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Related Experiment Video

Updated: Jun 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Probabilistic weather forecasting with machine learning.

Ilan Price¹, Alvaro Sanchez-Gonzalez², Ferran Alet²

¹Google DeepMind, London, UK. pricei@google.com.

|December 5, 2024

Summary

This summary is machine-generated.

GenCast, a new machine learning weather prediction model, generates probabilistic forecasts faster and more accurately than traditional methods. This advancement improves extreme weather prediction and decision-making for crucial weather-dependent applications.

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Area of Science:

Atmospheric Science
Artificial Intelligence
Data Science

Background:

Weather forecasting traditionally relies on Numerical Weather Prediction (NWP), which struggles to represent forecast uncertainty and risk.
Recent Machine Learning Weather Prediction (MLWP) models show promise but often lack the accuracy of NWP ensemble forecasts.

Purpose of the Study:

To introduce GenCast, a novel probabilistic ML weather model designed to outperform existing state-of-the-art ensemble forecasts.
To enhance the accuracy and efficiency of medium-range weather prediction, particularly for extreme events.

Main Methods:

GenCast is an ML weather prediction model trained on decades of atmospheric reanalysis data.
It generates a large ensemble of stochastic, 15-day global forecasts for over 80 variables at high resolution.
The model achieves this by leveraging advanced ML techniques for rapid, probabilistic weather prediction.

Main Results:

GenCast demonstrates superior skill compared to the European Centre for Medium-Range Weather Forecasts ensemble (ENS) on 97.2% of evaluated targets.
The model shows improved prediction capabilities for extreme weather events, tropical cyclone tracks, and wind power generation.
GenCast produces 15-day global forecasts in just 8 minutes, significantly faster than conventional methods.

Conclusions:

GenCast represents a significant advancement in operational weather forecasting, offering enhanced accuracy, speed, and probabilistic insights.
This ML-based approach facilitates more informed and efficient decision-making in weather-dependent sectors.
The development paves the way for the next generation of AI-driven weather prediction systems.