The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products
Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spat...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/239 |
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| author | Xuesong Bai Zhengkun Qin Juan Li Shupeng Zhang Lili Wang |
| author_facet | Xuesong Bai Zhengkun Qin Juan Li Shupeng Zhang Lili Wang |
| author_sort | Xuesong Bai |
| collection | DOAJ |
| description | Soil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spatial heterogeneity, implying strong local characteristics for both observational and background errors. To elucidate the impact of error localization on LDA, we constructed a land data assimilation system (LDAS) suitable for the Common Land Model (CoLM), based on the simplified extended Kalman filter (SEKF) method. Through practical assimilation experiments using soil moisture retrieval products from the Soil Moisture Active Passive (SMAP) and Fenyun-3D (FY3D) satellites, we investigated the influence of spatial static and dynamic observational and background errors on LDA. The results indicate that by incorporating dynamic errors that account for the spatial heterogeneity of soil, LDAS can adaptively absorb observational information, thereby significantly enhancing assimilation impact and subsequent model forecast accuracy. Compared to experiments applying static errors, dynamic errors increased the spatial correlation coefficients by 17.4% and reduced the root mean square error (RMSE) by 11.2%. The results clearly demonstrate that for soil variable assimilation studies with strong spatial heterogeneity, progressively refined dynamic error estimation is a crucial direction for improving land surface assimilation performance. |
| format | Article |
| id | doaj-art-0a452e7a4fed40f2b0e12a04c79ea889 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0a452e7a4fed40f2b0e12a04c79ea8892025-01-24T13:47:50ZengMDPI AGRemote Sensing2072-42922025-01-0117223910.3390/rs17020239The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval ProductsXuesong Bai0Zhengkun Qin1Juan Li2Shupeng Zhang3Lili Wang4The Joint Center for Data Assimilation Research and Applications, School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThe Joint Center for Data Assimilation Research and Applications, School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, ChinaThe State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaThe School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 510275, ChinaThe State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, ChinaSoil moisture is a key factor affecting the exchange of heat and water between the land and the atmosphere. Land data assimilation (LDA) methods that leverage the strengths of both models and observations can generate more accurate initial conditions. However, soil moisture exhibits significant spatial heterogeneity, implying strong local characteristics for both observational and background errors. To elucidate the impact of error localization on LDA, we constructed a land data assimilation system (LDAS) suitable for the Common Land Model (CoLM), based on the simplified extended Kalman filter (SEKF) method. Through practical assimilation experiments using soil moisture retrieval products from the Soil Moisture Active Passive (SMAP) and Fenyun-3D (FY3D) satellites, we investigated the influence of spatial static and dynamic observational and background errors on LDA. The results indicate that by incorporating dynamic errors that account for the spatial heterogeneity of soil, LDAS can adaptively absorb observational information, thereby significantly enhancing assimilation impact and subsequent model forecast accuracy. Compared to experiments applying static errors, dynamic errors increased the spatial correlation coefficients by 17.4% and reduced the root mean square error (RMSE) by 11.2%. The results clearly demonstrate that for soil variable assimilation studies with strong spatial heterogeneity, progressively refined dynamic error estimation is a crucial direction for improving land surface assimilation performance.https://www.mdpi.com/2072-4292/17/2/239soil moistureland data assimilationerror estimationsatelliteretrieval |
| spellingShingle | Xuesong Bai Zhengkun Qin Juan Li Shupeng Zhang Lili Wang The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products Remote Sensing soil moisture land data assimilation error estimation satellite retrieval |
| title | The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products |
| title_full | The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products |
| title_fullStr | The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products |
| title_full_unstemmed | The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products |
| title_short | The Impact of Spatial Dynamic Error on the Assimilation of Soil Moisture Retrieval Products |
| title_sort | impact of spatial dynamic error on the assimilation of soil moisture retrieval products |
| topic | soil moisture land data assimilation error estimation satellite retrieval |
| url | https://www.mdpi.com/2072-4292/17/2/239 |
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