scholarly journals Sea surface temperature patterns in Tropical Atlantic: principal component analysis and nonlinear principal component analysis

2014 ◽  
Vol 1 (1) ◽  
pp. 235-267 ◽  
Author(s):  
S. C. Kenfack ◽  
K. F. Mkankam ◽  
G. Alory ◽  
Y. du Penhoat ◽  
N. M. Hounkonnou ◽  
...  

Abstract. Principal Component Analysis (PCA) is one of the popular statistical methods for feature extraction. The neural network model has been performed on the PCA to obtain nonlinear principal component analysis (NLPCA), which allows the extraction of nonlinear features in the dataset missed by the PCA. NLPCA is applied to the monthly Sea Surface Temperature (SST) data from the eastern tropical Atlantic Ocean (29° W–21° E, 25° S–7° N) for the period 1982–2005. The focus is on the differences between SST inter-annual variability patterns; either extracted through traditional PCA or the NLPCA methods.The first mode of NLPCA explains 45.5% of the total variance of SST anomaly compared to 42% explained by the first PCA. Results from previous studies that detected the Atlantic cold tongue (ACT) as the main mode are confirmed. It is observed that the maximum signal in the Gulf of Guinea (GOG) is located along coastal Angola. In agreement with composite analysis, NLPCA exhibits two types of ACT, referred to as weak and strong Atlantic cold tongues. These two events are not totally symmetrical. NLPCA thus explains the results given by both PCA and composite analysis. A particular area observed along the northern boundary between 13 and 5° W vanishes in the strong ACT case and reaches maximum extension to the west in the weak ACT case. It is also observed that the original SST data correlates well with NLPCA and PCA, but with a stronger correlation on ACT area for NLPCA and southwest in the case of PCA.

2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.


2009 ◽  
Vol 20 (11) ◽  
pp. 1789-1802 ◽  
Author(s):  
CONSTANTIN ANDRONACHE

The framework of principal component analysis (PCA) based on singular value decomposition (SVD) is applied to the monthly sea surface temperature (SST) observations in the North Atlantic Ocean for the time interval 1856–2008. Multiyear time series of SST for each month are used to investigate the statistical relationship between SST variations from the 12 months. To obtain approximate stationary conditions, the trend and a multidecadal oscillation are removed from the data. The remaining SST residuals exhibit remarkable correlation between successive months, due largely to persistence. PCA demonstrates the dimension reduction of the data sets and provides a robust way of analyzing multivariate observations describing the climate system.


2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan

Abstract Rainfall is the primary driver of basin hydrologic processes. This article examines a recently developed rainfall predictive tool that combines wavelet principal component analysis (WPCA), an artificial neural networks-genetic algorithm (ANN-GA), and statistical disaggregation into an integrated framework useful for the management of water resources around the upper Blue Nile River basin (UBNB) in Ethiopia. From the correlation field between scale-average wavelet powers (SAWPs) of the February–May (FMAM) global sea surface temperature (SST) and the first wavelet principal component (WPC1) of June–September (JJAS) seasonal rainfall over the UBNB, sectors of the Indian, Atlantic, and Pacific Oceans where SSTs show a strong teleconnection with JJAS rainfall in the UBNB (r ≥ 0.4) were identified. An ANN-GA model was developed to forecast the UBNB seasonal rainfall using the selected SST sectors. Results show that ANN-GA forecasted seasonal rainfall amounts that agree well with the observed data for the UBNB [root-mean-square errors (RMSEs) between 0.72 and 0.82, correlation between 0.68 and 0.77, and Hanssen–Kuipers (HK) scores between 0.5 and 0.77], but the results in the foothills region of the Great Rift Valley (GRV) were poor, which is expected since the variability of WPC1 mainly comes from the highlands of Ethiopia. The Valencia and Schaake model was used to disaggregate the forecasted seasonal rainfall to weekly rainfall, which was found to reasonably capture the characteristics of the observed weekly rainfall over the UBNB. The ability to forecast the UBNB rainfall at a season-long lead time will be useful for an optimal allocation of water usage among various competing users in the river basin.


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