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Case Study of a Convective Cluster Over the Rain Shadow Region of Western Ghats Using Multi-platform Observations and WRF Model

  • Soumya SamantaEmail author
  • Kulkarni Gayatri
  • P. Murugavel
  • B. Balaji
  • N. Malap
  • Y. Jaya Rao
  • S. M. Deshpande
  • S. M. Sonbawne
  • P. Suneetha
  • Thara V. Prabha
Article
  • 103 Downloads

Abstract

Unique observational features of a convective cluster, occurred on 12-Sept-2015, over the rain shadow region in the leeward side of Western Ghats have been presented in this study. The synoptic environment had preconditioned the formation of a convergence zone over the study area. Moisture transport from the Arabian Sea was responsible for the moistening in the lower layers. Boundary layer convective thermals contributed to middle level moistening and subsequent onset of the cloud cluster was accompanied by a sudden surge of moist and warm air into the middle troposphere, and subsequent lifting of freezing level (FL) and wet bulb temperature zero (WBT0) levels. Sudden changes in the FL and WBT0 levels in association with the gust front prior to the initiation of the cloud system has been documented with high-resolution measurements using microwave radiometer and wind profiler. Thermodynamical parameters from radiometer illustrate the percussive conditions for formation of the cloud system. The cloud cluster had resulted in 25.50 mm rainfall, attributing to ~ 91% of convective rain. Intense fall velocity (10–12 ms−1) was noted up to ~ 7 km during the convective rain and the fall velocity was reduced to ~ 7 ms−1 (below the melting layer) during the stratiform counterpart. The cloud system was forecasted using WRF model (version 3.6.1), which was reproduced reasonably well as in the observations and the model output has been analyzed to understand the morphology of the system. The features such as formation of a cold pool, initiation of convective rainfall from the system were well forecasted by the model. Microphysical characteristics of the cloud cluster have also been examined. Riming was the dominant microphysical process within the convective regime. A major contribution to precipitation was from melting of ice hydrometeors especially graupel and snow was noted. Deep warm layer and associated production of supercooled liquid by the lifting of liquid water above the freezing level in updrafts exceeding 15 ms−1 was important for the production of a mixed-phase cloud system. Vapor deposition and aggregation process was noted in the stratiform/anvil counterpart, which also contained mixed phase hydrometeors, primarily of snow.

Keywords

Convective cluster Western Ghats rain shadow region cloud microphysics cloud simulation 

Notes

Acknowledgements

The authors would like to thank the Director, IITM for the support and providing necessary facilities to carry out the research work and Ministry of Earth Sciences, Government of India for funding the CAIPEEX project. We heartily thank all the members associated with CAIPEEX campaign for their effort in collecting the data. We also thank Dr. Kaustav Chakravarty, IITM for providing the disdrometer data, ‘Radar and Satellite Meteorology’ division, IITM for providing the X-Band Radar Data and ‘Thunderstorm Dynamics’ group, IITM for providing the Lightning Location Network (LLN) data. We would also like to thank European Centre for Medium-range Weather Forecast (ECMWF) for providing ERA-Interim reanalysis dataset, NOAA National Centers for Environmental Information (NCEI) for providing Global Land One-kilometer Base Elevation (GLOBE) digital elevation data set, National Centers for Environmental Prediction (NCEP) for providing the NCEP GFS data (used in WRF simulation), Indian Space Research Organisation (ISRO) for maintaining MOSDAC website and providing INSAT-3D data, National Aeronautics and Space Administration (NASA) for providing IMERG data product through Precipitation Measurement Mission and India Meteorological Department (IMD) for providing AWS data. We would like to acknowledge the National Center for Atmospheric Research (NCAR) for providing Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN) software. We would also like to acknowledge the High-Performance Computing (HPC) facility at IITM for model simulation. We sincerely thank the three anonymous reviewers from the journal for their insightful comments and valuable suggestions, which has helped us to improve the quality of the manuscript. In this study, Python Programming Language and NCL (NCAR Command Language) software are used for generating the plots.

