Somewhat related, but I found Andrew Blum's book The Weather Machine: A Journey Inside the Forecast an interesting read:
> In The Weather Machine, Andrew Blum takes readers on a fascinating journey through an everyday miracle. In a quest to understand how the forecast works, he visits old weather stations and watches new satellites blast off. He follows the dogged efforts of scientists to create a supercomputer model of the atmosphere and traces the surprising history of the algorithms that power their work. He discovers that we have quietly entered a golden age of meteorology—our tools allow us to predict weather more accurately than ever, and yet we haven’t learned to trust them, nor can we guarantee the fragile international alliances that allow our modern weather machine to exist.
I read Tubes back in 2012 when it was first published, and I enjoyed it. I knew a lot less about networking back then, so it would probably be a good book to re-read now with a different perspective, and to think about how things have might have changed since then.
As a sailor I’m really into getting a good weather prediction and most of the models are quite inaccurate. I learned on here about an ML based weather model startup that posted predictions on their website called Atmo.AI (edited) that was mind blowingly accurate and had incredible spacial resolution- it could accurately predict, e.g. the wind shadow behind islands, something none of the mainstream weather models even come close to doing. I used it everyday and it was almost always dead right at predicting wind speeds in almost any specific spot. I won a lot of sailboat races with it. I hope this tech gets taken up by NOAA, or other major weather predictors.
Anyone on here know what happened to Atmo and if the tech is ever going to come back so people can use it? It's a travesty that this tech exists but isn't being used- a lot of lives would be saved if NOAA were using this already existing technology.
Edit: Apparently as another poster pointed out I had just forgotten the correct name of it, and it is still around. I edited this to reflect that.
Weird, as a sailor I find GFS to be highly accurate taking into account an understanding of what it does, what it does not do, and how to interpret it.
GFS does not account for sea/shore breeze for instance, so if you're in an area where that may occur, then you will have to apply your own judgement about the conditions.
Now, if you were to feed weather station readings into an ML model, would the ML model be better at predicting the weather? Well I think it'd be better at predicting the things that the weather model does not model in the locality of the weather station, sure.
I've found GFS fine for that, but it is one of the worst models for inland or near shore sailing, even most of the other popular models that you can access from sailing weather apps like NAM and ECMWF are much more accurate in specific locations with unique geography. The resolution is just too low to account for any interacting geography with GFS. It gives the same forecast over huge areas with radically different conditions.
I don't use GFS just by looking at the wind layer though. Wind layer forecasts do not include terrain or local effects as you noted. But the necessary info is in the forecast and is accurate.
For instance, in the great lakes we tend to have large diurnal temperature swings and therefore strong sea/shore breezes. If the model is forecasting big temperature changes and an anticyclone with low wind-layer forecast, this is ripe for strong sea/shore breezes.
The biggest hazard we have in the great lakes is convective storms (squalls). They do not show up in forecasts because convective cells are very small. However, The GFS gribs do have pressure forecasts, and perception, and most importantly CAPE and CIN forecast layers. Combined with WPC synaptic charts you have the info needed to determine if 1) convective storms are likely to occur and 2) if they do occur, the probability that they will be severe.
The GFS is a coarser model which covers the entire globe, so while the overall situation at the synoptic scale will tend be modeled quite well (at least inside of a few days into the future), the resolution of smaller-scale weather phenomena taking into account local factors just isn't going to be there.
For something maybe more useful on the local scale, you can also look at a model like the HRRR (which I believe does take into account the terrain and other local effects from things like larger bodies of water). While this model only really covers the conterminous United States and southern Canada, I've generally found it good for showing the shorter-term, local weather details, including forecasting convective storms and winds on and around the Great Lakes.
Thanks, I think I need to spend some more time studying meteorology so I can also better interpret how the data predicts actual hazards, as you are doing. I'm not even familiar with many of the acronyms you mention. I am simply comparing how well the predicted wind speeds are reflected in real life, in specific places where I am potentially in the wind shadow of relatively small hills, islands, etc. It is the flow around these objects that requires a high resolution model, as small shifts in wind direction make the wind shadows shift and change in size a lot. The actual overall prevailing conditions are so identical here from day to day in Northern California, there is hardly a need for large scale models unless the rare storm comes through.
I have noticed that where I am, the inland/offshore temperature differential is alone a pretty good predictor of overall wind speeds near the coast, not accounting for geography.
Thanks, I'll check it out! I've read "High Performance Sailing" by Frank Bethwaite, who was a sailing meteorologist and covers some things- but it is more focused on extreme micro-prediction.
Many companies offer customized, high resolution weather simulations that resolve those sorts of features. It's purely a cost vs value proposition - only a tiny sliver of users actually need this sort of spatial resolution, and typically over a very small area, so there is no reason for a global weather forecast model to be cranked up to it.
> Anyone on here know what happened to Atmo and if the tech is ever going to come back so people can use it? It's a travesty that this tech exists but isn't being used- a lot of lives would be saved if NOAA were using this already existing technology.
They're a thriving start-up as far as I've heard. Weather is a tough industry. Given that hourly-refreshing, high-resolution forecasts are freely available already from NOAA, I doubt that proprietary forecasts like these really move the needle in terms of protecting public life / property.
For sailboat racing I care about “micrometeorology” - e.g. things like which of two slightly different courses only 1/10th of a mile apart to the same destination will be windier 45 minutes from now?
Amazingly, these models are starting to be able to actually predict that, but I agree that not a lot of people care about that level of detail.
It depends on the type of sailing and where the race will be taking you. The participants in a race such as the Vendée Globe [0] are almost certainly using the likes of synoptic scale models like the GFS and ECMWF to plan their routes.
Yes for the vast majority, the immediate weather conditions can be ascertained by looking out the window.
I'd like more accuracy for the next 1-5 days as that's the time horizion I tend to use to plan to work outside on various projects, and am often frustrated by rain when the prior day's forecast didn't anticipate any. Or the opposite.
Do you mean atmo.ai? Atmos.ai seems to be something very different: "Atmos AI streamlines the Carbon Accounting, GHG Emissions, and Environmental, Social, and Governance (ESG) reporting processes for companies of all shapes and sizes."
Ok, I am embarrassed, I simply forgot the name of it, and thought it had disappeared when I tried to find it again. Seems it is still alive and well. I swear this isn't viral marketing for them, I was legitimately confused.
I edited my post above to use the correct name, but realize it doesn't make too much sense anymore.
I've met the Atmo founders a few times, very nice people and the company seems to be doing well. The tech is definitely getting used, but I think they're working with goverments more than end users
How do I use Atmo for locations other than the bay area demo linked on their website? I tried URL tweaking without much luck. I have a friend who does a lot of sailing races who would love this!
I don't know... I am only using it in the Bay Area. I noticed there are coordinates in the URL that one could try changing, but it is possible they are only running the model for the Bay Area right now?
I've also wondered if they have hand tuned the model to match local observations and geography in the Bay Area, and if it could generally apply elsewhere without a lot of manual work? In particular, it is mind blowingly accurate at getting the wind shadow around Angel Island, which is a complex thing to do, because it involves ocean wind that divides into at least 3 separate streams, and then recombines in complex ways. No other model I've seen can predict which of those 3 stream will dominate, but they usually can.
I've noticed the same with e.g. car navigation/map software- it generally works much better in the Bay Area where the developers and companies making them actually live, than elsewhere. I could imagine that in both cases the developers use it themselves in the place they live, and investigate/fix local errors themselves.
I did try altering the URL and didn't have much luck! It would make a lot of sense if Atmo was only running the model for a smaller area to keep costs from running up. My friend races in SoCal (the Channel Islands and Catalina are common destinations) and the wind shadows and streams are complicated there too.
Your observation about navigation software is accurate in my experience too :)
I couldn’t make much of atmo given the SF constraint, but I use the UK2 model for UK forecasts and it seems to be a similar level of detail and does very well taking topographical and micro weather nuance into account. Depending on your location you might find an equivalent short range model
I don't think the title of the article matches the content. The GFS has not declined relative to the ECMWF. It just started from a lower skill level and its improvements have not significantly closed the gap with ECMWF (as it has also improved).
> Specifically, NOAA's global model, the UFS, is now in third or fourth place behind the European Center, the UK Meteorology Office, and often the Canadians.
