Improved Simulation of Monsoon Depressions and Heavy Rains From Direct and Indirect Initialization of Soil Moisture Over India
Improved Simulation of Monsoon Depressions and Heavy Rains From Direct and Indirect Initialization of Soil Moisture Over India
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DOI：
10.1029/2020jd032400
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发表时间：
2020-07
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影响因子：
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通讯作者：
K. Osuri;R. Nadimpalli;Kumar Ankur;H. Nayak;U. C. Mohanty;A. Das;D. Niyogi
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文献类型：
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作者：
K. Osuri;R. Nadimpalli;Kumar Ankur;H. Nayak;U. C. Mohanty;A. Das;D. Niyogi
本研究探讨直接与间接的初始化土壤水分（SM）和土壤温度（ST）对季风低压（MD）和印度暴雨模拟的影响。从高分辨率陆地数据同化系统（LDAS）获得的SM/ST产品用于ARW模拟系统中陆面条件的直接初始化。在间接方法中，初始SM通过通量调节地面数据同化系统（FASDAS）进行顺序调节。这两种方法进行了比较，控制实验（CNTL）涉及气候SM/ST条件下的8个MD在4公里的水平分辨率。LDAS运行模拟的地面场显示出最高的一致性，其次是FASDAS相对干燥的6月的情况下，但相对潮湿的8月的情况下，误差很高（~15-30%）。水汽收支表明，水汽辐合和局地影响对降水贡献较大。地面降雨反馈分析表明，地面条件和蒸发对降雨模拟有主导影响，这些耦合在LDAS运行中很明显。与FASDAS和CNTL相比，连续雨区（CRA）方法显示LDAS对特大暴雨分布和位置（ETS > 0.2）的性能更好。模式误差对总降水量误差的贡献最大，位移误差在8月份的降水量中大于6月份。总体分析表明，土地条件的作用是显着高，在干燥的月份（6月）比潮湿的月份（8月），SM/ST字段的直接初始化产生了改进的MD和大雨模拟。
This study investigates the impact of direct versus indirect initialization of soil moisture (SM) and soil temperature (ST) on monsoon depressions (MDs) and heavy rainfall simulations over India. SM/ST products obtained from high‐resolution, land data assimilation system (LDAS) are used in the direct initialization of land surface conditions in the ARW modeling system. In the indirect method, the initial SM is sequentially adjusted through the flux‐adjusting surface data assimilation system (FASDAS). These two approaches are compared with a control experiment (CNTL) involving climatological SM/ST conditions for eight MDs at 4‐km horizontal resolution. The surface fields simulated by the LDAS run showed the highest agreement, followed by FASDAS for relatively dry June cases, but the error is high (~15–30%) for the relatively wet August cases. The moisture budget indicates that moisture convergence and local influence contributed more to rainfall. The surface‐rainfall feedback analysis reveals that surface conditions and evaporation have a dominant impact on the rainfall simulation and these couplings are notable in LDAS runs. The contiguous rain area (CRA) method indicates better performance of LDAS for very heavy rainfall distribution, and the location (ETS > 0.2), compared to FASDAS and CNTL. The pattern error contributes the maximum to the total rainfall error, and the displacement error is more in August cases' rainfall than that in June cases. Overall analyses indicated that the role of land conditions is significantly high in the drier month (June) than a wet month (August), and direct initialization of SM/ST fields yielded improved MD and heavy rain simulations.