2025 WSC Weekly22期：為什麽天氣預報從不提前播報下個月的天氣?

WSC Weekly2025世界學者杯the World Scholar's Cup

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2025年度主題：重燃未來

Reigniting the Future

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鎖定每周WSC Weekly

在2025年世界學者杯第21期WSC Weekly欄目中，我們(men) 與(yu) 小學者一起了解了什麽(me) 是擬社交關(guan) 係。在上期的趣味Quiz中，你是否找到了正確答案？現在就讓我們(men) 一起來揭曉吧！

為(wei) 什麽(me) 越來越多的人沉迷聽播客？How Podcasts Became So Popular?

第21期Quiz答案揭曉：

Which of the following scenarioses can be BEST described as parasocial interaction? 以下哪種情況最適合被描述為(wei) 擬社交互動？

A. Jerry felt sad when his favorable figure in a novel died 傑瑞為(wei) 小說中他喜歡的人物的去世感到悲傷(shang)

B. Jerry chats with ChatGPT about his troubles and gets emotional comfort 傑瑞與(yu) ChatGPT 聊他的煩惱，並得到情感上的安慰

C. Jerry joined a online debate club to debate with strangers 傑瑞參加了一個(ge) 在線辯論俱樂(le) 部並與(yu) 陌生人辯論

D. Jerry often buys items on the online store of a famous influencer 傑瑞經常在知名網紅的網上商店購買(mai) 商品

E. Jerry enjoys “talking” with his dog 傑瑞喜歡和他的寵物狗聊天

正確答案：A

Key: A

2025年第22期

Weekly Intro

“30天後你所在的城市將迎來陰天間多雲(yun) 天氣，偶有分散陣雨，早晚有（輕)霧，氣溫22-27℃ ”

如果有人如此準確地告知您一個(ge) 月後的天氣預測，這是否可信呢....本期Weekly將基於(yu) 大氣混沌理論解讀天氣預測的數據模型以及其局限性，一起來看看吧！

2025 No.22

為(wei) 什麽(me) 天氣預報從(cong) 不提前播報下個(ge) 月的天氣?

Why Weather Forecasts Can’t Predict the Weather Months in Advance

想象一下，如果有人能夠告訴你距離今日整整兩(liang) 個(ge) 月後的某天你所在的城市將下多少毫米的雨，或者精確預報出三月第二個(ge) 星期四的氣溫是多少，聽起來是不是很厲害？但根據科學原理，這是不可能實現的。無論我們(men) 的超級計算機多麽(me) 強大，模型多麽(me) 先進，大氣的混沌性質都設定了一個(ge) 無法突破的硬性上限：我們(men) 無法無限期地準確預測未來的天氣。

Imagine today someone telling you exactly how much it will rain in your city on New Year’s Day two months from now. Or giving you the precise temperature for the second Thursday of March. It might sound impressive, but according to science, it’s not possible. And no matter how powerful our supercomputers or how advanced our models become,the chaotic nature of Earth’s atmosphere sets a hard limit on how far into the future we can predict the weather accurately.

天氣預測的數學模型

要理解長期天氣預報的局限性，我們(men) 需要了解天氣預報是如何進行的。氣象學家使用複雜的數學模型來模擬地球大氣係統。這些模型遵循物理定律，例如運動、溫度、氣壓和濕度，以預測氣團和天氣係統的變化。整個(ge) 地球被劃分成一個(ge) 巨大的三維網格係統，每一個(ge) 網格單元代表地表或大氣中的一個(ge) 特定區域。天氣模型通過逐步計算這些網格單元中條件隨時間的變化，來模擬天氣。但這些模型的準確性高度依賴於(yu) 它們(men) 的“初始狀態”，即當前大氣的詳細數據。這些數據來自全球範圍內(nei) 的衛星、氣象氣球、雷達係統和地麵觀測站。但即使擁有這些先進的技術，我們(men) 也不可能實時全麵地捕捉全球大氣的每一個(ge) 細節。這意味著模型一開始的“初始條件”總是不完全精確的，而這個(ge) 誤差會(hui) 隨著預測時長的延長而變得越來越嚴(yan) 重。

