Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture

IF 3 3区 地球科学 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES
Andrew R. Wade, Israel L. Jirak, Anthony W. Lyza
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引用次数: 0

Abstract

Abstract This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts to objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts to surface observations under certain warm sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.
允许对流模式预报近地表温度和湿度的区域和季节偏差
摘要:本研究探讨了对流允许模式预报近地表温度和露点的区域季节性偏差,这些区域对强局地风暴的预报具有特别重要的意义。一种方法是将模式预报与对报告的龙卷风流入扇区观测条件的客观分析进行比较。第二种方法获取更广泛的环境样本,将模式预测与某些暖区标准下的地面观测进行比较。这两种方法都揭示了在美国东南部冷季温暖地区测试的所有模型的冷偏倚。考虑到东南凉爽季节常见的不稳定限制恶劣天气的热力学敏感性,这是一个操作上重要的偏差。在大平原暖季,不同的模式之间没有明显的偏差,相反,不同的模式物理特性带来了更多的变化。
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来源期刊
Weather and Forecasting
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.
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