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Assimilation of remote sensed data for improved latent and sensible heat flux prediction: a comparative synthetic study / R. Pipunic in Remote sensing of environment, vol 112 n° 4 (15/04/2008)  

Public [article]  inRemote sensing of environment > vol 112 n° 4 (15/04/2008) . - pp 1295 - 1305 Titre : Assimilation of remote sensed data for improved latent and sensible heat flux prediction: a comparative synthetic study Type de document : Article/Communication Auteurs : R. Pipunic, Auteur ; J. Walker, Auteur ; A. Western, Auteur Année de publication : 2008 Article en page(s) : pp 1295 - 1305 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] chaleur [Termes IGN] filtre de Kalman [Termes IGN] flux de rayonnement [Termes IGN] humidité du sol [Termes IGN] image Terra-MODIS [Termes IGN] modèle numérique de surface [Termes IGN] SMOS Résumé : (Auteur) Predicted latent and sensible heat fluxes from Land Surface Models (LSMs) are important lower boundary conditions for numerical weather prediction. While assimilation of remotely sensed surface soil moisture is a proven approach for improving root zone soil moisture, and presumably latent (LE) and sensible (H) heat flux predictions from LSMs, limitations in model physics and over-parameterisation mean that physically realistic soil moisture in LSMs will not necessarily achieve optimal heat flux predictions. Moreover, the potential for improved LE and H predictions from the assimilation of LE and H observations has received little attention by the scientific community, and is tested here with synthetic twin experiments. A one-dimensional single column LSM was used in 3-month long experiments, with observations of LE, H, surface soil moisture and skin temperature (from which LE and H are typically derived) sampled from truth model run outputs generated with realistic data inputs. Typical measurement errors were prescribed and observation data sets separately assimilated into a degraded model run using an Ensemble Kalman Filter (EnKF) algorithm, over temporal scales representative of available remotely sensed data. Root Mean Squared Error (RMSE) between assimilation and truth model outputs across the experiment period were examined to evaluate LE, H, and root zone soil moisture and temperature retrieval. Compared to surface soil moisture assimilation as will be available from SMOS (every 3 days), assimilation of LE and/or H using a best case MODIS scenario (twice daily) achieved overall better predictions for LE and comparable H predictions, while achieving poorer soil moisture predictions. Twice daily skin temperature assimilation achieved comparable heat flux predictions to LE and/or H assimilation. Fortnightly (Landsat) assimilations of LE, H and skin temperature performed worse than 3-day moisture assimilation. While the different spatial resolutions of these remote sensing data have been ignored, the potential for LE and H assimilation to improve model predicted LE and H is clearly demonstrated. Copyright Elsevier Numéro de notice : A2008-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2007.02.038 En ligne : https://doi.org/10.1016/j.rse.2007.02.038 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29084 [article]