[HN Gopher] Project Aardvark: reimagining AI weather prediction
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Project Aardvark: reimagining AI weather prediction
Author : bentobean
Score : 282 points
Date : 2025-03-23 23:33 UTC (23 hours ago)
(HTM) web link (www.turing.ac.uk)
(TXT) w3m dump (www.turing.ac.uk)
| kgwgk wrote:
| Paper available here:
| https://www.nature.com/articles/s41586-025-08897-0
| abdullahkhalids wrote:
| Arxiv version here https://arxiv.org/pdf/2404.00411
| foofoo55 wrote:
| Why are the Arxiv and Nature versions so different, even the
| text?
| abdullahkhalids wrote:
| Its quite common to revise papers. For example, they might
| have uploaded to arxiv in order to submit to a conference.
| Later, they revised and submitted to Nature.
| kkylin wrote:
| As another comment mentioned, papers get revised during
| review, usually in response to reviewer comments. Also,
| some journals (not sure about Nature) do not allow authors
| to "backport" revisions made in response to reviewer
| comments to preprints; I guess they view the review process
| as part of their "value add".
| jamala1 wrote:
| Arxiv is mostly meant for preprints for peer review.
|
| In a Nature paper in particular, the final layout is
| typically done by the journal's professional production
| team, not the authors.
|
| Not all publishers grant permission for authors to upload
| the peer-reviewed and layouted postprints elsewhere.
| benob wrote:
| According to arxiv paper, code will be made available here:
| https://github.com/annavaughan/aardvark-weather-public (from
| 8 months ago)
| sunshinesnacks wrote:
| The Nature preprint references this zenodo archive with
| data and code in a big 13GB .tar file:
| https://doi.org/10.5281/zenodo.13158382
|
| I haven't downloaded it to see what's in it.
| dboreham wrote:
| Although based on their bios and UK population statistics, it
| seems likely they're both English, the article omits this
| information. They do work at a British institution, located in
| England..
| _joel wrote:
| The name Turing and turing.ac.uk should maybe give it away. I
| don't think the authors' nationalities are of importance,
| however.
| abdullahkhalids wrote:
| How robust will ML models be in the world of rapid climate
| change, where the past no longer predicts the future?
| zdragnar wrote:
| Climate is not weather.
|
| Weather predictions are for specific events and areas, made on
| the order of days- typically no more than 2-3 weeks into the
| future.
|
| Climate models predict future averages over large regions on
| the order of decades.
|
| "rapid climate change" is on the order of "within this
| century". Whether the climate changes or not doesn't really
| impact the weather models at all, because the climate's not
| changing on the same time scale.
| labster wrote:
| While I really want to agree with your sound argument, I have
| this suspicion that climate change significantly affected
| forecast skill on Hurricane Otis.
| melagonster wrote:
| Climate change: more typhoons in the future.
|
| Weather prediction: which path typhoons will follow?
| zdragnar wrote:
| The climate didn't change in the time from Otis forming to
| landing.
|
| Weather models look at current conditions and extrapolate
| what the next set of conditions will be.
|
| Another way to look at it is that climate change models
| reflect what we will experience. Weather models reflect the
| mechanics of humidity, temperature and air flow- given a
| current state, what is the next likely state. Climate
| change doesn't change how those mechanics interact. It only
| guesses what "current state" will look like in the far
| future.
| avianlyric wrote:
| Climate change doesn't impact the physics of weather, but
| our understanding of the inputs to a weather system is
| still very incomplete. Climate change does impact the
| relative importance of different physical inputs into a
| weather system, and inherently pushes weather systems
| away from the most studied and well understood aspects of
| weather physics. That in turn degrades the accuracy of
| our models, as it takes time to research and understand
| whats changed in a weather system, and what previously
| unimportant inputs have become much more important.
| zdragnar wrote:
| Let's say I live in the hypothetical state of Midwestia.
| It gets up to 100 degrees F in the summer and down to -40
| in the winter months.
|
| Over the course of ten years, the average daily
| temperature goes up by 1 degree F (which is many times
| faster than reality).
|
| That means that there's basically one day in the entire
| year that doesn't fit within the existing model, or maybe
| a handful if some other part of the year is cooler than
| normal. We see more variation between normal years than
| we expect to see average increase in both the short and
| medium terms.
