scholarly journals Forecasting the winter shower over India through a neurocomputing approach

2011 ◽  
Vol 14 (2) ◽  
pp. 512-522
Author(s):  
S. S. De ◽  
Goutami Chattopadhyay ◽  
Suman Paul ◽  
Soumyadip Chattopadhyay ◽  
D. K. Haldar
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Neural Net ◽  
Predictive Tool ◽  
Artificial Neural Net ◽  
Sea Surface Temperature Anomaly ◽  
Proper Training ◽  
Artificial Neural ◽  
Collinearity Diagnostics

The development of a neurocomputing technique to forecast the average winter shower in India has been modeled from 48 years of records (1950–1998). The complexities in the rainfall–sea surface temperature relationships have been statistically analyzed along with the collinearity diagnostics. The presence of multicollinearity has been revealed and a variable selection has been executed accordingly. The absence of persistence has also been revealed. For this reason, an Artificial Neural Net Model as a predictive tool for the said meteorological event in the form of a Multiple Layer Perceptron has been generated with a sea surface temperature anomaly and monthly average winter shower data over India during the above period. After proper training and testing, a Neural Net model with small prediction error is developed and the supremacy of the Artificial Neural Net over conventional statistical predictive procedures has been established statistically.

scholarly journals Basin-Scale Prediction of Sea Surface Temperature with Artificial Neural Networks

2018 ◽  
Vol 35 (7) ◽  
pp. 1441-1455 ◽  
Author(s):  
Kalpesh Patil ◽  
M. C. Deo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Prediction Skill ◽  
Basin Scale ◽  
Ann Models ◽  
Artificial Neural

AbstractThe prediction of sea surface temperature (SST) on the basis of artificial neural networks (ANNs) can be viewed as complementary to numerical SST predictions, and it has fairly sustained in the recent past. However, one of its limitations is that such ANNs are site specific and do not provide simultaneous spatial information similar to the numerical schemes. In this work we have addressed this issue by presenting basin-scale SST predictions based on the operation of a very large number of individual ANNs simultaneously. The study area belongs to the basin of the tropical Indian Ocean (TIO) having coordinates of 30°N–30°S, 30°–120°E. The network training and testing are done on the basis of HadISST data of the past 140 yr. Monthly SST anomalies are predicted at 3813 nodes in the basin and over nine time steps into the future with more than 20 million ANN models. The network testing indicated that the prediction skill of ANNs is attractive up to certain lead times depending on the subbasin. The ANN models performed well over both the western Indian Ocean (WIO) and eastern Indian Ocean (EIO) regions up to 5 and 4 months lead time, respectively, as judged by the error statistics of the correlation coefficient and the normalized root-mean-square error. The prediction skill of the ANN models for the TIO region is found to be better than the physics-based coupled atmosphere–ocean models. It is also observed that the ANNs are capable of providing an advanced warning of the Indian Ocean dipole as well as abnormal basin warming.

scholarly journals Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

2014 ◽  
Vol 142 (5) ◽  
pp. 1771-1791 ◽  
Author(s):  
Mohamed Helmy Elsanabary ◽  
Thian Yew Gan
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
River Basin ◽  
Principal Component ◽  
Sea Surface ◽  
Seasonal Rainfall ◽  
Blue Nile ◽  
Artificial Neural ◽  
Mean Square Errors ◽  
Global Sea Surface Temperature

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.

Time Series Analysis of CMIP5 Model and Observed Sea Surface Temperature Anomaly Along Indian Coastal Zones

2019 ◽  
Vol 86 (sp1) ◽  
pp. 239
Author(s):  
Dhanya Joseph ◽  
Vazhamattom Benjamin Liya ◽  
Girindran Rojith ◽  
Pariyappanal Ulahannan Zacharia ◽  
George Grinson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Anomaly ◽  
Sea Surface ◽  
Coastal Zones ◽  
Cmip5 Model ◽  
Series Analysis ◽  
Sea Surface Temperature Anomaly
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