References

  1. Arnaud, Y., Desbois, M., & Maizi, J. (1992). Automatic tracking and characterization of african convective systems on meteosat pictures. Journal of Applied Meteorology, 31, 443–453.  https://doi.org/10.1175/1520-0450(1992)031<0443:ATACOA>2.0.CO;2.CrossRefGoogle Scholar
  2. Balaji, B., Prabha, T. V., Rao, Y. J., Kiran, T., Dinesh, G., Chakravarty, K., et al. (2017). Potential of collocated radiometer and wind profiler observations for monsoon studies. Atmospheric Research, 194, 17–26.  https://doi.org/10.1016/j.atmosres.2017.04.023.CrossRefGoogle Scholar
  3. Cimini, D., Nelson, M., Güldner, J., & Ware, R. (2015). Forecast indices from a ground-based microwave radiometer for operational meteorology. Atmospheric Measurement Techniques, 8, 315–333.  https://doi.org/10.5194/amt-8-315-2015.CrossRefGoogle Scholar
  4. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., et al. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137, 553–597.  https://doi.org/10.1002/qj.828.CrossRefGoogle Scholar
  5. Dixon, M., & Wiener, G. (1993). TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—a radar-based methodology. Journal of Atmospheric and Oceanic Technology.  https://doi.org/10.1175/1520-0426(1993)010%3c0785:ttitaa%3e2.0.co;2.CrossRefGoogle Scholar
  6. Dudhia, J. (1989). Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the Atmospheric Sciences, 46, 3077–3107.  https://doi.org/10.1175/1520-0469(1989)046%3c3077:NSOCOD%3e2.0.CO;2.CrossRefGoogle Scholar
  7. Francis, P. A., & Gadgil, S. (2006). Intense rainfall events over the west coast of India. Meteorology and Atmospheric Physics, 94, 27–42.  https://doi.org/10.1007/s00703-005-0167-2.CrossRefGoogle Scholar
  8. Goyens, C., Lauwaet, D., Schröder, M., Demuzere, M., & Van Lipzig, N. P. M. (2011). Tracking mesoscale convective systems in the Sahel: Relation between cloud parameters and precipitation. International Journal of Climatology, 32, 1921–1934.  https://doi.org/10.1002/joc.2407.CrossRefGoogle Scholar
  9. Gunnell, Y. (1997). Relief and climate in south Asia: The influence of the western ghats on the current climate pattern of peninsular India. International Journal of Climatology, 17, 1169–1182.  https://doi.org/10.1002/(SICI)1097-0088(199709)17:11<1169::AID-JOC189>3.0.CO;2-W. CrossRefGoogle Scholar
  10. Hagos, S., Feng, Z., Burleyson, C. D., Lim, K.-S. S., Long, C. N., Wu, D., et al. (2014). Evaluation of convection-permitting model simulations of cloud populations associated with the Madden-Julian Oscillation using data collected during the AMIE/DYNAMO field campaign. Journal of Geophysical Research Atmospheres, 119(21), 12052–12068.  https://doi.org/10.1002/2014JD022143.CrossRefGoogle Scholar
  11. Hong, S.-Y., Noh, Y., & Dudhia, J. (2006). A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review, 134, 2318–2341.  https://doi.org/10.1175/MWR3199.1.CrossRefGoogle Scholar
  12. Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., & Jackson T. (2019). GPM IMERG final precipitation L3 half hourly 0.1 degree × 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC).  https://doi.org/10.5067/gpm/imerg/3b-hh/06. Accessed 09 June 2019.
  13. Kain, J. S. (2004). The Kain–Fritsch convective parameterization: An update. Journal of Applied Meteorology, 43, 170–181.  https://doi.org/10.1175/1520-0450(2004)043%3c0170:TKCPAU%3e2.0.CO;2.CrossRefGoogle Scholar
  14. Konwar, M., Das, S. K., Deshpande, S. M., Chakravarty, K., & Goswami, B. N. (2014). Microphysics of clouds and rain over the Western Ghat. Journal of Geophysical Research Atmospheres, 119, 6140–6159.  https://doi.org/10.1002/2014JD021606.CrossRefGoogle Scholar
  15. Lim, K.-S. S., & Hong, S.-Y. (2010). Development of an effective double-moment cloud microphysics scheme with prognostic cloud condensation nuclei (CCN) for weather and climate models. Monthly Weather Review, 138, 1587–1612.  https://doi.org/10.1175/2009MWR2968.1.CrossRefGoogle Scholar
  16. Maheskumar, R. S., Narkhedkar, S. G., Morwal, S. B., Padmakumari, B., Kothawale, D. R., Joshi, R. R., et al. (2014). Mechanism of high rainfall over the Indian west coast region during the monsoon season. Climate Dynamics, 43, 1513–1529.  https://doi.org/10.1007/s00382-013-1972-9.CrossRefGoogle Scholar