|
| Our weather models predict no more than two or three
| weeks into the future.
|
| There's literally nothing about climate change that will
| change how weather systems interact that will occur
| faster than our weather models will adapt to. Our weather
| patterns may change drastically, but that's a
| fundamentally different problem.
| avianlyric wrote:
| How quickly do you think our weather models adapt? Our
| current models are built on top of decades of detailed
| weather research by tens of thousands of researchers
| around the world, and has involved the launch of some the
| largest satellites in space. All to help us try and
| understand how weather works.
|
| Those models don't just "adapt", they're carefully and
| manually refined over time, slowly incorporating better
| physical models as they're developed, and better methods
| of collecting data as it become available. Climate change
| absolutely changes how weather systems interact faster
| than our models can adapt, primarily because we can only
| adapt models to changes _after_ their accuracy degrades,
| and it becomes possible to start identifying potential
| weaknesses in the existing approaches.
|
| You're example of seasonal temperature changes vs global
| average temperature increases, vastly underestimates how
| complicated weather models are, and how much impact
| changes in global climate systems (like the North
| Atlantic Current) can drastically impact local weather
| conditions. Weather models, like all models, will make
| assumptions about the behaviours of extremely large
| systems which aren't expected to change very much, or are
| very hard to measure in real-time. As those large systems
| change due to climate change, the assumptions made by
| weather models will grow increasingly incorrect. But that
| hard part is figuring our _which_ assumption is now
| incorrect, not always easy to identity exactly what
| assumptions have been made, or accurately measure the
| difference between the assumption and reality.
| danaris wrote:
| But when climate _changes_ even by what appear, to us, to be
| very small amounts, that can have huge effects on how weather
| systems behave.
|
| Thus, if you want your AI model that predicts _weather_ ,
| which was trained on data before that change, to predict
| weather _after_ that change, you may very well find that it
| rapidly loses accuracy.
| jvanderbot wrote:
| I think the naive answer is that physics doesn't change
| when the climate does, just the initial conditions for the
| day's simulation of the next week's weather.
|
| And every year over year change is within error bars of the
| prior years results, once you account for those initial
| conditions.
| danaris wrote:
| And if your model is based on physics, then that's fine.
|
| But if your model is an LLM (or close cousin) purely
| based on previous weather patterns, then it may struggle
| to accurately predict once the underlying conditions
| change.
| geysersam wrote:
| The less naive answer is that a typical weather model has
| hundreds of parameters that have been tuned manually over
| decades of use.
| abdullahkhalids wrote:
| One of the characteristics of the present climate change on
| Earth is that weather events (storms, heat waves, maximum and
| minimum temperatures, start of seasons) display a lot more
| variability, and consequently also a lot more extremes than
| in the recent past. This is already happening.
|
| If you train a weather model ML on past weather data, which
| has reduced variability, and use it to predict future
| weather, it is possible that it under predicts variability of
| weather variables (amount of expected rainfall, maximum
| temperature tomorrow etc). Variance not mean.
|
| I say "it is possible" because only rigorous modeling can
| tell us the truth. Which is why I asked the question
| originally. I don't think it can be denied by abstract
| arguments.
| labster wrote:
| AI is not just pattern matching. Assuming they have some idea
| of initial and boundary conditions, they should be fine.
| crm9125 wrote:
| "Tomorrow will be partly cloudy, with a chance of hurricane."
| KennyBlanken wrote:
| I wish HNers would ask themselves "If I, someone with nothing
| more than layman's knowledge, thought of this - would someone
| who has spent years studying in their field, possibly the head
| of their lab, the people they collaborated with, the people who
| approved their grant, and the people who reviewed the paper for
| publication - think of this?"
|
| I can hear the keyboard mashing already. YES, _I am aware there
| are problems in both research and publishing_.
|
| That does not mean that a HN'ers "did they think of X?" is any
| more valid. It's like saying that because bridges collapse, we
| should be allowed people with no relevant experience,
| knowledge, or training to look at new bridges and say "that bit
| there doesn't look strong enough to me!"
| throwup238 wrote:
| I wish HNers would stop and ask themselves "Do HNers need to
| read yet another misguided Dunning-Kruger rant from someone
| who clearly misread the parent comment?"