Would love to see evidence of that. It is well established that ECMWF is top in the game. I don't think it is reasonable to just state that Canada and UK are better without evidence.
When it comes to medium range predictions. If you look at nowcasting, I believe the top dog is metnet 3 (https://arxiv.org/abs/2306.06079).
On a tangential note, I worked on weather forecasting a bit before, and talking with some people still in the field it seems that these organizations (NOAA, ECMWF) are stuck with NWP and refuse to embrace AI models. Not sure if they can't attract talent and so are somewhat stuck or if their higher ups are "old school" and can't really see the potential that this approach has to offer. It is just sad that the private sector is outclassing institutions and that these better models won't hit the public domain.
> talking with some people still in the field it seems that these organizations (NOAA, ECMWF) are stuck with NWP and refuse to embrace AI models
This is trivially verifiable as not true. ECMWF is all over AI:
Windy.com offers ECMWF-AIFS models to paid subscribers. ECMWF seems to be all-in on the ML-based, as opposed to physical, NWP, but it's not clear ML approaches are superior.
I would love to see a multi-year skill plot for all global models similar to what is commonly shared for GFS vs. ECMWF. Instead of having to parse a bunch of individual month comparisons for specific observations.
Regardless, my criticism is towards the evidence presented in the article and the framing of the article.
They also want to get rid of most civil service employees and change their positions to political appointees. Not just the top management positions, but also the people that actually do the day to day work.
E.g., the scientists at the FDA who check the science on safety and effectiveness when processing approvals for new drugs would be political appointees who were vetted for loyalty to the President.
While I’d agree that accurate weather forecasts provide tremendous value, it’s not “free value”, as tax dollars are paying for it. That money could go to any number of ‘under-funded’ other causes, or be returned to the tax-payers.
This stuff has been a big bugaboo for GOP idiots for a long time.
Rick Santorum (a former conservative luminary) was pushing for a law that would prohibit making weather data available to the public, but would make it available to companies like Accuweather for free.
The brother of the Accuweather founder was Trump’s appointee to run the weather service.
It’s on of those things that illustrates that no public resource is safe from graft.
> I have written two peer-reviewed papers in a major meteorological journal (here)
The blogger is Cliff Mass, a professor at University of Washington. He has written two papers on this specific topic (decline of US weather predictions). In general, as a scientist, he has published 100+ papers.
He only linked to his second paper [1]. I also found his first paper [2].
This is a valuable blog on the subject, and his answer explains how the US could rectify.
But NWS didn’t correct their hurricane forecast to the whims of a mercurial President. Talking about the technicalities of the model library is fine but only after you pass the loyalty test.
Also I heard many smart people in NOAA left between 2016 and 2020 to go private and funding cuts. I would not be surprise many in NOAA are looking to leave now.
I wonder if the listeria issues occurring now is also a symptom of this plus the FDA funding cuts over the past 20+ years.
Project 2025 describes NOAA as a primary component “of the climate change alarm industry” and said it “should be broken up and downsized.” Talent is probably worried about the future political climate as well.
It's will known that AccuWeather's president Joel Myers and his younger brother (now former) AccuWeather CEO Barry Lee Myers want to kill public domain weather report data. https://en.wikipedia.org/wiki/AccuWeather#National_Weather_S... Barry was nominated (but never confirmated) for Under Secretary for Oceans and Atmosphere and Administrator, National Oceanic and Atmospheric Administration
While AccuWeather denies involvement in Project 2025, there's definitely overlap between TFG's administration and the authors of the plan.
> It's will known that AccuWeather's president Joel Myers and his younger brother (now former) AccuWeather CEO Barry Lee Myers want to kill public domain weather report data.
I haven't heard anything about NOAA having any new issues with workforce retention.
The trend you're seeing in private sector expansion / growth is linked to the massive investments in Earth observation and derivative applications. It's not really a response to anything happening in government. If anything, the federal government forced some of this growth through commitments like the Commercial Weather Data Program.
I just applied for a job at noaa after being asked by a director to apply for the job which was directly related to updating the core weather modelling libraries, some of which are 50+ years old Fortran, and also develop projects to integrate modern ai/ml into the modelling pipeline. I have 10+ years of experience working both as a researcher and a developer, a PhD in physics, and have won multiple grants either applying machine learning to geophysics problems or develop Bayesian spatiotemporal modeling libraries. At least on paper, I should get an interview for this job. I was rejected because:
> We are not able to determine your qualifications as your resume does not show complete information for each job entry, such as beginning and ending dates of employment, duties performed, and/or total hours worked per week
First, my cv did include everything but the total hours worked per week. Second there was no instruction that I could find for writing my CV such that I needed to list the number of hours worked per week. Third there was no way to contest this. Even the person who invited me to apply who would be the hiring manager had no control over this.
I can tell you, this assuredly leads to the unnecessary decline of any system when there’s an impenetrable hr system sitting between applicants and jobs.
That sucks. Did NOAA have their own application site or did you go through USAJOBs? I would recommend to you or others reading this that if you're applying through USAJOBs, then make sure to enter your resume information in the USAJobs resume builder to help prevent bureaucratic failures like this. HR probably didn't know what they were reading or weren't able to read your CV; they probably based their decision off of the USAJobs resume.
Because NOAA is part of the Federal government, expect job applications to be inspected technically detailed relative to compliance with a mountain of Federal statutes and regulations.
Because people who don't get hired can literally get their Congressional and Senate representatives involved.
This makes getting hired into the Civil Service hard. Internal candidates are usually at an advantage. Veterans always are.
> Internal candidates are usually at an advantage. Veterans always are.
The system is as you described. Internal candidates already know it and veterans, when transitioning out of uniformed service, get a full-blown class on it: how to prepare their "Federal resume" (which is unlike any resume that a reasonable person might otherwise prepare) and navigate the hiring process. So it's that on top of already having relevant experience and credentials or preference points.
I've been on the hiring side for Federal GS positions, and I'll tell you that it can be just as frustrating on that side too.
That's crazy. Why would the number of hours worked per week ever possibly be relevant to a hiring process? At most they might ask "full-time/part-time/other" or something like that.
I am sure somebody somewhere has tried to claim having had an academic position at a university, while actually they were working at 4 hours per week as a teaching assistant for one course.
Because Full-Time/Part-Time/Other is too imprecise for government. What if you consider full time is 25 hours because you were going to school? Well, you worked less on something but claim to have same experience at someone who worked 40.
Government has to comply with all these standards and by asking for hours, they can make sure themselves that someone meets them.
This is doubly stupid for an organization that would probably want to hire people right out of grad school, right? Where the hours are all day and the contracts are for however much the program has laying around at the moment.
HR in companies has changed radically in recent years.
If you throw away your assumptions, and solely look at the behaviors that the function exhibits, you will find striking resemblance to the behaviors under communism, or fascism.
Money has no value any more. Power is the only currency.
To the public, they are strictly compliant, but the tools, such as the CV, the interview process, feedback, approval process, and so on, are irrational to anyone involved, except the true decision makers.
Their objective is not no longer meritocratic, to hire the best qualified or best performing, but to hire people that strengthen the political power of those that already wield it. It's more than nepotism, it's empire building.
It's not the money that has value, but what money can be exchanged for.
If your money can't buy you admission into university,
or property that is sold off-market and privately,
or access to off-market and privately controlled jobs,
and so on,
then your money doesn't help you at all.
Power is the real currency in an unfree market.
As in communism.
I have lived in California for 36 years, 22 in SoCal and 14 in NorCal and I've noticed a trend that I find deeply disturbing: weather forecasts seem to be much less reliable now than they used to be. Before moving to California I lived on the east coast and the weather forecasts were generally unreliable, so I was really struck by how predictable the weather in California was, and how accurate the forecasts were. Rain began and ended when it was predicted to within a couple of hours. The temperature was within a few degrees of what it was predicted to be. And the window of predictability went out 5+ days.
Nowadays that has all changed. Forecast rain fails to materialize. Heat waves come out of nowhere and last much longer and are much more intense than they are forecast to be. Temperatures at my house regularly go 10 degrees above what is predicted, and regularly hit temperatures that would have been extremely anomalous when we first moved here.
This is all anecdotal. I haven't been keeping records, and I haven't done the math. It's possible that this is all confirmation bias or bad memory at work. But damn, it sure seems like a real phenomenon, and the most likely explanation is that the models were trained on data that don't reflect current conditions because of climate change.
