Satellite-Based Nowcasting of Extreme Rainfall Events Over Western Himalayan Region
| Autor: | Bipasha Paul Shukla, C. M. Kishtawal, Pradip K. Pal |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Meteorology Nowcasting Cloud top 0211 other engineering and technologies Terrain 02 engineering and technology 01 natural sciences Climatology Flash flood Environmental science Satellite Precipitation Computers in Earth Sciences Natural disaster 021101 geological & geomatics engineering 0105 earth and related environmental sciences Orographic lift |
| Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10:1681-1686 |
| ISSN: | 2151-1535 1939-1404 |
| DOI: | 10.1109/jstars.2017.2655105 |
| Popis: | Western Himalayan (WH) region is considered to be one of the most vulnerable spots for flash flooding-related natural disasters in the world. The confluence of moist air advected from Arabian sea with complex terrain has produced a series of extreme rainfall triggered disasters in recent past. However, the causal events leading to these enhanced episodes of precipitation lack clarity and thus flash flood forecasting still remains a big challenge. In this paper, we address the problem by studying cloud development over this region and its relationship with the underlying topography. Our results demonstrate that WH region is mostly inhabited by low-medium level clouds and governed by warm rain processes. Satellite-based analysis shows that in comparison to cloud top temperature, cloud top cooling rate (CTCR) is a better indicator for extreme rain producing events over this region. A model for nowcasting of extreme orographic rain events has thus been developed using the spatial characteristics of CTCR to predict potential locations for orographically induced severe precipitation events. The heavy rainfall nowcasts produced by this methodology, when compared with global precipitation fields, show very encouraging results. The probability of positive identification of a heavy rainfall event is 82.8%, with a false alarm rate of 29.7% and average lead time of 2–3 h. The insights provided by this study will give an impetus to the flash flood advance warning over WH region bringing about a significant beneficial societal impact. |
| Databáze: | OpenAIRE |
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