DOI: 10.1175/JCLI-D-13-00481.1
Scopus记录号: 2-s2.0-84909607997
论文题名: Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the Southeastern United States
作者: Tian D. ; Martinez C.J. ; Graham W.D. ; Hwang S.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 22 起始页码: 8384
结束页码: 8411
语种: 英语
Scopus关键词: Atmospheric temperature
; Forecasting
; Hydrology
; Oceanography
; Precipitation (chemical)
; Scales (weighing instruments)
; Surface properties
; Ensembles
; Model output statistics
; Sea surface temperatures
; Seasonal forecasting
; Seasonal precipitations
; Statistical downscaling
; Surface temperatures
; Teleconnections
; Climate models
; climate modeling
; downscaling
; ensemble forecasting
; precipitation (climatology)
; seasonal variation
; surface temperature
; teleconnection
; weather forecasting
; United States
英文摘要: This study compared two types of approaches to downscale seasonal precipitation (P) and 2-m air temperature (T2M) forecasts from the North American Multimodel Ensemble (NMME) over the states of Alabama, Georgia, and Florida in the southeastern United States (SEUS). Each NMME model forecast was evaluated. Two multimodel ensemble (MME) schemes were tested by assigning equal weight to all forecast members (SuperEns) or by assigning equal weights to each model's ensemble mean (MeanEns). One type of downscaling approach used was a model output statistics (MOS) method, which was based on direct spatial disaggregation and bias correction of the NMME P and T2M forecasts using the quantile mapping technique [spatial disaggregation with bias correction (SDBC)]. The other type of approach used was a perfect prognosis (PP) approach using nonparametric locally weighted polynomial regression (LWPR) models, which used the NMME forecasts of Niño-3.4 sea surface temperatures (SSTs) to predict local-scale P and T2M. Both SDBC and LWPR downscaled P showed skill in winter but no skill or limited skill in summer at all lead times for all NMME models. The SDBC downscaled T2M were skillful only for the Climate Forecast System, version 2 (CFSv2), model even at far lead times, whereas the LWPR downscaled T2M showed limited skill or no skill for all NMME models. In many cases, the LWPR method showed significantly higher skill than the SDBC. After bias correction, the SuperEns mostly showed higher skill than the MeanEns and most of the single models, but its skill did not outperform the best single model. © 2014 American Meteorological Society. 资助项目: NASA, National Oceanic and Atmospheric Administration
; NOAA, National Oceanic and Atmospheric Administration
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51234
Appears in Collections: 气候变化事实与影响
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作者单位: Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, United States; Agricultural and Biological Engineering Department, Water Institute, University of Florida, Gainesville, FL, United States; Agricultural Engineering Department, Gyeongsang National University, Jinju, South Korea; Civil and Environmental Engineering Department, Princeton University, Princeton, NJ, United States
Recommended Citation:
Tian D.,Martinez C.J.,Graham W.D.,et al. Statistical downscaling multimodel forecasts for seasonal precipitation and surface temperature over the Southeastern United States[J]. Journal of Climate,2014-01-01,27(22)