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Updated: Jan 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

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Published on: July 22, 2025

FuXi-ENS: A machine learning model for efficient and accurate ensemble weather prediction.

Xiaohui Zhong¹, Lei Chen^1,2, Hao Li^1,2

¹Artificial Intelligence Innovation and Incubation Institute, Fudan University, Shanghai, China.

Science Advances

|October 31, 2025

Summary

FuXi-ENS, a novel machine learning model, generates accurate global weather forecasts up to 15 days ahead. This advanced system enhances probabilistic weather prediction by optimizing ensemble forecasting, outperforming current standards.

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Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

Area of Science:

Meteorology
Machine Learning
Computational Science

Background:

Ensemble forecasting is crucial for quantifying uncertainty and probabilistic weather predictions.
Conventional global ensemble prediction systems face computational limitations, restricting ensemble size and scenario diversity.
Machine learning (ML) has shown promise in reducing computational costs and improving deterministic forecasting.

Purpose of the Study:

To introduce FuXi-ENS, an advanced ML model designed for global ensemble weather forecasting.
To address challenges in applying ML to ensemble forecasting, particularly uncertainties in initial conditions and models.
To generate 6-hourly global ensemble weather forecasts up to 15 days ahead at a 0.25° spatial resolution.

Main Methods:

Utilized a variational autoencoder framework for the FuXi-ENS model.
Optimized a loss function combining the continuous ranked probability score (CRPS) and Kullback-Leibler divergence.
Enabled flow-dependent perturbations for improved forecast accuracy.

Main Results:

FuXi-ENS demonstrated superior performance compared to the ECMWF ensemble.
Achieved better results in key forecast metrics, including CRPS and Brier score.
Successfully generated high-resolution global ensemble forecasts with reduced computational demands.

Conclusions:

FuXi-ENS represents a significant advancement in ML-driven ensemble weather forecasting.
The model effectively quantifies forecast uncertainty and improves probabilistic predictions.
FuXi-ENS offers a computationally efficient and accurate alternative to conventional ensemble systems.