The most striking thing about this is the speed with which things are changing. 36 years is nothing on what should be geologic time scales. I shudder to think what the future holds.
I've seen something similar happen and think I figured out the reason. Weather forecasts in my area were very accurate for decades, then they got much worse. They were off in timing for events, and incorrect about whether rain was going to fall or not. Then I realized that the change happened around the time the local nuclear power plant was shut down. The plant had been funding a local weather station and weather radio station as part of government requirements to provide early warning of problems to citizens. When the plant shut down the weather station went with it. So NOAA was no longer getting accurate data from this area and the forecasts were off because of that. I've been thinking of setting up my own station and linking it into the public data to see if that makes forecasts get more accurate.
Run your own code :-) I used to fly sailplanes and around Northern California at least there is an interesting intersection of computer nerds, aerodynamics nerds and weather nerds. One of my favorite things being Jack Glendening's weather code/models for sailplane pilots, culminating in the RASP forecasts that more technical sailplane (and hang glider etc) pilots ran for their own hyper-local forecasts. The localized terrain triggered thermal hotspots and convergence prediction of these RASP models were super impressive. http://www.drjack.info/rasp. Today specialized commercial forecast services are used by many sailplane pilots, and RASP and related forecasts predated or inspired those, but a well run RASP forecast on a fine grid is still something to behold.
As a European, I can tell you not to worry too much. The EU forecasts are mostly garbage. For some reason (possibly climate change) weather forecasting became unreliable over the last 10-15 years.
I regularly check 5 different forecasting models (through a subscription in the Windy app and elsewhere) and they are unable to agree on whether water will fall on my head later today. We're not talking long-range forecasting here, just tell me if it's going to rain in several hours or not. There is no forecasting model that gets this right.
Norwegian official weather forecasting service usually say they're quite accurate, and I was thinking how can that be? But in an interview last year they spilled the beans, they were talking about temperature and only missing by 1-2 degrees C most of the time.
However, I don't care if it's 22C or 20C, like you I care about am I gonna need a jacket or not.
I get that it's a difficult problem. And the inclusion of local weather radar to track actual rain has been very nice. But it only forecasts 90 minutes ahead, and rain can develop closer than that some times.
Freakingly accurate weather forecasts for the niche sport of gliding. Builds on top of ECMWF and incorporates refined soil moisture. It’s really good in predicting mountain waves (and used by Perlan: https://perlanproject.org/perlans-sponsors-enable-us-to-fly-... ). Also convergences and thermal activity which is very dynamic…
Mass/Maas uses European results in his blog often, often commenting that the European predictions for the PNW are more accurate. So, fine, that's great. Why does the US have to keep up? Will it stop the decline of the Empire? Is it a stab in the heart of our world posture? Are we doomed to having to be the best at everything for all of eternity? I don't get it.
I'm more upset about all the interest being paid on the US national debt that -could- have been spent on something great instead.
>In most nations, numerical weather prediction is the responsibility of one group. In the U.S., governmental global weather prediction is spread over FIVE efforts:
>NOAA, the National Oceanographic and Atmospheric Administration (the leading U.S. effort)
>The US Navy
>The US Air Force
>NASA
>U.S. Department of Energy
This doesn't make sense. Other nations don't have a NASA, and if they do have a space agency that space agency is almost certainly in the weather business.
They also don't have expeditionary militaries like the USAF and USN, which need weather for the middle of the ocean or remote nation, far from NOAA's ability to deploy weather balloons.
And as far as I can tell, the only exposure to weather that the DOE has is for climate change reasons, and their interest in that should be obvious.
There's no waste. NOAA does the US, DOD does DOD, NASA helps out, and the DOE is looking at how climate change will impact the US's energy security.
So reason number 1 is horseshit, and I stopped reading.
Interesting call for more horsepower behind numerical methods. One of the benefits of the emerging machine learning forecast models is dramatically lower need for computation (and therefore energy).
The motivation here is that numerical weather prediction (NWP) models are used for a lot more than just weather forecasting - they're critical research infrastructure and tools. NWP allows researchers to simulate complex atmospheric flows and phenomena which might not be readily or directly observed - or to manipulate observed flows in ways to evaluate dynamical theory and the underlying physics. You can't really do any of this with the AI emulators, which output a tiny, tiny sliver of information relative to the vast array of data you can dump from an NWP system.
Regarding energy usage, you're right up to a point. Today's ML weather models are trained to emulate very large reanalysis datasets (e.g., ECMWF's ERA5) which are produced using the physics-based models. No one has yet to demonstrate an end-to-end AI/ML weather forecast which sidesteps these reanalysis datasets (in fact, the trend so far has been to include _more_ physics-based model datasets for fine-tuning, including archives of historical NWP forecasts or GCM simulations). So there's a massive sunk cost in energy usage to create those training datasets, and then there's the ongoing energy cost of training AI emulators from the ground up. And of course, there's the future energy cost of running more physics-based models to better support the development of AI emulators.
For pure inference/forecasting? Sure. Much faster to run AI models and much lower energy usage. But that's quite literally the tip of the iceberg when it comes to forecast model development.
Sure, the AI stuff is not a complete solution because it relies on the results of numerical methods. I was only suggesting that in the question of making up the marginal difference between the best and fourth-best weather predictions, there might be some application of these other novel methods.
If the question is can the Americans build a gigantic supercomputer and write fortran programs, the answer to that seems obvious. Yes we can do that when the relevant bureaucracy gets pointed in the right direction.
> One of the benefits of the emerging machine learning forecast models is dramatically lower need for computation (and therefore energy).
Maybe but honestly, it doesn't make a ton of sense to me that something that is so power hungry it needs new power plants is going to save energy.
I'm also skeptical that AI can really model a chaotic system better than a.. chaotic system simulation. AI is a statistical pattern matcher. I'm really struggling to understand how that would be better at prediction than a simulation of the underlying phenomena. The whole thing seems like a solution in search of a problem.
> it doesn't make a ton of sense to me that something that is so power hungry it needs new power plants is going to save energy.
The fact that Sam Altman has a 200MW chatbot is not relevant here. For example the GraphCast system from DeepMind runs in under 1 minute on a single TPU device.
More like declaring the real plan of setting up one org that needs to be privatized.
There have been real attempts at privatization of weather services in the US, despite the fact that our current system costs pennies a day per person (whether it's used for weather apps, or safe flying/sailing).
unlikely that there is good precedent for a private weather organization. The gold standard for western weather prediction is the European Centre for Medium-Range Weather Forecasts and that is a collaboration between multiple government agencies.
> unlikely that there is good precedent for a private weather organization.
When has something being a bad idea or never having been tried stopped political apparatchik's from trying, especially given past appointee's opinions:
> The document describes NOAA as a primary component "of the climate change alarm industry" and said it "should be broken up and downsized."
[…]
> Project 2025 would not outright end the National Weather Service. It says the agency "should focus on its data-gathering services," and "should fully commercialize its forecasting operations."
> It said that "commercialization of weather technologies should be prioritized to ensure that taxpayer dollars are invested in the most cost-efficient technologies for high quality research and weather data." Investing in commercial partners will increase competition, Project 2025 said.
The "lets privatize essential parts of the government" folks do not care about precedent, or what is a good idea. They are driven by ideology and self dealing.
even Cliff himself (somewhat right leaning in today's American political environment) did not go so far in his blog. Cliff often speaks very highly of the European weather forecasts, and in his blog post it seems pretty clear that he is using it as the model for most of the suggestions.
Cliff Mass has this kind of weird position on climate change where here fully agrees that it is real and a serious concern, while simultaneously questioning the extent to which it is human-caused and disputing specific weather events as the negative effects of climate change.
I'm curious about where we draw the line on questioning scientific consensus.
Take Cliff Mass - he's a professor of Atmospheric Sciences at UW whose research focuses on weather modeling, climate systems, and atmospheric dynamics. When someone with his expertise raises questions about climate science conclusions, should we approach this differently than when non-experts do so?
This raises an interesting question about the role of expertise and scientific discourse: Is there a meaningful distinction between how we treat challenges to consensus from qualified researchers in the field versus those from outside it? Or should acceptance of consensus apply equally regardless of one's credentials?
