模型狂熱：ECMWF 和 GFS 模型是什么，它們為什么不同？

來源https://blog.weather.us/model-mania-what-are-the-ecmwf-and-gfs-models-and-why-are-they-different/

由杰克Sillin/2019年12月18日/沒有評論

這篇文章是我們的Model Mania系列文章的第三篇，希望為那些沒有嚴格大氣科學背景的人簡要介紹天氣模型。在前兩篇文章中，我解釋了天氣模型究竟是什么/它們如何工作以及區域和全球天氣模型之間的區別。這篇文章將深入探討兩個最著名的天氣模型，GFS 和 ECMWF。這些都是全球模型，這意味著它們都試圖服務于相同的目的（提前 3-10 天預測大規模天氣模式），但它們有何不同以及為何不同？

GFS 是美國政府在國家海洋和大氣管理局 (NOAA) 及其附屬機構的領導下運行的全球模型。GFS 模型由美國納稅人資助，這意味著任何想要它的人都可以免費獲得它的預測輸出。如果您想從 GFS 模型中獲得新鮮的原始輸出數據，您可以從 NOAA 的網站上免費下載。雖然原始數據是免費提供的，但使這些數據對最終用戶有用需要大量的后處理，其中原始數據被“按摩”成比模型生成的 1 和 0 字符串更容易被人類識別的格式本身。您在weather.us和weathermodels.com 上看到的地圖和圖表使用我們的氣象學家和程序員開發的后處理算法生成。

ECMWF 也是一個全球模式，但它不是由美國政府管理，而是由一個獨立的政府間實體管理，并得到 34 個歐洲國家的支持。請注意，這里的命名法有點混亂，但 ECMWF 代表歐洲中期天氣預報中心，是組織和模型的名稱。ECMWF 該組織的成立方式意味著他們可以并且確實為他們的預測數據收費，盡管國際法規定其中一些（歸類為 WMO-Essential）可用于公共利益。如果您訪問大多數免費天氣模型站點，您將看到的唯一 ECMWF 數據是 WMO-Essential，它僅包含少量分辨率非常低的參數。在撰寫本文時，除了我們的網站之外，我們還沒有發現任何網站可以讓您免費查看全分辨率 ECMWF 數據。然后應用您的后處理算法使其可用于人類。或者，您可以讓我們為您處理，并在weather.us或weathermodels.com 上查看最新的 ECMWF 輸出。

既然您知道每個模型的負責人以及對其信息的訪問有何不同，您可能想知道他們的預測有何不同。上圖是2019年12月14日晚美國ECMWF（左）和GFS（右）六天預報的對比，目前還不知道哪個模型預報更準確（我會在一分鐘內討論更多關于 GFS 和 ECMWF 模型的準確性），但立即可以看到一些關鍵差異。

首先，您會注意到 GFS 模型降水（陰影）場（在此上下文中的場是指網格數據集，或者在網格上的許多不同點有一個值的數據集）比 ECMWF 降水場更加像素化。這是因為 GFS 的運行分辨率低于 ECMWF 模型，這意味著它的網格點相距更遠（GFS 模型中每 13 公里放置一個網格點，而 ECMWF 模型中每 9 公里放置一個網格點）。正如您在本系列的前一期文章中所回憶的那樣，較低的分辨率通常意味著預測的準確性較低，因為模型不知道有更多的大氣和地形特征。

您還會注意到當時美國東部風暴系統預報的位置和結構存在重大差異。這些差異是由于分辨率差異（如上所述）、數據同化差異（告訴模型現在大氣中正在發生什么的過程）以及每個模型用來轉換其給定的控制方程組的差異的組合將初始條件轉化為預測。最后一句話中的術語聽起來很陌生嗎？查看本系列的第一篇博文，了解初始條件、控制方程以及運行天氣模型的更多基礎知識。

對每個模型采用的不同控制方程（有時稱為物理包）的詳細解釋需要大量的高級數學，所以我會給感興趣的讀者留下幾個鏈接，這些鏈接指向最新的詳細文檔（如of 12/9/19)ECMWF和GFS模型計算每個網格點的預測。詳細解釋每個模型的數據同化過程需要類似的數學，因此如果您希望將線性代數知識付諸實踐，我建議您閱讀 GFS 模型用于數據同化的技術（Ensemble卡爾曼濾波）。對于那些有興趣深入研究數據同化但不想處理線性代數或偏微分方程的人，ECMWF 開發了一個優秀的短期課程（約 1 小時），解釋了他們的（更高級的）數據如何同化過程無需深入數學即可工作。

