UC Davis

Extreme precipitation (PEx) events have major societal impacts. These events are rare and can have small spatial scale, making statistical analysis difficult. To mitigate these difficulties a methodology was developed to objectively define ``coherent" regions wherein data points have matching annual cycles of precipitation. Regions are found by training self-organizing maps (SOMs) on the annual cycle of precipitation for each grid point across the contiguous United States (CONUS). Multiple criteria are applied to identify useful numbers of regions for our future application. This methodology is applicable across data sets and is tested on both reanalysis and gridded observational data. This method of regionalization is then used, in conjunction with two automatic methods of determining the meteorological cause of PEx events, to determine the relationship between mean precipitation seasonality and the different types of PEx events. The first automatic method uses simple metrics, derived from the literature, which are ultimately unable to clearly distinguish between different types of PEx events. The second uses the Quasi-Geostrophic (QG) omega equation to identify fundamental weather patterns associated with different types of PEx events. These weather patterns are identified in a novel way using a SOM trained on a pressure-time series of vertical velocity from each of the advective forcing terms in the QG omega equation for each PEx event. Using the unsupervised learning of the SOM allows for the identification of the most descriptive set of 9 patterns in vertical velocity associated with precipitation extremes in the current climate.