Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work

2026-06-23

AI Summary

This work proposes a machine learning–oriented paradigm for weather forecasting that reimagines the traditionally complex and closed operational systems to meet the demands of efficiency, openness, and collaboration in the era of artificial intelligence. By integrating agent-driven software engineering, open compressed data formats, shared validation workflows, interactive computing environments, and generative AI techniques, the framework systematically transforms model development, data utilization, computational management, and service delivery. Designed to equip meteorological and climate centers with future-ready infrastructure, it establishes robust data governance mechanisms, quality assurance protocols, and pathways for workforce skill transformation. The approach maintains scientific rigor while substantially enhancing the accessibility, efficiency, and interactivity of forecasting services.

Abstract

Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from forecast output to the working practices that make prediction systems possible. We argue that machine learning and recent digital technologies will reshape the forecasting value chain: how models are coded and developed, how observations and Earth-system data are exploited, how data and computing are managed, how systems are verified, and how information is created, evaluated and turned into services. We discuss six non-exhaustive areas in which agentic software engineering, open and compressed data, shared verification workflows, interactive computing and generative methods may make modelling, evaluation and service creation faster, more interactive and more widely accessible. These changes will require weather and climate centres to adapt their infrastructures, data stewardship, trust and quality-assurance frameworks, skills and service delivery while maintaining scientific understanding, operational reliability, human expertise and their public-service role.

Problem

• machine learning
• weather forecasting
• digital transformation
• forecasting value chain
• operational infrastructure

Innovation

• agentic software engineering
• open and compressed data
• shared verification workflows
• interactive computing
• generative methods