Why Do Precipitation Intensities Tend to Follow Gamma Distributions?


Martinez-Villalobos C, Neelin JD. Why Do Precipitation Intensities Tend to Follow Gamma Distributions?. Journal of the Atmospheric Sciences [Internet]. 2019;76 :3611-3631.
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The probability distribution of daily precipitation intensities, especially the probability of extremes, impacts a wide range of applications. In most regions this distribution decays slowly with size at first, approximately as a power law with exponent between 0 and −1, and then more sharply, for values larger than a characteristic cutoff scale. This cutoff is important because it limits the probability of extreme daily precipitation occurrences in current climate. There is a long history of representing daily precipitation using a Gamma distribution—here we present theory for how daily precipitation distributions get their shape. Processes shaping daily precipitation distributions can be separated into non-precipitating and precipitating regime effects, the former partially controlling how many times in a day it rains, and the latter set by single-storm accumulations. Using previously developed theory for precipitation accumulation distributions—which follow a sharper power law range (exponent < −1) with a physically derived cutoff for large sizes—analytical expressions for daily precipitation distribution power law exponent and cutoff are calculated as a function of key physical parameters. Precipitating and non-precipitating regime processes both contribute to reducing the power-law range exponent for the daily precipitation distribution relative to the fundamental exponent set by accumulations. The daily precipitation distribution cutoff is set by the precipitating regime and scales with moisture availability, with important consequences for future distribution shifts under global warming. Similar results extend to different averaging periods, providing insight into how the precipitation intensity distribution evolves as a function of both underlying physical climate conditions and averaging time.

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Last updated on 11/06/2019