From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States
LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of su...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/12/2020 |
| _version_ | 1797564317613162496 |
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| author | Anthony Mucia Bertrand Bonan Yongjun Zheng Clément Albergel Jean-Christophe Calvet |
| author_facet | Anthony Mucia Bertrand Bonan Yongjun Zheng Clément Albergel Jean-Christophe Calvet |
| author_sort | Anthony Mucia |
| collection | DOAJ |
| description | LDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable. |
| first_indexed | 2024-03-10T18:55:37Z |
| format | Article |
| id | doaj.art-b77010772d1c4202aab9e20faf8d7265 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:55:37Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b77010772d1c4202aab9e20faf8d72652023-11-20T04:47:30ZengMDPI AGRemote Sensing2072-42922020-06-011212202010.3390/rs12122020From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United StatesAnthony Mucia0Bertrand Bonan1Yongjun Zheng2Clément Albergel3Jean-Christophe Calvet4CNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, FranceCNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, FranceCNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, FranceCNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, FranceCNRM, Université de Toulouse, Météo-France, CNRS, 31057 Toulouse, FranceLDAS-Monde is a global land data assimilation system (LDAS) developed by Centre National de Recherches Météorologiques (CNRM) to monitor land surface variables (LSV) at various scales, from regional to global. With LDAS-Monde, it is possible to jointly assimilate satellite-derived observations of surface soil moisture (SSM) and leaf area index (LAI) into the interactions between soil biosphere and atmosphere (ISBA) land surface model (LSM) in order to analyze the soil moisture profile together with vegetation biomass. In this study, we investigate LDAS-Monde’s ability to predict LSV states up to two weeks in the future using atmospheric forecasts. In particular, the impact of the initialization, and the evolution of the forecasted variables in the LSM are addressed. LDAS-Monde is an offline system normally driven by atmospheric reanalysis, but in this study is forced by atmospheric forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) for the 2017–2018 period over the contiguous United States (CONUS) at a 0.2° × 0.2° spatial resolution. These LSV forecasts are initialized either by the model alone (LDAS-Monde open-loop, without assimilation) or by the analysis (assimilation of SSM and LAI). These two forecasts are then evaluated using satellite-derived observations of SSM and LAI, evapotranspiration (ET) estimates, as well as in situ measurements of soil moisture from the U.S. Climate Reference Network (USCRN). Results indicate that for the three evaluation variables (SSM, LAI, and ET), LDAS-Monde provides reasonably accurate and consistent predictions two weeks in advance. Additionally, the initial conditions after assimilation are shown to make a positive impact with respect to LAI and ET. This impact persists in time for these two vegetation-related variables. Many model variables, such as SSM, root zone soil moisture (RZSM), LAI, ET, and drainage, remain relatively consistent as the forecast lead time increases, while runoff is highly variable.https://www.mdpi.com/2072-4292/12/12/2020ASCATdata assimilationsoil moistureleaf area indexevapotranspiration |
| spellingShingle | Anthony Mucia Bertrand Bonan Yongjun Zheng Clément Albergel Jean-Christophe Calvet From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States Remote Sensing ASCAT data assimilation soil moisture leaf area index evapotranspiration |
| title | From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States |
| title_full | From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States |
| title_fullStr | From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States |
| title_full_unstemmed | From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States |
| title_short | From Monitoring to Forecasting Land Surface Conditions Using a Land Data Assimilation System: Application over the Contiguous United States |
| title_sort | from monitoring to forecasting land surface conditions using a land data assimilation system application over the contiguous united states |
| topic | ASCAT data assimilation soil moisture leaf area index evapotranspiration |
| url | https://www.mdpi.com/2072-4292/12/12/2020 |
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