Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations - Météo-France Accéder directement au contenu
Article Dans Une Revue Weather and Forecasting Année : 2009

Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations

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To improve the assimilation of Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B) observations over land, three methods, based either on an estimation of the land emissivity or the land skin temperature directly from satellite observations, have been developed. Some feasibility studies have been performed in the Me´te´o-France assimilation system in order to choose the most appropriate method for the system. This study reports on three 2-month assimilation and forecast experiments that use different methods to estimate AMSU-A and -B land emissivities together with the operational run as a control experiment. The experiments and the control have been subjected to several comparisons. The performance of the observation operator for simulating window channel brightness temperatures has been studied. The study shows considerable improvements in the statistics of the window channels' first-guess departures (bias, standard deviation). The correlations between the observations and the model's simulations have also been improved, especially over snow-covered areas. The performances of the assimilation system, in terms of cost function change, have been examined: the cost function is generally improved during the screening and remains stable during the minimization. Moreover, comparisons have been made in terms of impacts on both analyses and forecasts.
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meteo-00463475 , version 1 (22-10-2021)

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Fatima Karbou, Florence Rabier, Elisabeth Gerard. Global 4DVAR Assimilation and Forecast Experiments Using AMSU Observations over Land. Part I: Impacts of Various Land Surface Emissivity Parameterizations. Weather and Forecasting, 2009, pp.PP. 5-19. ⟨10.1175/2009WAF2222243.1⟩. ⟨meteo-00463475⟩
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