As an imperfect analogy, Stephen Wolfram is a fairly successful theoretical physicist by academic standards with considerable expertise, yet his theories on fundamental physics and its interpretations are considered fringe in the physics community because he most of what he spins is untestable conjecture.
Individuals who disagree with the scientific consensus aren't always wrong, but most of the time they are. What separates these people is the amount of evidence they can bring to the table.
He doesnt challenge scientific consensus. What he does challenge is people making any claim they want then adding "because climate change" at the end which has become a problem. One side will deny climate change and the other side starting taking any claim at face value as long as you say "because climate change"
The problem is that his well-meaning statements are going to be used as ammo to privatize NOAA, much like someone's well-meaning statements were used to create turmoil in Springfield, Missouri.
It's possible it's in a superposition of states. Like if someone stupidly misremembered which Springfield, then it would be in many states at once until observed.
Yeah that was my take too considering most of his argument is "spin it out of NOAA" and consolidate. The whole thing seems like a standard MBA reorg plan.
Also I don't think his data supports his argument. He shows, for instance, that GFS and ECMWF track each other in his own quality metric, that modern GFS is basically the same quality as ECMWF was in 2012, and that they are slowly converging. This is hardly a "decline."
The reason why ECMWF and GFS track eachother is that they're using the same data! The dominant limit on how good weather prediction can be is the amount and quality of the initial state data. ECMWF is proof that GFS could improve still, but let's be real: it's very good and unlike ECMWF it is free.
That, I think, is the real problem this guy has. The Accuweather CEO (who is a Trump supporter) and a lot of the private meteorology industry want NOAA to get out of the business of weather forecasting so that they can charge people for lifesaving information.
They pump ECMWF mostly because it's not free. Even the data showing it is superior is often somewhat cherry picked. For instance, it missed Hurricane Milton for quite a while while GFS was predicting its development and ultimate path pretty accurately.
Cliff Mass has been tenured at U-Dub for decades. The guy could have moved private to finance (energy trading, other commodities highly correlated with weather) to multiply his income, but didn't.
I think the comment you are replying to is suggesting he's angling to get appointed NOAA administrator or some similar role by Trump, if he wins. I can see someone whose dedicated their life to weather and has major criticisms of government policy there wanting that job but not being interested in a mere well paying private sector job.
Being the NOAA administrator is a particularly not-fun role if you're primarily a research scientist. I _highly_ doubt Cliff Mass is on any short lists for this job. Several folks from private industry immediately come to mind as likely picks, mostly from the venture capital-infused weather analytics industry.
About as likely as some random mid level Microsoft PM getting a role in the Harris administration via blog post about Agile. Cliff isn't some giant in meteorology, he's just a prof and Seattle weather blogger. He says in the comments, "I am not endorsing a candidate on this blog. I am having FUN," but you are polarized enough to think that any form of non-critical satirizing is an endorsement. That's fine. Cliff is a moderate which many leftists in Seattle misread as hard trump supporter.
How many trump staffers read his blog, do you think? Are weather models a hot topic among them?
I am an amateur radio operator and looking into the history of it, you quickly run into the telegraph, and telegraph operators would talk about things like the weather. It didn't take long before they realized that the weather in a place 100 miles away could show up the next day. Then they would warn everyone around them if bad weather was coming.
Specifically, NOAA's global model, the UFS, is now in third or fourth place behind the European Center, the UK Meteorology Office, and often the Canadians.
So there's been no decline. In fact US weather prediction is getting better (by the article own chart). It's just The author is upset the US isn't moving ahead as quickly as other weather services. Maybe they should be upset by this but the way it's framed seems a bit dishonest.
Don't worry, UK Met Office are currently shitting the bed on a migration from on-prem HPC to Azure (3+ years late at this point I believe?) so the competition isn't exactly hot.
This is also the same process that the Conservative parties in the UK ans Canada have been carrying out with their respective healthcare systems, much to the suffering of their citizens.
Indeed. One cannot get absurdly wealthy without making money from millions and millions of others. It's the nature of such wealth, and it is a sign of a society's moral decay.
I'm not against making lots of money by providing a good product at a fair price, but that's not what right-wingers are doing. They are robber-barons robbing from the populace for their own selfish greed. And we are all left poorer, both literally and figuratively.
Once we shift our personal and governmental focus to raising the standard of living for our least cared-for -- instead of allowing our systems to add more riches to the wealthy -- we can change this world into a fun, beautiful, and healthy place for us ALL. But that will not happen without selfless compassion.
Selfish taking harms us all; selfless giving helps us all. To love someone is to want them to be happy, and we are in desperate need of far more lovingkindness in this world.
>> U.S. numerical weather prediction (NWP), which uses computer simulation to predict future weather, should be the best in the world.
The author makes an important point, on a subject that keep getting more important due to climate change, but appeal to blind nationalism doesn't work in his favor.
It's not so much a bias a willful blindness at this point. I have to tolerate it coming from politicians, but it is a different story when the author present himself as a serious scientist.
Yes, the U.S. could be the best at meteorology. they could be the best at anything, with their means. But not at everything. The U.S. are the best at military spending, and everything else is at best in second place.
Well historically the military has quite often been one of the primary customers of weather forecasting, so that doesn't necessarily mean we couldn't have the best meteorology (assuming for the sake of argument that we don't).
It doesn't seem likely that the military has a private model that is dramatically better. If we did, it would likely be exposed in a way similar to GPS.
Somewhat related, but I found Andrew Blum's book The Weather Machine: A Journey Inside the Forecast an interesting read:
> In The Weather Machine, Andrew Blum takes readers on a fascinating journey through an everyday miracle. In a quest to understand how the forecast works, he visits old weather stations and watches new satellites blast off. He follows the dogged efforts of scientists to create a supercomputer model of the atmosphere and traces the surprising history of the algorithms that power their work. He discovers that we have quietly entered a golden age of meteorology—our tools allow us to predict weather more accurately than ever, and yet we haven’t learned to trust them, nor can we guarantee the fragile international alliances that allow our modern weather machine to exist.
* https://www.goodreads.com/book/show/42079139-the-weather-mac...
* https://www.andrewblum.net/the-weather-machine-2
Also his book Tubes, the Internet's physical infrastructure. He also appears to be working on a book on the electrical grid:
* https://www.andrewblum.net/electric-earth
Thanks for the recommendation.
I read Tubes back in 2012 when it was first published, and I enjoyed it. I knew a lot less about networking back then, so it would probably be a good book to re-read now with a different perspective, and to think about how things have might have changed since then.
Read Tubes last year. Magnificent read. A must read for internet users.
Aren't all weather forecasts getting their data from Raytheon in the US via Weather Central, LP?
As a sailor I’m really into getting a good weather prediction and most of the models are quite inaccurate. I learned on here about an ML based weather model startup that posted predictions on their website called Atmo.AI (edited) that was mind blowingly accurate and had incredible spacial resolution- it could accurately predict, e.g. the wind shadow behind islands, something none of the mainstream weather models even come close to doing. I used it everyday and it was almost always dead right at predicting wind speeds in almost any specific spot. I won a lot of sailboat races with it. I hope this tech gets taken up by NOAA, or other major weather predictors.
Anyone on here know what happened to Atmo and if the tech is ever going to come back so people can use it? It's a travesty that this tech exists but isn't being used- a lot of lives would be saved if NOAA were using this already existing technology.
Edit: Apparently as another poster pointed out I had just forgotten the correct name of it, and it is still around. I edited this to reflect that.
Weird, as a sailor I find GFS to be highly accurate taking into account an understanding of what it does, what it does not do, and how to interpret it.
GFS does not account for sea/shore breeze for instance, so if you're in an area where that may occur, then you will have to apply your own judgement about the conditions.
Now, if you were to feed weather station readings into an ML model, would the ML model be better at predicting the weather? Well I think it'd be better at predicting the things that the weather model does not model in the locality of the weather station, sure.
Are you talking about for offshore/open ocean?
I've found GFS fine for that, but it is one of the worst models for inland or near shore sailing, even most of the other popular models that you can access from sailing weather apps like NAM and ECMWF are much more accurate in specific locations with unique geography. The resolution is just too low to account for any interacting geography with GFS. It gives the same forecast over huge areas with radically different conditions.
I do some offshore but mostly on the great lakes.
