天气预报算法多样
目前，英国气象局的预测引发了一场巨大的“风暴”。他们预测冬季将是温和、潮湿的冬季，而北爱尔兰的气温却是有记录以来最冷的，地面上有厚厚的积雪，这在 12 月通常很少见。
这是我很想尝试的东西，并不是我声称我可以击败他们，而是想知道人们目前正在使用哪些算法？他们基于什么数据集？
可能性大概包括神经网络建模输入，适应度是预测的准确性，复杂的数学模型，甚至是“与昨天相同”的预测，我听说（尽管没有看到证据）它对于单日预测更可靠（尽管在那之后显然会下降）。
理想情况下，希望听到气象中心的一些开发人员或可以使用超级计算机的开发人员的意见，听到方法会很有趣......
Currently there's a big 'storm' over the predictions by the MetOffice in the UK. They predicted a mild, wet winter, while we have the coldest temperature on record in Northern Ireland and solid snow on the ground, normally rare in December.
It's something I'd love to have a play with, not that I'm claiming I can beat them, but was wondering what algorithms are out there currently that people are working with? What datasets do they base it on?
Possibilities presumably include neural networks modelling input with fitness being the accuracy of the prediction, complex mathematical models, or even the 'same as yesterday' prediction which I've heard claim (although not seen evidence) that it's more reliable for single-day prediction (although obviously drops off after that).
Ideally like to hear from some developers in weather centres or who get access to the supercomputers, it'd be interesting to hear approaches...
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简而言之，如果您打算构建并运行自己的预报模型，您将面临三个主要问题：
获取观测数据
据我所知，获取良好的气象观测数据花费很多钱。
您需要从全球各地进行观测，并对整个地球的海洋和大气状况进行建模。或者，您需要从计算全局模型的人那里获得所谓的横向边界条件。
数学模型的开发
我不是，也从未隶属于英国气象局，但几年前，我曾将他们的统一模型的一个版本移植并优化到我们中心的一台超级计算机上。这是我记忆模型的方式。
英国气象局最近一直在开发统一模型 20 多年来，我们谈论了数百万行代码，其中包含最先进的海洋/大气模型和数值算法。请查看（过时的）用户指南的本节一睹风采他们的模型中使用的科学方法。这是一大群聪明人半个世纪以来资金充足、广泛研究的成果。如果有一个简单的解决方案能够始终比复杂模型提供更好的结果，那么现在可能已经有人实施它了。
总而言之，我想通过从头开始构建模型来获得天气预报方面的结果是非常困难的，除非你是大气物理学硕士/博士并且你有几年的空闲时间。
运行模型的计算能力
第一个预测模型是在 20 世纪中叶在无法与当今手机匹配的机器上运行的，因此，从技术上讲，您可以在 PC 上计算某些东西。然而，这种类型的工作通常是在非常非常强大的机器上完成的。事实上，Top500 中的 10 个系统专门致力于天气预报和气候研究。
有趣的阅读
更新可以获取免费提供WRF模型的源代码，以及一些气象数据。请注意，WRF、统一模型、COAMPS 和许多其他模型主要用 Fortran 编写。
In short, if you intend to build and run your own forecasting model, you will face three major problems:
Access to observation
As far as I know, access to good meteorological observations costs a lot of money.
You need to have observations from all over the globe and model the state of oceans and atmosphere for the whole planet. Alternatively, you need to obtain so-called lateral boundary conditions from someone who calculates a global model.
Development of a mathematical model
I'm not and I've never been affiliated with Met Office, but I used to port and optimize a version of their Unified Model to a supercomputer at our center a couple of years ago. Here's how I remember the model.
Met Office has been developing their Unified Model for the last 20+ years, we're talking about millions of lines of code that contain state of the art ocean/atmospheric models and numerical algorithms. Check out this section of (outdated) User Guide for a glimpse of scientific methods used in their model. It's a fruit of, give or take, half a century of well-funded, extensive research by a large community of smart people. If there was a simple solution that would consistently give better results than the complex models, someone would've probably implemented it by now.
To conclude, I guess it's very hard to get even remotely satisfactory results in weather forecasting by building a model from scratch, unless you're a MSc/PhD in atmospheric physics and you've got a couple of years of free time on your hands.
Computational power to run your model
The first forecasting models were run in the middle of 20th century on machines that cannot match with today's cellphones, so, technically, you could calculate something on your PC. However, this type of job is often done on very, very powerful machines. In fact, 10 systems in the Top500 are dedicated solely to weather forecasting and climate research.
Interesting reads
UPDATE It's possible to obtain the source code of the WRF model for free, together with some met data. Note that WRF, Unified Model, COAMPS, and many other models are written primarily in Fortran.
首先，您可以从 http://tgftp.nws.noaa.gov 和其他天气导入原始数据数据。计算机理解数据的最佳方法是将其放在地图上。地图上的每个点都会相互反应。每个点的数据可以代表温度、气压、风向和风向、云量、太阳在天空中的位置、能见度、最近 100 小时的降水量。您可以进行预测，然后将其与实际预测以及气象服务的预测进行比较。然后更新该数据点的气候模型。这样，它就可以成为一个自学习的神经网络。就计算能力而言，获得泰坦巨无霸！
First off, you can import raw data from http://tgftp.nws.noaa.gov and other weather data. The best way for the computer to understand the data is putting it on a map. Each point on the map reacts with each other. Data at each point can represent Temp, Pressure, Wind and Direction, Cloud Coverage, Where sun is in the sky, Visibility, last 100hrs of precipitation. You could make predictions, then compare them later to the actual predictions as well as the Weather Service's predictions. Then update a climate model for that data point. That way, it could be a self learning neural network. As far as computation power is concerned, Get a Titan, Big Mac!
似乎可以构建简单的预测模型。我的手表有气压计和温度计（根本无法使用，因为手表是用手加热的）。仅凭这些测量结果，它就多次警告我即将下雨，尽管互联网网站预报晴朗。 （左上角云图）
快速搜索引导我们找到Sager 算法，仅使用非常简单的输入数据。然而，虽然该实现声称是开源的，但我未能找到有关该算法的代码和科学论文。
It seems to be possible to construct simple forecast model. My watch features a barometer and a thermometer (which is not usable at all, because the watch is warmed by the hand). Solely on those measurements, it has several times warned me of incoming rain, in spite of sunny forecasts from internet sites. (the cloud picture at upper left corner)
A quick search leads us to the Sager Algorithm, which uses only very simple input data. However, while the implementation claims to be open-source, I have failed to locate both the code and scientific papers on the algorithm.