Tuesday, 24 January 2017
Extreme precipitation events are associated with numerous societal and environmental impacts. Recent observational analysis suggests increasing trends in precipitation intensity across portions of the Continental United States (CONUS) consistent with expectations associated with anthropogenic climate change. Therefore, a spatial understanding and intuitive means of monitoring extreme precipitation over time is critical. In support of the ongoing efforts of the US National Climate Assessment (NCA), we present a gridded climate indicator, based on high-resolution gridded NASA satellite-based precipitation data from NASA’s Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) product, to monitor and track extreme precipitation events over the CONUS. The indicator is based on categorized storm totals over the CONUS defined as 3-day total accumulated precipitation events, ensuring a spatially and temporally balanced regional representation of synoptic-scale and short-duration storm events alike. A precipitation categorization scheme mirroring that of the widely understood Saffir-Simpson hurricane intensity index is assigned to each 3-day precipitation event with each precipitation category referred to as a P-Cat. The magnitude of each event lies between P-Cat 1, the lightest category of storm totals, and P-Cat 5, the heaviest, allowing for easy interpretation and visualization. With all precipitation events assigned to a P-Cat, point-wise statistics are computed across the CONUS including the maximum P-Cat, the mean P-Cat, and the frequency of each P-Cat at each grid point providing a comprehensive climatology of precipitation event intensity and a baseline for monitoring change. A novel aspect of this indicator is that it will accurately display discernable spatial variations with regional specificity in extreme precipitation event frequency and intensity over relevant temporal scales. Changes in variability will be observable at various finite temporal scales due to natural climate variability, offering a platform to monitor changes at long-term climate scales associated with anthropogenically forced change as well. Additionally, the indicator provides a foundation for evaluating climate model simulation and projection output in future work.
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