Extreme weather events are occurring with increasing frequent due to the climate change. This increasing frequency may introduce more uncertainty in weather forecasting model performance, particularly when considering the intricate relationship of the land surface and atmosphere coupling system. In this study, we utilize data from the sophisticated New York State Mesonet to evaluate the performance of a forecasting system based on WRF Version 4 model, drawing insights from both dry and wet summers. Additionally, the model's performance is assessed on two land surface types: forest and farmland, to provide a comprehensive evaluation of impact of land surface heterogeneity. The surface meteorology, fluxes, and cloud development are assessed. The coupling between surface and atmosphere is diagnosed using a mixing diagram which serves to represent surface thermodynamic properties. The results reveal a systematic increase in warm season dry and warm biases, especially for forested sites during a drought year. The model exhibits heightened sensitivity to drought conditions, resulting in a substantial underestimation of latent heat fluxes during such period. During days with boundary layer clouds, the mixing diagram shows a notably slower growth of moist static energy in the model compared to observation. It is possible that these biases partly attribute to the underestimation of cloud optical depth due to not enough energy for the cloud development.

中文翻译：

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纽约州 Mesonet 的预测偏差比较：潮湿的夏季与干燥的夏季

由于气候变化，极端天气事件发生的频率越来越高。这种增加的频率可能会给天气预报模型的性能带来更多的不确定性，特别是在考虑陆地表面和大气耦合系统的复杂关系时。在这项研究中，我们利用来自复杂的纽约州 Mesonet 的数据来评估基于 WRF 第 4 版模型的预报系统的性能，并从干燥和潮湿的夏季汲取见解。此外，该模型的性能还针对森林和农田两种地表类型进行了评估，以综合评估地表异质性的影响。评估地表气象、通量和云发展。使用混合图来诊断表面和大气之间的耦合，该混合图用于表示表面热力学特性。结果表明，暖季干旱和温暖偏差系统性增加，特别是在干旱年份的森林地区。该模型对干旱条件表现出高度的敏感性，导致该时期的潜热通量被大大低估。在有边界层云的日子里，混合图显示与观测相比，模型中潮湿静态能量的增长明显较慢。这些偏差可能部分归因于云发展没有足够的能量而低估了云的光学深度。