|
| The GP asked _ _how_ _ robust the ML models will be, which is
| a perfectly good question to ask. Maybe a climate scientist
| specializing in ML can answer that question.
| RicoElectrico wrote:
| If you know about a particular field and read HN comments
| it's even worse.
| genewitch wrote:
| Murray Gell-Mann says "et tu?"
| graemep wrote:
| On the other hands experts do need to be questions and
| provide explanations.
|
| "It's like saying that because bridges collapse, we should be
| allowed people with no relevant experience, knowledge, or
| training to look at new bridges and say "that bit there
| doesn't look strong enough to me!""
|
| Not a valid comparison. Civil engineering is a field with a
| lot of known answers. bridge designs rarely rely on cutting
| edge research.
| interludead wrote:
| Honestly one of the biggest open questions in applying ML to
| weather and climate
| lenerdenator wrote:
| Hmmmmmmm.
|
| I have a challenge for the model:
|
| Accurate (within 3deg F) weather predictions for the Kansas City
| metro more than two days out. As of 2024 these were rarely
| accurate.[0]
|
| [0]https://www.washingtonpost.com/climate-
| environment/interacti...
| lytedev wrote:
| Indeed we have it tough out here. I like the excitement,
| though, I must say! Funny to find the sentiment here on HN,
| though!
| metaphor wrote:
| > _Accurate (within 3deg F) weather predictions_
|
| Sniff test: ASTM E230 standard tolerances for the venerable
| Type K thermocouple is +/- 2.2degC or +/- 0.75%, whichever is
| greater.
|
| Expectations are in need of recalibration if anyone thinks a
| single number is going to meaningfully achieve that level of
| accuracy across a volume representing any metro area at any
| given point in time, let alone two days out.
| genewitch wrote:
| I think you misunderstood the assignment. The temp
| predictions in my area wildly swing around, until the day of,
| and even then I have to adjust for my actual location. And
| I'm not in Kansas City. The only place that has ~3deg
| accuracy is San Diego. And maybe Antarctica.
| IshKebab wrote:
| The bigger the volume the _more_ accurate temperature you
| should be able to get.
|
| Do you think 2C global warming is irrelevant because a single
| thermocouple couldn't accurately measure it?
| kwertzzz wrote:
| I am not sure if type K thermocouple are used for
| meteorological air temperature measurements.
|
| These sensors (based on thermal resistance) for example have
| an accuracy of 0.2 degC under typical conditions [1].
|
| [1] https://www.ti.com/lit/ds/symlink/tmp1826.pdf?ts=17427997
| 638...
| zipy124 wrote:
| That is the accuracy of one sensor. The law of large numbers
| comes into play when using forecasts such as this, since
| accross any reasonably sized metro area you will have
| hundreds if not thousands or even tens of thousands of
| weather stations, accross which you can average to bring down
| error thresholds.
| baq wrote:
| What are your use cases for such level of accuracy on such long
| time frames?
| genewitch wrote:
| Is it safe to put my seedlings outside or is there going to
| be a soft freeze that kills them all in two days.
| bongodongobob wrote:
| Then use a thermometer. A city is a large area and can have
| variances of +/-10 degrees depending on where you are in
| the city.
| genewitch wrote:
| a thermometer will tell me if it's going to freeze in 2
| days?
|
| here's the scenario. I have started a bunch of seeds
| inside my house. I am waiting for the "last frost" as per
| the instructions on the seed packets. Now, how do i use a
| thermometer to tell if the last frost has passed? You
| need prediction models that can be accurate out at least
| a few days with temperature, and a 10% error "at
| freezing" means my plants either live or die, based on
| that error.
|
| there's no counterargument, here. "oh just cover the new
| plants" or "just wait longer" don't work, especially with
| larger gardens or "farms" or "homesteads". Most of us
| home-gamers just use environmental clues - number of
| bugs, buds on dogwood or pecan trees (native ones), when
| other trees flower. But this year i got a lesson that
| pecan trees get it wrong, too. They are in the process of
| leafing out and there was an _unpredicted_ , non-forecast
| cold snap that took the temperature so close to 32.0F
| (0.00006C) that i think any plant not equipped for that
| would have died that night. Now, _now_ i 'm fairly
| certain there's no more frost chance, but it's just a
| guess.
|
| Now imagine i lived anywhere outside of the subtropics,
| like nebraska or montana and needed to plant food for
| livestock or whatever.
