TL;DR
A DL-based model for weather forecasting over India, with inference time less than a minute for a seven-day prediction, is proposed, inspired by the Pangu-Weather (PW), a data-driven global weather forecasting model.
Flood-prone Bangladesh requires accurate short-term rainfall prediction, yet conventional numerical weather prediction (NWP) models remain computationally intensive and regionally limited in skill. This study evaluates and compares four deep learning (DL) architectures — Simple Long Short-Term Memory (LSTM), Stacked LS...
An improved AI-driven weather prediction system that enhances forecasting accuracy for temperature, humidity, wind speed, and atmospheric pressure through a Random Forest predictive model integrated with the Open Weather Map API and geolocation services is developed.
A machine learning-based framework for reconstructing missing hourly observations from automatic weather stations by integrating in situ AWS measurements with ERA5-Land reanalysis fields and Global Precipitation Measurement (GPM) satellite-derived products is presented.
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weat...
Medium-range weather forecasting underpins operational and planning decisions across the energy industry. Developing competitive weather models was once the domain of national meteorological centers, but recent advances in machine-learned weather prediction (MLWP) have opened the field to industry. We present Aries, a...
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.
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