Development of a perfect prognosis probabilistic model for prediction of lightning over south-east India

Abstract

A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the southeast Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time applications, the predictors are derived from a numerical weather prediction model. In the present study, we have developed the statistical model based on Binary Logistic Regression technique. For developing the statistical model, 115 cases of lightning that occurred over the southeast Indian region during the period 2006-2009 were considered. The probability of lightning (yes or no) occurring during the 12-hour period 0900-2100 UTC over the region was considered as the predictand. The thermodynamic and dynamic variables derived from the NCEP Final Analysis were used as the predictors. A three-stage strategy based on Spearman Rank Correlation, Cumulative Probability Distribution and Principal Component Analysis was used to objectively select the model predictors from a pool of 61 potential predictors considered for the analysis. The final list of six predictors used in the model consists of the parameters representing atmospheric instability, total moisture content in the atmosphere, low level moisture convergence and lower tropospheric temperature advection. For the independent verifications, the probabilistic model was tested for 92 days during the months of May, June and August 2010. The six predictors were derived from the 24-h predictions using a high resolution Weather Research and Forecasting model initialized with 00 UTC conditions. During the independent period, the probabilistic model showed a probability of detection of 77% with a false alarm rate of 35%. The Brier Skill Score during the independent period was 0.233, suggesting that the prediction scheme is skillful in predicting the lightning probability over the southeast region with a reasonable accuracy.

Nowcasting severe convective activity over southeast India using ground‐based microwave radiometer observations

“…By observing the variations of the composite time series of these thermodynamic indices, it is possible to explain the prerequisites necessary for the genesis of thunderstorm activity. In the recent study by [ Rajeevan et al ., ], a prediction scheme is developed to nowcast the probability of lightning over southeast India using various thermodynamical indices which explain the tropospheric instability. From the study it is inferred that the above mentioned eight parameters showed appreciable differences in threshold for the occurrence of thunderstorm and nonthunderstorm cases, 60% or more storms are formed when KI exceeds 42°C, MEANRH 950‐850hPa raises more than 75%, and PW value reaches more than 5.4 cm.…”

“…During the recent years, a variety of statistical techniques have been used to develop forecast models for thunderstorms and lightning [Reap, 1994;Lambert et al, 2005;Shafer and Fuelberg, 2006]. In a recent study by Rajeevan et al [2012], a statistical model based on binary logistic regression was developed for predicting probability of lightning occurrence over southeast India using the perfect prognostic method (PPM). For real time forecasts, predictors are derived from 12 h predictions using the WRF model.…”

“…In a recent study by Rajeevan et al . [], a statistical model based on binary logistic regression was developed for predicting probability of lightning occurrence over southeast India using the perfect prognostic method (PPM). For real time forecasts, predictors are derived from 12 h predictions using the WRF model.…”

Statistical characterization of cloud-to-ground lightning data and meteorological modelling of cloud-to-ground lightning days for the warm season in the province of León (northwest Spain)

“…There are several statistical models that allow prediction of the occurrence of a lightning day: classification and regression tree (CART) techniques (Burrows et al , ), neural networks and fuzzy logic (Kuk et al , ) and binary logistic regression (Mazany et al , ; Lambert et al , ; Shafer and Fuelberg, , ; Rajeevan et al , ).…”

Analysing the characteristic features of a pre-monsoon thunderstorm event over Pune, India, using ground-based observations and WRF model

“…) were also developed to forecast thunderstorm. Rajeevan et al (2012) developed a statistical model based on binary logistic regression for predicting the probability of lightning occurrence over southeast India using the perfect prognostic method (PPM). Though many studies were carried out using various techniques/observations, the prediction of severe convective events is still a difficult process which may be mainly because of (i) lack of observations of the initial state or (ii) relative coarse resolution.…”