Spatio-Temporal Transformer Framework for Weather Forecasting and Climate Analytics

Abstract

Weather forecasting is an essential element of modern society which is crucial for agricultural planning, disaster management, transport and logistic networks, aviation, power generation, water resources management, and environmental monitoring. The problem of weather prediction is exceptionally challenging since it involves the spatio-temporal behavior of the atmosphere which is subject to complex physical processes while constantly changing in time. Conventional statistical forecasting models and machine learning methods including Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) are effective in short-term forecasting but fail to account for long-range spatial and temporal weather patterns accurately. The historical weather data used in this research was obtained from public databases. The data consists of temperature, humidity, pressure, rainfall, wind speed, wind direction, solar radiation, and cloud cover. It undergoes numerous preprocessing steps before being fed into the developed framework as an input. Extracted features from the processed data are then encoded using an attention-based multi-head encoder to forecast the weather while being used to perform climate analytics such as identifying climate trends, seasonality, and climate anomalies detection. The evaluation results of the proposed framework on the forecasting performance using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination ($R^2$) are expected to demonstrate greater forecasting accuracy, more effective capturing of long-range spatial and temporal patterns, lower computational complexity, and faster processing speeds compared to conventional deep learning approaches. The framework contributes significantly to long-range climate forecasting while facilitating informed decision-making in disaster management, precision agriculture, smart grids, and environmental monitoring. The proposed solution, therefore, presents a novel and effective approach to weather prediction and climate analytics using the Spatio-Temporal Transformer framework.