Analysis of Climatological Rainfall Extremes over the Kennedy Space Center Complex Using a High-Density Observational Network

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Conference Proceeding

Publication/Presentation Date



The use of observational datasets to determine the occurrence frequencies of extreme weather events has gained a lot of recent interest due to concerns about the potential regional impacts from global climate change. Extreme-value theory can quantify the return frequency of the most extreme events using climatologically short data sets and the assumption that such short climatological periods are stationary. However, the resulting analyses must be used with caution since they may not accurately reflect the potential of extreme events in the future due to climate change and variability. Accurately predicting extreme-event likelihood is important for building realistic long-range planning scenarios for a number of weather- and climate-sensitive interests.

This study uses extreme-value theory to analyze a short-period (15-year), high-density rainfall dataset from NASA Kennedy Space Center's observational network. This data was acquired through the Tropical Rainfall Measurement Mission archive website. We employed the National Center for Atmospheric Research's Extremes statistical software package for the analysis of 24-hour rainfall at the locations of the 32 tipping-bucket gauges in the network. This type of analysis is highly sensitive to data that may have been misreported, invalid, or missing, therefore, additional quality control was required. The quality-controlled rainfall gauge data was subsequently gridded using a Barnes-style objective analysis with minimal smoothing, in order to estimate missing values while preserving maxima in the initial data. The high-resolution gridded rainfall data was used by the Extremes program to produce a series of event-return periods for different extreme rainfall-event thresholds over the study region.

Additional work includes a stratification of the rainfall data by season (dry and wet), and an extreme-value analysis of temperatures over the region. Even slight changes in mean temperature greatly affect energy usage, and knowledge of extremes can facilitate both smart infrastructure maintenance and planning as well as influence accurate budget allocations.


Atlanta, GA