TL;DR
This study evaluated fourteen forecasting methods on five weather variables over four horizons in Islamabad and Chakwal, Pakistan to help researchers and meteorologists to better identify which methods work well operationally in localized regions.
The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations.
Experimental results reveal that the DL methods achieve significantly better forecasting of temperature as compared to the conventional ARIMA model, providing strong evidence of the value of combining DL methods for weather prediction.
Weather prediction is a complex forecasting problem because atmospheric variables interact across time and space, observations are heterogeneous, and useful forecasts must remain reliable across different lead times and weather regimes. Numerical weather prediction (NWP) remains a major operational approach, while mach...
Recent machine learning weather prediction (MLWP) models have demonstrated remarkable forecasting skill on global reanalysis-based benchmarks. However, their performance remains unclear in challenging environments such as Northern Norway, where narrow fjords and rapidly changing weather result in highly variable local...
Accurate short‐ and medium‐term solar irradiance forecasting is vital for integrating solar power into the grid, but high variability and weather dependence make it challenging. Machine learning (ML) models offer promise, but their performance often depends on forecast horizon and training data size. In this study, f...
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