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

  • S. C. KarEmail author
  • Sarita Tiwari
Chapter

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.

Keywords

Western Himalayas Downscaling WRF Cloud microphysics Hydrometeors 

Notes

Acknowledgements

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.

References

  1. Chen F, Dudhia J (2001) Coupling an advanced land-surface/hydrology model with the Penn State/NCAR MM5 modeling system. Part I: model description and implementation. Mon Weather Rev 129:569–585CrossRefGoogle Scholar
  2. Cossu F, Hocke K (2014) Influence of microphysical schemes on atmospheric water in the Weather Research and Forecasting model. Geosci Model Dev 7:147–160CrossRefGoogle Scholar
  3. Crâetat J, Pohl B, Richard Y, Drobinski P (2012) Uncertainties in simulating regional climate of Southern Africa: sensitivity to physical parameterizations using WRF. Clim Dyn 38:613–634CrossRefGoogle Scholar
  4. 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
  5. Derksen C, LeDrew E, Goodison B (1998) SSM/I derived snow water equivalent data: the potential for investigating linkages between snow cover and atmospheric circulation. Atmosphere-Ocean 36(2):95–117CrossRefGoogle Scholar
  6. Dimri AP (2009) Impact of subgrid scale scheme on topography and landuse for better regional scale simulation of meteorological variables over the western Himalayas. Clim Dyn 32(4):565–574CrossRefGoogle Scholar
  7. Dimri AP, Chevuturi A (2014) Model sensitivity analysis study for western disturbances over the Himalayas. Meteorog Atmos Phys 123:155–180CrossRefGoogle Scholar
  8. Dimri AP, Niyogi D, Barros AP, Ridley J, Mohanty UC, Yasunari T, Sikka DR (2015) Western disturbances: a review. Rev Geophys 53(2):225–246CrossRefGoogle Scholar
  9. Flaounas F, Bastin S, Janicot S (2010) Regional climate modelling of the 2006 West African monsoon: sensitivity to Cumulus and planetary boundary layer parameterisation using WRF. Clim Dyn 36:1083–1105CrossRefGoogle Scholar
  10. Givati A, Lynn B, Liu Y, Rimmer A (2012) Using the WRF model in an operational streamflow forecast system for the Jordan River. J Appl Meteorol Climatol 51:285–299.  https://doi.org/10.1175/JAMC-D-11-082.1CrossRefGoogle Scholar
  11. Grell G, Devenyi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res Lett 29(14):38–31CrossRefGoogle Scholar
  12. Hong SY, Lim J (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorol Soc 42:129–151Google Scholar
  13. Hong SY, Pan HL (1996) Nonlocal boundary layer vertical diffusion in a medium range forecast model. Mon Weather Rev 124(10):2322–2339CrossRefGoogle Scholar
  14. Hong SY, Dudhia J, Chen SH (2004) A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon Weather Rev 132:103–120CrossRefGoogle Scholar
  15. Kar SC, Rana S (2014) Interannual variability of winter precipitation over northwest India and adjoining region: impact of global forcings. Theor Appl Climatol 116(3–4):609–623CrossRefGoogle Scholar
  16. Kar SC, Tiwari S (2016) Model simulations of heavy precipitation in Kashmir, India, in September 2014. Nat Hazards 81:167–188.  https://doi.org/10.1007/s11069-015-2073-3CrossRefGoogle Scholar
  17. Kim HJ, Wang B (2011) Sensitivity of the WRF model simulation of the East Asian summer monsoon in 1993 to shortwave radiation schemes and ozone absorption. Asia-Pac J Atmos Sci 47:167–180CrossRefGoogle Scholar
  18. 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
  19. Lo JC-F, Yang Z-L, Pielke RA Sr (2008) Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J Geophys Res 113:D09112.  https://doi.org/10.1029/2007JD009216CrossRefGoogle Scholar
  20. Patil R, Kumar PP (2016) WRF model sensitivity for simulating intense western disturbances over North West India. Model Earth Syst Environ 2:82.  https://doi.org/10.1007/s40808-016-0137-3CrossRefGoogle Scholar
  21. Rajeevan M, Kesarkar A, Thampi SB, Rao TN, Radhakrishna B, Rajasekhar M (2010) Sensitivity of WRF cloud microphysics to simulations of a severe thunderstorm event over Southeast India. Ann Geophys 28:603–619CrossRefGoogle Scholar
  22. Raju PV, Potty J, Mohanty UC (2011) Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteorog Atmos Phys 113:125–137CrossRefGoogle Scholar
  23. Sinha P, Mohanty UC, Kar SC, Dash SK, Kumari S (2013) Sensitivity of the GCM driven summer monsoon simulations to cumulus parameterization schemes in nested RegCM3. Theor Appl Climatol 112(1–2):285–306CrossRefGoogle Scholar
  24. Sinha P, Tiwari PR, Kar SC, Mohanty UC, Raju PVS, Dey S, Shekhar MS (2015) Sensitivity studies of convective schemes and model resolutions in simulations of wintertime circulation and precipitation over the Western Himalayas. Pure Appl Geophys 172(2):503–530CrossRefGoogle Scholar
  25. 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
  26. 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
  27. Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: implementation of a new snow parameterization. Mon Weather Rev 136(12):5095–5115CrossRefGoogle Scholar
  28. Tiwari PR, Kar SC, Mohanty UC, Dey S, Sinha P, Raju PVS (2015) The role of land surface schemes in the regional climate model (RegCM) for seasonal scale simulations over Western Himalaya. Atmosfera 28(2):129–142CrossRefGoogle Scholar
  29. Tiwari S, Kar SC, Bhatla R (2016) Interannual variability of snow water equivalent (SWE) over Western Himalayas. Pure Appl Geophys 173(4):1317–1335CrossRefGoogle Scholar
  30. Tiwari S, Kar SC, Bhatla R (2018) Dynamic downscaling over western Himalayas: Impact of cloud microphysics schemes. Atmos Res 201:1–16.  https://doi.org/10.1016/j.atmosres.2017.10.007CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Centre for Medium Range Weather ForecastingNoidaIndia
  2. 2.Geological Survey of IndiaHyderabadIndia

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