那么一般來說哪個模型更準確呢？

從統計上講，非常明確的答案是 ECMWF 始終比 GFS 表現更好，如上面的模型技能得分圖所示。自 2007 年以來（并且可能在此之前的一段時間內），GFS 從未對北半球 20 到 80N 之間的 5 天預測通常比 ECMWF 更準確。話雖如此，在很多情況下，對于特定風暴，GFS 比 ECMWF 更準確。例如，GFS 早在 ECMWF 之前就預測了熱帶風暴多里安的形成。也許一個更著名的例子是 2015 年 1 月 27 日的暴風雪，ECMWF 預測紐約市將有超過 2 英尺的雪，這將使這座城市完全停頓。另一方面，GFS 預測不到一英尺，這將是破壞性的，但肯定不會造成癱瘓。

盡管 GFS 偶爾會“失利”，但 ECMWF 仍然是中期天氣預報領域的一貫領導者，但為什么呢？

這個故事的第一部分是ECMWF組織有一個很大的責任范圍較窄比諾阿一樣。根據他們各自的網站，這里是每個組織的使命的快速比較。

請注意，ECMWF 的 2/3 目標與生成準確的天氣預報或進行研究有關，以生成更準確的天氣預報。雖然天氣預報是 NOAA 使命中至關重要的一部分，但這只是其整體職責中相對較小的一部分，其中包括更廣泛的環境問題。

承認這兩個機構都非常重要，并且以相對較小的成本為納稅人提供了非凡的社會價值，承認這一點非常重要。我解釋 NOAA 和 ECMWF 之間的組織差異的目的不是爭辯說一個比另一個更好，或者 NOAA 應該更像 ECMWF，反之亦然。話雖如此，如果您的目標是準確的天氣預報，則有助于將整個組織的大部分精力投入到該特定目標上。這并不是說歐洲不致力于預測氣候、海洋和海岸的變化，也不致力于保護和管理其沿海和海洋生態系統，但這些任務并未留給 ECMWF，這使其能夠更加專注于提高他們天氣預報的準確性。

也許此時您會認為 NOAA 和 ECMWF 之間的組織比較對美國不公平，正是因為 NOAA 負責的職責范圍如此廣泛。然而，即使在完全致力于天氣預報的 NOAA 子機構（國家環境預測中心，NCEP）的子機構（環境建模中心，EMC）內，也出現了類似的模式。雖然 ECMWF 運行一個全球模型和 51 個集合成員（更多關于我們集合系列中的成員），EMC 維護著一套綜合模型，范圍從 GFS 全局模型到 HRRR（高分辨率快速刷新）區域模型再到 HWRF （颶風天氣研究和預測）特定于颶風的區域模型。結果，資源池（時間、金錢、計算能力、

雖然這可能看起來效率低下，但請考慮這些其他模型提供的價值。HRRR 在預測大雪帶和雷暴群等小尺度特征何時何地形成以及它們的強度方面表現出相當大的技巧。此外，HRRR 每小時運行一次，這意味著與我們必須每六個小時等待一次全局模型運行相比，預測人員可以更頻繁地根據新信息重新評估他們的預測。說到颶風，ECMWF 和 GFS 模型在預測颶風或熱帶風暴可能去哪里方面做得相當好，但在確定強度方面卻出了名的糟糕這將是。HWRF 模型通過提供更準確的強度預測來幫助填補這一空白，這對于做出關于可能需要在風暴前疏散海岸線的哪些部分至關重要的決策。

雖然美國政府天氣預報系統支持的區域模型套件限制了我們全球模型 GFS 的準確性，但它可以更全面地了解我們的大氣層，而 ECMWF 并非旨在提供。當然，在美國進行天氣預報的真正雙贏將是為美國的天氣建模留出足夠的時間、金錢和計算能力，這樣我們就可以擁有一個與 ECMWF 技能相匹配的全球模型，并且一整套專業/區域模型，但您必須與您的國會代表討論。

