A New Variational Assimilation Method Based on Gradient Information from Satellite Data
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 systemat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2017/4861765 |
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| author | Bo Zhong Yun-Feng Wang Gang Ma Xin-Yuan Ma Lu Yang |
| author_facet | Bo Zhong Yun-Feng Wang Gang Ma Xin-Yuan Ma Lu Yang |
| author_sort | Bo Zhong |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ce32cca5a8aa4f49b129346beffedefa |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-ce32cca5a8aa4f49b129346beffedefa2025-02-03T01:02:09ZengWileyAdvances in Meteorology1687-93091687-93172017-01-01201710.1155/2017/48617654861765A New Variational Assimilation Method Based on Gradient Information from Satellite DataBo Zhong0Yun-Feng Wang1Gang Ma2Xin-Yuan Ma3Lu Yang4Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, ChinaInstitute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, ChinaNational Satellite Meteorological Center, Beijing, ChinaInstitute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, ChinaInstitute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing, ChinaWith 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.http://dx.doi.org/10.1155/2017/4861765 |
| spellingShingle | Bo Zhong Yun-Feng Wang Gang Ma Xin-Yuan Ma Lu Yang A New Variational Assimilation Method Based on Gradient Information from Satellite Data Advances in Meteorology |
| title | A New Variational Assimilation Method Based on Gradient Information from Satellite Data |
| title_full | A New Variational Assimilation Method Based on Gradient Information from Satellite Data |
| title_fullStr | A New Variational Assimilation Method Based on Gradient Information from Satellite Data |
| title_full_unstemmed | A New Variational Assimilation Method Based on Gradient Information from Satellite Data |
| title_short | A New Variational Assimilation Method Based on Gradient Information from Satellite Data |
| title_sort | new variational assimilation method based on gradient information from satellite data |
| url | http://dx.doi.org/10.1155/2017/4861765 |
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