AI Accurately Forecasts Extreme Weather Up to 23 Days Ahead
New research from the University of Washington is refining AI weather models using deep learning for more accurate predictions and longer-term forecasts. The study, published in
Geophysical Research Letters
, shows how adjusting initial atmospheric data enables advanced AI models to extend current forecast limits. As extreme weather becomes increasingly more severe and frequent due to climate change, giving governments, businesses, the public, and emergency responders more time to prepare for natural disasters such as floods, heatwaves, or hurricanes could help reduce loss of life and property.
“If a perfect weather model is given slightly imperfect initial conditions, the error compounds over time and results in an inaccurate forecast,” said lead author Trent Vonich, a PhD candidate at the University of Washington. “This is especially true when modeling a chaotic system such as the Earth’s atmosphere. There has been great focus recently on making better models, while somewhat ignoring the fact that a perfect model is only half the problem. Machine learning models help us address this because they are fully differentiable end-to-end, allowing us to capture nonlinear interactions between inputs and outputs—something legacy techniques cannot do.”
While state-of-the-art AI weather forecasting systems, such as Google’s
GraphCast
and Huawei’s
Pangu-Weather
, reliably predict upcoming weather up to 10 days ahead, they’re limited by the accuracy of the initial data fed into their system.
These models were trained on the massive ERA5 reanalysis dataset containing petabytes of information. The dataset captures hourly temperature, wind speed, humidity, air pressure, precipitation, and cloud cover across a global grid of 37 pressure levels. It includes historical weather conditions, dating back to 1979, and near-real-time data.
The researchers focused on refining the initial atmospheric variables leading up to the June 2021 Pacific Northwest Heat Wave to improve the accuracy of this extreme event. They applied nonlinear optimization using the GPU-accelerated JAX framework to optimize the data.
According to Vonich, it takes only 20 minutes to perform 100 initial conditions updates on an
NVIDIA A100 Tensor Core GPU
.
Figure 1. Comparison of atmospheric patterns and temperature anomalies on June 30, 2021, for ERA5 reanalysis and GraphCast 10-day forecasts using control, globally optimized, and regionally optimized initial conditions from 20 June 2021
The researchers tested their framework’s accuracy using atmospheric data captured during the 2021 Pacific Northwest heatwave, which was excluded from the original training dataset. The optimized data reduced 10-day forecast errors by 90%, successfully predicting the intensity and timing of the heatwave. It also more than doubled the prediction window, improving unoptimized forecasts up to 23 days in advance.
“This research may show that more accurate weather observations and measurements may be just as important as developing better models,” Vonich said. “If this technique can be used to identify systematic biases in the initial conditions, it could have an immediate impact on improving operational forecasts. Plus, more lead time enables greater preparation for communities. Aviation, shipping, and countless other industries rely on accurate weather forecasts, too. Improvements can translate to an economic benefit for them as well.”
Read the full news story on
Eos
.
Catch up on the study
Predictability Limit of the 2021 Pacific Northwest Heatwave From Deep-Learning Sensitivity Analysis
.
AI 提前 23 天实现极端天气高精度预测
华盛顿大学的新研究正在使用深度学习完善 AI 天气模型，以实现更准确的预测和更长期的预测。这项研究发表在
《地球物理研究通讯》
上，展示了调整初始大气数据如何使先进的 AI 模型能够扩展当前的预测极限。随着极端天气因气候变化而变得越来越严重和频繁，让政府、企业、公众和应急响应人员有更多的时间为洪水、热浪或飓风等自然灾害做好准备，有助于减少生命和财产的损失。
华盛顿大学博士生 Trent Vonich 说：“如果为完美的天气模型提供略微不完善的初始条件，误差会随着时间的推移而加剧，并导致预测不准确。”“在对地球大气等混沌系统进行建模时尤其如此。最近人们非常关注制作更好的模型，同时在某种程度上忽略了一个事实，即完美的模型只是问题的一半。机器学习模型帮助我们解决了这个问题，因为它们是完全可微的端到端模型，允许我们捕获输入和输出之间的非线性交互——这是传统技术无法做到的。”
虽然
Google
的
GraphCast
和
华为的 Pangu-Weather
等先进的人工智能天气预报系统能够可靠地预测未来 10 天的天气，但它们受到输入系统的初始数据准确性的限制。
这些模型基于包含 PB 级信息的大型 ERA5 再分析数据集进行训练。该数据集捕获 37 个大气压级别的全球电网中的小时温度、风速、湿度、气压、降水量和云量。它包括可追溯到 1979 年的历史天气条件和近乎实时的数据。
研究人员专注于细化 2021 年 6 月太平洋西北热波之前的初始大气变量，以提高这一极端事件的准确性。他们使用 GPU 加速的 JAX 框架应用非线性优化来优化数据。
Vonich 表示，在 NVIDIA A100 Tensor Core GPU 上执行 100 个初始条件更新只要 20 分钟。
图 1.
2021 年 6 月 30 日大气模式和温度异常的比较，用于 ERA5 再分析和 GraphCast 使用控制、全球优化和区域优化的初始条件从 2021 年 6 月 20 日起进行的 10 天预测。
研究人员使用在 2021 年太平洋西北热浪期间捕获的大气数据（这些数据不包含在原始训练数据集中）测试了他们的框架的准确性。经过优化的数据将 10 天预测误差减少了 90%，成功预测了热浪的强度和时间。它还将预测窗口期提高了一倍以上，使未经优化的预测提前了 23 天得到改善。
“这项研究可能表明，更准确的天气观测和测量可能与开发更好的模型同样重要，”Vonich 说。“如果这项技术可用于识别初始条件中的系统偏差，它可能会对改进运营预测产生直接影响。此外，更多的准备时间可以为社区提供更好的准备。航空、航运和无数其他行业也依赖于准确的天气预报。改进也可以为他们带来经济效益。”
阅读完整的新闻案例
Eos
。
通过深度学习灵敏度分析了解 2021 年太平洋西北热浪的可预测性极限
。