雖然這篇文章篇幅較長且文字較多，但我希望它有助于您理解 GFS 和 ECMWF 模型是什么、它們有何不同，以及為什么 ECMWF通常比 GFS 更熟練。在下一篇Model Mania 博文中，我將解釋為什么需要這兩種模型，盡管 GFS 在性能方面始終落后于 ECMWF。

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什么是天氣模型？

數值天氣預報

天氣模型，正式稱為“數值天氣預報”，是現代天氣預報的核心。您在weather.us 上看到的所有預報信息均由天氣模型提供支持，它們是什么以及它們如何工作？

天氣模型是對大氣未來狀態的模擬。數以百萬計的觀測被用作數萬億次計算的初始條件，生成了未來某個時間大氣可能是什么樣子的三維圖。大型計算機被用來以令人難以置信的速度進行這些計算，使模擬能夠覆蓋整個地球，并延長至未來兩周。

全球與區域模型

有兩種一般類型的天氣模型，全球模型和區域模型。全球模型為整個全球生成預測輸出，通常會延長一到兩周的未來。由于這些模型覆蓋的區域更廣，時間跨度更長，因此它們通常以較低的分辨率運行，無論是在空間上（每個給定區域的預測點較少）還是時間上（獲得預測的時間點較少）。

另一方面，區域模型具有更高的分辨率，但僅覆蓋全球的某些部分（區域），并且只能提供幾天的預報。這些模型的優勢在于其更高的分辨率讓他們能夠“看到”全局模型所遺漏的特征，尤其是雷暴。

為什么有這么多型號，它們有什么不同？

許多不同的國家氣象中心都有運行天氣模型的超級計算機。每一個都略有不同，使用不同的方程來解決塑造我們天氣模式的各種物理過程。它們中的許多還具有略有不同的分辨率，并使用略有不同的初始數據源組合。

這些細微的差異隨著時間的推移而倍增，因為大氣是一個混亂的系統。這也意味著模型在短期內產生的任何錯誤都會隨著時間呈指數增長。這就是為什么從現在起一周的預測遠不如明天的預測準確的原因。

天氣建模中心試圖通過運行每個使用略有不同的初始條件的集合系統來控制混沌的影響。每個集合“成員”然后產生一個預測，就好像它的初始條件集是正確的一樣。這提供了某種量化給定預測結果的可能性的方法，有助于顯示預測的不確定性。

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Model Mania: What Are The ECMWF and GFS Models, and Why Are They Different?

byJack Sillin/12/18/2019/ No Comments

Hello everyone!

This post is the third in ourModel Maniaseries which hopes to give a brief introduction to weather models for those without a rigorous atmospheric science background. In the previous two posts, I explainedwhat exactly weather models are/how they work andthe difference between regional and global weather models. This post will take a deeper dive into the two most famous weather models, the GFS and ECMWF. These are both global models, which means they’re both trying to serve the same purpose (predict large scale weather patterns 3-10 days in advance), but how and why are they different?

The GFS is the global model run by the US Government under the leadership of the National Oceanic and Atmospheric Administration (NOAA) and its subsidiary agencies. The GFS model is funded by American taxpayers, which means its forecast output is freely available to anyone who wants it. If you want the raw output data fresh from the GFS model, you candownload it for free off NOAA’s website. While the raw data is available for free, making that data useful to end users requires a substantial amount ofpost-processingwhere the raw data is ‘massaged’ into a format more recognizable to humans than the strings of 1’s and 0’s produced by the model itself. The maps and graphs you see atweather.usandweathermodels.comare produced using post-processing algorithms our meteorologists and programmers have developed.

The ECMWF is also a global model, but instead of being run by the US Government, it is run by an independent intergovernmental entity supported by 34 European nations. Note that the nomenclature here isa bit confusing, but ECMWF stands for the European Center for Medium-Range Weather Forecasts and is the name of both the organization and the model. The way ECMWF the organization is set up means that they can and do charge for their forecast data, though international law dictates that some of it (classified as WMO-Essential) is available for the public good. If you go to most free weather model sites, the only ECMWF data you’ll see is WMO-Essential, which only includes a small handful of parameters at a very low resolution. As of this writing, we are not aware of any site besides ours that lets you view full-resolution ECMWF data for free. If you want the raw forecast output from the ECMWF, you’ll have to pay for it andthenapply your post-processing algorithms to make it usable for humans. Alternatively, you could let us take care of that for you and view the latest ECMWF output atweather.usorweathermodels.com.

