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-scale meteorological patterns conducive to extreme weather. This work proposes a new tool for exploring patterns and trends of extreme weather; specifically, we propose an extremes analog to principal component analysis (PCA). To illustrate the method, we apply it to 3-day precipitation data from continental U.S. (CONUS) weather stations during hurricane season and investigate overall trends of extreme precipitation as well as relationships to El Niño–Southern Oscillation (ENSO). However, the method is not
-scale meteorological patterns conducive to extreme weather. This work proposes a new tool for exploring patterns and trends of extreme weather; specifically, we propose an extremes analog to principal component analysis (PCA). To illustrate the method, we apply it to 3-day precipitation data from continental U.S. (CONUS) weather stations during hurricane season and investigate overall trends of extreme precipitation as well as relationships to El Niño–Southern Oscillation (ENSO). However, the method is not
effective technique that can mitigate temporal deficiencies produced using the DA approach is encouraged. Plausible methods would include correlating coherent rain-rate information or appropriately recalculating the total daily rain rate. Both measures can be implemented by using a principal components analysis (PCA). The significant information from multidimensional remotely sensed images are compressed into fewer components, and a new pixel value is calculated based on their corresponding linear
effective technique that can mitigate temporal deficiencies produced using the DA approach is encouraged. Plausible methods would include correlating coherent rain-rate information or appropriately recalculating the total daily rain rate. Both measures can be implemented by using a principal components analysis (PCA). The significant information from multidimensional remotely sensed images are compressed into fewer components, and a new pixel value is calculated based on their corresponding linear
on the Stuttgart neural network [the recurrent neural network (RNN) method]. This paper simulates the brightness temperatures at the 35 frequency channels using upper-air ascent data of 6 yr in combination with the monochromatic radiative transfer model (MonoRTM), and establishes their relationship with the vertical profiles of temperature and humidity based on principal component analysis (PCA) and the stepwise regression method. The accuracy of this retrieval method would be determined by
on the Stuttgart neural network [the recurrent neural network (RNN) method]. This paper simulates the brightness temperatures at the 35 frequency channels using upper-air ascent data of 6 yr in combination with the monochromatic radiative transfer model (MonoRTM), and establishes their relationship with the vertical profiles of temperature and humidity based on principal component analysis (PCA) and the stepwise regression method. The accuracy of this retrieval method would be determined by
an instrument with respect to a measured variable through the principal component analysis (PCA). This robust technique has been widely used in atmospheric physics, that is, to classify and group cirrus clouds ( Dionisi et al. 2013 ) or clustering different air masses ( Borchi and Marenco 2002 ). The composite index will then represent solid help in building and optimizing a cost-effective network, bridging the gap between two very different worlds: the scientific need for precision and economic
an instrument with respect to a measured variable through the principal component analysis (PCA). This robust technique has been widely used in atmospheric physics, that is, to classify and group cirrus clouds ( Dionisi et al. 2013 ) or clustering different air masses ( Borchi and Marenco 2002 ). The composite index will then represent solid help in building and optimizing a cost-effective network, bridging the gap between two very different worlds: the scientific need for precision and economic
streamflow. Moreover, RBF-related networks further attract lots of attention on the improvement of its approximate ability as well as the construction of its architecture ( Shi et al. 2005 ). Some researchers—such as Dong and MacAvoy (1996) , Monahan (2000) , Hsieh (2001) , Lu et al. (2004) , and Ture et al. (2007) —have successfully combined the principal component analysis (PCA) with neural networks. They concluded that PCA can be employed to find a set of orthogonal components that minimize the
streamflow. Moreover, RBF-related networks further attract lots of attention on the improvement of its approximate ability as well as the construction of its architecture ( Shi et al. 2005 ). Some researchers—such as Dong and MacAvoy (1996) , Monahan (2000) , Hsieh (2001) , Lu et al. (2004) , and Ture et al. (2007) —have successfully combined the principal component analysis (PCA) with neural networks. They concluded that PCA can be employed to find a set of orthogonal components that minimize the
