March of buoyancy elements during extreme rainfall over India
- 326 Downloads
A major rain storm in Uttarakhand (India) caused heavy rains and major loss of life from floods and land slide during 16–18 June, 2013. The observed daily maximum rainfall rates (3-hourly) during the 16th and 17th June were 220 and 340 mm respectively. This event is addressed via sensitivity studies using a cloud resolving non-hydrostatic model with detailed microphysics. The streaming of moist air from the east-south-east and warmer air from the south-west contributed to the sustained large population and amplitude of buoyancy and the associated CAPE contributed to the longer period of heavy rains. This study addresses the concept of Buoyancy as a means for large vertical accelerations, stronger vertical motions, extreme rain rates and the mechanisms that relate to the time rates of change. A post-processing algorithm provides an analysis of time rate of change for the buoyancy. Moist air streams and warm/moist air intrusions into heavily raining clouds are part of this buoyancy enhancement framework. Improvements in modeling of the extreme rain event came from adaptive observational strategy that showed lack of moisture data sets in these vital regions. We show that a moist boundary layer near the Bay of Bengal leads to moist rivers of moisture where the horizontal convergence confines a large population of buoyancy elements with large magnitudes of buoyancy that streams towards the region of extreme orographic rains. The areas covered in this study include: (i) Use of high resolution cloud modeling (1-km), (ii) Now casting of rains using physical initialization with a Newtonian relaxation, (iii) Use of an adaptive observational strategy, (iii) Sensitivity of the evolution of fields and population of buoyancy elements to boundary layer moisture, (iv) Role of orography and details of buoyancy budget.
KeywordsMonsoon Mesoscale Buoyancy
This work is supported from three research grants to Florida State University, Ministry of Earth Sciences, Government of India MM/SERP/FSU-USA/2013/INT-8/002, NSF Grant Number AGS-1047282, and NASA Grant No. NNX13AQ40G.
- Baumhefner DP (1988) Forecast skill and predictability at the extended range using Monte-Carlo ensemble integrations from a general circulation model. In: ECMWF workshop ‘predictibility in the medium and extended range’, 16–18 May 1988, pp 25–44Google Scholar
- Draxler RR, Hess GD (1998) An overview of the HYSPLIT_4 modeling system of trajectories, dispersion, and deposition. Aust Meteorol Mag 47:295–308Google Scholar
- DurgaRao KHV, Rao VV, Dadhwal VK, Diwakar PG (2014) Kedarnath flash floods: a hydrological and hydraulic simulation study. Curr Sci 106:598–603Google Scholar
- Houze RA Jr, Wilton DC, Smull BF (2007) Monsoon convection in the Himalayan region as seen by the TRMM precipitation radar. Q J R Meteorol Soc 133:1389–1411Google Scholar
- Kain JS, Fritsch JM (1993) Convective parameterization for mesoscale models: the Kain–Fritsch scheme. In: Emanuel KA, Raymond DJ (eds) The representation of cumulus convection in numerical models, chap 16, vol 24. AMS Press, BostonGoogle Scholar
- Kotal SD, SenRoy S, Roy Bhowmik SK (2014) Catastrophic heavy rainfall episode over Uttarakhand during 16–18 June 2013—observational aspects. Curr Sci 107:1–12Google Scholar
- Krishnamurti TN, Kumar J (2016) Physical initialization in tropical cyclone forecasting. In: Mohanty UC, Gopalakrishnan (eds) Advanced numerical modeling and data assimilation techniques for tropical cyclone predictions. Capital Publishing Company, New Delhi, India, p 450Google Scholar
- IMD Report (2013) A preliminary report on heavy rainfall over Uttarakhand during 16–18 June 2013. http://www.imd.gov.in/doc/uttrakhand_report_04_09_2013.pdf. Accessed 20 Mar 2014
- Susmitha J, Sahai AK, Sharmila S, Abhilash S, Borah N, Chattopadhyay R, Pillai PA, Rajeevan M, Kumar A (2014) North Indian heavy rainfall event during June 2013: diagnostics and extended range prediction. Clim Dyn 44:2049–2065Google Scholar