Now that you know who’s in charge of each model and how access to their information differs, you’re probably wondering how their forecasts are different. The above graphic shows a comparison of the ECMWF (left) and GFS (right) six-day forecasts for the US on the evening of December 14th, 2019. I don’t yet know which model forecast will be more accurate (I’ll discuss more about the accuracy of the GFS and ECMWF models in a minute), but immediately a few key differences are visible.

First, you’ll notice the GFS model precipitation (shaded) field (fieldin this context means gridded dataset, or a dataset for which there is a value at many different points on a grid) is much more pixelated than the ECMWF precipitation field. This is because the GFS is run at a lowerresolutionthan the ECMWF model, meaning it has grid points located farther apart (one is placed every 13km in the GFS model compared to every 9km in the ECMWF model). As you’ll recall from the previous installment of this series, a lower resolution generally means a less accurate forecast, as there are more atmospheric and topographic features the model is unaware of.

You’ll also notice substantial differences in the placement and structure of the storm system forecast over the Eastern US at the time in question. These differences are due to a combination of resolution differences (mentioned above), differences indata assimilation(the process of telling the model what’s happening in the atmosphere right now), and differences in the sets of governing equations each model uses to turn its given initial conditions into a forecast. Did the terms in that last sentence sound unfamiliar? Check out the first post of this series to learn about initial conditions, governing equations, and more of the basics of running a weather model.

A detailed explanation of the different governing equations (sometimes referred to asphysics packages) employed by each model would require a substantial amount of advanced math, so I’ll leave the interested reader with a couple links to detailed documentation of how the latest (as of 12/9/19)ECMWFandGFSmodels calculate their forecasts at each gridpoint. A detailed effort to explain each model’s data assimilation process would require similar mathematics, so if you’re looking to put your knowledge of linear algebra to practical use, I’ll suggest reading up on the technique the GFS model uses for data assimilation (Ensemble Kalman Filtering). For those who are interested in a deeper dive into data assimilation but don’t want to deal with linear algebra or partial differential equations, theECMWF has developed an excellent short course(~1 hour) that explains how their (much more advanced) data assimilation process works without getting too deep into the math.

So which model isgenerally speakingmore accurate?

Statistically speaking, the very clear answer is that the ECMWF consistently performs better than the GFS, as the model skill score graph above shows. At no point since 2007 (and likely for a while before then) has the GFS produced an generally more accurate 5-day forecast for the Northern Hemisphere between 20 and 80N than the ECMWF. That being said, there have been many cases where the GFS has been more accurate than the ECMWF for specific storms. For example, the GFS predicted the formation of Tropical Storm Dorian long before the ECMWF did. Perhaps a more famous example was the snowstorm of January 27th, 2015 where the ECMWF forecasted over two feet of snow for New York City which would have brought the city to an absolute standstill. The GFS on the other hand predicted less than a foot, which would be disruptive but certainly not crippling. Central Park ended up recording 9.8″ of snow.

Despite the occasional ‘loss’ to the GFS, the ECMWF remains the consistent leader in medium-range weather prediction, but why?

The first part of this story is that ECMWF the organization has amuchnarrower range of responsibilities than NOAA does. According to their respective websites, here is a quick comparison of the mission each organization has.

Notice that 2/3 of ECMWF’s objectives relate to either producing accurate weather forecasts or conducting research for the purpose of producing more accurate weather forecasts. While weather forecasts are a crucially important part of NOAA’s mission, it is only one relatively small part of their overall responsibilities which includes a much broader range of environmental concerns.

It is extremely important to acknowledge thatboththese institutions are tremendously important, and provide extraordinary societal value for the relatively small costs they impart on taxpayers. My goal in explaining the organizational differences between NOAA and ECMWF is not to argue that one is better than the other, or that NOAA should be more like ECMWF or vice versa. That being said, if your goal is accurate weather forecasts, it helps to devote most of your entire organization to that specific aim. That’s not to say that Europe isn’t working on predicting changes in climate, oceans, and coasts, or working to conserve and manage its coastal and marine ecosystems, but those tasks are not left to ECMWF, which enables it to focus more intently on improving the accuracy of their weather forecasts.