To understand why long-term forecasting is so limited, it’s important to know how weather forecasts are made.Meteorologists use complex mathematical models to simulate Earth’s atmosphere. These models rely on the laws of physics, things like motion, temperature, air pressure, and humidity, to predict how air masses and weather systems will behave.The planet is divided into a massive 3D grid system, with each cube or "cell" representing a specific portion of the Earth’s surface and atmosphere. Weather models calculate how conditions in each of those grid cells change over time, step by step. But these forecasts are only as good as their starting point. The models need accurate, detailed data about the current state of the atmosphere, known as initialization. That data comes from a global network of instruments: satellites, weather balloons, radar systems, and surface stations. Even with all this technology, it’s impossible to capture every detail of the atmosphere everywhere on Earth in real time.That means the model always starts with a rough estimate of the present, which becomes more of a problem the further ahead you go.

大氣的混沌性質

這時就需要引入混沌理論來解釋了。大氣屬於(yu) 典型的混沌係統，極其微小的初始變化，可能引發完全不同的結果。這就是著名的“蝴蝶效應”：理論上，一個(ge) 蝴蝶在巴西扇動翅膀，可能會(hui) 在幾周後導致美國德州出現一場龍卷風。在實際操作中，這意味著哪怕是當前氣溫、風速或濕度的微小觀測誤差，隨著時間推移也會(hui) 被放大。到了第10到第 14天，這些初始誤差就會(hui) 演變成大的偏差，使天氣預測嚴(yan) 重失準。這不是一個(ge) 理論假設，而是被幾十年的實證研究證實的結果。

2019年《大氣科學雜誌》發表的一項裏程碑式研究表明：即使使用完美的模型和無限的計算能力，也無法準確進行超過14到15天的逐日天氣預報。同樣，美國氣象學會(hui) 指出，在現實條件下，詳細的天氣預報在第8到第10天後就不再可靠了。這就是為(wei) 什麽(me) 像美國國家氣象局和歐洲中期天氣預報中心（ECMWF）這樣的權威機構，也隻發布最多約7–10天的逐日天氣預報。再往後的預報則轉向趨勢判斷或概率展望，而不會(hui) 再給出具體(ti) 的預測，比如“3月28日將有5毫米降雨”。

Here’s where chaos theory comes in.The atmosphere is what scientists call a chaotic system—one in which tiny changes can lead to vastly different outcomes.This is the famous “butterfly effect”: a butterfly flapping its wings in Brazil could, in theory, lead to a tornado in Texas weeks later. In practical terms, this means that even the smallest uncertainties in today’s weather data—such as slightly incorrect readings of temperature, wind, or humidity—can grow larger and larger over time. By the time you reach 10 or 14 days ahead, those tiny initial errors have compounded into major inaccuracies. This isn’t just a theory. Decades of testing and analysis support it.A landmark 2019 study in the Journal of the Atmospheric Sciences found that even with perfect models and unlimited computing power, accurate day-by-day forecasts are physically impossible beyond 14 or 15 days.Similarly, the American Meteorological Society states that detailed forecasts lose reliability beyond 8 to 10 days under real-world conditions. That’s why official forecasts from national agencies like the U.S. National Weather Service and the European ECMWF only publish daily forecasts up to about 7–10 days out. Beyond that, they switch to general trends or probabilistic outlooks, but never specific predictions like “5 millimeters of rain on March 28.”