respect to the equator and to be most sensitive to warm sea surface temperatures of the Pacific. c. Motivation of NLPC analysis for the MJO It has become common practice to identify the MJO with a linear principal component analysis (LPCA). The LPCA can be either univariate or multivariate ( CLIVAR Madden–Julian Oscillation Working Group 2009 ), that is, the linear principal components (LPCs) can be defined either separately or jointly for the fields under investigation. For example, Maloney and
respect to the equator and to be most sensitive to warm sea surface temperatures of the Pacific. c. Motivation of NLPC analysis for the MJO It has become common practice to identify the MJO with a linear principal component analysis (LPCA). The LPCA can be either univariate or multivariate ( CLIVAR Madden–Julian Oscillation Working Group 2009 ), that is, the linear principal components (LPCs) can be defined either separately or jointly for the fields under investigation. For example, Maloney and
together with the diurnal sea breeze can result in flooding and significant economic losses ( Wu et al. 2007 ). The main aim of the paper is to demonstrate the usefulness of principal component analysis (PCA) (e.g., Jackson 1991 ) in assessing a mesoscale model’s ability in reproducing the diurnal rainfall variability in the MC. PCA have been used to assess low-frequency atmospheric responses to the El Niño–Southern Oscillation in models ( Renshaw et al. 1998 ; Peng et al. 2000 ; Wang et al. 2009
together with the diurnal sea breeze can result in flooding and significant economic losses ( Wu et al. 2007 ). The main aim of the paper is to demonstrate the usefulness of principal component analysis (PCA) (e.g., Jackson 1991 ) in assessing a mesoscale model’s ability in reproducing the diurnal rainfall variability in the MC. PCA have been used to assess low-frequency atmospheric responses to the El Niño–Southern Oscillation in models ( Renshaw et al. 1998 ; Peng et al. 2000 ; Wang et al. 2009
1660JOURNAL OF CLIMATE AND APPLIED METEOROLOGYVOLUME 23Complex Principal Component Analysis: Theory and ExamplesJ. D. HORELClimate Research Group. Scripps Institution of Oceanography. University of Ca! jfornia, San Diego, La Jolla, CA 92093(Manuscript received 14 March 1984, in final form 25 September 1984)ABSTRACTComplex principal component (CPC) analysis is shown to be a useful method for identifying traveling andstanding waves in geophysical data sets. Combinations of simple progressive and
1660JOURNAL OF CLIMATE AND APPLIED METEOROLOGYVOLUME 23Complex Principal Component Analysis: Theory and ExamplesJ. D. HORELClimate Research Group. Scripps Institution of Oceanography. University of Ca! jfornia, San Diego, La Jolla, CA 92093(Manuscript received 14 March 1984, in final form 25 September 1984)ABSTRACTComplex principal component (CPC) analysis is shown to be a useful method for identifying traveling andstanding waves in geophysical data sets. Combinations of simple progressive and
is dealt with implicitly, as our proposed modifications likewise separate the estimation of missing AVHRR principal component (PC) and ground station information. The third reconstruction, utilizing standard principal component analysis, appears in S09 ’s supplementary information and is not accompanied by sufficient information for a quantitative comparison. However, as this version also utilized the same number of retained AVHRR PCs as the T IR reconstruction, our criticisms apply to the
is dealt with implicitly, as our proposed modifications likewise separate the estimation of missing AVHRR principal component (PC) and ground station information. The third reconstruction, utilizing standard principal component analysis, appears in S09 ’s supplementary information and is not accompanied by sufficient information for a quantitative comparison. However, as this version also utilized the same number of retained AVHRR PCs as the T IR reconstruction, our criticisms apply to the
AxJGusx1978 DONALD M. HARDY AND JOHN J. WALTON 1153Principal Components Analysis of Vector Wind Measurements~ DONALD M. HAm)VSolar Energy Research Institute, Golden, CO 80401 JOHN J. WALTONLawrence Livermore Laboratory, Livermore, CA 94550(Manuscript received 6 July 1977, in final form 20 February 1978)ABSTRACT The method of principal components analysis (also known as empirical eigenvector
AxJGusx1978 DONALD M. HARDY AND JOHN J. WALTON 1153Principal Components Analysis of Vector Wind Measurements~ DONALD M. HAm)VSolar Energy Research Institute, Golden, CO 80401 JOHN J. WALTONLawrence Livermore Laboratory, Livermore, CA 94550(Manuscript received 6 July 1977, in final form 20 February 1978)ABSTRACT The method of principal components analysis (also known as empirical eigenvector
