Channel
@metwarn
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Posts edited after publishing · Citations · Telegram's recommendations · Cite this entry
24,326subscribers
-73 since we began measuring on 5 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001394006413 |
|---|---|
| Type | Channel |
| Username | @metwarn |
| Description | 捉得緊現在，先可以預報未來。 MET WARN 天氣預警頻道提供最新天氣消息，請善用Forward功能轉發信息。 想請我地食件Cake、提供更專業可靠預報，可以撳呢度⬇️ https://www.metwarn.hk/donate/ 免責聲明: 1. 此頻道的天氣資訊均為非官方資訊，如欲獲得官方資訊，請到香港天文台網頁。 2. MET WARN 天氣預警 不會對使用本頻道資料而導致的任何損失而負責。 網頁： www.metwarn.hk |
| Created | 31 May 2019 — measured — cross-checked against a third-party dataset (ext.tg_channel) |
| First recorded | 6 August 2026 |
| Last confirmed live | 27 September 2026 |
| Measurements held | 30 |
| Confirmed unchanged | 1 time, most recently 27 September 2026 |
| On Telegram | t.me/metwarn |
Other / unclassifiable — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 67% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and why it has no browse page of its own yet.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 27 Sept 2026, 18:18 | 24,326 | -41 |
| 17 Sept 2026, 15:36 | 24,367 | -2 |
| 15 Sept 2026, 14:21 | 24,369 | -1 |
| 13 Sept 2026, 23:39 | 24,370 | -3 |
| 12 Sept 2026, 05:18 | 24,373 | -7 |
| 6 Sept 2026, 14:55 | 24,380 | -7 |
| 4 Sept 2026, 03:37 | 24,387 | +1 |
| 2 Sept 2026, 15:35 | 24,386 | -5 |
| 1 Sept 2026, 15:23 | 24,391 | +4 |
| 31 Aug 2026, 11:46 | 24,387 | +3 |
| 30 Aug 2026, 13:54 | 24,384 | -3 |
| 29 Aug 2026, 16:16 | 24,387 | -6 |
| 28 Aug 2026, 13:56 | 24,393 | +1 |
| 27 Aug 2026, 16:58 | 24,392 | +4 |
| 26 Aug 2026, 20:28 | 24,388 | +7 |
| 25 Aug 2026, 19:14 | 24,381 | +2 |
| 24 Aug 2026, 19:48 | 24,379 | +2 |
| 23 Aug 2026, 03:37 | 24,377 | -1 |
| 21 Aug 2026, 13:28 | 24,378 | +1 |
| 20 Aug 2026, 13:45 | 24,377 | first reading |
110 posts held, back to 31 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 62 pages of Telegram’s post history, 20 posts per page.
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate.
| Window | Rolling 30 days · latest post in window 26 September 2026 |
|---|---|
| Posts held | 110 (31 July 2026 – 26 September 2026) |
| Views total | 185,360 |
| Reactions total | 2,060 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 28 Sept 2026, 12:59 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Lifetime counters from Telegram’s own channel header, read 28 September 2026 — not the date at the top of this page, which is when the subscriber count was last read. A count marked ≈ was rounded by Telegram before we ever saw it — t.me prints these counters in full below 1,000 and to three significant figures above, so ≈142,000 means somewhere between 141,500 and 142,499.
