Impact of Cloud Microphysical Processes on the Dynamic Downscaling for Western Himalayas Using the WRF Model
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.
KeywordsWestern Himalayas Downscaling WRF Cloud microphysics Hydrometeors
This work has been carried out as a part the project “Dynamics of Himalayan ecosystem and its impact under changing climate scenario in Western Himalaya” under the National Mission on Himalayan Studies (NMHS) of the Ministry of Environment, Forest & Climate Change, Government of India.
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette J-J, Park B-K, Peubey C, de Rosnay P, Tavolato C, Thépaut J-N, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J Roy Meteorol Soc 137:553–597CrossRefGoogle Scholar
- Hong SY, Lim J (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
- Liang X-Z, Xu M, Yuan X, Ling T, Choi HI, Zhang F, Chen L, Liu S, Su S, Qiao F, He Y, Wang JL, Kunkel KE, Gao W, Joseph E, Morris V, Yu T-W, Dudhia J, Michalakes J (2012) Regional climate–weather research and forecasting model. B Am Meteorol Soc 93:1363–1387. https://doi.org/10.1175/BAMS-D-11-00180.1CrossRefGoogle Scholar
- Skamaraock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2, NCAR Technical Note. www.wrf-model.org
- Srivastava AK, Rajeevan M, Kshirsagar SR (2009) Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmos Sci Lett. https://doi.org/10.1002/asl.232