This study investigates the influence of soil moisture initialization on the North American Monsoon System (NAMS) using the Weather Research and Forecasting (WRF) model coupled with Noah‐MP Land Surface Model. Five sets of experiments using different soil moisture initializations were conducted from May 1 to October 1 in 2015–2017. The model forecast for each set of experiments consists of five ensemble members. The simulation using North American Regional Reanalysis is the control run (WRF‐Ctrl). Four sets of sensitivity experiments were conducted with extremely wet (WRF‐Wet) and dry (WRF‐dry) initial conditions and using different soil moisture products from the Global Land Data Assimilation System (WRF‐GLDAS) and NASA Soil Moisture Active Passive L4 (WRF‐SMAP). Results show that the WRF model can capture the key features of the NAMS, but it exhibits a wet bias in the higher elevation regions and a dry bias in the lower elevation regions. Our analysis of extremely wet and dry cases reveals that initial soil moisture conditions can affect precipitation through both modulating surface energy partitioning and influencing large‐scale pressure and wind patterns. When different soil moisture products are used to initialize the model, it can lead to substantial changes in spatial pattern of the NAM precipitation. Initializing the model with SMAP and GLDAS can reduce the overestimation of precipitation over high terrain areas. The results indicate that high‐quality soil moisture products have the potential to improve model representation of the NAMS, especially with future improvements in model convective parameterization.

中文翻译：

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土壤水分初始化对WRF模拟的北美季风系统的影响

本研究使用天气研究与预报（WRF）模型与Noah-MP地表模型相结合，研究了土壤湿度初始化对北美季风系统（NAMS）的影响。2015-2017年5月1日至10月1日进行了五组使用不同土壤湿度初始化的实验。每组实验的模型预测由五个集合成员组成。使用北美区域再分析进行的模拟是控制运行（WRF-Ctrl）。在极端潮湿（WRF‐Wet）和干燥（WRF‐dry）初始条件下，使用全球土地数据同化系统（WRF‐GLDAS）和NASA土壤水分主动被动L4（ WRF-SMAP）。结果表明，WRF模型可以捕获NAMS的关键功能，但在较高海拔区域表现出湿偏斜，在较低海拔区域表现出干偏斜。我们对极端潮湿和干燥情况的分析表明，初始土壤湿度条件可通过调节表面能分配并影响大规模压力和风型来影响降水。当使用不同的土壤水分产物初始化模型时，可能导致NAM降水的空间格局发生实质性变化。使用SMAP和GLDAS初始化模型可以减少高海拔地区降水的高估。结果表明，高质量的土壤水分产品具有改善NAMS的模型表示的潜力，尤其是在模型对流参数化方面的未来改进方面。