The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model
Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/1/59 |
| _version_ | 1797439793670389760 |
|---|---|
| author | Suping Nie Xiaolong Jia Weitao Deng Yixiong Lu Dongyan He Liang Zhao Weihua Cao Xueliang Deng |
| author_facet | Suping Nie Xiaolong Jia Weitao Deng Yixiong Lu Dongyan He Liang Zhao Weihua Cao Xueliang Deng |
| author_sort | Suping Nie |
| collection | DOAJ |
| description | Based on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST. |
| first_indexed | 2024-03-09T11:59:16Z |
| format | Article |
| id | doaj.art-1a757dd77c96449cad4bdc0de090d968 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:59:16Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-1a757dd77c96449cad4bdc0de090d9682023-11-30T23:05:12ZengMDPI AGRemote Sensing2072-42922022-12-011515910.3390/rs15010059The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate ModelSuping Nie0Xiaolong Jia1Weitao Deng2Yixiong Lu3Dongyan He4Liang Zhao5Weihua Cao6Xueliang Deng7Earth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, ChinaNational Climate Center, China Meteorological Administration, Beijing 100081, ChinaKey Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaEarth System Modeling and Prediction Centre, China Meteorological Administration, Beijing 100081, ChinaAnhui Climate Center, Hefei 230031, ChinaInstitute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, ChinaInstitute of Urban Meteorology, China Meteorological Administration, Beijing 100089, ChinaHefei Meteorological Bureau, Hefei 230031, ChinaBased on the Beijing Climate Center’s land surface model BCC_AVIM2.0, an ensemble Kalman filter (EnKF) algorithm is developed to assimilate the land surface temperature (LST) product of the first satellite of Fengyun-4 series meteorological satellites of China to study the influence of LST data with different time frequencies on the surface temperature data assimilations. The MODIS daytime and nighttime LST products derived from Terra and Aqua satellites are used as independent validation data to test the assimilation results. The results show that diurnal variation information in the FY-4A LST data has significant effect on the assimilation results. When the time frequencies of the assimilated FY-4A LST data are sufficient, the assimilation scheme can effectively reduce the errors and the assimilation results reflect more reasonable spatial and temporal distributions. The assimilation experiments with a 3 h time frequency show less bias as well as RMSEs and higher temporal correlations than that of the model simulations at both daytime and nighttime periods. As the temporal frequency of assimilated LST observations decreases, the assimilation effects gradually deteriorate. When diurnal variation information is not considered at all in the assimilation, the assimilation with 24 h time frequency showed the largest errors and smallest time correlations in all experiments. The results demonstrate the potential of assimilating high-frequency FY-4A LST data to improve the performance of the BCC_AVIM2.0 land surface model. Furthermore, this study indicates that the diurnal variation information is a necessary factor needed to be considered when assimilating the FY-4A LST.https://www.mdpi.com/2072-4292/15/1/59FY-4A satelliteLST datahigh-frequency observationsdata assimilationclimate model |
| spellingShingle | Suping Nie Xiaolong Jia Weitao Deng Yixiong Lu Dongyan He Liang Zhao Weihua Cao Xueliang Deng The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model Remote Sensing FY-4A satellite LST data high-frequency observations data assimilation climate model |
| title | The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model |
| title_full | The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model |
| title_fullStr | The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model |
| title_full_unstemmed | The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model |
| title_short | The Influence of FY-4A High-Frequency LST Data on Data Assimilation in a Climate Model |
| title_sort | influence of fy 4a high frequency lst data on data assimilation in a climate model |
| topic | FY-4A satellite LST data high-frequency observations data assimilation climate model |
| url | https://www.mdpi.com/2072-4292/15/1/59 |
| work_keys_str_mv | AT supingnie theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT xiaolongjia theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT weitaodeng theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT yixionglu theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT dongyanhe theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT liangzhao theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT weihuacao theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT xueliangdeng theinfluenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT supingnie influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT xiaolongjia influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT weitaodeng influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT yixionglu influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT dongyanhe influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT liangzhao influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT weihuacao influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel AT xueliangdeng influenceoffy4ahighfrequencylstdataondataassimilationinaclimatemodel |