A226-0014
Predictability of Subseasonal Extreme Precipitation Events in the United States
Predictability of Subseasonal Extreme Precipitation Events in the United States
Wednesday, 16 December 2020
Poster
Abstract:
Extreme precipitation can cause devastating impacts to many sectors of society and the economy, including water management, infrastructure, public health and transportation. In order to better prepare for these risks, further work is needed to understand extreme precipitation events that occur at the subseasonal to seasonal timescales (S2S event). For example, in May 2015, Oklahoma and Texas received up to 20+ inches of rain throughout the month that resulted in widespread flooding that caused mandatory evacuations. Extreme precipitation events lasting 14-days were found for seven regions across the continental U.S. for the time period of 1981 to 2018 using Parameter Elevation Regression on Independent Slopes Model (PRISM) daily precipitation data. S2S events are determined based on a set of criteria including: percentile thresholds, areal extent, and temporal distribution of precipitation. These events have been shown to be related to many on-the-ground impacts, such as flooding. The potential for predictability of precipitation at the sub-seasonal to seasonal time scale will help to mitigate the risks associated with extreme events. Large scale variables will be analyzed using principal component analysis (PCA) and maximum covariance analysis (MCA) for event days in all seven regions including: 500hPa geopotential height, precipitable water, and integrated vapor transport. Preliminary PCA results show a geopotential height anomaly dipole near the region of interest. When looking into coupled patterns between geopotential heights and precipitable water, using MCA, it was found that days with anomalously high heights and precipitable water experience significantly more precipitation. Investigation of how often these patterns occur in association with our events, as well as during non-events, will give an insight of using large scale variables for predictability purposes. Improving the prediction of S2S events will help mitigate the many impacts for our stakeholders.