Measured directly from 1 video with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
7,382 reactions across 109 posts, in 53 distinct kinds. The most used accounts for 18.5% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🐳 | 1,366 | 18.5% | |
| 🔥 | 943 | 12.8% | |
| 🤡 | 715 | 9.69% | |
| 👍 | 680 | 9.21% | |
| ❤ | 578 | 7.83% | |
| 🫡 | 463 | 6.27% | |
| 🥱 | 370 | 5.01% | |
| 💩 | 340 | 4.61% | |
| 🤣 | 201 | 2.72% | |
| 👏 | 160 | 2.17% | |
| 🙏 | 154 | 2.09% | |
| 🕊 | 143 | 1.94% | |
| 👎 | 142 | 1.92% | |
| 😴 | 140 | 1.90% | |
| 😢 | 109 | 1.48% | |
| 🖕 | 84 | 1.14% | |
| 🤯 | 75 | 1.02% | |
| 😨 | 72 | 0.975% | |
| 😁 | 59 | 0.799% | |
| 😱 | 56 | 0.759% | |
| 33 further kinds | 532 | 7.21% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 110 of the 110 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 7,423 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 110 most recent posts we hold, published 31 July 2026 to 26 September 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
近日東太平洋颶風季異常活躍，當中五級颶風Polo更寫下多項氣象紀錄。 Polo於本港時間晚間再度增強至五級颶風，是其生命週期中第三次，創下東太平洋氣象觀測史上首例。另外，美國氣象部門於當地時間週二進行的一次飛機實測中，在風暴中心錄得最低氣壓為892百帕，在東太平洋颶風中僅次於2015年颶風Patricia的879百帕紀錄。 東太平洋颶風活躍，且發展至極端強度，與超級厄爾尼諾令該區海溫上升有關。相比之下，鄰近的大西洋今年風季則異常不活躍，至今仍未出現首個達一級颶風強度的熱帶氣旋，同樣打破紀錄。
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樺加沙襲港一週年 人工智能模式表現如何？ 去年 9 月 24 日，超強颱風樺加沙掠過本港以南，為本港帶來同年內第二個十號颶風信號，是1964年來首次「一年雙十」。事隔一年「賽後檢討」，人工智能能否提供更精確的預報？ 樺加沙在南海創下多項紀錄。其中心附近最高持續風速達每小時 230 公里，為 1950 年有紀錄以來南海第二強的熱帶氣旋。雖然它於本港以南約 120 公里處掠過，成為 1946 年以來距離香港最遠的十號風球，但十號信號生效時間長達 10 小時 40 分鐘，為有紀錄以來第二長。樺加沙在本港造成廣泛破壞，當中越堤浪及風暴潮對本港東部及南部的影響相當顯著。 天文台於今年六月發表研究，公布人工智能（AI）模式對樺加沙的預報驗證數據。對比傳統電腦模式，AI 模式的路徑預報表現顯著較佳，部分模式五天路徑預報誤差更少於100公里；但強度預報的表現則較參差，只有FNV3等個別 AI 模式比傳統模式優勝。 研究指出，這種差異可能…
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【中秋天氣展望】唔經唔覺又到咗中秋，雖然今年嘅滿月要去到星期日（27日）凌晨嘅12點47分先出現，即係追月嗰晚嘅午夜之後。不過根據天文台最新預測，星期四迎月（24日）同星期五中秋（25日）大致天晴，星期六追月（26日）就部分時間有陽光。所以只要天氣配合，呢段時間都適合賞月。但要留意嘅係由於早晚都會部分時間多雲，所以可能只能夠係雲隙之間睇到月亮。而呢幾日嘅天氣都會比較熱，秋意暫時仍然欠奉，有意外出活動嘅讀者就要留意喇。 MET WARN 喺度先預祝各位中秋節快樂～
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位於菲律賓以東的熱帶低氣壓較可能在台灣以東轉向日本，對本港直接威脅不大。 電腦模式普遍預料，該風暴會發展成強度較高、環流正常至偏細的熱帶氣旋。受惠於較低垂直風切變、較高海水溫度，以及良好高空流出，風暴有機會發展至強颱風或以上強度。 值得留意的是，風暴的路徑預報在過去數天的變化較大。部分電腦模式一度預料，風暴會以高強度靠近廣東沿岸；但最近兩日的趨勢反映，風暴轉向日本的可能性逐漸增加。這是由於西風槽南下幅度較早前預測稍大，風暴較可能沿副熱帶高壓脊北上。 是次案例正正反映「秋颱難測」。秋季正值大氣環流調整週期，多個天氣系統同時影響熱帶氣旋的動向及發展，因此「秋颱」的動向會較「夏颱」難捉摸。 本港方面，受高空反氣旋影響，下週初可能出現酷熱天氣。下週中至後期則將會受到東北季候風影響，氣溫稍為下降。風暴要採取明顯偏西路徑，才會對本港構成顯著風雨影響。
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【玉兔事件？淺談董建華任內的「派比安事件前傳」】 被坊間認為間接導致派比安事件發生的「前傳」，其實早於此事五年前（2001年），即近日離世的首任行政長官董建華任內發生；氣象界普遍稱之為「玉兔事件」。 2001年7月25日，八號風球懸掛逾19小時，股市停止運作一日。由於玉兔路徑比預期稍為偏西，烈風僅影響本港南部地區，市區部分地區風力未達烈風程度。惹來商界強烈質疑並抨擊天文台懸掛八號風球的決定。當時商界的回應異常高調，時任自由黨主席田北俊接受《華爾街日報》訪問時指八號風球懸掛的早上在其寓所看見陽光，平日繁忙的街道因八號波高掛而顯得冷清，令其懷疑天文台是否誤判。田北俊更於數日後向時任天文台台長追問如何作出懸掛八號風球及維持該信號逾19小時的決定。 香港總商會更於同年9月致函時任政務司司長曾蔭權，要求檢討制度。函中雖表明認同安全優先，惟認為不應承受因熱帶氣旋警告系統「過度保守」或「過時」，或因「數據解讀不準確」而造成的經濟損失。總…
