Quantitative Precipitation Estimation of Extremes in CONUS With Radar Data

Constructing an accurate, continental, in-situ-based, kilometer-scale, long-term record of the precipitation field and its spatiotemporal changes remains a significant challenge. Here, we determine the extreme-value behavior of the NEXRAD Stage IV radar-based quantitative precipitation estimate. We find that the climatology of 5-year daily return values in the contiguous United States East of the Rocky Mountains shows only slight variability on spatial scales smaller than similar to 100 km. In light of this finding, we test whether rain-gauge-only daily precipitation data sets can produce accurate extreme-value behavior at spatial scales finer than the spacing between gauges. We find that the 5-year daily return values are accurate at locations far from rain gauges only if the interpolation between gauges is carried out appropriately for extremes. Precipitation statistics derived from in-situ rain gauge data are therefore of sufficient spatial resolution to faithfully capture daily extremes over much of the eastern United States. Plain Language Summary Accurate measurement of the amount of precipitation that falls within a given region and time period is crucial for environmental modeling, climate change research, and resource and risk management. For all of those applications, it is desirable to understand not only how much precipitation falls on average, but also how much precipitation falls during an extreme event, such as a severe storm. Using data from weather radar, we show that certain statistical properties of extreme rainfall are highly correlated on spatial scales up to 100 km over the eastern United States. This means that rain gauge networks, which have typical intergauge spacings of roughly 30 km over the eastern United States, are dense enough to accurately measure these statistical properties. However, it is imperative to interpolate between the rain gauge measurements in a way that explicitly captures extremes if the application of interest requires capturing extremes accurately. Our research represents a step toward constructing an accurate, continental-scale, long-term, high-resolution precipitation data set.