Could AI solve the weather prediction dilemma? Countries rush to find out

India Today

24-07-2025

As Artificial Intelligence (AI) revolutionises various aspects of our daily lives, accurate local weather prediction is also now on the radar. The National Oceanic and Atmospheric Administration's (NOAA) Global Systems Laboratory (GSL) in the US has prepared a new regional weather forecast system, HRRR-Cast.This innovative AI-powered system provides highly localised forecasts in the range of 1 to 6 hours. It delivers fine-grained, timely predictions essential for localised weather events.advertisementAs the first of its kind developed by NOAA, HRRR-Cast represents a major step in integrating artificial intelligence into environmental modelling. Built as a data-driven counterpart to NOAA's renowned High-Resolution Rapid Refresh (HRRR) model, HRRR-Cast aims to maintain comparable forecast accuracy while delivering results faster and at lower computational costs.
HRRR-Cast is developed under the umbrella of NOAA's Project EAGLE (Experimental Artificial Intelligence Global and Limited-area Ensemble forecast system). By leveraging a comprehensive three-year dataset generated by the physics-based HRRR model, the AI system learns to enhance forecasting techniques. The project is a collaborative effort involving NOAA's GSL, the Cooperative Institute for Research in Environmental Sciences (CIRES), and the Cooperative Institute for Research in the Atmosphere (CIRA).PREDICT SEVERE WEATHER EVENTSOne of HRRR-Cast's key innovations is its ability to produce ensemble forecasts using AI-driven methods such as diffusion techniques.These ensembles generate multiple forecast scenarios, helping improve predictions for severe weather events. Early testing has shown promising results in capturing the dynamics of changing weather patterns, which is crucial for timely warnings.While HRRR-Cast showcases the power of artificial intelligence, researchers emphasize that it is not meant to replace traditional physics-based models.Instead, the goal is a hybrid approach that combines AI's computational efficiency with the reliability of physics-driven simulations, potentially transforming the future of weather prediction.This breakthrough is part of NOAA's broader initiative to integrate AI technologies into meteorological research, supported by the NOAA Center for Artificial Intelligence (NCAI) and partnerships with industry leaders like Google.
Photo: Reuters
EUROPE LEADING THE CHARGE IN GLOBAL AI WEATHER PREDICTIONThe US is not the only country making such advancements; several countries and organisations are advancing the field of AI-based weather forecasting by developing models that operate primarily at global scales, with some exploring regional applications akin to the HRRR-Cast model.These AI-driven systems utilise cutting-edge machine learning architectures to improve forecast accuracy, efficiency, and the handling of extreme weather phenomena.advertisementOne prominent example is GraphCast, developed by Google DeepMind in the UK. Utilising Graph Neural Networks (GNNs) and trained on ECMWF's ERA5 reanalysis data, GraphCast produces global forecasts up to 10 days ahead at approximately 25 km resolution.Remarkably, it offers higher accuracy than traditional numerical weather prediction (NWP) models like ECMWF's Integrated Forecasting System (IFS) and runs much faster, completing forecasts in minutes on a single TPU. NOAA's Project EAGLE builds on GraphCast by fine-tuning it with operational data, improving practical applicability.CHINA AND OTHER COUNTRIES CATCHING UPHuawei's Pangu-Weather, a Chinese initiative, employs a 3D Earth-specific transformer architecture for forecasting up to seven days. It excels in predicting tropical cyclone tracks and extreme events with high resolution and efficiency.NOAA has also tested Pangu-Weather using its global data systems, demonstrating its potential as a competitive global alternative that balances computational speed with accuracy.NVIDIA's FourCastNet from the US adopts a vision transformer model for high-resolution weather forecasts of up to seven days. Designed for GPU scalability, it supports both global and experimental regional forecasting, offering versatility similar to HRRR-Cast.NOAA researchers have incorporated FourCastNet in their AI-driven forecasting investigations, adapting it to their global datasets.advertisementEurope's ECMWF contributes through the Artificial Intelligence Forecasting System (AIFS), which integrates AI models based on transformer architectures into its existing forecasting pipelines. AIFS complements traditional NWP like the IFS with faster, data-driven predictions and encourages regional AI innovation through initiatives such as the AI Weather Quest.Lastly, China's FengWu, developed by the Shanghai Artificial Intelligence Laboratory, provides high-accuracy medium-range global forecasts. With a transformer-based model trained on extensive reanalysis data, FengWu emphasises scalable operational use and aligns with global trends toward efficient, AI-enhanced meteorology.WHERE DOES INDIA STAND?India is also in the advanced stages of developing its own AI-based weather forecasting model. Institutions under the Ministry of Earth Sciences (MoES) are actively integrating Artificial Intelligence (AI) and Machine Learning (ML) techniques into both research and operational frameworks. These technologies are being applied to monitor and predict Tropical Cyclone Heat Potential (TCHP), a key factor in forecasting cyclone intensity. The India Meteorological Department (IMD) is also using AI and ML to correct biases in Numerical Weather Prediction (NWP) model outputs. To accelerate the development of indigenous forecasting systems, the government is providing dedicated funding support. Additionally, the Ministry has established a virtual center focused on AI, ML, and Deep Learning (DL) at the Indian Institute of Tropical Meteorology (IITM), Pune. advertisementThis center is leading the effort to develop AI/ML-based applications specifically designed for localized weather and climate analysis.These systems focus on improving forecast accuracy, efficiency, and extreme weather handling through advanced machine learning architectures, reflecting a worldwide trend toward smarter, faster weather prediction methods, now advancing at both global and regional scales. - EndsTune InMust Watch