Wikindx Resources

Journal Article: BibTeX citation key:  DeRosnay2009
De Rosnay, P., Drusch, M., Boone, A., Balsamo, G., Decharme, B., Harris, P. P., Kerr, Y. H., Pellarin, T., Polcher, J. & Wigneron, J.-P. (2009) AMMA Land Surface Model Intercomparison Experiment coupled to the Community Microwave Emission Model: ALMIP-MEM. IN Journal of Geophysical Research - Atmospheres, 114. D05108.
Added by: Aaron Boone 2009-06-03 09:40:24    Last Edited by: Fanny Lefebvre 2011-01-21 11:47:37
 B  
Categories: Land surface processes, Land surface-atmosphere feedback
Keywords: Data assimilation, Modelling, Satellites, Surface Flux
Creators: Balsamo, Boone, Decharme, Drusch, Harris, Kerr, Pellarin, Polcher, De Rosnay, Wigneron
Collection: Journal of Geophysical Research - Atmospheres
Bibliographies: cnrm, Prior150410

Peer reviewed
Number of views:  1158
Popularity index:  61.27%

 
Abstract
This paper presents the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Models Intercomparison Project (ALMIP) for Microwave Emission Models (ALMIP-MEM). ALMIP-MEM comprises an ensemble of simulations of C-band brightness temperatures over West Africa for 2006. Simulations have been performed for an incidence angle of 55°, and results are evaluated against C-band satellite data from the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E). The ensemble encompasses 96 simulations, for 8 Land Surface Models (LSMs) coupled to 12 configurations of the Community Microwave Emission Model (CMEM). CMEM has a modular structure which permits combination of several parameterizations with different vegetation opacity and soil dielectric models. ALMIP-MEM provides the first intercomparison of state-of-the-art land surface and microwave emission models at regional scale. Quantitative estimates of the relative importance of land surface modeling and radiative transfer modeling for the monitoring of low-frequency passive microwave emission on land surfaces are obtained. This is of high interest for the various users of coupled land surface microwave emission models. Results show that both LSMs and microwave model components strongly influence the simulated top of atmosphere (TOA) brightness temperatures. For most of the LSMs, the Kirdyashev opacity model is the most suitable to simulate TOA brightness temperature in best agreement with the AMSR-E data. When this best microwave modeling configuration is used, all the LSMs are able to reproduce the main temporal and spatial variability of measured brightness temperature. Averaged among the LSMs, correlation is 0.67 and averaged normalized standard deviation is 0.98.
Document
Added by: Aaron Boone    Last Edited by: Fanny Lefebvre