Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary
In this study, a Land Data Assimilation System (LDAS) is applied over the Carpathian Basin at the Hungarian Meteorological Service to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale (spatial resolution of 8 km × 8...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/12/8/944 |
| _version_ | 1797524697194168320 |
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| author | Helga Tóth Balázs Szintai |
| author_facet | Helga Tóth Balázs Szintai |
| author_sort | Helga Tóth |
| collection | DOAJ |
| description | In this study, a Land Data Assimilation System (LDAS) is applied over the Carpathian Basin at the Hungarian Meteorological Service to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale (spatial resolution of 8 km × 8 km) in quasi-real-time. In this system the SURFEX model is used, which applies the vegetation growth version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) photosynthesis scheme to describe the evolution of vegetation. SURFEX is forced using the outputs of the ALADIN numerical weather prediction model run operationally at the Hungarian Meteorological Service. First, SURFEX is run in an open-loop (i.e., no assimilation) mode for the period 2008–2015. Secondly, the Extended Kalman Filter (EKF) method is used to assimilate Leaf Area Index (LAI) Spot/Vegetation (until May 2014) and PROBA-V (from June 2014) and Soil Water Index (SWI) ASCAT/Metop satellite measurements. The benefit of LDAS is proved over the whole country and to a selected site in West Hungary (Hegyhátsál). It is demonstrated that the EKF can provide useful information both in wet and dry seasons as well. It is shown that the data assimilation is efficient to describe the inter-annual variability of biomass and soil moisture values. The vegetation development and the water and carbon fluxes vary from season to season and LDAS is a capable tool to monitor the variability of these parameters. |
| first_indexed | 2024-03-10T09:00:32Z |
| format | Article |
| id | doaj.art-931babfc598445db856b80e7a95594cc |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T09:00:32Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-931babfc598445db856b80e7a95594cc2023-11-22T06:46:46ZengMDPI AGAtmosphere2073-44332021-07-0112894410.3390/atmos12080944Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in HungaryHelga Tóth0Balázs Szintai1Hungarian Meteorological Service, 1024 Budapest, HungaryHungarian Meteorological Service, 1024 Budapest, HungaryIn this study, a Land Data Assimilation System (LDAS) is applied over the Carpathian Basin at the Hungarian Meteorological Service to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale (spatial resolution of 8 km × 8 km) in quasi-real-time. In this system the SURFEX model is used, which applies the vegetation growth version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) photosynthesis scheme to describe the evolution of vegetation. SURFEX is forced using the outputs of the ALADIN numerical weather prediction model run operationally at the Hungarian Meteorological Service. First, SURFEX is run in an open-loop (i.e., no assimilation) mode for the period 2008–2015. Secondly, the Extended Kalman Filter (EKF) method is used to assimilate Leaf Area Index (LAI) Spot/Vegetation (until May 2014) and PROBA-V (from June 2014) and Soil Water Index (SWI) ASCAT/Metop satellite measurements. The benefit of LDAS is proved over the whole country and to a selected site in West Hungary (Hegyhátsál). It is demonstrated that the EKF can provide useful information both in wet and dry seasons as well. It is shown that the data assimilation is efficient to describe the inter-annual variability of biomass and soil moisture values. The vegetation development and the water and carbon fluxes vary from season to season and LDAS is a capable tool to monitor the variability of these parameters.https://www.mdpi.com/2073-4433/12/8/944land surface modelingdata assimilationsatellite dataLAISWI |
| spellingShingle | Helga Tóth Balázs Szintai Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary Atmosphere land surface modeling data assimilation satellite data LAI SWI |
| title | Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary |
| title_full | Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary |
| title_fullStr | Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary |
| title_full_unstemmed | Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary |
| title_short | Assimilation of Leaf Area Index and Soil Water Index from Satellite Observations in a Land Surface Model in Hungary |
| title_sort | assimilation of leaf area index and soil water index from satellite observations in a land surface model in hungary |
| topic | land surface modeling data assimilation satellite data LAI SWI |
| url | https://www.mdpi.com/2073-4433/12/8/944 |
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