Perhaps at this point you’re thinking that the organizational comparison between NOAA and ECMWF is unfair to the US precisely because NOAA is charged with such a broad range of responsibilities. However, even within the sub-agency (the Environmental Modeling Center, EMC) of the sub-agency (the National Centers for Environmental Prediction, NCEP) of NOAA devoted entirely to weather prediction, a similar pattern emerges. While the ECMWF runs one global model and 51 ensemble members (more on those to come in our ensemble series), EMC maintains a comprehensive suite of models ranging from the GFS global model to the HRRR (High Resolution Rapid Refresh) regional model to the HWRF (Hurricane Weather Research and Forecasting) hurricane-specific regional model. As a result, the pool of resources (time, money, computational power, and personnel) the US has dedicated to numerical weather prediction is split among a wide range of models, each with a specific purpose.

While this might seem inefficient, consider the value provided by these other models. The HRRR has demonstrated considerable skill in predicting when and where small-scale features like heavy snow bands and thunderstorm clusters will form, and how intense they’ll be. Additionally, the HRRR is run every hour which means that forecasters can re-evaluate their predictions based on new information much more frequently than if we had to wait for a run of the global models every six hours. When it comes to hurricanes, the ECMWF and GFS models do a fairly good job predictingwherea hurricane or tropical storm might go, but are notoriously bad at figuring outhow strongit will be. The HWRF model helps to fill in that gap by providing more accurate intensity forecasts that are critically important to making decisions about which parts of the coastline might need to be evacuated ahead of a storm.

While the suite of regional models supported by the US Government’s weather prediction system has limited the accuracy of our global model, the GFS, it enables a much more comprehensive view of our atmosphere that ECMWF isn’t designed to provide. Of course, the real win-win-win for weather prediction here in the US would be to have enough time, money, and computational power set aside to weather modelling in the US so we can have both a global model that matches ECMWF’s skillanda full suite of specialized/regional models, but you’ll have to take that up with your congressional representatives.

While this post was on the longer and text-heavier side, I hope it has been helpful to your understanding of what the GFS and ECMWF models are, how they’re different, and why the ECMWF isgenerallymore skillful than the GFS. In the nextModel Maniapost, I’ll explain why both models are needed despite the fact that the GFS consistently lags behind the ECMWF in terms of performance.

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What are weather models?

Numerical Weather Prediction

Weather models, known formally as "Numerical Weather Prediction" are at the core of modern weather forecasts. All the forecast information you see at weather.us is powered by weather models, do what are they and how do they work?

Weather models are simulations of the future state of the atmosphere out through time. Millions of observations are used as initial conditions in trillions of calculations, producing a three dimensional picture of what the atmosphere might look like at some time in the future. Massive computers are used to do these calculations at incredibly fast speeds to enable simulations to cover the entire globe, and extend up to two weeks into the future.

Global vs Regional models

There are two general types of weather models, global models and regional models. Global models produce forecast output for the whole globe, generally extending a week or two into the future. Because these models cover a wider area, and a longer timespan, they’re generally run at a lower resolution, both spatially (fewer forecast points per given area) and temporally (fewer time points get a forecast).

Regional models on the other hand have much higher resolutions, but only cover some part (region) of the globe, and only provide forecasts a couple days out in time. The advantage with these models is that their higher resolution lets them "see" features that the global models miss, most notably including thunderstorms.

Why are there so many models and how are they different?

Many different national weather centers have supercomputers that run weather models. Each of these is slightly different, using different equations to solve for various physical processes that shape our weather patterns. Many of them also have slightly different resolutions, and use slightly different combinations of initial data sources.

These slight differences multiply out through time because the atmosphere is a chaotic system. This also means any errors that the models make in the near term become exponentially larger with time. This is why the forecast for a week from now is far less accurate than the forecast for tomorrow.

Weather modelling centers attempt to control for the influence of chaos by running ensemble systems that each use slightly different initial conditions. Each ensemble "member" then produces a forecast as if its set of initial conditions were correct. This provides some way of quantifying how likely a given forecast outcome is, helping to show forecast uncertainty.

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總結

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