Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data
| العنوان: | Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data |
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| المؤلفون: | Daniel Cooley, Elizabeth Mannshardt-Shamseldin, Stephan R. Sain, Linda O. Mearns, Richard Smith |
| المصدر: | Ann. Appl. Stat. 4, no. 1 (2010), 484-502 |
| بيانات النشر: | Institute of Mathematical Statistics, 2010. |
| سنة النشر: | 2010 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Statistics and Probability, Rain gauge, Global warming, reanalysis, Statistics - Applications, Regression, Modeling and Simulation, Climatology, projections of extreme events, Generalized extreme value distribution, Environmental science, Applications (stat.AP), Climate model, Precipitation, Generalized Pareto Distribution (GPD), Statistics, Probability and Uncertainty, Extreme value theory, Generalized Extreme Value (GEV) distribution, Downscaling |
| الوصف: | 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. Comment: Published in at http://dx.doi.org/10.1214/09-AOAS287 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org) |
| وصف الملف: | application/pdf |
| تدمد: | 1932-6157 |
| DOI: | 10.1214/09-aoas287 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8b74c06bec826c885fba9c279fd0e3c https://doi.org/10.1214/09-aoas287 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....c8b74c06bec826c885fba9c279fd0e3c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19326157 |
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| DOI: | 10.1214/09-aoas287 |