The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation,surface radiation,etc.,that in turn induces changes in the model simulated outputs.The present study deals with the effects of vegetation in climate modeling over the Indian region using the MM5 mesoscale model.The main objective of the present study is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization)versus that of USGS (United States Geological Survey)vegetation dataset on the simulation of the Indian summer monsoon.The present study has been conducted for five monsoon seasons (1998 –2002),giving emphasis over the two contrasting southwest monsoon seasons of 1998 (normal)and 2002 (deficient).

The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the monsoon.Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India and along the western coast.The MM5- USGS has greater tendency of overestimation of rainfall.It has higher standard deviation indicating that it induces a dispersive effect on the rainfall simulation.Among the five years of study,it is seen that the RMSE of July and JJAS (June –July –August –September)for All India Rainfall is mostly lower for MM5-ISRO.Also,the bias of July and JJAS rainfall is mostly closer to unity for MM5-ISRO.The wind fields at 850 hPa and 200 hPa are also better simulated by MM5 using ISRO vegetation.The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the veri fication analysis by ISRO vegetation.The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India,showing greater spatial variability over the region. However,the JJAS total rainfall over north India and Deccan coast is better simulated using the USGS vegetation.Sensible heat flux over north-west India is also better simulated by MM5-USGS.

An analysis system experiment was conducted for the month of June 2008 with Gridpoint Statistical Interpolation (GSI) analysis scheme using NCMRWF’s (National Centre for Medium Range Weather Forecasting) T254L64 model. Global analyses were carried out for all days of the month and respective forecast runs are made up to 120-hr. These analyses and forecasts are inter-compared with the operational T254L64 model outputs which uses Spectral Statistical Interpolation (SSI) analysis scheme. The prime objective of this study is to assess the impact of GSI analysis scheme with special emphasis on Indian summer monsoon as compared to SSI.

GSI analysis scheme do have positive impact over India and its surrounding regions. Though not for all but for some fields it is in edge over Spectral Statistical Analysis Scheme. Patterns for the forecast mean error; anomaly correlation and $S_1$ scores with respect to the respective analyses are same for both GSI and SSI. Both have increasing $S_1$ scores, decreasing mean errors and anomaly correlation with the advance of forecast days. The vector wind RMSE of the model forecasts with respect to the analyses is lower for GSI at 850 hPa and higher at 250 hPa. But over tropics GSI is better at both levels. The temperature field of GSI has higher correlation and lower RMSE at both 850 and 250 hPa pressure levels. There are improvements in systematic errors for 850 and 200 hPa temperature field in GSI compared to that in SSI. The depression centre in GSI analysis is closer to observation but has produced more intense depression compared to that of SSI. Rainfall forecast of SSI is better at day-1 whereas GSI is closer to the observation at day-5 forecasts valid at the same day.