提升降水量级预报精度,有助于优化灾害预警与决策支持。选取2018年1月1日至2021年1月山东省逐12 h降水观测数据和欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasting,ECMWF)的集合预报集合平均(Ensemble P...提升降水量级预报精度,有助于优化灾害预警与决策支持。选取2018年1月1日至2021年1月山东省逐12 h降水观测数据和欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasting,ECMWF)的集合预报集合平均(Ensemble Prediction Ensemble Mean,EPEM)结果进行72 h内逐12 h降水量级预报统计订正,然后对比ECMWF集合平均降水预报插值的原始预报(EC_EPEM)、基于EC_EPEM的输出统计(Model Output Statistics,MOS)预报(EC_EPEM_MOS)、利用最优TS(Threat Score)评分订正(Optimal Threat Score,OTS)预报(EC_EPEM_OTS)的效果。结果表明:EC_EPEM_MOS在较小量级上表现最优,但在大量级上订正效果稍差,甚至略低于EC_EPEM;EC_EPEM_OTS仅在0.1、10 mm量级上低于EC_EPEM_MOS,其他量级均为最优,尤其在较大量级上订正效果更明显。在50、100 mm大量级上,EC_EPEM_OTS在12~72 h时效订正效果均最优,这是由于EC_EPEM_OTS在稍大量级上提高订正系数使得大量级降水漏报率减小,同时对大量级降水使用较小订正系数也减小了空报率。在较小量级降水中短期预报时效除了山东中部山区外EC_EPEM_MOS表现最佳,山区EC_EPEM_OTS最佳;中等以上量级、尤其较大量级降水,山东大部分地区EC_EPEM_OTS表现最佳。EC_EPEM_MOS订正预报有效地减小了EC_EPEM的空报问题。EC_EPEM_OTS的订正效果最佳,在大范围强降雨过程中与实况降雨分布更为接近,降水总体分布把握较好。展开更多
The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thr...The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.展开更多
基金primarily supported by the National 973 Fundamental Research Program of China(Grant No.2013CB430103)the Department of Transportation Federal Aviation Administration(Grant No.NA17RJ1227)through the National Oceanic and Atmospheric Administration+1 种基金supported by the National Science Foundation of China(Grant No.41405100)the Fundamental Research Funds for the Central Universities(Grant No.20620140343)
文摘The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias. In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias. When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.
