Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin
Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin
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DOI：
10.3390/rs9090963
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Xiaoduo Pan;Xin Li;G. Cheng;Y. Hong
中科院分区：
文献类型：
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作者：
Xiaoduo Pan;Xin Li;G. Cheng;Y. Hong
单独地，地面、原位观测、遥感和区域气候模拟不能提供水文预测所需的高质量降水数据，特别是在复杂地形上。数据同化技术可以通过将地面观测和遥感产品同化到模式中来改善降水模拟和预报，从而弥合观测和模式之间的差距。然而，卫星反演降水产品同化研究在干旱区复杂地形上只开展了一小部分。本文利用气象研究与预报（WRF）模式，对地形复杂的中国西北干旱区典型内陆河流域黑河流域的两颗卫星降水产品（热带降雨测量任务：TRMM 3B42和风云2d: FY-2D）采用4D-Var数据同化方法进行同化。结果表明，遥感降水产品的同化可以改善湿度和温度的初始WRF场，从而改善降水预报，缩短自旋启动时间。因此，利用WRF 4D-Var吸收TRMM和FY-2D遥感降水产品可被视为朝着提高数值天气预报模式的准确性和提前期迈出的积极一步，特别是在地形复杂的地区。
Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF) model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D) using the 4D-Var data assimilation method for a typical inland river basin in northwest China’s arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.