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Weather researchers are using artificial intelligence (AI) systems to improve existing weather prediction methods.
气象研究人员正在使用人工智能（AI）系统来改进现有的天气预报方法。
But, experts say the AI tools currently face limitations and should be used along with traditional prediction methods to be most effective.
但是，专家表示，人工智能工具目前具有局限性，应该与传统的预测方法一起使用，以达到最佳效果。
AI systems trained to predict, or forecast, weather events are now being used by many government agencies and organizations worldwide.
现在，世界各地的许多政府机构和组织都在使用经过训练来预测或预报天气事件的人工智能系统。
Such systems aim to produce weather predictions faster and at a lower cost than traditional forecasting methods.
这样的系统旨在以比传统预报方法更快、成本更低的方式进行天气预报。
One weather predicting system that has shown promise is the Google-financed GraphCast method.
谷歌资助的GraphCast是一个有前景的天气预报系统。
This machine learning-based system trains directly on weather data that has already been collected and examined.
这个基于机器学习的系统直接用已经收集和检查过的天气数据进行训练。
Such methods have demonstrated an ability to outperform traditional forecasting systems.
这些方法已经证明了其能力优于传统的预测系统。
The system works by combining past weather predictions with modern forecasting models to provide the most complete picture of weather and climate.
该系统的工作原理是将过去的天气预报与现代预报模型结合起来，提供最完整的天气和气候情况。
In Europe, the European Center for Medium-Range Weather Forecasts (ECMWF) has been using AI prediction tools since January.
在欧洲，欧洲中期天气预报中心（ECMWF）自1月份以来一直在使用人工智能预测工具。
The organization provides detailed weather forecasts four times per day to nations across Europe.
该组织每天向欧洲各国提供四次详细的天气预报。
The ECMWF technology is called the Artificial Intelligence/Integrated Forecasting System (AIFS).
ECMWF技术称为人工智能/综合预报系统（AIFS）。
The group describes the system as a “data-driven" forecasting model.
该组织称该系统为“数据驱动”的预测模型。
It is designed to make many predictions quickly, including for extreme events involving powerful storms and heatwaves.
它的设计目的是快速做出许多预测，包括强风暴和热浪等极端事件。
AI-supported data from the ECMWF correctly predicted intense rains last month across parts of Europe that resulted in widespread flooding.
ECMWF的人工智能支持数据正确预测了上个月欧洲部分地区导致了大范围洪水的强降雨。
But while the predictions were right, destruction caused by the flooding could not be avoided.
虽然预测正确了，但洪水造成的破坏却无法避免。
Experts told Reuters this is largely because it is still difficult to gather and fully utilize some collected weather data.
专家告诉路透社，这主要是因为收集并充分利用一些收集到的天气数据仍然很困难。
In addition, there is a need to strengthen and improve current AI models used to predict weather.
此外，还需要加强和改进目前用于预测天气的人工智能模型。
Andrew Charlton-Perez is a professor of meteorology – the scientific study of weather processes – at the University of Reading in Britain.
安德鲁·查尔顿-佩雷斯是英国雷丁大学的气象学教授，气象学是研究天气过程的科学。
He told Reuters, "In some cases and for some variables, AI models can beat physics-based models, but in other cases vice versa."
他告诉路透社，“在某些情况下，对于某些变量，人工智能模型比基于物理的模型更好用，但在其他情况下，反之亦然。”
Charlton-Perez said one problem is that the effectiveness of an AI model is based on the information it is given.
查尔顿-佩雷斯说，一个问题是，人工智能模型的有效性是基于它所提供的信息。
Weather disasters can be harder to predict if there is too little data to enter into AI systems.
如果人工智能系统中的数据太少，天气灾害可能更难预测。
This can also be true if extreme events happen repeatedly at different times of the year or in different areas.
如果极端事件在一年中的不同时间或不同地区反复发生，情况也可能如此。
Charlton-Perez said he thinks the best use of AI-based weather forecasts would be to use them in combination with traditional weather predicting tools.
查尔顿-佩雷斯说，他认为基于人工智能的天气预报的最佳用法是将其与传统的天气预报工具结合起来。
This, he noted, could utilize AI data to produce weather predictions based on large sets of information collected from multiple sources.
他指出，这样可以让人工智能数据根据从多个来源收集的大量信息进行天气预测。
Thomas Wostal is with the weather observatory GeoSphere in Austria.
Thomas Wostal就职于奥地利GeoSphere气象观测站。
He told Reuters his group’s models correctly predicted 300 to 400 millimeters of local rains in September.
他告诉路透社，他的团队的模型正确地预测了9月份当地降雨量为300到400毫米。
And records show that same amount actually fell in the storms.
记录显示，风暴带来的实际降雨量与预测数据相同。
But scientists say even in cases where predictions are correct, effective communication is needed to get the information out to communities and local officials so they can effectively prepare.
但是科学家们说，即使在预测正确的情况下，也需要有效的沟通将信息传达给社区和地方官员，以便他们能够有效地做好准备。
Shruti Nath is a research assistant in weather prediction and climate at Britain’s Oxford University.
施卢蒂·纳特是英国牛津大学天气预报和气候研究助理。
She told Reuters, "I think what happened with (the recent floods) ... is that it's so rare - a one in 150- to 200-year event - that even if the weather models capture it, there's a reasonable degree of uncertainty."
她告诉路透社：“我认为（最近的洪水）……它非常罕见——150到200年才出现一次——即便天气模型捕捉到了它，也存在一定程度的不确定性。”
Nath said AI-supported forecasts need to be clearly communicated to the public in a way that warns of the severity and possible destruction of extreme events.
纳特说，人工智能支持的预测需要清晰地传达给观众，警告他们极端事件的严重性和可能造成的破坏。
This way, people might see the importance of taking action before severe weather happens in order to prevent costly cleanup and recovery efforts.
这样，人们可能会意识到在恶劣天气发生之前采取行动的重要性，以防止后期成本高昂的清理和恢复工作。
I’m Bryan Lynn.
布莱恩·林恩报道。
