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The complex topographical features of the Indian sub continent such as the high Himalayan ranges have started showing the affects of global climate change. Himalaya modulates weather over the entire northern belt of the Indian sub-continent. It also influences the Western Disturbances (WDs) during winter over the region. Some recent trends in the parameters of weather and climate over the Western Himalayan region have been studied and explanations have been given in context of climate change over these regions. The studies show how the climate changes in long term period with reference to the total precipitation over the station locations of these regions. Data analysis of Western Himalaya shows significant variations in temperature and total precipitation trends in the past few decades. The results show negative Correlation Coefficients (CCs) between the mean maximum and mean minimum temperature with the fresh snowfall the different station locations over Western Himalaya. The possible effects of the climate change on the number of snowfall days over the Western Himalayan region have also been analyzed. There is clear sign of shrinking winter over the Western Himalaya. Necessary efforts have been directed to understand the science of climate change more in regional scale over this complex orographic region.

In North India, due to the geomorphology, high altitude and different orientations of Western Himalayan mountain ranges, prediction of weather parameters becomes a complex problem. During winter, a large amount of precipitation is received in this region due to Western Disturbances (WDs). This area is also avalanche prone. The information of the site specific weather plays an important role in the prediction of avalanche. In the present study, we have used the Model Output Statistics (MOS) technique to develop a Statistical – Dynamical Model (SDM) to better predict three weather parameters (ambient temperature, wind speed and precipitation) of 11 SASE surface observatories situated in the Western Himalayan mountainous region in North India. We have used 26 parameters from MM5 model output and observed surface weather data from 11 surface observatories for the winter months of three consecutive years from 2004 to 2006 to develop this model. The performances of the model have been assessed using the independent data set from the winter months for the year 2007. This assessment shows that model output statistics (MOS) technique improves the forecast over specific station locations.