用传统统计方法提升AI天气模型的预报精度
Statistical post-processing yields accurate probabilistic forecasts from Artificial Intelligence weather models
- 将气象局已有统计后处理系统应用于AI天气模型
- 后处理使AI模型在均值和概率预报上达到与传统模型相当的准确度
- 可融合AI与传统模型，提升整体预报能力，适合气象部门渐进式应用
人工智能（AI）天气模型在部分变量上已达到业务级性能，但与传统数值天气预报（NWP）模型一样存在系统性偏差和可靠性问题。本文测试了澳大利亚气象局现有的统计后处理系统IMPROVER对欧洲中期天气预报中心（ECMWF）确定性AI预报系统（AIFS）的应用效果，并与ECMWF HRES和ENS模型的后处理结果进行对比。在不修改处理流程的情况下，后处理对AIFS的准确度提升效果与传统NWP预报相当，同时在期望值和概率输出上均表现良好。研究显示，将AIFS与NWP模型融合能提升整体预报技能，即使AIFS本身并非最精确的成分。结果表明，为NWP开发的统计后处理方法可直接应用于AI模型，使国家气象中心能够以低风险、渐进方式整合AI预报。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion.
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