Forecasting Climate Extremes-A Data Driven Approach

Rana Muhammad Ebrahim; Muhammad Taha Pasha; Hanzallah Ahmed Khan; Muhammad Faraz Khan; Muhammad Umer; Muhammad Wasim; Lubaid Ahmed

Author(s)

Rana Muhammad Ebrahim Usman Institute of Technology, Karachi, Pakistan

Muhammad Taha Pasha Usman Institute of Technology, Karachi, Pakistan

Hanzallah Ahmed Khan Usman Institute of Technology, Karachi, Pakistan

Muhammad Faraz Khan Usman Institute of Technology, Karachi, Pakistan

Muhammad Umer Usman Institute of Technology, Karachi, Pakistan

Muhammad Wasim Usman Institute of Technology, Karachi, Pakistan

Lubaid Ahmed Usman Institute of Technology, Karachi, Pakistan

Abstract

Climate extremes such as heavy rainfall, droughts, and heatwaves pose an increased risk to human life, agriculture, buildings, and nature. Currently Pakistan relies on traditional statistical models that are built on limited station-level data, while machine-learning-based forecasts remain underutilized. The objective of this paper is to introduce a provincial-based approach for accurate and precise short-term extreme weather forecasts adapted to the unique climatic conditions of Pakistan. The research uses the Extreme Gradient Boosting algorithm along with 25 years of historical weather data of Pakistan retrieved from Numerical Weather Prediction models, specifically, GFS-Global Forecast System and ICON-Icosahedral Nonhydrostatic Model, in improving the prediction of heatwaves, droughts, and heavy rainfall events. The models showed excellent performance in identifying heatwaves, classifying droughts, and identifying rainfall severity. The results show the potential of refining data from physics-based weather models with machine learning models to significantly improve forecasts of climate extremes, filling a vital gap in Pakistan's weather prediction landscape. This approach would prove beneficial to emergency management agencies in disaster preparedness and response as well as to the general public to make better decisions.

Publication Details

Page(s) 66-72

DOI DOI not available

Published Journal: Journal of Information & Communication Technology (JICT), Volume: 19, Issue: 2, Year: 2025

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Keywords

Climate extremes prediction, XGBoost, Heatwave prediction, Pakistan Climate, Machine learning, Drought classification, Rainfall prediction

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