| bongodongobob wrote:
| Forecasting isn't fine grained enough to get your
| backyard temp within 2 degrees. What your asking for is
| silly. The temp across your city is going to vary by more
| than 2 degrees every single day.
| genewitch wrote:
| you're still not listening. Once more, the forecast can't
| tell me _with any certainty_ whether or not there is
| going to be a frost, specifically in my area, and
| apparently in Kansas City, as well.
|
| What i am asking for is what is promised by weather
| forecasters and the models. If it can't say with any
| certainty if it's going to freeze, it's completely
| worthless _for this common circumstance_. Like most
| people, i don 't care if the forecast is 75F and it's
| actually 70F or 80F (or 68F), but what i do care about is
| a forecast for lows of 50F and it ends up being 32.15F.
| If you were a roofer and you were off by 18 degrees,
| you'd still be in prison.
| Joker_vD wrote:
| Well, take the actual weather observations for the past year.
| Take the actual weather observations for the last week. Overlay
| and slide this week of observations on top of the observations
| of the past year, until you find the window that matches "the
| best" -- then take the day right after this window and predict
| that the weather will be just like that (or maybe try to tweak
| the values a bit).
|
| I wonder how poorly this thing operates, and whether taking
| several years of history to look at would help much.
| counters wrote:
| Extremely poorly, because forecasting the weather is all
| about forecasting the deviations from the expected seasonal
| patterns - the "eddies" in the atmospheric flow which give
| rise to storm systems and interesting, impactful weather.
| bongodongobob wrote:
| Where in the city?
| kubav027 wrote:
| According to paper model grid resolution is 1.5 degrees. I do
| not think it can predict accurate weather in any location. It
| show global weather trends.
| scellus wrote:
| It's lower than many other medium-range AI forecasts, but
| note that those other models get state-of-the-art with pretty
| coarse grids, 0.5deg or so. The point is that upper
| atmosphere and broad patterns are smooth, so with ML/AI they
| don't require high resolution (while simulating them with
| physical models does require). And at the forecast lag of say
| 5-10 days, all local detail is lost anyway, so what skill
| remains comes from broad patterns, in all models. (Some extra
| skill can be gained by running local models initialized with
| the broad patterns, for there are clear cases like mountains
| where fine resolution is useful.)
| counters wrote:
| 1.5 degrees is perfectly fine for predicting large-scale
| (synoptic) weather patterns. They're not just "global
| trends." But yes, typical global NWP models and their MLWP
| competitors are run at 0.25 degrees or finer. All forecasts
| are statistically post-processed and biased-corrected to
| create local forecasts.
| MostlyStable wrote:
| I'm curious if some future, hypothetical AGI agent, which had
| been trained to have these kinds of abilities, would be akin to
| how most humans see a ball in flight and just instinctively know
| (within reason) where the ball is going to go? We don't
| understand, consciously and in the moment, how our brain is doing
| these calculations (although obviously we can get to similar
| results with other methods), but we still trust the outputs.
|
| Would some hypothetical future AI just "know" that tomorrow it's
| going to be 79 with 7 mph winds, without understanding exactly
| how that knowledge was arrived at?
| defrost wrote:
| > would be akin to how most humans see a ball in flight and
| just instinctively know (within reason) where the ball is going
| to go?
|
| Up to a point .. and that point is more or less the same as the
| point where humans can no longer catch a spinning tennis
| raquet.
|
| We understand the gravitional rainbow arc of the centre of
| mass, we fail at predicting the low order chaotic spin of
| tennis raquet mass distributions.
|
| Other butterflies are more unpredictable, and the ones that
| land on a camels back breaking a dam of precariously balanced
| rocks are a particular problem.
|
| * https://en.wikipedia.org/wiki/Tennis_racket_theorem
|
| * Dzhanibekov effect demonstration in microgravity:
| https://www.youtube.com/watch?v=1x5UiwEEvpQ
|
| * https://en.wikipedia.org/wiki/Horseshoe_map
| MostlyStable wrote:
| Yes, humans are obviously limited in the things we can
| instinctively, intuitively predict. That's not really the
| point. The point is whether something that has been trained
| to do more complicated predictions will have the a similar
| feeling when doing those predictions (of being intuitive and
| natural), or if it will feel more explicit, like when a human
| is doing the calculus necessary to predict where the same
| ball is going to go.
