Channel
@tropicalwave
On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Handles named that no longer answer · Cite this entry
177subscribers
+0 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of Under 1,000.
| Telegram ID | -1001410008052 |
|---|---|
| Type | Channel |
| Username | @tropicalwave |
| Description | 💡Les amateurs de météo en Chine • The important archives of them • This channel is for academic research purposes ONLY and welcome enthusiasts to follow • i.e. The operator is NOT funded by any source • More Information @ChinaWeatherInfomationUpdate |
| Created | Between 1 April 2019 and 30 September 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 8 August 2026 |
| Last confirmed live | 13 August 2026 |
| Measurements held | 2 |
| On Telegram | t.me/tropicalwave |
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 8 Aug 2026, 13:31 | 177 | no change |
| 7 Aug 2026, 01:13 | 177 | first reading |
15 posts held, back to 6 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 1 pageof 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. It is computed over the 1 of 10 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 4 August 2026 |
|---|---|
| Posts held | 15 (6 July 2026 – 4 August 2026) |
| Views total | 325 |
| Reactions total | 1 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 13:31 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 8 August 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 2 videos 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.
1 reaction across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 🤨 | 1 | 100.0% |
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 1 of the 15 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 1reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 15 most recent posts we hold, published 6 July 2026 to 4 August 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.
中国气象局：在强到超强厄尔尼诺事件背景下，8月我国暴雨、高温、台风将偏多 中新网8月4日电 中国气象局8月4日召开新闻发布会。国家气候中心副主任贾小龙介绍，最新监测显示，2026年6—7月厄尔尼诺状态持续加强。6月Ni#241;o3.4区海温指数达1.60℃，较5月上升0.64℃。7月第2候起指数稳定高于2.0℃。预计赤道中东太平洋海表温度将持续升高，夏秋季形成一次东部型强到超强厄尔尼诺事件，发生超强事件的概率增大，秋冬季达到峰值，2027年春夏季逐步衰减结束。本次事件发展快、强度强、需高度重视。
