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.
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.
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.
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.
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.
- 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.
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.
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.
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.
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.
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。