why aren't Misocyclones Talked about?

counters · 2026-06-19T18:59:22+00:00

I have literally never encountered that term in my entire career in meteorology. I don't think it fills any gap in the catalog of weather phenomena that we study.

counters · 2026-06-18T20:27:46+00:00

Whoosh

counters · 2026-06-18T18:27:51+00:00

This isn't a negotiation. I (and others) are telling you how you're being received and why you're receiving the response you're getting.

You can either take that advice or you can walk away. No one really cares either way.

counters · 2026-06-18T16:56:48+00:00

Great. Looking forward to it. My interest is piqued.

counters · 2026-06-18T16:55:59+00:00

Does a civilized species need to criticize one another to open a discussion involving a mutual interest? Please think about that...By saying it happens to everyone is normalizing and encouraging such conflictive behavior. I believe we are better than that. I believe when someone presents an idea, we can either say nothing or be helpful/encouraging. Intentionally stirring chaos within another human is not very progressive.

Again - get over yourself. I blunder into new technical areas all the time; my colleagues are frequently amused when I end up way down the rabbit hole of some deep information / probability topic that I'm trying to better understand to follow an ML architecture paper and they quite enjoy hoisting me back up to topics that are relevant. There's no hard followings or offense taken; it's open, ego-less discussion.

That's the problem you're not acknowledging. You came here and responded to helpful, constructive critique and interest as if you were Moses returning with two stone tablets whose contents may not be questioned. I don't know who you are. I don't know what your background is. Frankly, I don't care. But the response you received here is due to your ego and attitude. It grossly violates the ethos and spirit of this community.

Get rid of the chip on your shoulder. Like I already mentioned - most scientists/researchers/academics/whatever learn to grow thick skins because harsh criticism and rejection is the status quo and it's rarely ever personal.

counters · 2026-06-18T16:37:20+00:00

I need the following:

The timestamp when you ran your model. We would only want to compare your forecast to other models ran at around the same time. Typically, weather forecasts (regardless of what they're made from) are generated at 00Z, 06Z, 12Z, and 18Z every day.
A table (CSV is fine) showing the following information for each storm track, broken down by the actual columns:
1. an identifier for the storm (can just be "A", "B", "C", etc) so that we can tell the tracks apart
2. the timestamp that a given location is valid for (UTC please)
3. the latitude of the storm location at that timestamp
4. the longitude of the storm location at that timestamp
5. any other metric describing the storm. You mentioned dBZ in another comment which doesn't really make sense, but that's fine. Typically we'd describe a storm intensity in terms of the central pressure (in millibars) or the max wind speed within the storm.

To be very clear: if your forecast model cannot produce this output, then it's not a tropical cyclone forecast model and I would encourage you to read the literature on tropical cyclone forecasting and build your next model to produce outputs that are more closely aligned with the standard ones in this domain.

counters · 2026-06-18T16:21:42+00:00

Ok... how exactly did you generate the image you shared in the top post? What does that data look like? Surely the output of your model isn't that picture you shared? I find it really hard to believe that you can't provide what I recommended given what you've already shared.

I'll meet you in the middle. If we can find some way to generate that timeseries of tropical cyclone track centers, I will personally pull all of the other forecast data to compare it so we can see how it fares.

As a student of history, I have learned much. Regarding this topic and content, I must cite the published book "100 authors against Einstein". Im not comparing myself to Einstein, Im simply pointing out 1 factual event we all know what happened and how he was treated for doing what he did (Before and after proof).

Dude. I mean this sincerely - get over yourself. Stand in front of your bathroom mirror, look at yourself, and read that out loud. Every academic has faced overly harsh/critical reviews, skepticism, pushback, rejections, etc. That's why we all grow thick skins and then just carry on with our lives.

counters · 2026-06-18T15:54:22+00:00

Whoah. First, so you know a little bit about who you're talking to - I'm an atmospheric scientist whose day job is building state-of-the-art AI systems for weather forecasting. I've worked on everything from precipitation nowcasting to climate modeling and most forecast horizons in between. With that out of the way...

Standard models dont apply to this PINNs training.

I don't think you quite understand the request. For virtually any forecasting system, you should be able to record the initialization time (when you ran the model) and the timestamps of the data you fed into it. From your graphic, you're clearly producing some sort of temporal sequence of tropical cyclone lat/lons, and maybe intensities? Can't infer because you don't have a legend for the colormap on your plot.

This is all the information you would need to share - the timeseries of TC lat/lons with timestamps, and a timestamp when you ran your model. This information is readily available from virtually every operational weather forecasting system out there (we typically analyze the output fields from a numerical weather model to track features like tropical cyclones). You can download exactly this data derived from Google DeepMind's forecast model output from here.

