A Probabilistic Gridded Product for Daily Precipitation Extremes Over the United States
A specialized spatial extreme value analysis is used to characterize the climatology of extreme precipitation over the contiguous United States. The essence of the method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to their native scales as well as quantify the resulting uncertainty.
The paper presents a new climatological gridded product (with uncertainty) based on in situ measurements that is specifically tailored to characterize extreme precipitation. Furthermore, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined resolves the issue of how to properly compare station data with output from earth system models. Finally, we demonstrate that a commonly used gridded product is systematically biased with respect to precipitation extremes and, for the most part, underestimates extreme precipitation (by an average of approximately 25% in wintertime).
Gridded data products are commonly used as a convenient substitute for direct observations; however, when the goal is to characterize the high-resolution climatology of extreme precipitation over a spatial domain (e.g., a map of return values), then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. We argue that our approach yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing.