Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

中文翻译：

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乌尔米亚湖干涸对极端降雪事件的影响：使用 WRF-Lake 模型的案例研究

伊朗西北部的乌尔米耶湖近几十年来经历了严重的干涸，面积比原来小得多。本研究采用 WRF-Lake 耦合建模系统来检查 2017 年 2 月 1 日至 2 日发生的一场大雪期间观测到的湖泊物理变化的影响。引入了修改来表示收缩、盐结的湖面和周围通过更新土地利用、土壤特征、湖泊大小和深度以及反照率数据来应对干旱环境。结合湖泊-大气耦合和陆地表面细化的实验（Lake_CTL、Lake_LULC、Lake_AL）在再现乌尔米亚湖周围站点观测到的降水模式方面优于没有湖泊的模拟（WRF_noLake）。所有模拟都低估了乌尔米耶站记录的 290 毫米极端降雪量；然而，Lake_LULC 和 Lake_AL 改善了湖周围其他站点的降雪表现。在积雪验证方面，Lake_LULC 和 Lake_AL 与卫星观测表现出更高的相关性，并且乌尔米亚湖附近的积雪检测有了显着改善。它们的检测概率 (POD) 为 0.94，关键成功指数 (CSI) 为 0.91，而默认湖模型 (Lake_CTL) 和 WRF_noLake 实验的 POD 为 0.79-0.80，CSI 为 ∼0.77 。此外，调整初始湖面温度以匹配观测结果可大大减少乌尔米亚湖近地表气温的冷偏差。不过，WRF-Lake 缺乏湖泊温度更新对模型提出了持续的挑战。总之，这项研究表明，在高分辨率耦合模型中完善干湖及其周围环境的表示将改善受湖泊与大气相互作用影响的气象过程的模拟。