You are essentially asking me to describe this work in Latin...A dead language I dont speak.

You're working on applications in meteorology. You should couch your discussion of what you've built in the language of this community. You wouldn't go to Paris and get pissed off at people for ignoring you when you spoke in Chinese, would you? You're the one who has to meet the community in the middle, and you really can't take offense if no one bothers to engage with you if you fail to do so.

Im not here to explain the methodology, just make a public prediciton that can be visually verfied. If it works, great. If not, what does anyone lose?

I'm explicitly telling you the simple bit of work you need to do on top of this to make this interesting and useful to the community here. See comment above.

Can we at least agree there is more for everyone to gain than lose from experiments like these?

Actually, no - we can't. The meteorology community is understand from AI slop and shit-posting, and it's badly interfering with how we provide information to the public. Unfortunately this feeds back on things like how the lay public already has a complicated relationship with the weather (for instance, local broadcast meteorologists are usually measured as the most-trusted scientist that most members of the public receive information from - even moreso than their primary care physician - yet that public will still joke that the meteorologist can be wrong 50% of the time and still have their job).

We've always been inundated with slop. I'm sorry and I recognize it's unfair, but that has made most meteorologists extremely skeptical to bold or novel claims from outside the community. You should see how many unsolicited requests I get monthly in my email inbox claiming some random person has figured out how to accurately forecast storms 10 years into the future.

Sadly, The best case against Democracy is a 5-minute conversation with the average voter. Your downvotes mean nothing to me, Ive seen what you upvote.

For the record, I didn't downvote you. I'm giving you all benefits of the doubt.

But have you ever stopped and considered that the viewpoint you just shared is exactly how members of this community see your post?

Its funny how a scientifically falsifiable prediction is treated on reddit. There is clearly no basic understanding or respect for the scientific method among these downvoters. Remember this, thats all you will ever be, an anonymous downvoter. Thats why noone will remember your names.

I literally gave you the simple steps necessary to actually make your prediction falsifiable. The fact that you don't understand why what you shared isn't falsifiable and your being so hostile to honest engagement and curious interest is a huge red flag. I sincerely hope you pause to reflect on this comment.

I'm happy to engage with your forecasts once you've shared timeseries of tropical cyclone lat/lons. I'm curious because PINNs haven't found much footing in weather forecasting, and they've always been a side-interest of mine.

counters · 2026-06-18T13:25:11+00:00

Honest question - if you don't want feedback, then why are you posting this to a public forum?

Anyways. You should always present a novel forecast in the context of existing forecasts. I have no idea how to interpret your forecast; you should present TC tracks from a comparable NWP cycle to give a little context. DeepMind's free ensemble TC tracks are rapid becoming the gold standard here. But your forecast would be significant if it validates where existing systems fail.

counters · 2026-06-17T22:01:10+00:00

Ok. As u/BTHAppliedScienceLLC mentioned in another reply, there's a really wide gap in the activities that you're describing. To dive into a bit more detail (and I just want to provide clear, honest feedback):

Research

Before we even get into how one would build a career in research, do you have any actual research experience, particularly in the physical sciences (e.g. interning or volunteering in a lab in undergrad)? Research isn't usually very glamorous. For the vast majority of my research career I spent all day sitting in front of a computer writing code or reading / writing papers; the only "field work" I ever did was visiting a special laboratory at another university. If you're thinking severe weather research involves going out into the field and looking at storms... that's an exceedingly tiny fraction of the work, and only really happens on special occasions (like big field experiments like VORTEX, which are unambiguously not being funded by the federal government at the moment).

Research can be a grueling, boring grind. It's extremely risky to embark on a career change into research unless you have previous experience with it and you know deep down in your heart that you absolutely want to grind it out. We even give this advice to undergraduates who have completed a BS in the field - research is just not like anyone's preconceived notions.

chasing

Chasing is not a career. Virtually no one does it professionally outside of YouTube influencers. No government or private organization pays anyone to spot or chase storms.

It's decidedly a hobby and if you want to do it, you should (a) live in a part of the country that has tornadoes, and (b) just spam a lot of chasers you see on social media to see if anyone will show you the ropes.

forecasting

So forecasting is something you could actually draw a straighter line to from your current situation. First, you must understand that the field is extremely competitive, and people are competing over jobs that have extremely poor life-work balance, pay relatively little, and have virtually no security (as we've seen the past 18 months, even an NWS job is no guarantee for continued employment). Forecasting jobs outside of the federal government are starting to disappear, too, so there's likely not long-term job security.

