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Updated: Jan 8, 2026

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

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

Published on: July 22, 2025

Understanding machine learning weather prediction by designing a cost-efficient model with knowledge-oriented

Minjong Cheon^1,2, Jeong-Hwan Kim¹, Yumi Choi¹

¹Climate and Environmental Research Institute, Korea Institute of Science and Technology, Seoul, South Korea.

Scientific Reports

|December 14, 2025

Summary

This summary is machine-generated.

A new deep learning model, KARINA, offers efficient global weather forecasting with competitive accuracy. It utilizes novel modules to improve predictions while reducing computational costs, advancing machine learning in meteorology.

Related Experiment Videos

Last Updated: Jan 8, 2026

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

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

Published on: July 22, 2025

Area of Science:

Meteorology
Artificial Intelligence
Computational Science

Background:

Deep learning models are increasingly used for global weather forecasting, outperforming traditional numerical models.
Training these advanced models requires substantial computational resources and limits understanding of their internal processes and accuracy variations.
The specific contributions of individual components to model performance and predictability remain unclear.

Purpose of the Study:

To introduce KARINA, a novel data-driven model for enhanced weather forecasting with reduced computational demands.
To investigate the effectiveness of Geocyclic Padding and SENet modules within the ConvNeXt backbone.
To provide a framework for understanding machine learning weather model mechanisms and improving their performance.

Main Methods:

Developed KARINA, a data-driven model integrating Geocyclic Padding and SENet modules with a ConvNeXt backbone.
Conducted comprehensive trials to assess the impact of KARINA's modular components.
Compared KARINA's performance against established data-driven models (Pangu-Weather, GraphCast) and numerical weather prediction (ECMWF IFS).

Main Results:

KARINA achieved competitive performance against Pangu-Weather and GraphCast with significantly lower training costs.
KARINA surpassed ECMWF IFS numerical weather prediction accuracy up to a 10-day lead time.
Geocyclic Padding enhanced horizontal advection modeling, while SENet improved the capture of atmospheric convection dynamics.

Conclusions:

KARINA demonstrates that knowledge-oriented techniques can yield reliable and efficient machine learning-based weather prediction.
The modular design facilitates understanding of component contributions, paving the way for future improvements in AI weather forecasting.
This work offers a pathway to deeper insights into model mechanisms and enhances the development of superior machine learning weather prediction systems.