1. 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 , 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

2. Meteosat-7 Water vapor radiances and GSICS coefficients
Meteosat-7 clear sky water vapor radiances in bufr format, from EUMETSAT (
-METEOSAT7-MVIRI-MTPCSKR-NA Z bfr
GSICS correction coefficients in netcdf format, from EUMETSAT (
-W_XX-EUMETSAT-Darmstadt,SATCAL+NRTC+GEOLEOIR,MET7+MVIRI-MetOpA+IASI_C_EUMG_ _demo_03

3. 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

4. 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: D-Var
Assimilation Cycle: hour
Bias Correction : Variational Bias Correction

5. 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)

6. 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.

7. 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.