天氣預測的局限性

如今的天氣預測技術已經極為(wei) 先進。比如歐洲中期天氣預報中心（ECMWF）使用的是一個(ge) 涵蓋900萬(wan) 個(ge) 網格單元、垂直分為(wei) 137層的大氣模型，並運行在世界上最快的超級計算機之一上。但即便如此，其預測能力也會(hui) 在大約10天後大幅下降，尤其是在局部天氣（如降雨或冷空氣）預測方麵。原因之一在於(yu) 天氣受到宏觀係統和微觀係統的雙重影響。

我們(men) 可以提前幾天預測大型急流或氣壓係統的移動，但像山地風、湖泊氣流、暖空氣團等微觀因素通常隻能提前一兩(liang) 天估算，而這些小規模係統對本地天氣卻往往有著不成比例的影響。更何況，要初始化一個(ge) 全球模型所需的數據量是巨大的。盡管我們(men) 擁有成百上千顆衛星和成千上萬(wan) 的觀測站，但整個(ge) 大氣係統過於(yu) 龐大和複雜，無法在任意時刻完全捕捉。這就是為(wei) 什麽(me) 在像北極或南半球等觀測稀疏地區，天氣預報往往準確率較低。

Modern weather prediction is incredibly sophisticated. The European ECMWF model, for example, uses 9 million grid cells across 137 vertical levels of the atmosphere and runs on one of the fastest supercomputers in the world. Yet even it loses skill after about 10 days, especially when it comes to localized forecasts like rainstorms or cold snaps.One reason is that weather is influenced by both macro- and micro-scale systems.We can predict the movement of a large jet stream or pressure system several days in advance, but small, local factors, like mountain winds, lake breezes, or pockets of warm air, can only be estimated a day or two ahead. These small-scale systems often have disproportionate effects on local weather, and they’re harder to model accurately. Additionally, the sheer amount of data needed to initialize a global model is staggering.Even with hundreds of satellites and thousands of observation stations, the atmosphere is too vast and complex to fully capture at any one time.That’s why forecast confidence is often low for regions like the Arctic or parts of the Southern Hemisphere, where observation data is sparse.

天氣預報的商業(ye) 假象

盡管如此，一些商業(ye) 天氣公司仍然發布詳細的45天或90天天氣預報。例如，一家名為(wei) AccuWeather的公司會(hui) 提前好幾周公布某些節假日的降雪量等精確預測。但來自科學界的氣象學家普遍批評這些長期預報是誤導性的、缺乏科學依據的。

專(zhuan) 家指出，這類預報給公眾(zhong) 一種虛假的精確感，破壞了大眾(zhong) 對天氣科學的信任。問題不僅(jin) 僅(jin) 是準確性，還有透明度。例如，AccuWeather 並不會(hui) 定期發布驗證報告來說明他們(men) 的長期預報到底有多準確。在缺乏客觀證據的情況下，許多氣象學家認為(wei) 這些預報更像是營銷手段，而非真正的科學成果。

Despite this, some commercial weather companies, like AccuWeather, continue to publish detailed 45-and 90-day forecasts. These often include bold claims such as exact snowfall predictions for holidays many weeks in advance. However, meteorologists across the scientific community criticize these forecasts for being misleading and unscientific.Experts say they give people a false sense of precision and damage public trust in weather science. The criticism is not only about accuracy. It’s also about transparency.AccuWeather, for example, does not routinely release verification reports to prove how often their long-term forecasts are correct.Without this kind of objective evidence, meteorologists argue that these forecasts function more as marketing tools than legitimate science.

Weekly關(guan) 鍵詞 Key Words

►Weather forecast 天氣預報

Chaos theory 混沌理論

所屬話題

In Futurity, Someone Prophetic Sees

相關(guan) 閱讀

https://theconversation.com/whats-the-difference-between-climate-and-weather-models-it-all-comes-down-to-chaos-244914

https://www.washingtonpost.com/weather/2019/11/07/science-says-specific-weather-forecasts-cant-be-made-more-than-ten-days-advance/

Weekly FUN Quiz

相信現在你已經知道了為(wei) 什麽(me) 無法準確地進行長期天氣預報了吧！那就快來參與(yu) 本期Weekly FUN Quiz👇，告訴老師你的答案吧！

Quiz

An AI driven weather forecast system is LEAST accurate in performing which of the following tasks? 基於(yu) 人工智能的天氣預報係統在執行以下哪項任務時準確性最低？