DOI: 10.1002/jgrd.50751
论文题名: Minimax filtering for sequential aggregation: Application to ensemble forecast of ozone analyses
作者: Mallet V. ; Nakonechny A. ; Zhuk S.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 19 起始页码: 11294
结束页码: 11303
语种: 英语
英文关键词: air quality
; ensemble forecast
; Kalman filtering
; minimax filtering
; sequential aggregation
; uncertainty estimation
Scopus关键词: Aggregates
; Air quality
; Covariance matrix
; Estimation
; Kalman filters
; Mean square error
; Ozone
; Regression analysis
; Uncertainty analysis
; Ensemble forecasts
; Kalman-filtering
; Minimax filtering
; Root mean square errors
; Uncertain parameters
; Uncertainty estimates
; Uncertainty estimation
; Weighted linear combinations
; Forecasting
; air quality
; algorithm
; data assimilation
; ensemble forecasting
; error analysis
; filtration
; Kalman filter
; linearity
; numerical model
; ozone
; prediction
; two-dimensional modeling
; uncertainty analysis
; Europe
英文摘要: This paper presents a new algorithm for sequential aggregation of an ensemble of forecasts. At any forecasting step, the aggregation consists of (1) computing new weights for the ensemble members represented by different numerical models and (2) forecasting with a weighted linear combination of the ensemble members. We assume that the time evolution of the weights is described by a linear equation with uncertain parameters and apply a minimax filter (and also Kalman filter, for comparison) in order to estimate the vector of weights given "observations". The "observation" equation for the filter compares the aggregated forecast with the analysis determined in a data assimilation cycle together with its variance. The minimax approach allows one to work with flexible uncertainty description: deterministic bounding sets for uncertain parameters in weight's equation, and error covariance matrices for the "observational" errors. Our key contribution is an uncertainty estimate of the aggregated forecast, for which we introduce an evaluation test. The performance of the method is assessed for the forecast of ground-level ozone daily peaks over Europe, for the year 2001. Compared to forecasts generated by classical data assimilation, the root mean square error is decreased by 16% for prediction of the analyses and by 20% for prediction of the observations. Key Points The minimax filter is applied for sequential aggregation of ensemble forecasts The approach allows to forecast 2D ozone analyses, with uncertainty estimation The filter is compared to Kalman filter and to discounted ridge regression ©2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63273
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
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作者单位: INRIA, Le Chesnay 78 153, France; CEREA, Joint Laboratory École des Ponts ParisTech/EDF RandD, Université Paris Est, Marne-la-Vallée, France; Cybernetics Faculty, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine; IBM Research - Ireland, Dublin, Ireland
Recommended Citation:
Mallet V.,Nakonechny A.,Zhuk S.. Minimax filtering for sequential aggregation: Application to ensemble forecast of ozone analyses[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(19)