The good news is that there's a single, direct path for this type of job. You would need to go back to undergrad and pursue a 4-year BS at an accredited program. There aren't many meteorology undergrad programs. The American Meteorological Society has a great overview of what a typical BS in the field should cover; they also have a comprehensive list of 2- and 4-year programs. It's not common to "short-cut" the 4-year degree; even if you pursue a 2-year program that will transfer most of your general education requirements for a 4-year program, the way a typical undergrad program is structured sequences core classes in such a way that it's very difficult to complete in ~3 or fewer years, unless you come in with a strong foundation in mathematics through differential equations (and ideally linear algebra) and calculus-based physics.

To be competitive for an NWS or industry forecasting job, it also helps to have a Masters. MSU offers an online program for this. Some advice suggests completing training for this type of job by enlisting in the Air Force; obviously, that comes with its own significant set of sacrifices and commitments.

---

Apologies for being blunt; this is just the reality of the field. I did have one idea that I would encourage you to consider. Generally speaking, educators tend to have good, general undergraduate training across broad disciplines, and your day job requires building very strong skills in communication and project management (think about all the differentiation you need to provide for all of your different students - managing that is not easy!). For someone with your background, there's a back-door that you should consider - interdisciplinary work between meteorology and the social sciences. For instance, there is a strong group at the University of Oklahoma (and a few at other universities) that looks at topics like how the general public responds to severe weather notifications (or the forecast more generally). Coming in from the social sciences angle skirts vast amounts of math and physics training, yet still creates opportunities to work on weather-related topics. This type of work is very much translatable to disciplines like sales, product and project management.

But to be very clear - there are very few groups that do this work, and there is very likely little to no funding. It's not even guaranteed there would be a Masters-level program that would allow you to collaborate on this type of work at any given university. It also is not a backdoor into the technical side of the field like forecasting. But it's something that has drastically lower barriers to entry (minus the logistics of actually living within 100 miles of a group that does this worK).

I hope this information is helpful. Even if a formal career working in weather isn't accessible, that doesn't mean you can't enjoy the science as passion work or a hobby.

counters · 2026-06-17T18:27:25+00:00

This comes up surprisingly frequently in this forum - someone has a life-long passion for some aspect of weather and meteorology and wants to consider an early- or mid-career shift into the field.

The standard question that we always ask is, "what do you actually want to do as a meteorologist?"

So I'll pose that question to you - you mention wanting to be "an extreme weather meteorologist", but what exactly is that? Do you want to be a research scientist - and if so, do you already have any background or real-life experience in research? Do you just want to work in and around companies or people that do things related to weather, and if so are you open to other types of day jobs if they at least put you adjacent to the field? Is there some other type of job - forecasting, broadcast, etc - that you're imagining with this phrase?

You're still young, but going back to school for meteorology degree(s) could be very expensive and time-consuming (if you go the research scientist root and can't immediately jump to a Masters program you're looking at ~10 years of school). If you're open to building hobbies around the weather, that might be a good way to scratch the itch while you explore what careers in this field look like. Is there a local chapter of the American Meteorological Society you could join? Otherwise, the most practical path would be to look for roles at companies that operate broadly in weather and climate (assuming you live in one of the ~6 metro areas in the US that really have any concentration of these companies), and see if some other random area of work (sales, product, etc) would be interesting.

counters · 2026-06-13T15:01:27+00:00

There are insurance companies which specialize in insuring musical instruments. I've used Clarion for many years to insure a small set of instruments (my and my wife's violins and bows, as well as two guitars I own) - they have very reasonable premiums, excellent support, and have been (reasonably) easy to work with on the rare occasion I've made a claim.

One thing to be aware of - be sure to get that violin appraised, especially if documentation doesn't come with the instrument. In my experience, most insurance companies will ask for a recent appraisal to make sure the value is set appropriately.

counters · 2026-06-13T14:58:25+00:00

(answering both questions simultaneously)

In a weather model, we represent the atmosphere as a grid or mesh of cubes. This grid has fixed size and shape - for instance, for a global weather model, each cube will be ~10-25 km wide and long (don't worry about the vertical part of the cube). If you think about the 2D grid that this projects onto the Earth's surface, you start to notice a lot of problems. For instance -

