Using Self-Organizing Maps to Identify Coherent CONUS Precipitation Regions
We present an objective method to group areas of the Contiguous United States (CONUS) together based on the local annual cycle of precipitation.
This objective method yields regions that have several key differences to popular, subjectively chosen, precipitation regions. The number of regions is a tunable parameter but novel criteria for choosing the most appropriate number of regions are a key innovation.
The rarity and small spatial scale of extreme precipitation events make statistical analysis difficult; both factors are mitigated by combining events over a region. A methodology is presented 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). To identify useful numbers of region criteria assess these properties for each region: having many more events than experienced by a single grid point, connectedness, and compactness. Our methodology is applicable across datasets and is tested here on both reanalysis and gridded observational data. Precipitation regions obtained align with large-scale geographical features and are readily interpretable. Useful numbers of regions balance two conflicting preferences: larger regions contain more events and thereby have more robust statistics, but more compact regions allow weather patterns associated with extreme events to be aggregated with confidence.