The Global SMOS Microwave Emission Model (SMOS-MEM) Intercomparison and SMOS Bias Correction for ECMWF Numerical Weather Prediction
de Rosnay, Patricia¹; Muñoz Sabater, Joaquín¹; Drusch, Matthias²; Albergel, Clément¹; Gianpaolo, Balsamo¹; Boussetta, Souhail¹; Isaksen, Lars¹; Thépaut, Jean-Noël^1
1European Centre for Medium-Range Weather Forecasts, UNITED KINGDOM; ²ESA

Soil moisture initialisation is crucial for Numerical Weather Prediction (NWP). New generations of satellites, such as ASCAT (Advanced Scatterometer) and SMOS (Soil Moisture and Ocean Salinity)provide highly suitable data from active and passive microwave sensors for soil moisture remote sensing. In order to make it possible to combine use of satellite, in situ and proxy observations to analyse soil moisture, ECMWF implemented an Extended Kalman Filter (EKF) soil moisture analysis which is used for operational NWP in the ECMWF Integrated Forecasting System (IFS) (de Rosnay et al., 2012). In this presentation we focus on recent activities in radiative transfer modelling conducted to prepare SMOS brightness temperature data assimilation in the ECMWF IFS. First, the ECMWF Community Microwave Emission Modelling Platform (CMEM) and its recent updates are presented (de Rosnay et al., 2009). CMEM has been used for near real time SMOS brightness temperature monitoring since 2010 (Muñoz Sabater et al., 2011a,b). Second, the SMOS Microwave Emission Models (SMOS-MEM) intercomparison experiment is presented. It consists in intercomparing a range of state-of-the art microwave emission models included in CMEM. The ERA-Land surface re-analysis (Balsamo et al., 2012), conditions of soil moisture, temperature, snow depth, vegetation cover and surface characteristics are used as input of CMEM, so that a consistent version of the Land Surface Model is used for CMEM simulations for 2010-2011, as well as in the current operational IFS. In CMEM, combinations of three soil dielectric models, three vegetation opacity models and four soil roughness parameterizations are investigated, leading to intercompare and to evaluate 36 microwave emission model configurations. Global scale forward simulations of dual polarization L-band (1.4 GHz) brightness temperature have been conducted for each radiative transfer model and compared to SMOS brightness temperature data for the 2010-2011 reprocessed data set of the SMOS near real time product. Best microwave emission model performances are obtained with the Wang and Schmugge dielectric model combined with the Wigneron vegetation opacity model and the Wigneron 2001 roughness model. Finally the best CMEM configuration is used to simulate multi-angular SMOS brightness temparature at 30, 40 and 50 degrees incidence angle. A multi-angular bias correction approach based on a CDF-matching is proposed and shown to reduce the SMOS bias with respect to ECMWF simulated brightness temperature. Bias corrected SMOS brightness temperature provide relevant information to be assimilated in the ECMWF IFS, as presented in the companion paper by Muñoz Sabater. References: Balsamo G., C. Albergel, A. Beljaars, S. Boussetta, E. Brun, H. Cloke, D. Dee, E. Dutra, F. Pappenberger, P. de Rosnay, J. Muñoz-Sabater, T. Stockdale, F. Vitart, 2012: ERA-Interim/Land: A global land-surface reanalysis based on ERA-Interim meteorological forcing, ERA-Report series, n. 13, September 2012 de Rosnay P., M. Drusch, D. Vasiljevic, G. Balsamo, C. Albergel and L. Isaksen: A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF, Q. J. R. Meteorol. Soc., 2012 doi: 10.1002/qj.2023 de Rosnay, P., M. Drusch and J. Muñoz Sabater: Milestone 1 Tech Note - Part 1: SMOS Global Surface Emission Model November 2009 http://www.ecmwf.int/publications/library/do/references/show?id=89525 Muñoz Sabater J., M. Dahoui, P. de Rosnay, L. Isaksen: Technical Note, Phase II, WP1100: SMOS Monitoring Report; December 2011a http://www.ecmwf.int/publications/library/do/references/show?id=90375 Muñoz Sabater J., A. Fouilloux and P. de Rosnay, " Technical implementation of SMOS data in the ECMWF Integrated Forecasting System", Geosci. Remote Sens. Let., 2011b doi: 10.1109/LGRS.2011.2164777