Prediction of all India summer monsoon rainfall using error-back-propagation neural networks

Venkatesan, C.; Raskar, S. D.; Tambe, Sanjeev S.; Kulkarni, Bhaskar D.; Keshavamurty, R. N.

doi:10.1007/bf01029704

Cited by 73 publications

(33 citation statements)

References 52 publications

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“…It has been proven by several studies that the artificial neural networks are useful tools for predicting summer rainfall (e.g. Guhathakurta et al, 1999;Navone and Ceccatto, 1994;Sahai et al, 2003;Sahai et al, 2000;Venkatesan et al, 1997). Nevertheless, all of these studies deal with the Indian summer monsoon rainfall.…”

Section: Introductionmentioning

confidence: 99%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society

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Section: Introductionmentioning

confidence: 99%

Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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“…e present work is different from the majority of the work reported on the forecasting of rainfall at different time scale, where the time series data of either rainfall or meteorological parameters are utilized to forecast the timelagged rainfall. In the present scenario of rainfall forecasting, researchers have utilized various sets of surface meteorological variables such as minimum and maximum temperature [35,39,41,[43][44][45][46]. Some other researchers have utilized extended meteorological parameters in addition to temperature variables such as relative humidity and previous day rainfall [62].…”

Section: Summary Discussion and Conclusionmentioning

confidence: 99%

“…e advantage of an ANN approach is that it can be used to develop a functional relationship, including a nonlinear relationship, amongst the various parameters of the process under study even in the absence of full understanding of its mathematical model [34]. e ANN techniques such as feedforward, feedback, and competitive neural networks are extensively used for rainfall forecasting at different time scales such as, yearly [35][36][37], monthly/seasonal [13,14,[38][39][40][41][42][43][44], and weekly/daily basis [45][46][47]. By using the soft computing techniques, the rainfall forecasting can be categorized in two groups: either by using historical time series rainfall data [48] or by using historical time series data of meteorological variables [45].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

Misra

Deshamukhya

Sharma³

et al. 2018

Advances in Meteorology

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In the present work, daily rainfall is simulated over the core monsoon region of India by using a feedforward multilayer perceptron (MLP) model. Daily rainfall is found to be optimally dependent on four concurrent meteorological parameters, namely, geopotential height, specific humidity, zonal, and meridional wind at 1000 mb, 925 mb, 850 mb, and 700 mb pressure levels during 00, 06, 12, and 18 Greenwich Mean Time (GMT). e architecture of the optimized feedforward MLP model consists of 64 nodes in the input layer, 10 nodes in the hidden layer, and 1 node in the output layer. e results from the model are compared with the 3B42 (version 7) rainfall product. In terms of root mean square error (rmse) and correlation coefficient (cc), the model is performing better compared to the satellite-derived 3B42 rainfall product, whereas in terms of bias, the performance of the 3B42 product is better compared to the model. e weight matrices of the feedforward MLP model are estimated at a particular location (22.5°N, 82.5°E). ese weight matrices are able to simulate daily rainfall at neighbourhood locations also with reasonably good accuracy with cc in the range of 0.41 to 0.55. e performance of the model improves in case of an aerial average of daily rainfall with significantly enhanced cc (0.72). e model is able to capture monthly and intraseasonal variation of rainfall with reasonably good accuracy, with cc of 0.88 and 0.68, respectively. e simulation model has a limitation that it is not able to simulate extreme high rainfall events (>60 mm/day). Overall, the developed model is performing reasonably well. is approach has a potential to be used as a rain parameterization scheme in the dynamical atmospheric and coupled models to simulate daily rainfall. Nevertheless, the present approach can also be used for multistep prediction of rainfall.

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“…This paper proposes a method for calculating an instability metric called a Shape Factor (SF) that can be used as a metric for forecasting local weather conditions. Once the SF has become perfected as a suitable instability index it can serve as one of several inputs into a neural network computational model to more adequately warn aviation authorities of hazardous severe storms (Chauvin and Rumelhart, 1995;Venkatesan et al, 1997). Other possible inputs can come from vertical wind shear data (Ahrens, 1982) or from radars, lidars, surface mesonet stations, soundings and rapid scanning satellites (Wilson, 2004).…”

Section: Introductionmentioning

confidence: 99%

The development of a shape factor instability index to guide severe weather forecasts for aviation safety

Walker

Chakrapani

Elmahboub

2008

Meteorological Applications

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An instability index, called the shape factor (SF), for weather forecasting was proposed using temperature profiles from a NASA/LaRC-hosted web-page featuring NOAA Rapid Update Cycle (RUC) radiosonde data used for short-term weather predictions. The index of instability was predicated on the sign and magnitude of the gradient of the equivalent potential temperature. A training data set of temperature profiles was obtained for both clear and severe weather conditions and the SF index was computed. The data showed marked differences in the magnitudes of the shape factors for the two weather conditions. Calculations of SF were compared with the several well-established instability indices and there appeared marked agreement between the SF and these other metrics. It is being proposed that this new index could be used as a parameter for input to statistical weather forecasting models to enhance overall aviation safety and air traffic management efficiency. Of particular relevance is the fact that more comprehensive results can be garnered since the SF index is calculated from the entire temperature profile and not just from selected temperatures as is the case for other indices of instability.

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Prediction of all India summer monsoon rainfall using error-back-propagation neural networks

Cited by 73 publications

References 52 publications

Predicting summer rainfall in the Yangtze River basin with neural networks

Predicting summer rainfall in the Yangtze River basin with neural networks

Simulation of Daily Rainfall from Concurrent Meteorological Parameters over Core Monsoon Region of India: A Novel Approach

The development of a shape factor instability index to guide severe weather forecasts for aviation safety

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