A Diagnostic Metric for Predicting Tropical Cyclone and Mid-Latitude Floods
This study details a dynamic and thermodynamic metric (i.e., Extreme Flood Index [EFI]) designed to diagnose the frequency and intensity of extreme precipitation events associated with stagnant mid-latitude flow patterns (i.e., Rex blocks). As the global climate warms, rapid Arctic warming may be he...
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| Format: | Text |
| Language: | unknown |
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Scholarly Commons
2018
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| Online Access: | https://commons.erau.edu/beyond/vol2/iss1/2 https://commons.erau.edu/context/beyond/article/1031/viewcontent/MS1031.pdf |
| Summary: | This study details a dynamic and thermodynamic metric (i.e., Extreme Flood Index [EFI]) designed to diagnose the frequency and intensity of extreme precipitation events associated with stagnant mid-latitude flow patterns (i.e., Rex blocks). As the global climate warms, rapid Arctic warming may be helping to slow the mid-latitude westerly jet stream, resulting in increased mid-latitude flow stagnation. The combination of long-duration ascent associated with easterly winds and warm moist air increases the severity of extreme precipitation events; as such, the EFI is specifically designed to detect this potent combination of ingredients. In 2013, a Rex block stalled a low-pressure system over Alberta which caused the worst Canadian flood disaster ever seen. To that end, the recent billion-dollar flood catastrophe produced by Tropical Cyclone (TC) Harvey was also associated with a Rex Block (dynamics) in the presence of warm, moist air (thermodynamics). Despite dynamic differences between TC-related and mid-latitude floods, the EFI is successfully able to detect both. The dynamics component of the EFI is derived from two atmospheric blocking criteria, used operationally by the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Oceanic and Atmospheric Administration (NOAA), respectively, and adapted here for the shorter duration of extreme precipitation events. The EFI’s thermodynamic component utilizes standardized anomalies of equivalent potential temperature. Finally, the ability of the EFI to diagnose and predict high-impact flood events using reanalysis data and operational numerical weather prediction models is explored. |
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