Impact of Meteosat-7 Water Vapor Radiances on Mesocale Weather Forecasts Dr. Randhir Singh Indian Institute of Remote Sensing (IIRS) Indian Space Research Organisation (ISRO) Dept. of Space, Govt. of India Dehradun - 248 001, India Dr. Tim Hewison European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Am Kavalleriesand 31, D-64295, Darmstadt, Germany Dr. Manik Bali CMNS-Earth System Science Interdisciplinary Center University of Maryland, College Park, MD 20742,USA

Meteosat-7 Water vapor radiances and GSICS coefficients Meteosat-7 clear sky water vapor radiances in bufr format, from EUMETSAT ( www.eumetsat.int) -METEOSAT7-MVIRI-MTPCSKR-NA-1-20140731030259.000000000Z-1145972.bfr GSICS correction coefficients in netcdf format, from EUMETSAT (www.eumetsat.int) -W_XX-EUMETSAT-Darmstadt,SATCAL+NRTC+GEOLEOIR,MET7+MVIRI-MetOpA+IASI_C_EUMG_20140703000000_demo_03

Monitoring of Meteosat-7 Water Vapor Radiances Entire July 2014, at every 6 hours GSICS corrected radiances have less bias compared to without GSICS corrected radiances. Standard deviations are same for GSICS and without GSICS corrected radiances

Model Configuration and Assimilation Methodology Entire July 2014 Model Domain Model: WRF V3.7 Horizontal Resolution: 25 km Cumulus Scheme: Kain Fritsch Moisture Scheme : WSM 6 Class LW Radiation Scheme : RRTM SW Radiation Scheme: Dudhia PBL Scheme: YSU DA Method: 3D-Var Assimilation Cycle: 6 hour Bias Correction : Variational Bias Correction

Assimilation Experiment with Meteosat-7 water vapor radiances Three assimilation experiments were performed at every 6 hours during the entire July 2014 CNTL – Assimilation of only conventional data (e.g. Radiosonde, Synop, Ships, AMVs ) EXP-1 – CNTL plus Meteosat-7 water vapor radiances (without GSICS correction) EXP-2 – CNTL plus Meteosat-7 water vapor radiances (with GSICS correction)

Assimilation Results : averaged over one month period EXP-1 EXP-2 Vertical variation of domain averaged improvement parameter ( %) for (a) specific humidity, (b) temperature and (c) winds. Positive and negative values signify the positive and negative impact of the Meteosat-7 water vapor radiances.

Conclusions When compared with the RT model simulated radiance (using NCEP GFS analyzed atmospheric state), GSICS corrected Meteosat-7 water vapor radiances show less bias (-1.2 K, e.g. cold bias) compared to without GSICS corrected radiances (3.7 K, e.g. warm bias) . However, standard deviations of both these products are similar. Assimilation of the Meteosat-7 water vapor radiances shows positive impact (as large as 5 %) , particularly in the moisture field around 300 to 400 hPa. Overall the impact of these two products is similar, except GSICS corrected radiances that show broad structure of improvements in moisture field. The reason for this similar impact could be the fact that prior to their assimilation , these products were corrected for the biases with respect to the first guess radiances, which is a prerequisite to any data assimilation system.