AI for Extreme Weather Prediction and Response: A 2015–2024 Review

This blog post reviews a recent systematic analysis showing how artificial intelligence is transforming the forecasting and management of extreme-weather events.

The study, led by researchers at the National Institute of Meteorological Sciences in South Korea, synthesized trends in 8,642 peer-reviewed papers from 2015–2024 to map how AI and machine learning are improving accuracy, speed, and actionable intelligence in weather and climate extremes.

Table of Contents

What the large-scale review reveals about AI in extreme-weather forecasting

The investigation applied topic modeling to a decade of literature and identified the dominant research directions shaping the field.

This synthesis provides a quantitative, evidence-based view of where AI delivers the greatest value for operational weather services, disaster management, and environmental assessment.

Five major research themes identified

The review clusters the AI literature into five themes that cover both predictive modeling and post-event analysis.

These themes reflect where research effort and practical deployment are concentrating.

Forecasting extreme-weather events — machine learning approaches aimed at predicting storms, hurricanes, and high-impact weather more reliably than traditional systems.

Flood prediction — hydrological modeling enhanced by data-driven techniques for improved runoff and inundation forecasts.

Drought and agricultural risk assessment — AI-driven indicators and crop-impact models for early warning and resilience planning.

Ecosystem responses to climate extremes — fine-scale assessments of ecological impacts, recovery dynamics, and vulnerability mapping.

Disaster detection through deep learning — automated identification and mapping of damage from remote sensing and high-resolution imagery.

How modern AI techniques push forecasting performance

Across the corpus, AI-driven models demonstrate consistent gains over conventional meteorological models in both speed and accuracy.

This performance edge is particularly apparent when systems need to ingest and interpret large, heterogeneous data streams in real time.

Key methods making a difference

A number of emerging AI architectures are singled out for their practical benefits:

Diffusion models — improving rainfall and wind-field forecasts by enabling probabilistic, high-fidelity generation of spatial patterns.

Recurrent neural networks (RNNs) — effective for time-series tasks such as runoff prediction and hydrological sequence modeling.

Transformer-based vision models — increasingly used for rapid, high-resolution damage mapping after disasters using satellite and aerial imagery.

From data integration to faster responses

One of the clearest strengths of AI systems reported in the review is their ability to fuse diverse hydrometeorological data: radar, satellite, in-situ sensors, and numerical model output.

This integration supports operational capabilities that are difficult for classical physics-based pipelines to match at scale.

Operational impacts and societal benefits

By enabling real-time warnings and accelerating disaster-response workflows, AI techniques contribute directly to reducing social and economic losses.

They also allow for more precise ecological impact assessments at finer spatial and temporal resolutions — vital for targeted relief, recovery prioritization, and longer-term resilience planning.

Why this review matters

This is the first large-scale, quantitative mapping of AI research in extreme-weather forecasting.

By aggregating nearly a decade of progress, the study provides an empirical roadmap for researchers, meteorological services, and emergency managers seeking to adopt AI-driven tools responsibly and effectively.

Here is the source article for this story: Frontiers | AI in Extreme Weather Events Prediction and Response: A Systematic Topic-Model Review (2015–2024)