DOI: 10.1007/s00382-016-3000-3
Scopus记录号: 2-s2.0-84957614065
论文题名: Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model
作者: Wang J. ; Han Y. ; Stein M.L. ; Kotamarthi V.R. ; Huang W.K.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 47, 期: 2017-09-10 起始页码: 2833
结束页码: 2849
语种: 英语
英文关键词: Dynamical downscaling
; Generalized extreme value (GEV) distribution
; Temperature extremes
英文摘要: The weather research and forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximum temperature through comparison with North American regional reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting a novel bootstrap procedure that makes no assumption of temporal or spatial independence within a year, which is especially important for climate data. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns. © 2016, Springer-Verlag (outside the USA). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53496
Appears in Collections: 过去全球变化的重建
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作者单位: Environmental Science Division, Bldg. 240, Rm. 6A22, Argonne National Laboratory, 9700 South Cass Ave., Argonne, IL, United States; Department of Statistics, University of Chicago, Chicago, IL, United States; Environmental Science Division, Argonne National Laboratory, Argonne, IL, United States; Department of Statistics, Purdue University, West Lafayette, IN, United States
Recommended Citation:
Wang J.,Han Y.,Stein M.L.,et al. Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model[J]. Climate Dynamics,2016-01-01,47(2017-09-10)