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Assimilation of SMOS retrievals in the land information system / Clay B. Blankenship in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6320 - 6332 Titre : Assimilation of SMOS retrievals in the land information system Type de document : Article/Communication Auteurs : Clay B. Blankenship, Auteur ; Jonathan L. Case, Auteur ; Bradley T. Zavodsky, Auteur ; William L. Crosson, Auteur Année de publication : 2016 Article en page(s) : pp 6320 - 6332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] carte de la végétation [Termes IGN] Etats-Unis [Termes IGN] filtre de Kalman [Termes IGN] humidité du sol [Termes IGN] image radar [Termes IGN] image SMOS [Termes IGN] modèle numérique de surface [Termes IGN] radiométrie [Termes IGN] système d'information foncière Résumé : (Auteur) The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in roughly the upper 5 cm with a 30-50-km resolution and a mission accuracy requirement of 0.04 cm3/cm-3. These observations can be used to improve land surface model (LSM) soil moisture states through data assimilation (DA). In this paper, SMOS soil moisture retrievals are assimilated into the Noah LSM via an Ensemble Kalman Filter within the National Aeronautics and Space Administration Land Information System. Bias correction is implemented using cumulative distribution function (cdf) matching, with points aggregated by either land cover or soil type to reduce the sampling error in generating the cdfs. An experiment was run for the warm season of 2011 to test SMOS DA and to compare assimilation methods. Verification of soil moisture analyses in the 0-10-cm upper layer and the 0-1-m root zone was conducted using in situ measurements from several observing networks in central and southeastern United States. This experiment showed that SMOS DA significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10-cm layer. Time series at specific stations demonstrates the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared with using a simple uniform correction curve. Numéro de notice : A2016-913 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2579604 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2579604 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83135 [article]

A real-time MODIS vegetation product for land surface and numerical weather prediction models / Jonathan L. Case in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 3 (March 2014) . - pp 1772 - 1786 Titre : A real-time MODIS vegetation product for land surface and numerical weather prediction models Type de document : Article/Communication Auteurs : Jonathan L. Case, Auteur ; Frank J. Lafontaine, Auteur ; Jordan R. Bell, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 1772 - 1786 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Etats-Unis [Termes IGN] image Terra-MODIS [Termes IGN] indice de végétation [Termes IGN] modèle de simulation [Termes IGN] modèle météorologique Résumé : (Auteur) A technique is presented to produce real-time, daily vegetation composites at 0.01° resolution (~1 km) over the Conterminous United States (CONUS) for use in the NASA Land Information System (LIS) and weather prediction models. Green vegetation fraction (GVF) is derived from direct-broadcast swaths of normalized difference vegetation index from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observing System satellites. The real-time data and increased resolution compared to the 0.144° (~16 km) resolution monthly GVF climatology in community models result in an improved representation of vegetation in high-resolution models, especially in complex terrain. The MODIS GVF fields show seasonal variations that are similar to the community model climatology, and respond realistically to temperature and precipitation anomalies. The wet spring and summer 2010 over the U.S. Plains led to higher regional GVF than in the climatology. The GVF substantially decreased over the U.S. Southern Plains from 2010 to 2011, consistent with the transition to extreme drought in summer 2011. LIS simulations depict substantial sensitivity to the MODIS GVF, with regional changes in heat fluxes around 100 Wm-2 over the northern U.S. in June 2010. CONUS LIS simulations during the 2010 warm season indicate that the larger MODIS GVF in the western U.S. led to higher latent heat fluxes and initially lower sensible heat fluxes, with a net drying effect on the soil. With time, the drier soil eventually lead to higher mean sensible heat fluxes such that the total surface energy output increased by late summer 2010 over the western U.S. A sensitivity simulation of a severe weather event using real-time MODIS GVF data results in systematic changes to low-level temperature, moisture, and instability fields, and improves the evolution of simulated precipitation. Numéro de notice : A2014-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2255059 En ligne : https://doi.org/10.1109/TGRS.2013.2255059 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33018 [article]

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