Using Object-Based Verification to Assess Improvements in Forecasts of Convective Storms Between Operational HRRR Versions 3 and 4

IF 3 3区地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES

Weather and Forecasting Pub Date : 2023-08-02 DOI:10.1175/waf-d-22-0181.1

Jeffrey D. Duda, David D. Turner

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引用次数: 0

Abstract

The object-based verification procedure described in a recent paper (Duda and Turner 2021) was expanded herein to compare forecasts of composite reflectivity and 6-h precipitation objects between the two most recent operational versions of the High-Resolution Rapid Refresh (HRRR) model, versions 3 and 4, over an expanded set of warm season cases in 2019 and 2020. In addition to analyzing all objects, a reduced set of forecast-observation object pairs was constructed by taking the best forecast match to a given observation object for the purposes of bias-reduction and unequivocal object comparison. Despite the apparent signal of improved scalar metrics such as the object-based threat score in HRRRv4 compared to HRRRv3, no statistically significant differences were found between the models. Nonetheless, many object attribute comparisons revealed indications of improved forecast performance in HRRRv4 compared to HRRRv3. For example, HRRRv4 had a reduced over-forecasting bias for medium and large-sized reflectivity objects, and all objects during the afternoon. HRRRv4 also better replicated the distribution of object complexity and aspect ratio. Results for 6-h precipitation also suggested superior performance in HRRRv4 over HRRRv3. However, HRRRv4 was worse with centroid displacement errors and more severely over-forecast objects with a high maximum precipitation amount. Overall, this exercise revealed multiple forecast deficiencies in the HRRR, which enables developers to direct development efforts on detailed and specific endeavors to improve model forecasts.

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使用基于对象的验证来评估运行HRRR版本3和版本4之间对流风暴预报的改进

本文扩展了最近一篇论文（Duda和Turner 2021）中描述的基于对象的验证程序，以比较高分辨率快速刷新（HRRR）模型的两个最新操作版本（版本3和版本4）在2019年和2020年一组扩展的暖季情况下对复合反射率和6小时降水对象的预测。除了分析所有对象外，为了减少偏差和明确的对象比较，还通过对给定观测对象进行最佳预测匹配，构建了一组简化的预测观测对象对。尽管与HRRRv3相比，HRRRv4中的基于对象的威胁得分等标量指标明显改善，但在模型之间没有发现统计学上的显著差异。尽管如此，许多对象属性比较显示，与HRRRv3相比，HRRRv4的预测性能有所改善。例如，HRRRv4对中等和大尺寸反射率物体以及下午的所有物体的过度预测偏差都有所减少。HRRRv4也更好地复制了对象复杂度和纵横比的分布。6小时降水的结果也表明，HRRRv4的性能优于HRRRv3。然而，HRRRv4在质心位移误差的情况下更差，在最大降水量较高的预测对象上更严重。总的来说，这项工作揭示了HRRR中的多个预测缺陷，这使开发人员能够将开发工作引向详细和具体的努力，以改进模型预测。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Weather and Forecasting 地学-气象与大气科学

CiteScore

5.20

自引率

17.20%

发文量

131

审稿时长

6-12 weeks

期刊介绍： Weather and Forecasting (WAF) (ISSN: 0882-8156; eISSN: 1520-0434) publishes research that is relevant to operational forecasting. This includes papers on significant weather events, forecasting techniques, forecast verification, model parameterizations, data assimilation, model ensembles, statistical postprocessing techniques, the transfer of research results to the forecasting community, and the societal use and value of forecasts. The scope of WAF includes research relevant to forecast lead times ranging from short-term “nowcasts” through seasonal time scales out to approximately two years.