AI天气预报的崛起

The Rise of AI Weather Forecasting

尽管人工智能尚无法取代传统天气预报，但它有望成为重要辅助工具，为公众提供更准确的极端天气预警。GenCast是多个AI天气预报模型之一，其目标是提高天气预测的准确性。

Although AI cannot yet replace traditional weather forecasting, it promises to become an important supplementary tool, providing the public with more accurate severe weather warnings. GenCast is one of several AI models aiming to enhance forecast accuracy.

“天气影响着生活的方方面面，同时也是科学界的重大挑战之一，”DeepMind高级研究科学家Ilan Price说。“GenCast是我们致力于利用AI造福人类的重要成果之一。”

“Weather affects every aspect of our lives and is one of the big scientific challenges,” says Ilan Price, a senior research scientist at DeepMind. “GenCast is one important contribution to our mission of advancing AI for the benefit of humanity.”

模型性能超越传统预报 | Performance Outshines Traditional Forecasting

在与ENS的对比测试中，GenCast在97.2%的情况下表现优于ENS。GenCast基于1979年至2018年的历史天气数据进行训练，通过识别模式进行预测，而传统模型如ENS依赖复杂方程和物理模拟。

In comparison tests with ENS, GenCast outperformed the model 97.2% of the time. GenCast was trained on historical weather data from 1979 to 2018, recognizing patterns to make predictions, while traditional models like ENS rely on complex equations and physical simulations.

研究显示，在预测热带气旋路径时，GenCast平均能提前12小时发出预警。此外，GenCast在预测极端天气和风力发电等方面表现更出色，能够提前15天提供更准确的结果。

Studies show that GenCast provided an average of 12 hours of additional warning for tropical cyclone paths. It also excelled in predicting extreme weather and wind power production, delivering more accurate results up to 15 days in advance.

快速高效的AI预测工具 | A Fast and Efficient AI Forecasting Tool

GenCast的速度是其显著优势之一。利用谷歌云TPU v5，GenCast仅需8分钟即可完成一个15天的预测，而传统的物理模型可能需要数小时。

Speed is one of GenCast's notable advantages. Using Google Cloud TPU v5, it takes just 8 minutes to generate a 15-day forecast, compared to several hours for traditional models.

相比之下，GenCast无需复杂方程计算，因此能以更低的计算成本完成预测。这种高效性也可能缓解人们对高能耗AI数据中心环境影响的担忧。

Unlike traditional methods, GenCast bypasses complex equations, allowing for lower computational costs. This efficiency might also address concerns about the environmental impact of energy-intensive AI data centers.

未来的改进空间 | Areas for Future Improvement

尽管GenCast已经取得重要成果，但它仍有改进空间。例如，提升分辨率或缩短预测间隔（目前为12小时）将使其预测更实用。

While GenCast has made significant strides, there is still room for improvement, such as increasing resolution or shortening its forecast intervals (currently every 12 hours).

University of Florida气象学教授Stephen Mullens指出：“实际应用中，人们不仅需要知道6点和18点的风速，还需要了解全天的风速变化。”

Stephen Mullens, a meteorology professor at the University of Florida, points out, “In real-world applications, people need to know wind speeds throughout the day, not just at 6 AM and 6 PM.”

开放合作推动社会影响 | Open Collaboration to Drive Social Impact

为了进一步验证其价值，DeepMind已将GenCast代码开源，供气象学家和实际应用者使用。

To validate its utility, DeepMind has made GenCast’s code open-source, allowing meteorologists and practitioners to use it.

Price表示，希望AI模型与传统模型协同作用，为实际天气预报和社会福利带来深远影响。他相信，一旦这些工具被更多实践者采用，将增强公众的信任和信心。

Price envisions AI models working alongside traditional models to make a profound impact on weather forecasting and societal welfare. He believes widespread adoption will build public trust and confidence.

GenCast的发展标志着天气预报领域的重要里程碑，它不仅展示了AI在科学应用中的潜力，也为应对气候变化和极端天气提供了新的解决方案。

The development of GenCast marks a significant milestone in weather forecasting, showcasing AI’s potential in scientific applications and offering new solutions to address climate change and extreme weather.