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Remote Sens. 2017, 9(9), 963; doi:10.3390/rs9090963

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

1,2
,
1,3,* , 1,4
and
5
1
Key Laboratory of Remote Sensing of Gansu Province, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
2
Heihe Remote Sensing Experimental Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou 730000, China
3
CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing 100101, China
4
Institute of Urban Development, Shanghai Normal University, Shanghai 200234, China
5
Department of Hydraulic Engineering, Tsinghua University, Beijing 10084, China
*
Author to whom correspondence should be addressed.
Received: 23 June 2017 / Revised: 1 September 2017 / Accepted: 14 September 2017 / Published: 17 September 2017
(This article belongs to the Section Atmosphere Remote Sensing)
View Full-Text   |   Download PDF [4249 KB, uploaded 17 September 2017]   |  

Abstract

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. View Full-Text
Keywords: TRMM; FY-2D; GPM; data assimilation; inland river basin TRMM; FY-2D; GPM; data assimilation; inland river basin
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Pan, X.; Li, X.; Cheng, G.; Hong, Y. 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. Remote Sens. 2017, 9, 963.

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