使用机器学习进行概率天气预报
Ilan Price1, Alvaro Sanchez-Gonzalez2, Ferran Alet2
1Google DeepMind, London, UK. pricei@google.com.
Nature
|December 5, 2024
概括
一个新的机器学习天气预测模型GenCast比传统方法更快,更准确地生成概率预测. 这一进步改善了极端天气预测和决策,
科学领域:
- 大气科学
- 人工智能
- 数据科学
背景情况:
- 天气预报传统上依赖于数值天气预报 (NWP),它难以代表预报的不确定性和风险.
- 最近的机器学习天气预测 (MLWP) 模型有希望,但往往缺乏NWP整体预测的准确性.
研究的目的:
- 介绍GenCast,一个新的概率机器天气模型,旨在超越现有的最先进的天气预报.
- 提高中期天气预报的准确性和效率,特别是极端天气预报.
主要方法:
- 基因Cast是一个基于几十年大气再分析数据的ML天气预测模型.
- 它以高分辨率对80多个变量产生大量的随机,15天的全球预测.
- 该模型通过利用先进的机器学习技术来实现快速的概率天气预测.
主要成果:
- 与欧洲中期天气预报中心 (ENS) 相比,GenCast在97.2%的评估目标上表现出卓越的技能.
- 该模型显示了极端天气事件,热带气旋轨迹和风力发电的预测能力.
- 仅用8分钟就能产生15天的全球预报, 比传统方法快得多.
结论:
- 在运营天气预报方面,GenCast代表了显著的进步,提供了更高的准确性,速度和概率洞察力.
- 这种基于ML的方法有助于在依赖天气的行业做出更明智和更有效的决策.
- 这种发展为下一代人工智能驱动的天气预测系统铺平了道路.
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