在天气预报中应用机器学习

原文发表于 2017年7月21日 ，是由英国气象信息部门（Met Office Informatics Lab, UK）发表的。

Authors list ：Rachel Prudden, Niall Robinson, Alberto Arribas , Charles Ewen

In the 1950s, there was a revolution in weather forecasting. Advances in technology made it possible to simulate the atmosphere using dynamical models, quickly and accurately enough to be used for operational forecasts. Dynamical models are now a central part of weather forecasting. Starting from basic physical laws, they make it possible to predict events such as storms before they have even begun to form.

二十世纪五十年代，天气预报有了革命性的变化。技术进步使我们可以使用模式来模拟大气运动，这种方法在预报业务中是快速而准确的。模式直到现在仍是天气预报的核心。通过基本的物理学原理，模式可以在暴风雨形成之前便做出预测。

A crucial challenge in the coming decade will be the integration of direct physical simulations on the one hand, and data-driven approaches on the other. Such a hybrid approach holds many opportunities for weather forecasting, as well as countless other fields.

未来十年的一个关键挑战将是直接物理模拟与数据驱动方式融合应用。这种混合方式为天气预报以及无数其他领域带来许多机会（可能性）。

From model to outcomes 从模式到结果

• Localisation and super-resolution (downscaling) 局地和超高分辨率（降尺度）
• Links to the real world 与其他领域结合

Operational weather models are usually run at a resolution of between 1km and 10km, that is, everything within the same square kilometer is represented by a single grid cell. This resolution is fine enough to capture a wide range of phenomena, but will obviously be unable to capture very localised details.

目前业务运行的天气模式的空间分辨率在1公里和10公里之间，这意味着在这个分辨率网格内只有一个值。这个分辨率对于一个大尺度的天气现象是够用的，但是对于一些局地性的天气却是不够的。

It may be possible to perform this kind of localisation using models trained on historical data, providing a mapping between the large-scale predictions of the simulation and the small-scale effects. This is an area of active research which could make forecasts more useful for day-to-day activities.

可以尝试使用历史数据训练的模型（机器学习的方法）来预测局地效应，之后建立一个大尺度模型预测与小规模效应之间的映射关系。此类研究现在非常活跃，有助于提升天气预测对日常活动的价值。

As well as predicting weather at finer scales, similar techniques could help to link weather forecasts with their broader impacts. Many things are affected by the weather, either directly or indirectly; these include traffic, hayfever, flight delays, and hospital admissions. While some effects may not be easy to simulate, using data-driven models could help to provide advance warning of significant impacts.

除了在更细微的尺度上预测天气，类似的技术可以帮助将天气预报与更广泛的领域联系起来。许多事情直接或间接地受到天气的影响，包括交通、花粉过敏、飞行延误和住院率，这些事情不容易通过模型来推理，但可以使用数据驱动的模型来预测进而提供预警。

Emulation

• Faster components (emulation) 局部加速
• Hybrid models 混合模式

Once a machine learning model has been trained, it is often much faster to run than a full simulation. This is the motivation for a technique called model emulation. The idea is to build a fast statistical model which closely approximates a far more expensive simulation. Emulators are already being applied to problems such as climate sensitivity. An area of current interest is using the same tools to speed up some components of the weather model.

机器学习模型一旦被建立，通常是要比完整的数值模拟工程要快。可以使用一种模式仿真（model emulation）的方法，建立一个非常接近于数值模式的统计学模型，这种方法已经应用于气候敏感性研究。现在比较热的领域是使用机器学习工具加速天气模式的部分 组件。

There are some aspects of weather prediction which require a full physical simulation; this is what lets you predict unseen events with confidence. Other places this is not possible or even justified, and a statistical approximation may be the best you can do. This second case is where emulation can be useful in operational forecasting.

天气预测中的一些场景是需要通过大气物理模式来实现，但有些场景使用模式却是不可能或不合理的，这些场景下使用统计学趋近是最好的选择，模式仿真（model emulation）在预报业务中会有效果。

Beyond emulators, there is broader potential for hybrid models with both learned and simulated components. Such models would combine data-driven and physically-driven approaches. For example, it may be possible to adapt statistical components of the model to the local terrain, based on previous observations.

除了模式仿真（model emulation），建立融合机器学习与数值模拟的混合模式也是非常有潜力的。这种混合模型可以融合数据驱动和物理驱动两种方法。比如，在局地地形对天气影响方面，可以基于前期观测的结果训练模型，融合到数值模式中。

Descriptive learning 描述学习

• Finding features 特征识别
• Exploring and summarising 信息汇总

An area where machine learning has made dramatic progress is feature detection. You can see examples of this in apps which not only detect your face, but add glasses and a moustache in real-time.

机器学习取得了显着进步的一个领域是特征检测。一些基于机器学习的应用程序不仅可以检测到您的脸部，还可以实时在脸上添加眼镜和胡子。

There is currently a lot of interest in applying similar methods to hazard detection, especially to storm tracking. Trained experts are able to recognise storms and trace their paths from weather imagery; in principle there is no reason an algorithm could not learn to do the same.

目前有很多研究在使用类似的方法做灾害监测，特别是风暴跟踪。训练有素的专家能够识别风暴，并从天气图像中追踪路径，理论上算法也可以做得到。

Another application could address the challenges posed by data volume and complexity when dealing with data from physical simulations. The fields output by such models are highly multidimensional; making sense of them is a complex task, requiring many “screens” of information. An algorithm which could summarise the salient features and bring them to the forecaster’s attention would help streamline this task.

预报员在使用观测数据和数值预报结果时，需要处理大量的多维度的数据，理解这些数据是一项复杂的工作，经常需要切换多个屏幕来查阅信息。通过算法可以自动识别这些数据中的关键信息，然后汇总到预报员的桌面，从而简化这项工作。

Summary 总结

Exploring combinations of machine learning and numerical simulation is an area of great interest and promise for the Met Office. Not only does it offer an advance in scientific capability, but the challenges arising from the attempt could drive new research in the field of machine learning. This article has given an outline of a few research directions within meteorology, but a similar story holds across a range of scientific disciplines.

探索机器学习和数值模拟的组合是 Met Office 非常感兴趣且抱有期望的领域。它不仅促进了预报能力的进步，而且可能会推动机器学习领域的新研究。本文概述了气象学中的一些研究方向，在其他科学学科中，机器学习的应用的方向与本文所述类似。