A probabilistic gridded product for daily precipitation extremes over the United States

Abstract

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, 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. The essence of our 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 a fine grid. We argue that our method 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. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https ://bit.ly/2CXdn uj).

Maximizing ENSO as a source of western US hydroclimate predictability

“…We apply the spatial data analysis outlined in Risser et al (2019) to characterize the spatially-complete climatological distribution of extreme precipitation based on measurements from weather stations, as well as to quantify the relationship between the ENSO indices and daily extremes. An important feature of the Risser et al (2019) analysis is that it allows one to estimate the distribution of extreme precipitation even for locations where no data are available. Furthermore, their methodology can be used for a large network of weather stations over a heterogeneous spatial domain like the western US, which is critical for the problem at hand.…”

“…Using station-specific estimates of the climatological coefficients, we next apply spatial statistical methods (via second-order nonstationary Gaussian processes) to infer the underlying climatology over a fine grid via kriging (again see Risser et al 2019 for full details). This approach yields fields of best estimates of the climatological coefficients, denoted ̂0 � ,̂1 � ,̂ � ,̂ � ∶ � ∈ G , where G is the 0.25° grid of M = 13,073 grid cells over CONUS.…”

“…In order to utilize a highquality network of stations with sufficient spatial coverage over the western US, we focus on daily measurements during the wet season from 1951 to 2017. Full details on the quality assurance procedure are given in Risser et al (2019); after pre-processing, we select the subset of stations that have a minimum of 66.7% nonmissing daily precipitation measurements between 1950 and 2017. This procedure yields a high-quality set of daily precipitation measurements for n = 5202 stations over CONUS ( Fig.…”

North American extreme precipitation events and related large-scale meteorological patterns: a review of statistical methods, dynamics, modeling, and trends

“…Cavanaugh and Gershunov (2015) assessed the impact of spatio-temporal averaging on the tail structure of precipitation distributions and showed that spatial averaging can result in greater reduction in volatility than does temporal averaging, in regions where relatively long-lived extreme precipitation-producing systems such as atmospheric rivers are important. Due to the fractal nature of daily or sub-daily precipitation, the wisdom of gridding such a heterogeneous function has been called into question by Risser et al (2018) who offer an alternative approach using spatial statistics to grid precipitation extremes from station data directly.…”

Flood hazard and change impact assessments may profit from rethinking model calibration strategies

“…the precipitation product used to drive the models (Te Linde et al, 2007), which was here different for mHM than for the other models. These products may underestimate extreme rainfall or the spatial dependence of extreme precipitation at different locations because spatial smoothing or averaging during the gridding process reduces variability (Risser et al, 2019). The importance of input uncertainty is particularly pronounced if we are interested in future changes (Chen et al, 2014).…”