Cyclone tracks decide who evacuates and who stays safe. Most AI models trade accuracy for speed. This system does not. It beats the operational IFS ensemble on accuracy. Read the study: https://doi.org/10.1007/s00376-026-5784-3 #AAS #TropicalCyclones #AIWeather #Climatescience @springer.springernature.com via @aasjournal.bsky.social - Adv. Atmos. Sci.
中国气象报时评：治理谣言也是防灾减灾 台风“红霞”尚未登陆时，网上便疯传“13号台风白海豚已经生成”的说法。而所谓视频画面，不过是广西钦州三娘湾的白海豚雕塑被大风刮倒的现场，被胡乱拼接成了“新台风生成”的谣言。 造谣一张嘴，辟谣跑断腿。 这句话在每年汛期都格外应景。随着极端天气来得密、来得猛，人们的焦虑感也随之攀升。一些人趁虚而入，“把灾害当成流量密码”——移花接木的视频、张冠李戴的图片、凭空捏造的“水库溃坝”，在微信群和短视频平台上一轮轮刷屏。 技术的发展让谣言的生产成本趋近于零，辨识成本却指数级上升。当人工智能让“眼见”不再“为实”，灾害传播的信任基础设施需要重建。 防灾救灾争分夺秒的时刻，一则谣言绝不只是“吓唬人”“博流量”那么简单。水库溃坝的假消息，可能让应急队伍白跑一趟、让资源调度错位；有人因假消息慌了神，盲目撤离或盲目留守，都是在给真实的救援添乱。信息正常传播通道一旦被堵塞，预警信息被挤压，最终承受…
🤨1
科普丨预警级别下调并不意味可放松警惕 预警级别下调并不意味可放松警惕 伴随着台风强度减弱，暴雨等预警信号等级会相应下调，但这并非意味风险解除。 需要明确的是，台风强度与其造成的风雨影响并不完全成正比。弱台风在移动过程中若遇到有利条件，也可能激发出强烈降水。若台风登陆后恰逢西南季风卷入其环流系统，就如同为其配备了一台“水泵”。即便登陆后远离海洋，台风仍能短暂维持充足的水汽输送，继续积蓄能量，造成持续性强降水。今年第10号台风“美莎克”的峰值强度为强热带风暴级，但给广西带来持续而极端的降水。 有时，台风带来的暴雨平息了，而隐形灾害则藏于表面的平静之下。滑坡、崩塌、泥石流等地质灾害滞后发生——农村切坡建房区域、山区道路边坡以及覆盖风化土层的山体在经历长时间雨水浸泡后，土体自重显著增加，抗滑能力急剧下降，极易整体下滑；高陡崖壁、边坡及风化岩发育的山区，雨水持续渗入其岩体裂隙后，结构面软化，叠加台风大风扰动作用，岩石或土块容…
我国蜂群无人机首次实现台风过境全程立体观测探索极端天气下低空飞行安全边界 随着台风“红霞”逐渐逼近，自7月24日起，中国气象科学研究院专项气象保障技术研究中心联合深圳市国家气候观象台，在位于深圳市大鹏半岛的中国气象局粤港澳大湾区低空经济无人航空气象保障野外科学试验基地，首次采用蜂群无人机观测方式，针对台风登陆全过程开展高频率、多维度观测试验。 这是我国首次将蜂群无人机技术应用于台风过境全过程观测。试验持续同步获取台风过境前后低空温度、湿度、风场、湍流等关键气象参数的垂直廓线观测资料，以及无人机飞行姿态、航迹偏移、电量功耗等运行参数，系统探索极端气象条件下无人机安全飞行的气象约束边界。 “我们聚焦新域新质气象装备发展和应用，依托蜂群无人机同步开展台风边界层探测试验与极端天气适航性双重研究，既要让蜂群无人机精准探测台风边界层结构，也要观察天气怎样影响不同型号无人机的飞行姿态。”试验负责人、中国气象科学研究院研究员郭建平介绍…
台风登陆地点又变了？其实是预报更准了 台风“红霞”即将登陆，在哪登陆成为媒体关注的焦点。尤其是这两天，“登陆地点又发生变化”的报道见诸媒体，吸引不少目光。一个“又”字，让一些不明就里的人认为台风预报结论变幻无常，总是在调整。 情况真就如此吗？其实不然。从科学的角度上讲，公众看到的预报信息中登陆点的改变，本质上是随着时间的趋近，预报越来越准了。 首先，台风是一个在复杂大气环境中不断演变的巨大系统——海温、副热带高压、冷空气、季风……任何一个变量的更新，都可能让其路径偏移几十公里甚至上百公里。因此，气象部门在预报台风时从不会钉死一个登陆点不变。 气象部门在进行登陆点研判时，往往会给出一个空间范围参考，并随台风逼近一步步缩小范围。比如提前几天划出一个大致区间，之后不断缩小范围，然后再锁定具体地段。整个过程看似在变，实则是逐步聚焦、不断逼近真实路径的科学过程，背后反映的正是预报员根据最新实况不断修正并无限接近的过程。 …