I don't use GFS just by looking at the wind layer though. Wind layer forecasts do not include terrain or local effects as you noted. But the necessary info is in the forecast and is accurate.
For instance, in the great lakes we tend to have large diurnal temperature swings and therefore strong sea/shore breezes. If the model is forecasting big temperature changes and an anticyclone with low wind-layer forecast, this is ripe for strong sea/shore breezes.
The biggest hazard we have in the great lakes is convective storms (squalls). They do not show up in forecasts because convective cells are very small. However, The GFS gribs do have pressure forecasts, and perception, and most importantly CAPE and CIN forecast layers. Combined with WPC synaptic charts you have the info needed to determine if 1) convective storms are likely to occur and 2) if they do occur, the probability that they will be severe.
The GFS is a coarser model which covers the entire globe, so while the overall situation at the synoptic scale will tend be modeled quite well (at least inside of a few days into the future), the resolution of smaller-scale weather phenomena taking into account local factors just isn't going to be there.
For something maybe more useful on the local scale, you can also look at a model like the HRRR (which I believe does take into account the terrain and other local effects from things like larger bodies of water). While this model only really covers the conterminous United States and southern Canada, I've generally found it good for showing the shorter-term, local weather details, including forecasting convective storms and winds on and around the Great Lakes.
Thanks, I think I need to spend some more time studying meteorology so I can also better interpret how the data predicts actual hazards, as you are doing. I'm not even familiar with many of the acronyms you mention. I am simply comparing how well the predicted wind speeds are reflected in real life, in specific places where I am potentially in the wind shadow of relatively small hills, islands, etc. It is the flow around these objects that requires a high resolution model, as small shifts in wind direction make the wind shadows shift and change in size a lot. The actual overall prevailing conditions are so identical here from day to day in Northern California, there is hardly a need for large scale models unless the rare storm comes through.
I have noticed that where I am, the inland/offshore temperature differential is alone a pretty good predictor of overall wind speeds near the coast, not accounting for geography.
The best weather book for sailors I've ever read is Modern Marine Weather by David Burch
Thanks, I'll check it out! I've read "High Performance Sailing" by Frank Bethwaite, who was a sailing meteorologist and covers some things- but it is more focused on extreme micro-prediction.
Many companies offer customized, high resolution weather simulations that resolve those sorts of features. It's purely a cost vs value proposition - only a tiny sliver of users actually need this sort of spatial resolution, and typically over a very small area, so there is no reason for a global weather forecast model to be cranked up to it.
> Anyone on here know what happened to Atmo and if the tech is ever going to come back so people can use it? It's a travesty that this tech exists but isn't being used- a lot of lives would be saved if NOAA were using this already existing technology.
They're a thriving start-up as far as I've heard. Weather is a tough industry. Given that hourly-refreshing, high-resolution forecasts are freely available already from NOAA, I doubt that proprietary forecasts like these really move the needle in terms of protecting public life / property.
For sailboat racing I care about “micrometeorology” - e.g. things like which of two slightly different courses only 1/10th of a mile apart to the same destination will be windier 45 minutes from now?
Amazingly, these models are starting to be able to actually predict that, but I agree that not a lot of people care about that level of detail.
It depends on the type of sailing and where the race will be taking you. The participants in a race such as the Vendée Globe [0] are almost certainly using the likes of synoptic scale models like the GFS and ECMWF to plan their routes.
[0] https://en.wikipedia.org/wiki/Vend%C3%A9e_Globe
Yes for the vast majority, the immediate weather conditions can be ascertained by looking out the window.
I'd like more accuracy for the next 1-5 days as that's the time horizion I tend to use to plan to work outside on various projects, and am often frustrated by rain when the prior day's forecast didn't anticipate any. Or the opposite.
Do you mean atmo.ai? Atmos.ai seems to be something very different: "Atmos AI streamlines the Carbon Accounting, GHG Emissions, and Environmental, Social, and Governance (ESG) reporting processes for companies of all shapes and sizes."
Ok, I am embarrassed, I simply forgot the name of it, and thought it had disappeared when I tried to find it again. Seems it is still alive and well. I swear this isn't viral marketing for them, I was legitimately confused.
I edited my post above to use the correct name, but realize it doesn't make too much sense anymore.
I've met the Atmo founders a few times, very nice people and the company seems to be doing well. The tech is definitely getting used, but I think they're working with goverments more than end users
How do I use Atmo for locations other than the bay area demo linked on their website? I tried URL tweaking without much luck. I have a friend who does a lot of sailing races who would love this!
I don't know... I am only using it in the Bay Area. I noticed there are coordinates in the URL that one could try changing, but it is possible they are only running the model for the Bay Area right now?
I've also wondered if they have hand tuned the model to match local observations and geography in the Bay Area, and if it could generally apply elsewhere without a lot of manual work? In particular, it is mind blowingly accurate at getting the wind shadow around Angel Island, which is a complex thing to do, because it involves ocean wind that divides into at least 3 separate streams, and then recombines in complex ways. No other model I've seen can predict which of those 3 stream will dominate, but they usually can.
I've noticed the same with e.g. car navigation/map software- it generally works much better in the Bay Area where the developers and companies making them actually live, than elsewhere. I could imagine that in both cases the developers use it themselves in the place they live, and investigate/fix local errors themselves.
I did try altering the URL and didn't have much luck! It would make a lot of sense if Atmo was only running the model for a smaller area to keep costs from running up. My friend races in SoCal (the Channel Islands and Catalina are common destinations) and the wind shadows and streams are complicated there too.
Your observation about navigation software is accurate in my experience too :)
I couldn’t make much of atmo given the SF constraint, but I use the UK2 model for UK forecasts and it seems to be a similar level of detail and does very well taking topographical and micro weather nuance into account. Depending on your location you might find an equivalent short range model
I don't think the title of the article matches the content. The GFS has not declined relative to the ECMWF. It just started from a lower skill level and its improvements have not significantly closed the gap with ECMWF (as it has also improved).
> Specifically, NOAA's global model, the UFS, is now in third or fourth place behind the European Center, the UK Meteorology Office, and often the Canadians.
Would love to see evidence of that. It is well established that ECMWF is top in the game. I don't think it is reasonable to just state that Canada and UK are better without evidence.
With that said, I agree the US should improve.
When it comes to medium range predictions. If you look at nowcasting, I believe the top dog is metnet 3 (https://arxiv.org/abs/2306.06079).
On a tangential note, I worked on weather forecasting a bit before, and talking with some people still in the field it seems that these organizations (NOAA, ECMWF) are stuck with NWP and refuse to embrace AI models. Not sure if they can't attract talent and so are somewhat stuck or if their higher ups are "old school" and can't really see the potential that this approach has to offer. It is just sad that the private sector is outclassing institutions and that these better models won't hit the public domain.
> talking with some people still in the field it seems that these organizations (NOAA, ECMWF) are stuck with NWP and refuse to embrace AI models This is trivially verifiable as not true. ECMWF is all over AI:
https://www.ecmwf.int/en/newsletter/178/news/aifs-new-ecmwf-...
https://github.com/ecmwf-lab/ai-models
And they are hiring with competitive salaries (for Europe).
If only we'd put forecasting on the blonkchain so we could defend against 51% tornadoes
> When it comes to medium range predictions. If you look at nowcasting, I believe the top dog is metnet 3 (https://arxiv.org/abs/2306.06079).
I don't consider 24 hour forecasts "medium range". And I don't really view MetNet-3 a competitor to GFS or ECMWF at all (currently).
Windy.com offers ECMWF-AIFS models to paid subscribers. ECMWF seems to be all-in on the ML-based, as opposed to physical, NWP, but it's not clear ML approaches are superior.
There is plenty of evidence - UKMO reliably outperforms GFS, as does DWD and many other centres.
https://wmolcdnv.ecmwf.int/scores/mean/msl
But the forecasting hasn't declined. It's better than it was.
I would love to see a multi-year skill plot for all global models similar to what is commonly shared for GFS vs. ECMWF. Instead of having to parse a bunch of individual month comparisons for specific observations.
Regardless, my criticism is towards the evidence presented in the article and the framing of the article.
Like this?
https://emc.ncep.noaa.gov/users/verification/global/gfs/ops/...
