Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1175/JAS-D-17-0235.1 |
Calculating State-Dependent Noise in a Linear Inverse Model Framework | |
Martinez-Villalobos, Cristian1,2; Vimont, Daniel J.1; Penland, Cecile3; Newman, Matthew4,5; Neelin, J. David2 | |
2018-02-01 | |
发表期刊 | JOURNAL OF THE ATMOSPHERIC SCIENCES
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ISSN | 0022-4928 |
EISSN | 1520-0469 |
出版年 | 2018 |
卷号 | 75期号:2页码:479-496 |
文章类型 | Article |
语种 | 英语 |
国家 | USA |
英文摘要 | The most commonly used version of a linear inverse model (LIM) is forced by state-independent noise. Although having several desirable qualities, this formulation can only generate long-term Gaussian statistics. LIM-like systems forced by correlated additive-multiplicative (CAM) noise have been shown to generate deviations from Gaussianity, but parameter estimation methods are only known in the univariate case, limiting their use for the study of coupled variability. This paper presents a methodology to calculate the parameters of the simplest multivariate LIM extension that can generate long-term deviations from Gaussianity. This model (CAM-LIM) consists of a linear deterministic part forced by a diagonal CAM noise formulation, plus an independent additive noise term. This allows for the possibility of representing asymmetric distributions with heavier-or lighter-than-Gaussian tails. The usefulness of this methodology is illustrated in a locally coupled two-variable ocean-atmosphere model of midlatitude variability. Here, a CAM-LIM is calculated from ocean weather station data. Although the time-resolved dynamics is very close to linear at a time scale of a couple of days, significant deviations from Gaussianity are found. In particular, individual probability density functions are skewed with both heavy and light tails. It is shown that these deviations from Gaussianity are well accounted for by the CAM-LIM formulation, without invoking nonlinearity in the time-resolved operator. Estimation methods using knowledge of the CAM-LIM statistical constraints provide robust estimation of the parameters with data lengths typical of geophysical time series, for example, 31 winters for the ocean weather station here. |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000425753300006 |
WOS关键词 | SEA-SURFACE TEMPERATURE ; NORTHERN-HEMISPHERE WINTER ; STOCHASTIC CLIMATE MODELS ; GENERAL-CIRCULATION MODEL ; FLUCTUATION-DISSIPATION ; DIFFERENTIAL-EQUATIONS ; PROBABILITY DENSITY ; NON-GAUSSIANITY ; OPTIMAL-GROWTH ; EL-NINO |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/29270 |
专题 | 地球科学 |
作者单位 | 1.Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI 53706 USA; 2.Univ Calif Los Angeles, Dept Atmospher & Ocean Sci, Los Angeles, CA 90095 USA; 3.NOAA, Phys Sci Div, Earth Syst Res Lab, Boulder, CO USA; 4.Univ Colorado, Cooperat Inst Res Environm Sci, Boulder, CO 80309 USA; 5.NOAA, Earth Syst Res Lab, Boulder, CO USA |
推荐引用方式 GB/T 7714 | Martinez-Villalobos, Cristian,Vimont, Daniel J.,Penland, Cecile,et al. Calculating State-Dependent Noise in a Linear Inverse Model Framework[J]. JOURNAL OF THE ATMOSPHERIC SCIENCES,2018,75(2):479-496. |
APA | Martinez-Villalobos, Cristian,Vimont, Daniel J.,Penland, Cecile,Newman, Matthew,&Neelin, J. David.(2018).Calculating State-Dependent Noise in a Linear Inverse Model Framework.JOURNAL OF THE ATMOSPHERIC SCIENCES,75(2),479-496. |
MLA | Martinez-Villalobos, Cristian,et al."Calculating State-Dependent Noise in a Linear Inverse Model Framework".JOURNAL OF THE ATMOSPHERIC SCIENCES 75.2(2018):479-496. |
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