吉林：松辽流域开展台风“巴威”复盘总结 推动跨区域协同与技术升级 7月21日，松辽流域气象中心召开台风“巴威”极端强降水过程复盘总结会，推动业务理念与联防模式的升级，实现跨行政区域、跨业务层级的全链条协同与深度互鉴。会议联合内蒙古、黑龙江、辽宁、吉林四省（自治区）气象部门共同参与，并邀请国家气象中心流域水文气象预报中心专家进行技术指导。 复盘总结过程中，各省（自治区）气象局及沈阳、通化等子流域代表市局围绕降水实况、环流成因、模式预报偏差、预警服务及应急联动等进行了系统复盘。经过复盘，各省预报业务人员总结出本次极端强降水预报技术方面的有关经验，沈阳市气象局介绍了“1316431”递进式预报服务模式，国家气象中心专家则从技术层面给予了专业点评和指导。 针对当前松辽流域正值“七下八上”防汛关键期，会议提出三点要求：一要强化新型观测资料应用和流域风险研判，充分运用雷达、风云卫星、北斗探空等资料，加快研发流域水文气象模型，提升山…
青海：强化高原公共健康风险预警合作 近日，青海省气象服务中心与西宁市疾病预防控制中心围绕高原气候适应性城市建设中的公共健康风险开展研讨，并就下一阶段数据共享、健康预警干预及产品研发等达成合作共识，旨在提升“气象+健康”服务能力，支撑西宁深化气候适应型城市试点建设。 双方针对高原特有公共健康风险展开分析，重点分析了低温高寒、呼吸系统敏感性、强紫外线、沙尘天气对人居健康的影响，以及避暑旅游旺季人流激增带来的气候灾害暴露度增加等内容。双方将进一步拓展数据共享的深度与广度，并明确针对气象敏感性疾病、地方病及慢性病等疾病的气象预警干预合作方式，健全风险预报预警合作和信息发布机制，提升气象与健康综合干预能力。市疾病预防控制中心提出了对高原气象健康产品研发的新需求，省气象服务中心则针对极端复合灾害情景，提出下一步“气象+健康”协同服务建议，深化高原气候变化与健康领域科技交流合作，筑牢“气象+健康”协同防护网，保障人民群众生命健康安全。 …
重庆：风洞计量自动化实验室建成 打造智能化风要素计量保障新标杆 日前，重庆市气象数据中心70m/s风洞计量自动化设备安装调试圆满完成，标志着风洞计量自动化实验室建成。重庆风要素计量保障正式迈入智能自动化、精准高效的新阶段，为精密气象监测筑牢了坚实的计量根基。 风传感器的量值精准度是气象数据的“生命线”，直接关系到灾害性天气监测、预报预警及气候评估的可靠性。针对传统人工检定模式存在的流程繁琐、周期长、批量能力弱等瓶颈，风洞计量自动化实验室建成为这一难题给出了解决方案。 据悉，该实验室集成了智能控制系统、自动化机械臂及全流程数据自动采集分析一体化平台，实现了风速传感器检定、出证的全流程自动化闭环。其核心优势体现在三个方面：一是效率实现“大跃升”，检定能力提升5倍以上，高效化解了批量检定积压难题，确保自动气象站风设备周期检定任务按时清零；二是精度实现“再提质”，依托高均匀性、高稳定性的闭环风场与标准化程控体系，严格对标国家规…
国务院成立广西六蓝水库“7·6”溃坝灾害调查评估组 中新网7月24日电 据应急管理部网站消息，7月6日，广西南宁横州市六蓝水库发生溃坝，造成重大人员伤亡。根据国家有关法律法规规定，国务院成立调查评估组，由应急管理部牵头，自然资源部、住房城乡建设部、水利部、农业农村部、国家发展改革委、工业和信息化部、公安部、财政部、交通运输部、中国气象局、国家能源局、国家消防救援局和广西壮族自治区人民政府等相关方面参加，对广西南宁横州市六蓝水库“7·6”溃坝灾害进行调查评估。
“我们低估了这次台风的影响” | 原文
China Meteorological Amateurs 中国气象爱好者纪事 pinned «广西洪水溃坝：极端台风无法避免的气候灾难 | 原文»
Showing the 12 most recent of 15 posts we hold for @tropicalwave. 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.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
@tropicalwave named 2 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all.A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
References a handle that is not a live channel — we have no record it ever was one.
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 8 August 2026 — this entry's latest reading, not the date you are reading this.
“China Meteorological Amateurs 中国气象爱好者纪事” (@tropicalwave), 177 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/tropicalwave.
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.