Abstract

The set of the present thesis work deals with the intricacies associated with prediction of wintertime weather over western Himalayas. This region is comprised of complex mountain ranges having different altitudes and orientations. Due to this highly variable altitude and orientation of orographic barriers the prevailing weather conditions over the region are very complex. The foremost part of this thesis deals with the circulation features, dynamics and energetics associated with wintertime, December, January, February and March (DJFM), circulations over the western Himalayas.

Large scale balances of kinetic energy, vorticity, angular momentum, heat and moisture over the western Himalayas are studied using 40 years (1958-97) NCEP reanalysis during Surplus and deficient years of seasonal (DJFM) precipitation over the western Himalayas.

Western Himalayas is visited by many synoptic weather systems, WDs, that produce large spatial and temporal variability in weather and climate during winter. These WDs are simulated using MM5 to understand the behavioral pattern of circulation fields and prediction of precipitation.Precipitation and circulation features associated with the intense WDs are well simulated by the model. Forecast errors indicate that high resolution meso-scale model could simulate the weather associated with the WDs with reasonable accuracy.

Site and time specific prediction of some of the atmospheric variables cannot be achieved by numerical model outputs only. Therefore, dynamical statistical models are developed based on Perfect Prognostic Method (PPM) concept for forecasting maximum and minimum temperatures at three stations situated in the western Himalayas. Realtime observations and numerical analyses or numerical model outputs are used for model development during winter season and tested with independent data. Analysis data from the NCEP, USA and station data of the India Meteorological Department (IMD), India are used for the model development. For model performance with independent data sets as predictors, four sets of experiment are carried out with predictors selected from four different types of sources viz., reanalysis data of NCEP, National Center for Medium Range Weather Forecasting (NCMRWF) operational analysis, T80 spectral model day 1 forecast and MM5 day 1 forecast respectively.
Further, PPM is used to forecast Probability of Precipitation (PoP) occurrence/non – occurrence and Quantitative Precipitation Forecast (QPF). Analysis data of the NCEP, USA and upper air and surface observations at three stations of the IMD, India are used for the development of dynamical statistical models for winter season, i.e., DJFM. It is found that by using numerical model outputs from MM5 as predictors in dynamical statistical model based on PPM concept, definite improvements in PoP and QPF are obtained as compared to the direct numerical model outputs.

AP Dimri
apdimri@hotmail.com