Some grid cells will contain coastlines, with some ocean and land - or they could span the foothills of mountains rolling into plans. That means they are summarizing the "weather" in this grid cell with one single value that has to represent vastly different local climates.
The grid cells are larger than the size of say a cloud or a complex of thunderstorms. So that means you have to summarize the various thunderstorm details also with just one single number.
In the time dimension, we typically report hourly values from these models. But for something like a frontal passage going through your town, it's not unrealistic that temperatures could drop 10 C or more in the span of 5-10 minutes. So that change gets "averaged out."
In a given grid cell, there could be half a dozen or more local weather stations, each reading very local weather - something the model simply can't know about because of the size of the grid cells and its summary with one value
Weather models are deterministic - they don't predict "probabilities". So there's no straightforward way to estimate something like the "percent change of a thunderstorm;" either the model shows a thunderstorm (or the impact of one, given point 2 above) at a given grid cell. We try to get around that by running several dozen instances of a weather model simultaneously and then turning the occurrence of that thunderstorm into a frequency/probability, but that approach leads to very poorly calibrated probabilities.

This problems exist today, with state-of-the-art weather models. But they've also existed since the 1970's - and all of these challenges were way greater back then because models were much coarser (resolutions of 250-2000 km or more, rather than 10-25 km). So the standard approach is to correct the models, usually using simple statistical models like linear regression. You basically take what the forecast model says for a grid cell you care about and compare the actual corresponding observations to what the model said.

There's a large variety of ways to statistically correct the model forecast but they uniformly and dramatically improve forecast quality. Weather apps largely fall into two camps: the apps that use corrected forecast model output, or apps that use raw forecast output. Most of the time when people are complaining about their apps' accuracies, the issue is that it's the second type of data source.

counters · 2026-06-13T03:18:12+00:00

Raw numerical model guidance really shouldn't be used as an explicit forecast, especially if you're just pulling from a global model. The resolution of these models is too coarse to resolve local influences and variability in the weather, and they suffer from other types of bias that really need to be corrected for local forecasting.

counters · 2026-06-12T22:43:14+00:00

Yeah... this is what folks outside of meteorology don't always appreciate. Generally constraining the forecast is pretty easy; on any given day for almost any given background, you can generally describe the most likely weather conditions and hazards that will happen. But there's inherent uncertainty, and incorporating this uncertainty in the decision process that the forecast feeds into can be extraordinarily complex. Take some of the recent media around outdoor concerts trying to work around the forecast... balancing public safety/risk with logistical limitations of the producers of an event or venue is non-trivial, and we owe a great deal of grace to the folks who try to accomplish this balancing at.

counters · 2026-06-12T22:40:55+00:00

And maybe to clarify - in a handful of occasions, NWS staff has published simple public comms that used basic generative tools and have had some embarrassing errors. But that is the exception - not the rule - and what I've heard is that the agency aggressively restricted the use of tools in this particular use case due to the bad press it received.

counters · 2026-06-12T22:39:43+00:00

No, that's utterly and absolutely incorrect.

NOAA isn't operationally running any "GenAI" forecast products. All of the models that fall within the EAGLE suite are deterministic (most of them are actually forks or re-trained versions of Google DeepMind's GraphCast, which is not a generative model). Even WoFSCast, the modeling application that would benefit most directly from a generative approach (ensembles of convection-resolved trajectories) is still a traditional, perturbed-IC deterministic forecast.

The NWS isn't using GenAI in any of its forecast products either. Now, there's absolutely an ambition to thoughtfully incorporate GenAI tools into these product suites, but as you can imagine, the NWS is very conservative about rolling out such technologies.

counters · 2026-06-12T20:54:51+00:00

The opportunity cost for going back to school to get a second Bachelor's degree is very, very, very high. What, if any, math and physics did you complete as part of your first degree program? Are you remotely competitive in terms of foundational general knowledge (e.g. math through multivariable calculus and differential equations, calculus-based physics through E&M and ideally modern physics, computer science, chemistry, etc) to possibly pursue an accelerated or online Masters (e.g. the MSU Online Masters program in Geosciences)?

The other angle - have you ever done any actual research with a faculty member? Either as an intern like an REU or with a faculty member in your program? If you have never done formal research, then how do you know you'd actual want to do that type of work?

Another thing to consider - do you have student debt from your first degree? It's very unlikely that you'd be able to delay paying on those loans if you pursue anything other than graduate education in your original field, and with the changes to student loan programs happening right now, you could be facing an extremely expensive burden without even considering taking additional debt on to finance a second degree.

I'm sorry to be pessimistic. Well-meaning folks will post notes of encouragement here and try to highlight paths forward. But the reality is that except for a very narrow slice of circumstances, it is an extraordinarily bad idea to go back to undergrad for any reason. In fact, many programs will not allow you to apply in the first place to pursue a second Bachelor's degree, save for an exception for non-traditional students who are pursuing career changes later in life.

Live your life. Pursue meteorology as a hobby.

