow prediction skill
Bias correction of ensemble precipitation forecasts in the improvement of summer streamflow prediction skill
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集合降水预报偏差修正提高夏季径流预报技术
DOI：
10.1016/j.jhydrol.2020.124955
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发表时间：
2020-09
影响因子：
6.4
通讯作者：
Xiang Su
中科院分区：
文献类型：
--
作者：
Chunlei Yang;Huiling Yuan;Xiang Su
近年来，集合降水预报能够提供更多的不确定性信息，在流域尺度的水文预报中发挥着越来越重要的作用。美国国家环境预报中心(NCEP)发布的全球集合预报系统(GEFS)再预报资料具有长期的数据档案库和相对稳定的系统误差，在水文应用中显示出巨大的潜力。本文旨在对GEFS再预报降水预报进行偏差修正，并分析经过偏差修正的集合降水预报对提高夏季径流预报水平的影响。在GEFS再预报集合降水预报的驱动下，应用变入渗量(VIC)分布式水文模式对淮河流域2000-2010年夏季径流进行了模拟。结果表明，GEFS再预报资料普遍低估了随提前时间增加而增加的降水量，而径流模拟由于空间降水误差而有低估峰值的趋势。本文采用频率匹配法(FMM)和模拟方法对GEFS再预报集合降水预报进行了检验。在流域尺度面雨量的基础上，改进了相似方法的搜索条件，经过偏差修正后，FMM方法主要提高了1~8d提前期的小雨和中雨预报技巧。相比之下，模拟方法改进了概率降水预报，增加了集合扩散，以缓解1-8d提前期的原始集合预报的欠分散。模拟方法还可以利用观测降水的降尺度信息来改善空间分布，从而产生更好的概率降水预报。用偏差修正的降水预报进行的径流预报与观测的径流更接近。模拟修正后的降水预报能有效地改进1~5d提前期的径流模拟，其中提前期2~3d的模拟效果最好。对于2003年夏季极端暴雨引发的特大洪涝事件，集合平均预报技巧有限，而概率降水预报在预报极端降雨强度方面表现出很大的潜力。同时，利用模拟校正后的降水预报进行的集合径流预报具有较大的集合扩散和较高的极值径流纳什系数。结果表明，再预报集合数据集对于提高业务应用中的水文气象预报具有重要价值。
In recent years, ensemble precipitation forecasting has been able to provide more uncertainty information and plays an increasingly important role in basin-scale hydrologic predictions. The Global Ensemble Forecast System (GEFS) reforecast data released by the National Centers for Environmental Prediction (NCEP) has a long-term data archive and relatively stable systematic error, which shows great potential in hydrologic applications. This paper aims to bias correct the GEFS reforecast precipitation forecasts and analyze the impact of bias-corrected ensemble precipitation forecasts on the improvement of summer streamflow prediction skill. Driven by the GEFS reforecast ensemble precipitation forecasts, the Variable Infiltration Capacity (VIC) distributed hydrological model is applied to simulate the 2000–2010 summer streamflow over the Huaihe River basin.The results show that the GEFS reforecast data generally underestimates the precipitation amount with the increasing leadtime and the streamflow simulation tends to underestimate the peak due to the spatial precipitation error. In this study, both the frequency matching method (FMM) and the analog method are applied to calibrate the GEFS reforecast ensemble precipitation forecasts. The searching criteria of the analog method has been improved based on the basin-scale areal precipitation.After bias correction, the FMM method mainly improves the forecast skill of light rain and moderate rain for the 1–8 d leadtime. By contrast, the analog method improves probabilistic precipitation forecasts and increases ensemble spread to alleviate the underdispersion of raw ensemble forecasts for the 1–8 d leadtime. The analog method can also improve spatial distributions with the downscaling information from the observed precipitation, producing better probabilistic precipitation forecasts. The streamflow prediction using bias-corrected precipitation forecasts better resembles the observed streamflow. The analog-corrected precipitation forecasts can effectively improve the streamflow simulations for the 1–5 d leadtime, with the maximum improvements at the 2–3 d leadtime. The FMM method only improves the streamflow predictions for the 2 d leadtime.For the heavy flood events caused by extreme rainstorms in the summer of 2003, the ensemble mean forecasts have limited forecast skill, but probabilistic precipitation forecasts show great potential in predicting the magnitude of extreme rainfall events. Meanwhile, the ensemble streamflow predictions using the analog-corrected precipitation forecasts have larger ensemble spread and a higher Nash coefficient of extreme streamflow.The encouraging results suggest that the reforecast ensemble dataset exhibits a great value to improve hydrometeorological predictions for operational applications.
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