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厄爾尼諾繼續發展。每週更新的相對海洋尼諾指數（即剔除氣候變化影響後的海溫偏差）已達2.0度水平，代表今次厄爾尼諾已達到非常強或「超級」的級別。 根據本月最新氣候模式預報，海洋尼諾指數（即未有考慮氣候變化影響）有機會於年末達到4.0度水平，相對海洋尼諾指數則介乎2.5度至3.0度水平。若預測成真，兩者均屬打破紀錄的程度。
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現時位於大鵬半島一帶的雷雨區正向西南緩慢移動，預料未來一兩小時內會影響本港，新界東北部地區將首當其衝，讀者應提高警覺。
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零散大驟雨正在本港境內發展。 過去一小時，九龍東部的新蒲崗錄得超過40毫米雨量。 不穩定天氣仍有機會在午飯時間影響本港部分地區，讀者外出時應留意天氣變化。
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受南海中部季風低壓及東北季候風的共同影響，預料本港週末風勢較大，驟雨可能增多，氣溫亦會稍降。 該季風低壓現時結構仍較鬆散。電腦模式預料，季風低壓會在未來兩三日移至海南島附近海域，整合較為緩慢，但不排除可發展為較弱的熱帶氣旋。由於季風低壓與本港保持相當距離，預料對本港的直接影響不大。 然而，在季風低壓及東北季候風的共同影響下，週末高地及離岸風勢可能達強風程度，海有湧浪。同時，季風低壓相關的暖濕東南風會與東北季候風在南海北部匯聚，廣東沿岸屆時可能受廣闊雲雨帶影響。此外，隨著地面東北風增強，預料本港週末氣溫稍降。 週末計劃進行戶外活動的讀者，應留意天氣變化可能帶來的影響。
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日本近日多處受列車效應降雨影響，愛知縣、岐阜縣、東京都伊豆群島和鹿兒島縣屋久島一度發布特別警報，名古屋更錄得破137年觀測紀錄的時雨量。 今次列車效應降雨的幕後元兇為秋雨鋒面。此類鋒面成因與初夏的梅雨鋒面類似，均是暖濕氣流與冷空氣碰撞而成。本月上旬熱帶氣旋科羅旺和另一個熱帶低氣壓在日本以南徘徊，加強暖濕氣流往日本本土輸送，是秋雨鋒面較強和持續的原因。 近年日本氣象廳亦加強對列車效應降雨的預測。根據最近發布的預測檢驗數據，半日前預測準確度為15%，捕捉率則為47%。所謂準確度，是指預測列車效應降雨會發生的個案中，預測成真的比例，亦即是用作評估虛報率的數據。至於捕捉率，是指列車效應降雨實際發生的個案中，有作出提前預測的比例，亦即是用作評估漏報率的數據。 基於上述數據，日本氣象廳對列車效應降雨的半日前預測，仍然有超過八成的虛報，以及超過五成的漏報個案，可見暴雨預測難度之高，絕非僅限於香港。
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三年前今日，本港受到與海葵殘留相關的環流影響，更出現頗長時間的「列車效應」降雨。 素來為本港帶來嚴重影響的暴雨，其雷達回波往往不是「大紅大紫」，而是「黃黃橙橙」的長命雨。今集《氣象通識》會與大家探討此類極端暴雨的其中一個幕後元兇，「列車效應」。 https://youtu.be/j-isSIfrLCY?is=hDrXrHm1saA5tf_N
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Google宣布與國泰航空合作，研究透過人工智能減少航跡雲的形成，從而減輕航空業對氣候變化的負面影響。 航跡雲是指飛機引擎排放的熱能，遇上大氣高空寒冷和潮濕的空氣時，迅速凝結形成的雲。不少航跡雲會迅速消散，但視乎大氣高空的濕度，部分航跡雲可能擴散成更廣泛的雲層，並將熱量困在大氣層。部分研究顯示，航空業對氣候變化的影響中，航跡雲佔約三分之一。 Google計劃透過最新的人工智能技術，以及衛星雲圖和氣象觀察數據，預測容易形成航跡雲的空域。與避開氣流類似，機師可以透過稍微調整飛行高度，以避免航跡雲的形成。 Google與國泰航空的合作於去年展開，至今已在超過100班航班上應用，特別是香港往返新加坡的空域，並視為經常具備有利航跡雲形成的氣象條件。 Google近年加快人工智能研究。氣象方面，除了與國泰航空的合作，亦推出WeatherNext等電腦模式，與傳統電腦模式旗鼓相當。
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Showing the 12 most recent of 110 posts we hold for @metwarn. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
@metwarn edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Read from Telegram’s recommendation API, most recently 16 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
This channel appears in 12 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 27 September 2026 — this entry's latest reading, not the date you are reading this.
“MET WARN 天氣預警” (@metwarn), 24,326 subscribers as measured 27 September 2026. Telegram Register, tgregister.com/channel/metwarn.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.