  17. Mathon, V., & Laurent, H. (2001). Life cycle of Sahelian mesoscale convective cloud systems. Quarterly Journal of the Royal Meteorological Society, 127, 377–406.  https://doi.org/10.1002/qj.49712757208.CrossRefGoogle Scholar
  18. Mathon, V., Laurent, H., & Lebel, T. (2002). Mesoscale convective system rainfall in the Sahel. Journal of Applied Meteorology, 41, 1081–1092.  https://doi.org/10.1175/1520-0450(2002)041%3c1081:MCSRIT%3e2.0.CO;2.CrossRefGoogle Scholar
  19. Min, K. H., Choo, S., Lee, D., & Lee, G. (2015). Evaluation of WRF cloud microphysics schemes using radar observations. Weather and Forecasting, 30(6), 1571–1589.  https://doi.org/10.1175/WAF-D-14-00095.1.CrossRefGoogle Scholar
  20. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research, 102, 16663–16682.  https://doi.org/10.1029/97JD00237.CrossRefGoogle Scholar
  21. Murthy, B. S., Latha, R., & Madhuparna, H. (2018). WRF simulation of a severe hailstorm over Baramati: A study into the space–time evolution. Meteorology and Atmospheric Physics, 130, 153–167.  https://doi.org/10.1007/s00703-017-0516-y.CrossRefGoogle Scholar
  22. Patwardhan, S. K., & Asnani, G. C. (2000). Meso-scale distribution of summer monsoon rainfall near the Western Ghats (INDIA). International Journal of Climatology, 20, 575–581.  https://doi.org/10.1002/(SICI)1097-0088(200004)20:5<575::AID-JOC509>3.0.CO;2-6.CrossRefGoogle Scholar
  23. Prabha, T. V., Goswami, B. N., Murthy, B. S., & Kulkarni, J. R. (2011). Nocturnal low-level jet and ‘atmospheric streams’ over the rain shadow region of Indian Western Ghats. Quarterly Journal of the Royal Meteorological Society, 137, 1273–1287.  https://doi.org/10.1002/qj.818.CrossRefGoogle Scholar
  24. Rao, Y. P. (1976). Southwest Monsoon. Google Scholar
  25. Segele, Z. T., Leslie, L. M., & Lamb, P. J. (2013). Weather research and forecasting model simulations of extended warm-season heavy precipitation episode over the US southern great plains: Data assimilation and microphysics sensitivity experiments. Tellus Series A Dynamic Meteorology and Oceanography, 65, 1–28.  https://doi.org/10.3402/tellusa.v65i0.19599.CrossRefGoogle Scholar
  26. Soman, M. K., & Kumar, K. (1990). Some aspects of daily rainfall distribution over India during the south-west monsoon season. International Journal of Climatology, 10, 299–311.CrossRefGoogle Scholar
  27. Song, H. J., & Sohn, B. J. (2018). An evaluation of WRF microphysics schemes for simulating the warm-type heavy rain over the Korean Peninsula. Asia-Pacific Journal of Atmospheric Sciences, 54(2), 225–236.  https://doi.org/10.1007/s13143-018-0006-2.CrossRefGoogle Scholar
  28. Tawde, S. A., & Singh, C. (2015). Investigation of orographic features influencing spatial distribution of rainfall over the Western Ghats of India using satellite data. International Journal of Climatology, 35, 2280–2293.  https://doi.org/10.1002/joc.4146.CrossRefGoogle Scholar
  29. Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J., & Cuenca, R. H. (2004). Implementation and verification of the unified noah land surface model in the WRF model. In 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, American Meteorological Society, Seattle, WA, US, pp. 11–15.Google Scholar
  30. Utsav, B., Deshpande, S. M., Das, S. K., & Pandithurai, G. (2017). Statistical characteristics of convective clouds over the western ghats derived from weather radar observations. Journal of Geophysical Research Atmospheres, 122, 10050–10076.  https://doi.org/10.1002/2016JD026183.CrossRefGoogle Scholar
  31. Wu, D., Zhao, K., Kumjian, M. R., Chen, X., Huang, H., Wang, M., Didlake, A. C. Jr., Duan, Y., & Zhang, F. (2018). Kinematics and microphysics of convection in the outer rainband of typhoon nida (2016) revealed by polarimetric radar. Monthly Weather Review, 146(7), 2147–2159.  https://doi.org/10.1175/MWR-D-17-0320.1.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Soumya Samanta
    • 1
    • 2
    Email author
  • Kulkarni Gayatri
    • 1
  • P. Murugavel
    • 1
  • B. Balaji
    • 1
  • N. Malap
    • 1
  • Y. Jaya Rao
    • 1
  • S. M. Deshpande
    • 1
  • S. M. Sonbawne
    • 1
  • P. Suneetha
    • 2
  • Thara V. Prabha
    • 1
  1. 1.Indian Institute of Tropical MeteorologyPuneIndia
  2. 2.Department of Meteorology and Oceanography, College of Science and TechnologyAndhra UniversityVisakhapatnamIndia

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