Remote Sensing Applications and Decision Support

Assimilating moderate resolution imaging spectroradiometer radiance with the weather research and forecasting data assimilation system

[+] Author Affiliations
Feiyue Mao

Wuhan University, School of Remote Sensing and Information Engineering, Wuhan, China

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan, China

Collaborative Innovation Center for Geospatial Technology, Wuhan, China

Qilong Min

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan, China

State University of New York, Atmospheric Sciences Research Center, Albany, New York, United States

Guangyi Liu

Global Energy Interconnection Research Institute, San Jose, California, United States

Chun Liu, Shuanglei Feng, Shuanglong Jin, Ju Hu

State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems, China Electric Power Research Institute, Beijing, China

Wei Gong

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan, China

Collaborative Innovation Center for Geospatial Technology, Wuhan, China

Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy, Wuhan, China

Chen Li

CRRC Zhuzhou Institute Co. Ltd., Zhuzhou, China

J. Appl. Remote Sens. 11(3), 036002 (Jul 10, 2017). doi:10.1117/1.JRS.11.036002
History: Received February 22, 2017; Accepted June 19, 2017
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Abstract.  The performance of the weather research and forecasting (WRF) model relies on the accuracy of the initial field provided by data assimilation. The initial field usually contains large uncertainties, especially for regions where observations are sparse or lacking. Assimilating additional observations is an efficient way to reduce this uncertainty. The moderate resolution imaging spectroradiometer (MODIS) is one of the most critical data sources of various indirect assimilating applications due to its wide swath and remarkable quality. Therefore, assimilating MODIS data into WRF data assimilation (WRFDA) system is meaningful to improve the accuracy of weather forecasts. This study developed a module to directly assimilate the MODIS radiances into the WRFDA based on the 3-D variational data assimilation method and community radiative transfer model. An assimilation experiment was carried out on August 2014, from which the background field has been relatively improved. Specifically, the improvement of the temperature, humidity, and wind speed at the near surface layer is about 0.2°C, 1.2%, and 0.2  m/s, respectively. Additional capabilities and increased potential of MODIS data assimilation based on WRFDA need to be further investigated and tested under various conditions and applications.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Topics

MODIS ; Humidity

Citation

Feiyue Mao ; Qilong Min ; Guangyi Liu ; Chun Liu ; Shuanglei Feng, et al.
"Assimilating moderate resolution imaging spectroradiometer radiance with the weather research and forecasting data assimilation system", J. Appl. Remote Sens. 11(3), 036002 (Jul 10, 2017). ; http://dx.doi.org/10.1117/1.JRS.11.036002


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