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Advances in Meteorology
Volume 2017 (2017), Article ID 4861765, 12 pages
Research Article

A New Variational Assimilation Method Based on Gradient Information from Satellite Data

1Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, China
2National Satellite Meteorological Center, Beijing, China

Correspondence should be addressed to Yun-Feng Wang

Received 22 December 2016; Revised 27 March 2017; Accepted 10 April 2017; Published 24 April 2017

Academic Editor: Rossella Ferretti

Copyright © 2017 Bo Zhong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. A gradient information assimilation method is proposed in this paper to eliminate systematic bias. This method uses a gradient operator for gradient transformation between the model variable and observation variable and reaches the objective of eliminating systematic bias. An ideal experiment of variational data assimilation is conducted using the Community Radiative Transfer Model (CRTM) and Advanced Microwave Sounding Unit-A (AMSU-A) data, indicating that only assimilating gradient information can eliminate the smooth systematic bias in observation data. Then, a numerical simulation of tropical cyclone (TC) Megi and data assimilation experiment are conducted using the Weather Research Forecast (WRF) and WRF Data Assimilation (WRFDA) model as well as the Atmospheric Infrared Sounder (AIRS) data. The results show that the method of gradient information assimilation can improve the accuracy of TC tracks forecast and is also applicable for dealing with unreliable satellite data.