Smart weather prediction and forecasting system

Abstract

The smart weather prediction and forecasting system is an innovative project that explores the possibility of using predictive machine learning models to accurately predict current weather conditions and forecast weather to replace the traditional Numerical Weather Prediction (NWP) models. Scientists have attempted various techniques to predict meteorological attributes, and there have been trade-offs between accuracy and cost. Therefore, the system aims to minimize the high costs and complexities associated with NWP models from ultra-high-speed computers and the costly HPC nodes required to predict the weather accurately. Hence, the project analyzes historical weather datasets using state-of-art predictive machine-learning models to predict weather conditions with high accuracy and precision efficiently. The system also aims to impact meteorological organizations in Ghana, Africa, and the world by providing a cheaper and more convenient alternative to traditional NWP models. The system can help these organizations make better decisions and improve operational efficiency by accurately predicting and forecasting future weather. Overall, this project presents a significant advancement in meteorology and has the potential to revolutionize how we predict and forecast weather.