Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data

By Elizabeth Mannshardt-Shamseldin, Richard L. Smith, Stephan R. Sain, L. Mearns, Daniel Cooley. 2010

88 citations recorded by the source corpus.

There is substantial empirical and climatological evidence that precipitation extremes have become more extreme during the twentieth century, and that this trend is likely to continue as global warming becomes more intense. However, understanding these issues is limited by a fundamental issue of spatial scaling: most evidence of past trends comes from rain gauge data, whereas trends into the future are produced by climate models, which rely on gridded aggregates. To study this further, we fit the Generalized Extreme Value (GEV) distribution to the right tail of the distribution of both rain gauge and gridded events. The results of this modeling exercise confirm that return values computed from rain gauge data are typically higher than those computed from gridded data; however, the size of the difference is somewhat surprising, with the rain gauge data exhibiting return values sometimes two or three times that of the gridded data. The main contribution of this paper is the development of a family of regression relationships between the two sets of return values that also take spatial variations into account. Based on these results, we now believe it is possible to project future changes in precipitation extremes at the point-location level based on results from climate models.

Subfields: Finance, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2f58c0cd25a9713e8f4aee87427064c3798e2e91

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Citations in the corpus

Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, background): “2016), or stochastic models, often formulated as Bayesian hierarchical models, such as Cooley, Nychka, and Naveau (2007). GAM forms can also be used to model the extremal index.”
Toward enhanced understanding and projections of climate extremes using physics-guided data mining techniques (2014, background): “Statistical downscaling (Bürger et al., 2012; Mannshardt-Shamseldin et al., 2010; Robertson et al., 2004) model outputs are relatively computationally inexpensive to generate, but criticisms (Eden et al.”
The emergence of heat and humidity too severe for human tolerance (2020, background): “In contrast, reanalysis products and high-resolution regional models satisfy the need for spatiotemporal continuity and consistency and allow analysis of additional variables, but often underestimate extremes ( 34 ).”