DOI: 10.1175/JCLI-D-12-00722.1
Scopus记录号: 2-s2.0-84881247258
论文题名: Statistical downscaling prediction of sea surface winds over the global ocean
作者: Sun C. ; Monahan A.H.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 20 起始页码: 7938
结束页码: 7956
语种: 英语
Scopus关键词: Buoy observations
; Mean and standard deviations
; Monte Carlo experiments
; Multiple linear regressions
; Probability modeling
; Standard deviation
; Statistical downscaling
; Statistical prediction
; Linear regression
; Probability density function
; Sampling
; Statistics
; Vectors
; Wind effects
; downscaling
; geostatistics
; global ocean
; Monte Carlo analysis
; multiple regression
; prediction
; probability density function
; sea surface
; vector
; wind velocity
; Atlantic Ocean
; Atlantic Ocean (North)
; Pacific Ocean
; Pacific Ocean (North)
英文摘要: The statistical prediction of local sea surface winds from large-scale, free-tropospheric fields is investigated at a number of locations over the global ocean using a statistical downscaling model based on multiple linear regression. The predictands (the mean and standard deviation of both vector wind components and wind speed) calculated from ocean buoy observations on daily, weekly, and monthly scales are regressed on upperlevel predictor fields from reanalysis products. It is found that in general the mean vector wind components are more predictable than mean wind speed in the North Pacific and Atlantic, while in the tropical Pacific and Atlantic the difference in predictive skill between mean vector wind components and wind speed is not substantial. The predictability of wind speed relative to vector wind components is interpreted by an idealized model of the wind speed probability density function, which indicates that in the midlatitudes the mean wind speed is more sensitive to the vector wind standard deviations (which generally are not well predicted) than to the mean vector winds. In the tropics, the mean wind speed is found to be more sensitive to the mean vector winds. While the idealized probability model does a good job of characterizing month-to-month variations in the mean wind speed in terms of the vector wind statistics, month-to-month variations in the standard deviation of speed are not well modeled. A series of Monte Carlo experiments demonstrates that the inconsistency in the characterization of wind speed standard deviation is the result of differences of sampling variability between the vector wind and wind speed statistics. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51595
Appears in Collections: 气候变化事实与影响
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作者单位: School of Earth and Ocean Sciences, University of Victoria, Victoria BC, Canada
Recommended Citation:
Sun C.,Monahan A.H.. Statistical downscaling prediction of sea surface winds over the global ocean[J]. Journal of Climate,2013-01-01,26(20)