Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition

Lee, Taesam; Ouarda, Taha B. M. J.

doi:10.1029/2011wr010660

Cited by 47 publications

(43 citation statements)

References 56 publications

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“…The trends are identified through the empirical mode decomposition (EMD) technique, developed to process nonlinear and nonstationary data [ Huang et al , ; Vecchio and Carbone , ], and successfully applied in many different fields [ Loh et al , ; Echeverria et al , ; Coughlin et al , ; Vecchio et al , ; Laurenza et al , ; Capparelli et al , ; Lee and Ouarda , , ]. EMD decomposes a time series into a finite number of intrinsic mode functions (IMFs) and a residual by using an adaptive basis derived from the time series through a so‐called “sifting” process, namely,

T (t) = \sum_{j = 0}^{m - 1} θ_{j} (t) + r_{normalm} (t),

where T denotes the temperature time series and each IMF θ j ( t ) and residual r m ( t ) are time‐dependent.…”

Section: Methodsmentioning

confidence: 99%

A spatiotemporal analysis of U.S. station temperature trends over the last century

et al. 2013

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[1] This study presents a nonlinear spatiotemporal analysis of 1167 station temperature records from the United States Historical Climatology Network covering the period from 1898 through 2008. We use the empirical mode decomposition method to extract the generally nonlinear trends of each station. The statistical significance of each trend is assessed against three null models of the background climate variability, represented by stochastic processes of increasing temporal correlation length. We find strong evidence that more than 50% of all stations experienced a significant trend over the last century with respect to all three null models. A spatiotemporal analysis reveals a significant cooling trend in the South-East and significant warming trends in the rest of the contiguous U.S. It also shows that the warming trend appears to have migrated equatorward. This shows the complex spatiotemporal evolution of climate change at local scales.

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T (t) = \sum_{j = 0}^{m - 1} θ_{j} (t) + r_{normalm} (t),

where T denotes the temperature time series and each IMF θ j ( t ) and residual r m ( t ) are time‐dependent.…”

Section: Methodsmentioning

confidence: 99%

A spatiotemporal analysis of U.S. station temperature trends over the last century

et al. 2013

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“…Unlike other time frequency analyses (e.g. wavelet analysis), the EMD procedure directly decomposes the original signal into a finite number of components (Lee and Ouarda, 2010, 2011, 2012).…”

Section: Methods and Datamentioning

confidence: 99%

An orchestrated climate song from the Pacific and Atlantic Oceans and its implication on climatological processes

Lee

Ouarda²,

2012

Intl Journal of Climatology

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ABSTRACT:A climate index is a time series that quantifies the temporal evolution of a climate process in a particular region. Various climatic patterns, such as the El Niño-Southern Oscillation, Pacific Decadal Oscillation (PDO), Arctic Oscillation, and North Atlantic Oscillation (NAO), have been summarized into climate indices corresponding to the respective regions; a comparison among these indices enables various further inferences. In this study, we investigated the interconnection between the Pacific and Atlantic Oceans using the representative climate indices, i.e. the PDO and NAO indices, respectively. Using empirical mode decomposition (EMD) and statistical analysis, it was shown that these two indices share the same long-term oscillation phase in the low-frequency domain, while in the high-frequency domain, the cross-correlation and the serial correlations of the two indices vary according to the phase of the long-term oscillation. This implies that a certain long-term oscillatory forcing influences both the Atlantic and Pacific regions. Three global gridded climate variables [i.e. sea-level pressure (SLP), precipitable water (PW), and sea-surface temperature (SST)] were studied over three different periods (i.e. the negative phase period of the long-term oscillation centered on 1960 and the positive phase centered on 1990). The mean 11 year anomalies revealed a noticeable particular spatial pattern and opposing tendencies for these periods. Furthermore, the global spatial patterns inducing the cross-correlation between the NAO and PDO indices and the lag-1 auto-correlations of the NAO index are presented. Based on the results presented in the current study, a long-term oscillation with a 70 to 80 year cycle may exist in the Pacific and Atlantic regions simultaneously. Because the slow and cyclic long-term oscillation can be predictable using EMD, if the same climate conditions of the Pacific and Atlantic Oceans continue as the last several decades, the spatial evolution of climate variables might also be inferred according to the phase of the long-term oscillation. Further physical study and analysis of long proxy records should help provide more conclusive results.

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“…최근 기후변화로 인한 극치사상들의 발생이 빈번해지고 있 어, 미래 기후변화에 따른 수문 기상학적인 변화를 예측 분석 하는 연구가 활발히 이루어지고 있다. Kim et al(2010) (Boe et al, 2007;Lee et al, 2012). 그러나 일 강수량으로 유출 모의를 할 경우 짧고 강한 강우의 특성들이 평균화되기 때문에 유출량을 과소평가하게 된다 (Eagleson, 1978).…”

Section: 서 론 일반 기후 모델(Gcm)과 Gcm을 공간적으로 상세화한 형 태인 지역 기후모델(Rcm)은 최상의 유효unclassified

Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region

Lee¹,

Park²,

Lee³

et al. 2014

Journal of Korean Society of Hazard Mitigation

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Hourly hydro-meteorological data are vital to flood control system and to assess the hydrological effects of climate change on medium and small watersheds. However, finder than daily hydro-meteorological data are not easy to obtain. A temporal downscaling method to obtain finer time series, e.g. hourly, might be very useful for assessing the hydrological effects of the variations of precipitation by climate change. In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with Genetic Algorithm (GA) is tested. The tested model was applied to Busan-Gyeongnam station in South Korea for a historical time period to validate the model performance. The results revealed that the applied model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate scenarios for the Busan-Gyeongnam area were also analyzed. The results illustrated that the mean significantly increased while the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. The downscaled hourly precipitation adequately reproduced the statistical behaviors of the historical hourly precipitation data for all durations considered. Overall, the results demonstrated that the applied temporal downscaling model is a good alternative method for downscaling simulated daily precipitation data to hourly especially for assessing the impacts of climate change especially from the variations of precipitation.

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Stochastic simulation of nonstationary oscillation hydroclimatic processes using empirical mode decomposition

Cited by 47 publications

References 56 publications

A spatiotemporal analysis of U.S. station temperature trends over the last century

A spatiotemporal analysis of U.S. station temperature trends over the last century

An orchestrated climate song from the Pacific and Atlantic Oceans and its implication on climatological processes

Temporal Downscaling of Precipitation from Daily to Hourly Based on Nonparametric Approach: Assessment of the Climate Change Impacts on the Hourly Precipitation for the Gyeongnam Region

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