4.7 Article

Early Detection and Analysis of an Unpredicted Convective Storm over the Negev Desert

Journal

REMOTE SENSING
Volume 15, Issue 21, Pages -

Publisher

MDPI
DOI: 10.3390/rs15215241

Keywords

convective storm; flash floods; cloud detection; nowcasting

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On 15 September 2015, a convective storm caused the strongest flash flood in the South Negev Desert in the last 50 years. None of the forecast models predicted the event, leading to a lack of warning. Researchers used satellite, radar, and numerical weather prediction model data to analyze the event and found that a combination of temporal Fourier analysis and satellite and model-based analysis could provide early warning for flood risks several hours ahead.
On 15 September 2015, a convective storm yielded heavy rainfalls that caused the strongest flash flood in the last 50 years in the South Negev Desert (Israel). None of the operational forecast models predicted the event, and thus, no warning was provided. We analyzed this event using satellite, radar, and numerical weather prediction model data. We generated cloud-free climatological values on a pixel basis using Temporal Fourier Analysis on a time series of MSG geostationary satellite data. The discrepancy between the measured and climatological values was used to detect cloud-contaminated pixels. This simple, robust, fast, and accurate method is valuable for the early detection of convection. The first clouds were detected 30 min before they were detected by the official MSG cloud mask, 4.5 h before the radar, and 10 h before the flood reached the main road. We used the severe storms RGB composite and the satellite-retrieved vertical profiles of cloud top temperature-particle's effective radius relations as indicators for the development of a severe convective storm. We also reran the model with different convective schemes, with much-improved results. Both the satellite and model-based analysis provided early warning for a very high probability of flooding a few hours before the actual flooding occurred.

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