Evaluation of satellite Land SAF and SRB products

Evaluation of satellite Land. SAF and SRB products, ECMWF ISF and AMMA re-analysis surface incoming radiation with AMMA flux station data: Role of clouds and aerosols David Ramier (1), Françoise Guichard (2), Bernard Cappelaere (1), Laurent Kergoat (3), Jean-Louis Roujean (2), Sylvie Galle (4), Nicolas Boulain (1), Jean-Martial Cohard (4), Christopher M. Taylor (5), and Anna Agusti-Panareda (6) (1) Hydroscience Montpellier (IRD/CNRS), France, (2) CNRM (CNRS/METEOFRANCE), Toulouse, France, (3) LMTG (CNRS/IRD/UPS°, Toulouse, france, (4) LTHE (IRD/CNRS/INPG/UJF), Grenoble, france, (5) CEH, Wallingford, UK, (6) ECMWF, Reading, UK Sites and Data Results: daily time step Bamba Wankama Agoufou Bamba Nalohou - In situ : CNR 1 (Kipp and Zonen) at Bamba, Agoufou, Wankama, Nalohou Agoufou Wankama - Satellite : Land-SAF for SW and LW (as of 2005, used in ALMIP), Gewex SRB 3. 0 for SW Nalohou Bamba Agoufou Wankama Nalohou - Models : ECMWF IFS and ECMWF AMMA reanalysis Results : 3 hourly time step Temporal evolution of meteo data (Relative humidity in black, Temperature in blue rain at the top) Results : monthly time step Bamba Agoufou Wankama May Bamba Nalohou Agoufou Wankama Nalohou Augus Monthly average and standard-deviation for Swin (top) and Lwin (bottom) for Ground data (black), ECMWF (blue), LSA-SAF (red); SRB (green) Bamba Agoufou Wankama Nalohou Results : AMMA re-analysis for 2006 Bamba Agoufou Wankama Nalohou - OBS (some gaps due to 9 days running means) - ECMWF IFS 2006 - ECMWF AMMA re-analysis (May-Sept) AOT and angstrom coefficients from AERONET sun photometers near Agoufou (orange), Wankama (green), Nalohou (blue). BSRN sites added for comparison Tamanrasset (pink), Ilorin (cyan). Improvement for Nalohou SW due to better cloud simulations few changes for Sahelian sites. Monthly average bias for ECMWF (blue bars), LSA-SAF (red bars), SRB (green bars). Black line is cloud cover from ECMWF (top) and AOT (bottom, inverted y axis). Conclusion : Significant biases for satellites products and models affect SW and LW, coinciding with large AOT values. These biases spread from January-March in Nalohou, to March-May in Wankama, and March-June in Agoufou and Bamba. Overestimation of SW and underestimation of LW are primilary due to dust, since they are higher when dust AOT is larger than biomass burning AOT. Surprinsingly, clouds are less of a problem. The ECMWF AMMA reanalysis is close to the IFS in terms of surface incoming radiation. There is some compensation between SW and LW errors at daily to monthly time steps, but 3 -hourly data more significantly biased, resulting in a distorted diurnal cycle. Overall, there is a very consistent bias in satellites and models, which impacts the gradient of surface energy budget in the hot and moist season preceeding the monsoon rainfalls. This feature is nicely evidenced by the latitudinal instrument setup (CATCH and AERONET). References : Guichard et al, 2009, Surface thermodynamics and radiative budget in the Sahelian Gourma: Seasonal and diurnal cycles, J. Hydrol. , 375, 161– 177 Guyot et al. , 2009, Combined analysis of energy and water balances to estimate latent heat flux of a sudanian small catchment, J. Hydrol. 375, 227– 240 Ramier et al. 2009, Towards an understanding of coupled physical and biological processes in the cultivated Sahel – 1. Energy and water, J. Hydrol. 375, 204– 216