The article leaves out that members of one political party have proposed eliminating the NOAA entirely https://www.washingtonpost.com/weather/2024/07/22/project-20...
They also want to get rid of most civil service employees and change their positions to political appointees. Not just the top management positions, but also the people that actually do the day to day work.
E.g., the scientists at the FDA who check the science on safety and effectiveness when processing approvals for new drugs would be political appointees who were vetted for loyalty to the President.
Lol I shouldn't have clicked on that link, that shit really makes my blood boil.
I don't think most people realize how much free value they get out of NOAA weather predictions.
While I’d agree that accurate weather forecasts provide tremendous value, it’s not “free value”, as tax dollars are paying for it. That money could go to any number of ‘under-funded’ other causes, or be returned to the tax-payers.
A relief to know NOAA isn’t doing this pro bono, I was beginning to wonder whether they were simply donating forecasts.
This stuff has been a big bugaboo for GOP idiots for a long time.
Rick Santorum (a former conservative luminary) was pushing for a law that would prohibit making weather data available to the public, but would make it available to companies like Accuweather for free.
The brother of the Accuweather founder was Trump’s appointee to run the weather service.
It’s on of those things that illustrates that no public resource is safe from graft.
> I have written two peer-reviewed papers in a major meteorological journal (here)
The blogger is Cliff Mass, a professor at University of Washington. He has written two papers on this specific topic (decline of US weather predictions). In general, as a scientist, he has published 100+ papers.
He only linked to his second paper [1]. I also found his first paper [2].
[1] "The Uncoordinated Giant II" (2023) https://journals.ametsoc.org/downloadpdf/view/journals/bams/...
[2] "The Uncoordinated Giant" (2006) https://www.e-education.psu.edu/files/meteo410/image/Lesson3...
This is a valuable blog on the subject, and his answer explains how the US could rectify.
But NWS didn’t correct their hurricane forecast to the whims of a mercurial President. Talking about the technicalities of the model library is fine but only after you pass the loyalty test.
Also I heard many smart people in NOAA left between 2016 and 2020 to go private and funding cuts. I would not be surprise many in NOAA are looking to leave now.
I wonder if the listeria issues occurring now is also a symptom of this plus the FDA funding cuts over the past 20+ years.
You can draw a strong connection from in 2019[1] when the USDA allowed slaughterhouses to self inspect to the recent listeria outbreaks.
[1] https://www.npr.org/sections/thesalt/2019/09/17/761682926/us...
It would be really interesting to interview all the listeria victims and find out how they voted.
Elections have consequences.
Project 2025 describes NOAA as a primary component “of the climate change alarm industry” and said it “should be broken up and downsized.” Talent is probably worried about the future political climate as well.
https://static.project2025.org/2025_MandateForLeadership_FUL...
It's will known that AccuWeather's president Joel Myers and his younger brother (now former) AccuWeather CEO Barry Lee Myers want to kill public domain weather report data. https://en.wikipedia.org/wiki/AccuWeather#National_Weather_S... Barry was nominated (but never confirmated) for Under Secretary for Oceans and Atmosphere and Administrator, National Oceanic and Atmospheric Administration
While AccuWeather denies involvement in Project 2025, there's definitely overlap between TFG's administration and the authors of the plan.
> It's will known that AccuWeather's president Joel Myers and his younger brother (now former) AccuWeather CEO Barry Lee Myers want to kill public domain weather report data.
John Oliver of all people had a segment on this:
* https://www.youtube.com/watch?v=qMGn9T37eR8
* https://www.imdb.com/title/tt11110660/
* https://www.theguardian.com/culture/2019/oct/14/john-oliver-...
* https://time.com/5699545/john-oliver-weather-last-week-tonig...
I haven't heard anything about NOAA having any new issues with workforce retention.
The trend you're seeing in private sector expansion / growth is linked to the massive investments in Earth observation and derivative applications. It's not really a response to anything happening in government. If anything, the federal government forced some of this growth through commitments like the Commercial Weather Data Program.
Yes, indeed, the more they start sucking at things, the more money we should give them. This will incentivize not sucking.
Hello, the spirit of No Child Left Behind
I just applied for a job at noaa after being asked by a director to apply for the job which was directly related to updating the core weather modelling libraries, some of which are 50+ years old Fortran, and also develop projects to integrate modern ai/ml into the modelling pipeline. I have 10+ years of experience working both as a researcher and a developer, a PhD in physics, and have won multiple grants either applying machine learning to geophysics problems or develop Bayesian spatiotemporal modeling libraries. At least on paper, I should get an interview for this job. I was rejected because:
> We are not able to determine your qualifications as your resume does not show complete information for each job entry, such as beginning and ending dates of employment, duties performed, and/or total hours worked per week
First, my cv did include everything but the total hours worked per week. Second there was no instruction that I could find for writing my CV such that I needed to list the number of hours worked per week. Third there was no way to contest this. Even the person who invited me to apply who would be the hiring manager had no control over this.
I can tell you, this assuredly leads to the unnecessary decline of any system when there’s an impenetrable hr system sitting between applicants and jobs.
That sucks. Did NOAA have their own application site or did you go through USAJOBs? I would recommend to you or others reading this that if you're applying through USAJOBs, then make sure to enter your resume information in the USAJobs resume builder to help prevent bureaucratic failures like this. HR probably didn't know what they were reading or weren't able to read your CV; they probably based their decision off of the USAJobs resume.
https://help.test.usajobs.gov/how-to/account/documents/resum...
Because NOAA is part of the Federal government, expect job applications to be inspected technically detailed relative to compliance with a mountain of Federal statutes and regulations.
Because people who don't get hired can literally get their Congressional and Senate representatives involved.
This makes getting hired into the Civil Service hard. Internal candidates are usually at an advantage. Veterans always are.
> Internal candidates are usually at an advantage. Veterans always are.
The system is as you described. Internal candidates already know it and veterans, when transitioning out of uniformed service, get a full-blown class on it: how to prepare their "Federal resume" (which is unlike any resume that a reasonable person might otherwise prepare) and navigate the hiring process. So it's that on top of already having relevant experience and credentials or preference points.
I've been on the hiring side for Federal GS positions, and I'll tell you that it can be just as frustrating on that side too.
I’m currently a civil servant of Norway. They seem to have it figured out. Haha. But yeah I get you.
That's crazy. Why would the number of hours worked per week ever possibly be relevant to a hiring process? At most they might ask "full-time/part-time/other" or something like that.
I am sure somebody somewhere has tried to claim having had an academic position at a university, while actually they were working at 4 hours per week as a teaching assistant for one course.
Because Full-Time/Part-Time/Other is too imprecise for government. What if you consider full time is 25 hours because you were going to school? Well, you worked less on something but claim to have same experience at someone who worked 40.
Government has to comply with all these standards and by asking for hours, they can make sure themselves that someone meets them.
This is doubly stupid for an organization that would probably want to hire people right out of grad school, right? Where the hours are all day and the contracts are for however much the program has laying around at the moment.
Wait, they asked for a resume and you provided a CV. Gonna have a hard time when you start on the wrong foot with government work ;p
I was waiting for the classic in triplicate req wasn't followed.
What did the director who asked you to apply say when you told them?
They said to follow the guidelines for writing your CV that are posted in the job ad. So I did.
A simple explanation is: corruption.
HR in companies has changed radically in recent years.
If you throw away your assumptions, and solely look at the behaviors that the function exhibits, you will find striking resemblance to the behaviors under communism, or fascism.
Money has no value any more. Power is the only currency.
To the public, they are strictly compliant, but the tools, such as the CV, the interview process, feedback, approval process, and so on, are irrational to anyone involved, except the true decision makers.
Their objective is not no longer meritocratic, to hire the best qualified or best performing, but to hire people that strengthen the political power of those that already wield it. It's more than nepotism, it's empire building.
> Money has no value any more. Power is the only currency.
Money can be exchanged for goods and services.
Thank you, Homer.
I feel like the opposite is true. Money is all that matters because it seems like everyone has a price.
It's not the money that has value, but what money can be exchanged for.
If your money can't buy you admission into university, or property that is sold off-market and privately, or access to off-market and privately controlled jobs, and so on, then your money doesn't help you at all.
Power is the real currency in an unfree market. As in communism.
Yeah, I nope out of any application that requires manual entry of each employer etc.