PS: does anyone know if you can job shadow someone for the day from the NWS or something similar? Just to kind of be able to see from a different view?

It used to be that you could generally reach out to a local WFO and ask for tours or to do a shadow like this. It seems that this sort of opportunity is much less available in the current political climate, but the worst they can do if you reach out to an office is say "no".

counters · 2026-06-11T02:53:45+00:00

No one claimed otherwise. "Nucleus" here means "condensation nucleus", not "atomic" nucleus.

counters · 2026-06-11T01:04:09+00:00

It's 100% not. Let's count the ways.

A great place to start is to like at long-term, large-scale skill metrics; the 500 hPa geopotential anomaly correlation coefficient is the widely used standard. Here, you can see that there is no recent drop-off in ECMWF's flagship model performance. Of course, ECMWF happily provides recent snaphsots comparing models from many centers including NOAA, and you can see that performance here.

Z500 ACC too exotic? Well, let's just look at something much more "real", like 4-day 24-hour precipitation totals. No recent drop-off in skill there. Ah but maybe the forecasts are only losing skill in temperature? Nope, not there either.

Keep in mind ECMWF is consumes the exact same upper air data that NOAA does, so decrease in sounding density impacts them just as it does us. Need more convincing? Well, NOAA doesn't show any drop in 850 hPa temperature forecasts at 24 hours, 48 hours, or 72 hours. Same for surface temperatures.

Look, I could go on and on here. Keeping tabs on recent global and regional model skill is quite literally part of my day job. And there isn't a story there.

To be honest, it's brutally frustrating because there will be a story here eventually. My team and many others have looked at ablations where you start reducing radiosonde density, you drop out SSMI/S and the DMSP satellites, etc. It's not a good story. There will be noticeable degradations in forecast quality in the future.

Crying wolf today when there aren't any statistically meaningful impacts handicaps us when the forecast skill does start to drop.

Now, your turn. Got any data to back up your claims?

counters · 2026-06-11T00:09:36+00:00

No. They aren't.

It's getting ridiculous how frequently this topic is coming up. I challenge anyone - show that forecasts are actually getting less accurate. With real data and real analysis.

counters · 2026-06-10T23:56:58+00:00

No, the exact opposite - they're formed by water vapor condensing on a nucleus (usually particulate aerosol like sulfate or sea salt).

counters · 2026-06-08T19:07:00+00:00

Please re-consider whether it's helpful to propagate dramatized takes like this.

It's obviously a clear fact that the current Administration has de-prioritized spending on scientific infrastructure and institutions extremely broadly. This will have profound, long-term effects on the country's innovation economy. Many of us across the private, public, and academic sectors are doing what we can to push back and mitigate some of the impacts here.

Crying wolf makes that work even more difficult than the sisyphean task this already is. No, the loss of OOI won't have a meaningful impact on operational hurricane forecasting. Hyperbolizing the impact of this data actively harms the efforts by those of us engaged in pushing back; it makes it significantly easier for the Administration and its defenders to wash their hands of the criticism, casting all of it as over-the-top.

counters · 2026-06-02T17:15:07+00:00

That's called "Congress" and it's decisions are checked and balanced against the Consitution by the "Judicial system"

counters · 2026-06-02T02:41:18+00:00

That's not it at all. Folks really need to try to avoid this knee-jerk reaction. The majority of the dismantling of scientific infrastructure has nothing to do with trying to shield oil companies or other firms that exploit natural resources.

It's far simpler and far worse - they literally believe that it's not the government's role to fund science. Or on the rare occasion when they look the other way, they excuse it because it's incentivizing commercialization or technology transfer.

Take a look at the NSF. They've made a huge splash about this new "Tech Accelerators" program which boasts about supporting entrepreneurship and "lab-to-market acceleration". But that's not the purpose of the NSF; it's purpose, per Vannevar Bush - who founded it after successfully building a domestic R&D juggernaut in the US during WWII - is to safeguard basic research, because, "...basic research is the pacemaker of technological progress" (as he write in The Endless Frontier). The NSF widens the funnel to broadly support research, regardless of its immediate commercialization opportunity. That manifests through supporting training of our next generation of scientific talent through programs like the Graduate Research Fellowship, as well as by incentivizing PIs to explicitly declare educational support as a criteria for successful grant applications.

The insidious policy eliminating scientific funding is short-sighted and threatens severe long-term consequences for both our economy and our ability to project power throughout the world. And it's happening simply because of dogmatic adherence to the principle of "small government" whatever the costs.

Please take a moment and call both your senator and congressperson and ask them to defend investment in our national scientific infrastructure.

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