Forecasting monthly precipitation in Central Chile: a self-organizing map approach using filtered sea surface temperature

2011 ◽  
Vol 107 (1-2) ◽  
pp. 1-13 ◽  
Author(s):  
Diego Rivera ◽  
Mario Lillo ◽  
Cintia B. Uvo ◽  
Max Billib ◽  
José Luis Arumí
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Monthly Precipitation ◽  
Self Organizing Map ◽  
Central Chile ◽  
Map Approach ◽  
Self Organizing

scholarly journals Sea Surface Temperature Patterns on the West Florida Shelf Using Growing Hierarchical Self-Organizing Maps

2006 ◽  
Vol 23 (2) ◽  
pp. 325-338 ◽  
Author(s):  
Yonggang Liu ◽  
Robert H. Weisberg ◽  
Ruoying He
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Self Organizing Map ◽  
West Florida Shelf ◽  
The West ◽  
Florida Shelf ◽  
Network Analyses ◽  
Sst Anomaly ◽  
Self Organizing

Abstract Neural network analyses based on the self-organizing map (SOM) and the growing hierarchical self-organizing map (GHSOM) are used to examine patterns of the sea surface temperature (SST) variability on the West Florida Shelf from time series of daily SST maps from 1998 to 2002. Four characteristic SST patterns are extracted in the first-layer GHSOM array: winter and summer season patterns, and two transitional patterns. Three of them are further expanded in the second layer, yielding more detailed structures in these seasons. The winter pattern is one of low SST, with isotherms aligned approximately along isobaths. The summer pattern is one of high SST distributed in a horizontally uniform manner. The spring transition includes a midshelf cold tongue. Similar analyses performed on SST anomaly data provide further details of these seasonally varying patterns. It is demonstrated that the GHSOM analysis is more effective in extracting the inherent SST patterns than the widely used EOF method. The underlying patterns in a dataset can be visualized in the SOM array in the same form as the original data, while they can only be expressed in anomaly form in the EOF analysis. Some important features, such as asymmetric SST anomaly patterns of winter/summer and cold/warm tongues, can be revealed by the SOM array but cannot be identified in the lowest mode EOF patterns. Also, unlike the EOF or SOM techniques, the hierarchical structure in the input data can be extracted by the GHSOM analysis.

scholarly journals Application of Z-numbers to teleconnection modeling between monthly precipitation and large scale sea surface temperature

2021 ◽  
Author(s):  
Hessam Najafi ◽  
Vahid Nourani ◽  
Elnaz Sharghi ◽  
Kiyoumars Roushangar ◽  
Dominika Dąbrowska
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Complex Problem ◽  
Sea Surface ◽  
Drought Monitoring ◽  
Monthly Precipitation ◽  
Fuzzy Approach ◽  
Suggested Model ◽  
Resources Management

Abstract The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using of Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability, can be used in various sectors of water resources management such as decision-making and drought monitoring.

Holocene sea surface temperature variations recorded in corals from Kikai Island, Japan

2017 ◽  
Vol 51 (4) ◽  
pp. e9-e14 ◽  
Author(s):  
Hiroto Kajita ◽  
Atsuko Yamazaki ◽  
Takaaki Watanabe ◽  
Chung-Che Wu ◽  
Chuan-Chou Shen ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Temperature Variations
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