Yeah it always makes me feel like they are going to be more than happy to waste my time since they waste their own with such things.
I have lived in California for 36 years, 22 in SoCal and 14 in NorCal and I've noticed a trend that I find deeply disturbing: weather forecasts seem to be much less reliable now than they used to be. Before moving to California I lived on the east coast and the weather forecasts were generally unreliable, so I was really struck by how predictable the weather in California was, and how accurate the forecasts were. Rain began and ended when it was predicted to within a couple of hours. The temperature was within a few degrees of what it was predicted to be. And the window of predictability went out 5+ days.
Nowadays that has all changed. Forecast rain fails to materialize. Heat waves come out of nowhere and last much longer and are much more intense than they are forecast to be. Temperatures at my house regularly go 10 degrees above what is predicted, and regularly hit temperatures that would have been extremely anomalous when we first moved here.
This is all anecdotal. I haven't been keeping records, and I haven't done the math. It's possible that this is all confirmation bias or bad memory at work. But damn, it sure seems like a real phenomenon, and the most likely explanation is that the models were trained on data that don't reflect current conditions because of climate change.
The most striking thing about this is the speed with which things are changing. 36 years is nothing on what should be geologic time scales. I shudder to think what the future holds.
I've seen something similar happen and think I figured out the reason. Weather forecasts in my area were very accurate for decades, then they got much worse. They were off in timing for events, and incorrect about whether rain was going to fall or not. Then I realized that the change happened around the time the local nuclear power plant was shut down. The plant had been funding a local weather station and weather radio station as part of government requirements to provide early warning of problems to citizens. When the plant shut down the weather station went with it. So NOAA was no longer getting accurate data from this area and the forecasts were off because of that. I've been thinking of setting up my own station and linking it into the public data to see if that makes forecasts get more accurate.
Run your own code :-) I used to fly sailplanes and around Northern California at least there is an interesting intersection of computer nerds, aerodynamics nerds and weather nerds. One of my favorite things being Jack Glendening's weather code/models for sailplane pilots, culminating in the RASP forecasts that more technical sailplane (and hang glider etc) pilots ran for their own hyper-local forecasts. The localized terrain triggered thermal hotspots and convergence prediction of these RASP models were super impressive. http://www.drjack.info/rasp. Today specialized commercial forecast services are used by many sailplane pilots, and RASP and related forecasts predated or inspired those, but a well run RASP forecast on a fine grid is still something to behold.
As a European, I can tell you not to worry too much. The EU forecasts are mostly garbage. For some reason (possibly climate change) weather forecasting became unreliable over the last 10-15 years.
I regularly check 5 different forecasting models (through a subscription in the Windy app and elsewhere) and they are unable to agree on whether water will fall on my head later today. We're not talking long-range forecasting here, just tell me if it's going to rain in several hours or not. There is no forecasting model that gets this right.
Norwegian official weather forecasting service usually say they're quite accurate, and I was thinking how can that be? But in an interview last year they spilled the beans, they were talking about temperature and only missing by 1-2 degrees C most of the time.
However, I don't care if it's 22C or 20C, like you I care about am I gonna need a jacket or not.
I get that it's a difficult problem. And the inclusion of local weather radar to track actual rain has been very nice. But it only forecasts 90 minutes ahead, and rain can develop closer than that some times.
One of the fee startups I am genuinely impressed: skysight.
https://magazine.weglide.org/skysight-interview-matthew-scut...
Freakingly accurate weather forecasts for the niche sport of gliding. Builds on top of ECMWF and incorporates refined soil moisture. It’s really good in predicting mountain waves (and used by Perlan: https://perlanproject.org/perlans-sponsors-enable-us-to-fly-... ). Also convergences and thermal activity which is very dynamic…
Incredibly impressive.
Mass/Maas uses European results in his blog often, often commenting that the European predictions for the PNW are more accurate. So, fine, that's great. Why does the US have to keep up? Will it stop the decline of the Empire? Is it a stab in the heart of our world posture? Are we doomed to having to be the best at everything for all of eternity? I don't get it.
I'm more upset about all the interest being paid on the US national debt that -could- have been spent on something great instead.
>In most nations, numerical weather prediction is the responsibility of one group. In the U.S., governmental global weather prediction is spread over FIVE efforts:
>NOAA, the National Oceanographic and Atmospheric Administration (the leading U.S. effort)
>The US Navy
>The US Air Force
>NASA
>U.S. Department of Energy
This doesn't make sense. Other nations don't have a NASA, and if they do have a space agency that space agency is almost certainly in the weather business.
JAXA: https://earth.jaxa.jp/en/data/2547/index.html
They also don't have expeditionary militaries like the USAF and USN, which need weather for the middle of the ocean or remote nation, far from NOAA's ability to deploy weather balloons.
And as far as I can tell, the only exposure to weather that the DOE has is for climate change reasons, and their interest in that should be obvious.
There's no waste. NOAA does the US, DOD does DOD, NASA helps out, and the DOE is looking at how climate change will impact the US's energy security.
So reason number 1 is horseshit, and I stopped reading.
“In the U.S., governmental global weather prediction is spread over FIVE efforts:
NOAA
The US Navy
The US Air Force
NASA
U.S. Department of Energy
Thus, scientific and technical talent and computer resources are diffused over five groups, greatly undermining progress.“
I suppose if Elon has his way, the overlapping functions from these are on the chopping block.
Interesting call for more horsepower behind numerical methods. One of the benefits of the emerging machine learning forecast models is dramatically lower need for computation (and therefore energy).
Sort of.
The motivation here is that numerical weather prediction (NWP) models are used for a lot more than just weather forecasting - they're critical research infrastructure and tools. NWP allows researchers to simulate complex atmospheric flows and phenomena which might not be readily or directly observed - or to manipulate observed flows in ways to evaluate dynamical theory and the underlying physics. You can't really do any of this with the AI emulators, which output a tiny, tiny sliver of information relative to the vast array of data you can dump from an NWP system.
Regarding energy usage, you're right up to a point. Today's ML weather models are trained to emulate very large reanalysis datasets (e.g., ECMWF's ERA5) which are produced using the physics-based models. No one has yet to demonstrate an end-to-end AI/ML weather forecast which sidesteps these reanalysis datasets (in fact, the trend so far has been to include _more_ physics-based model datasets for fine-tuning, including archives of historical NWP forecasts or GCM simulations). So there's a massive sunk cost in energy usage to create those training datasets, and then there's the ongoing energy cost of training AI emulators from the ground up. And of course, there's the future energy cost of running more physics-based models to better support the development of AI emulators.
For pure inference/forecasting? Sure. Much faster to run AI models and much lower energy usage. But that's quite literally the tip of the iceberg when it comes to forecast model development.
Sure, the AI stuff is not a complete solution because it relies on the results of numerical methods. I was only suggesting that in the question of making up the marginal difference between the best and fourth-best weather predictions, there might be some application of these other novel methods.
If the question is can the Americans build a gigantic supercomputer and write fortran programs, the answer to that seems obvious. Yes we can do that when the relevant bureaucracy gets pointed in the right direction.
> One of the benefits of the emerging machine learning forecast models is dramatically lower need for computation (and therefore energy).
Maybe but honestly, it doesn't make a ton of sense to me that something that is so power hungry it needs new power plants is going to save energy.
I'm also skeptical that AI can really model a chaotic system better than a.. chaotic system simulation. AI is a statistical pattern matcher. I'm really struggling to understand how that would be better at prediction than a simulation of the underlying phenomena. The whole thing seems like a solution in search of a problem.
> it doesn't make a ton of sense to me that something that is so power hungry it needs new power plants is going to save energy.
The fact that Sam Altman has a 200MW chatbot is not relevant here. For example the GraphCast system from DeepMind runs in under 1 minute on a single TPU device.
Don't forget that ML models are trained on existing data. This makes them unsuitable for long-term climate models.
True, but I believe we are discussing forecasts on the scale of hours, not decades.
Red hat at the end positions this as a job application?
More like declaring the real plan of setting up one org that needs to be privatized.
There have been real attempts at privatization of weather services in the US, despite the fact that our current system costs pennies a day per person (whether it's used for weather apps, or safe flying/sailing).
unlikely that there is good precedent for a private weather organization. The gold standard for western weather prediction is the European Centre for Medium-Range Weather Forecasts and that is a collaboration between multiple government agencies.
