Impact of Cloud Microphysical Processes on the Dynamic Downscaling for Western Himalayas Using the WRF Model

Abstract

Dynamic downscaling of climate is a useful procedure to downscale the climate especially over the data sparse regions of the Himalayas. The global reanalysis data are too coarse to represent the hydroclimate over the regions with sharp orography gradient in the western Himalayas. The present study attempts to carry out dynamic downscaling of ERA-Interim dataset (January to May) over the western Himalayas using the weather research and forecasting (WRF) model. Sensitivity studies have been carried out using four microphysics parameterization schemes (namely WSM3, WSM6, Morrison and Thompson schemes). It is seen that the model is able to simulate large scale patterns of precipitation, temperature and winds reasonably well. The impact of the Morrison and Thompson schemes is to shift the zone of maximum precipitation more downwind as compared to WSM6 during winter. The WSM6 favors precipitation on the slopes of the terrain, Morrison and Thompson schemes simulate more precipitation on the mountain top (more snow) as the snow particles get advected more downwind. The Morrison scheme simulates less amount of graupels over the region than the WSM6. The narrow zone of sharply rising orography is the area where the WSM6 scheme simulates more rain than the Morrison scheme. This study emphasizes that a correct representation of the microphysical processes in the models is crucial for long-term climate simulations for correct representation of partitioning atmospheric water into vapor, cloud liquid water, cloud ice etc. leading either to solid or liquid precipitation.