Quantifying the Relationship between Embedded Rotation and Extreme Rainfall Rates in Observations of Tropical Storm Imelda (2019)

Abstract

Abstract Previous work on continental convective systems has indicated that there is a positive relationship between short-term rainfall rates and storm-scale to mesoscale rotation. However, little has been done to explore this relationship in dense observing networks or in landfalling tropical cyclone (LTC) environments. In an effort to quantify the relationship between rainfall rates and embedded rotation of this scale, we use several sets of observations that were collected during Tropical Storm Imelda (2019). First, a meteorological overview of the event is presented, and the ingredients that led to its flash flood–producing rainfall are discussed. Then, two analyses that investigate the relationship between rainfall rates and storm-scale to mesoscale rotation in the LTC remnants are examined. The first method relies on products from the Multi-Radar Multi-Sensor system, where two spatial averaging approaches are applied to the 0–2-km accumulated rotation track and gauge bias-corrected quantitative precipitation estimate products over hourly time periods. Using these fields as proxies for rotation and rain rates, the results show a positive spatiotemporal relationship between the two products. The second method time matches subjectively identified radar-based rotation and 5-min surface rain gauge observations. There, we show that nearly twice the amount of rain was recorded by the gauges when storm-scale to mesoscale rotation was present nearby, and the differences in 5-min rainfall observations between when rotation was present versus not was statistically significant. Together, these results indicate that more rain tended to fall in locations where there was rotation embedded in the system. Significance Statement Tornadoes and flash floods frequently occur in unison over the same locations, which can complicate forecasting, warning, and communication efforts within the meteorology community. Previous work has furthered the understanding of the interconnectedness of these hazards by suggesting a relationship between two of their predecessors: storm rotation and rainfall rates. We build on this research by quantifying the relationship of these two processes using observations from Tropical Storm Imelda: a system that brought devastating flooding to southeast Texas in September 2019. Our results show across multiple observational datasets that more rain tended to fall in locations where there was rotation embedded in the tropical storm remnants.