> unlikely that there is good precedent for a private weather organization.
When has something being a bad idea or never having been tried stopped political apparatchik's from trying, especially given past appointee's opinions:
* https://www.youtube.com/watch?v=qMGn9T37eR8
* https://www.imdb.com/title/tt11110660/
* https://www.theguardian.com/culture/2019/oct/14/john-oliver-...
* https://time.com/5699545/john-oliver-weather-last-week-tonig...
See also Project 2025:
> The document describes NOAA as a primary component "of the climate change alarm industry" and said it "should be broken up and downsized."
[…]
> Project 2025 would not outright end the National Weather Service. It says the agency "should focus on its data-gathering services," and "should fully commercialize its forecasting operations."
> It said that "commercialization of weather technologies should be prioritized to ensure that taxpayer dollars are invested in the most cost-efficient technologies for high quality research and weather data." Investing in commercial partners will increase competition, Project 2025 said.
* https://www.politifact.com/factchecks/2024/sep/26/jared-mosk...
The "lets privatize essential parts of the government" folks do not care about precedent, or what is a good idea. They are driven by ideology and self dealing.
even Cliff himself (somewhat right leaning in today's American political environment) did not go so far in his blog. Cliff often speaks very highly of the European weather forecasts, and in his blog post it seems pretty clear that he is using it as the model for most of the suggestions.
Cliff Mass has this kind of weird position on climate change where here fully agrees that it is real and a serious concern, while simultaneously questioning the extent to which it is human-caused and disputing specific weather events as the negative effects of climate change.
I'm curious about where we draw the line on questioning scientific consensus.
Take Cliff Mass - he's a professor of Atmospheric Sciences at UW whose research focuses on weather modeling, climate systems, and atmospheric dynamics. When someone with his expertise raises questions about climate science conclusions, should we approach this differently than when non-experts do so?
This raises an interesting question about the role of expertise and scientific discourse: Is there a meaningful distinction between how we treat challenges to consensus from qualified researchers in the field versus those from outside it? Or should acceptance of consensus apply equally regardless of one's credentials?
As an imperfect analogy, Stephen Wolfram is a fairly successful theoretical physicist by academic standards with considerable expertise, yet his theories on fundamental physics and its interpretations are considered fringe in the physics community because he most of what he spins is untestable conjecture.
Individuals who disagree with the scientific consensus aren't always wrong, but most of the time they are. What separates these people is the amount of evidence they can bring to the table.
He doesnt challenge scientific consensus. What he does challenge is people making any claim they want then adding "because climate change" at the end which has become a problem. One side will deny climate change and the other side starting taking any claim at face value as long as you say "because climate change"
The problem is that his well-meaning statements are going to be used as ammo to privatize NOAA, much like someone's well-meaning statements were used to create turmoil in Springfield, Missouri.
Ohio?
Nobody is sure what state Springfield is in.
It's possible it's in a superposition of states. Like if someone stupidly misremembered which Springfield, then it would be in many states at once until observed.
Yeah that was my take too considering most of his argument is "spin it out of NOAA" and consolidate. The whole thing seems like a standard MBA reorg plan.
Also I don't think his data supports his argument. He shows, for instance, that GFS and ECMWF track each other in his own quality metric, that modern GFS is basically the same quality as ECMWF was in 2012, and that they are slowly converging. This is hardly a "decline."
The reason why ECMWF and GFS track eachother is that they're using the same data! The dominant limit on how good weather prediction can be is the amount and quality of the initial state data. ECMWF is proof that GFS could improve still, but let's be real: it's very good and unlike ECMWF it is free.
That, I think, is the real problem this guy has. The Accuweather CEO (who is a Trump supporter) and a lot of the private meteorology industry want NOAA to get out of the business of weather forecasting so that they can charge people for lifesaving information.
They pump ECMWF mostly because it's not free. Even the data showing it is superior is often somewhat cherry picked. For instance, it missed Hurricane Milton for quite a while while GFS was predicting its development and ultimate path pretty accurately.
ECMWF is working towards being free/open-data by 2026.
>We eat her models
Oh, the keming.
But I agree. Seeing that privatization of government agencies is part of the platform, this sounds like "I'm not endorsing, but..."
> Gr eat again
Cliff Mass has been tenured at U-Dub for decades. The guy could have moved private to finance (energy trading, other commodities highly correlated with weather) to multiply his income, but didn't.
I think the comment you are replying to is suggesting he's angling to get appointed NOAA administrator or some similar role by Trump, if he wins. I can see someone whose dedicated their life to weather and has major criticisms of government policy there wanting that job but not being interested in a mere well paying private sector job.
Being the NOAA administrator is a particularly not-fun role if you're primarily a research scientist. I _highly_ doubt Cliff Mass is on any short lists for this job. Several folks from private industry immediately come to mind as likely picks, mostly from the venture capital-infused weather analytics industry.
I’m saying that the hat is a pretty clear declaration.
But I’m not saying just what role he might find tempting. Focusing on the particular role is a red herring.
There are lots of opportunities for disruptive people with big plans who don’t mind wearing the hat to get there. As we have seen recently.
About as likely as some random mid level Microsoft PM getting a role in the Harris administration via blog post about Agile. Cliff isn't some giant in meteorology, he's just a prof and Seattle weather blogger. He says in the comments, "I am not endorsing a candidate on this blog. I am having FUN," but you are polarized enough to think that any form of non-critical satirizing is an endorsement. That's fine. Cliff is a moderate which many leftists in Seattle misread as hard trump supporter.
How many trump staffers read his blog, do you think? Are weather models a hot topic among them?
I am an amateur radio operator and looking into the history of it, you quickly run into the telegraph, and telegraph operators would talk about things like the weather. It didn't take long before they realized that the weather in a place 100 miles away could show up the next day. Then they would warn everyone around them if bad weather was coming.
> Our nation invented the technology...
Lewis Fry Richardson might have something to say about that.
Specifically, NOAA's global model, the UFS, is now in third or fourth place behind the European Center, the UK Meteorology Office, and often the Canadians.
So there's been no decline. In fact US weather prediction is getting better (by the article own chart). It's just The author is upset the US isn't moving ahead as quickly as other weather services. Maybe they should be upset by this but the way it's framed seems a bit dishonest.
Don't worry, UK Met Office are currently shitting the bed on a migration from on-prem HPC to Azure (3+ years late at this point I believe?) so the competition isn't exactly hot.
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This is also the same process that the Conservative parties in the UK ans Canada have been carrying out with their respective healthcare systems, much to the suffering of their citizens.
Indeed. One cannot get absurdly wealthy without making money from millions and millions of others. It's the nature of such wealth, and it is a sign of a society's moral decay.
I'm not against making lots of money by providing a good product at a fair price, but that's not what right-wingers are doing. They are robber-barons robbing from the populace for their own selfish greed. And we are all left poorer, both literally and figuratively.
Once we shift our personal and governmental focus to raising the standard of living for our least cared-for -- instead of allowing our systems to add more riches to the wealthy -- we can change this world into a fun, beautiful, and healthy place for us ALL. But that will not happen without selfless compassion.
Selfish taking harms us all; selfless giving helps us all. To love someone is to want them to be happy, and we are in desperate need of far more lovingkindness in this world.
Missed a chance to reword that epic as a failure to an epic failure
>> U.S. numerical weather prediction (NWP), which uses computer simulation to predict future weather, should be the best in the world.
The author makes an important point, on a subject that keep getting more important due to climate change, but appeal to blind nationalism doesn't work in his favor.
It's not so much a bias a willful blindness at this point. I have to tolerate it coming from politicians, but it is a different story when the author present himself as a serious scientist.
Yes, the U.S. could be the best at meteorology. they could be the best at anything, with their means. But not at everything. The U.S. are the best at military spending, and everything else is at best in second place.
Well historically the military has quite often been one of the primary customers of weather forecasting, so that doesn't necessarily mean we couldn't have the best meteorology (assuming for the sake of argument that we don't).
It doesn't seem likely that the military has a private model that is dramatically better. If we did, it would likely be exposed in a way similar to GPS.
They'll release an intentionally kneecapped model until it's clear that workarounds are well known enough to allow release of the actual data?
https://en.wikipedia.org/wiki/Error_analysis_for_the_Global_...