Prediction of occurrence of daily summer monsoon precipitation over Karnataka

Abstract

Karnataka, a State in south peninsular India, receives 73% of its annual rainfall during the southwest monsoon season. Because of the complex physiographical features, the rainfall pattern over the State shows large spatial variation from 50 to 350 cm. The co-efficient of interannual variation of the monsoon rainfall is about 15% over coastal Karnataka (CK) and between 20 and 30% over interior Karnataka. It is, therefore, a difficult task to predict the location specific daily rainfall over Karnataka. In this study, an attempt has been made to develop an objective tool for forecasting the occurrence and non-occurrence of precipitation during a 24 h period for the 19 stations in Karnataka during the monsoon season. The probability of precipitation (POP) model is developed using forward stepwise regression with the available surface and upper air parameters from synoptic and radiosonde and radio wind stations in and around Karnataka as potential predictors. The POP model has been developed based on the data from 1981 to 1996 and verified with the data from 1997 to 2002. Different skill scores are computed using a yes/no contingency table. The POP model performs very well, with percentages of correct (PC) forecasts for occurrence/non-occurrence of precipitation being 57-91% for the independent data. Comparing the results of the POP model with that of the conventional method of forecast for Bangalore City, the PC forecasts improves from 44 to 56% with the use of the POP model.

Evaluation of classification and decision trees in predicting daily precipitation occurrences

“…Also, the relative humidity and maximum daily temperature had the highest correlation of the occurrence of daily precipitation. The results are also agree with Agnihotri & Mohapatra (2012), which examined the forecast of daily summer monsoon rains in the Karnataka region of India and concluded that the highest correlation of daily rainfall is with relative humidity parameters and minimum daily air temperature.…”

Evaluation of the prediction capability of AHP and F-AHP methods in flood susceptibility mapping of Ernakulam district (India)

“…About 40 million hectares (almost 12% of the area) of India are susceptible to floods (Husain 2012). India receives 75% of the rain during the S-W monsoon season (June-September) (Agnihotri and Mohapatra 2012). Most of the rivers overflow during this period, resulting in intense recurring floods.…”

Statistical Downscaling of ERA-Interim Forecast Precipitation Data in Complex Terrain Using LASSO Algorithm

“…Other methods focus on investigating the relationship between local precipitation and large-scale atmosphere circulations, which vary from linear regression (e.g., [30][31][32][33][34][35]) to complex nonlinear models (e.g., [36][37][38][39][40]). Although numerous studies were carried out, a general standardized precipitation downscaling method still does not exist, especially for complex terrains.…”

“…Several previous studies have used stepwise regression for different purposes. For example, Harpham and Wilby[39], Hessami et al[42], and Huth[66] adopted Stepwise for predictor selection; Agnihotri and Mohapatra[30] applied it to occurrence estimation of daily summer monsoon precipitation. In this study, stepwise regression is adopted to test LASSO.…”