Year: 2013
Type: article
Abstract: Thunderstorm forecasting is a challenging job.Machine learning techniques are being applied nowadays in meteorological fields for prediction purpose.This study presents the application of different machine learning tools based on multiple correlation, Multi-layer Perceptron (MLP), K-nearest neighbor (K-nn) method, and modified K-nn method to predict seasonal severe thunderstorms associated with squall occurring in Kolkata, North-East India.The models are trained and tested with the radiosonde data recorded in the early morning at 00:00UTC.The predictors are moisture difference and dry adiabatic lapse rate at different geopotential heights of the atmosphere.Our aim in this paper is to find how much correctly one can nowcast 10 to 14 hours before the 'occurrence'/ 'no occurrence' of evening squall-storms by using a few upper air diagnostic predictors.Modified K-nn method is found to yield very promising prognostic information with high prediction accuracy.The results indicate that forecasting can be done correctly up to 82.02% both for 'squall-storm/no storm' events, and up to 91.11% for 'squall-storm' events using modified K-nn based approach.In this article, modified K-nn method is proved as the best method in comparison with the other methods for the squall-storm prediction.
Institutions: University of Calcutta, Indian Statistical Institute
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: bronze
Funder: University Grants Commission
OpenAlex ID: W2334316632