| defrost wrote:
| Humans have both intuition and explicit calculation,
| predictive calculation can be stable or inherently
| unstable.
|
| The point is whether a LLM has any _feelings_ ...
| MostlyStable wrote:
| My phrase "future, hypothetical" was trying very
| specifically to avoid any discussions about whether
| current AI have qualia or internal experiences. I was
| trying to think about whether something which did have
| some kind of coherent internal experience (assumed for
| the sake of the idle thought), but which had far greater
| predictive abilities than humans, would have the same
| intuitive feeling when making those much more complicated
| predictions.
|
| It was an idle thought that was only barely tangentially
| related to the article in question, and was not meant to
| be a comment at all on the model in the article or on any
| current (or even very near future) AI model.
|
| I don't expect anyone would have an answer, given the
| extreme degree of hypothetical-ness.
| criddell wrote:
| When you say "The point is whether a LLM has any
| feelings..." are you thinking specifically about an LLM,
| or AI in general? I've seen nothing to indicate that
| Project Aardvark is using an LLM for weather prediction.
| avianlyric wrote:
| I think "chain-of-thought" LLMs, with access to other tools
| like Python, already demonstrate two types of "thinking".
|
| We can query an LLM a simple question like "how many 'r' are in
| the word strawberry", and an LLM with know access to tools will
| quite confidently, and likely incorrectly, give you an answer.
| There's no actual counting happening, and any kind of
| understanding of the problem, the LLM will just guess an answer
| based on its training data. But that answer tends to be wrong,
| because those types of queries don't make up a large portion of
| its training set, and if they do, there's a large body of
| similar queries with vastly different answers, which ultimately
| results in confidentiality incorrect outputs.
|
| But provide an LLM tools like Python, and a "chain-of-thought"
| prompt that allows it to recursively re-prompt itself, while
| also executing external code and viewing the outputs, and an
| LLM can easily get the correct answer to query "how many 'r'
| are in the word strawberry". By simply writing and executing
| some Python to compute the answer.
|
| Those two approaches to problem solving are strikingly similar
| to intuitive vs analytical thinking in humans. One approach is
| driven entirely by pattern matching, and breaks down when
| dealing with problems that require close attention to specific
| details, the other is much more accurate, but also slower
| because directed computation is required.
|
| As for your hypothetical "weather AI", I think it's pretty easy
| to imagine an AGI capable of confidently predicting the weather
| tomorrow, not be capable of understanding how it computed the
| prediction, beyond a high level hand wavy explanation. Again,
| that's basically what LLM do today, confidently make
| predictions of the future, with zero understanding of how or
| why they made those predictions. But you can happily ask an LLM
| how and why it made a prediction, and it'll give you a very
| convincing answer, that will also be a complete and total
| deception.
| klabb3 wrote:
| > would be akin to how most humans see a ball in flight and
| just instinctively know (within reason) where the ball is going
| to go?
|
| Generally no. If I show you a puddle of water, can you tell me
| what shape was the ice sculpture that it was melted from?
|
| One is Newtonian motion and the other is a complex chaotic
| system with sparse measurements of ground truth. You can
| minimize error propagation but it's a diminishing returns
| problem, (except in rare cases like for natural disasters where
| a 6h warning can make a difference).
| mnky9800n wrote:
| While generally correct there has been evidence that machine
| learning models can predict multiple Lyapunov times past
| traditional models.
|
| [1] https://link.aps.org/doi/10.1103/PhysRevLett.120.024102
|
| [2] https://link.aps.org/doi/10.1103/PhysRevResearch.5.043252
| sebastiennight wrote:
| I remember learning that humans trying to catch a ball are not
| actually able to predict where the ball will land, but rather,
| will move in a way that maintains the angle of movement
| constant.
|
| As a result a human running to catch the ball over some
| distance (eg during a baseball game) runs along a curved path,
| not linearly to the point where the ball will drop (which would
| be evidence of having an intuition of the ball's destination).
| 01HNNWZ0MV43FF wrote:
| Sounds a bit like proportional navigation from missile
| guidance
| https://en.wikipedia.org/wiki/Proportional_navigation
| eszed wrote:
| This hypothesis could be tested, now that major league
| baseball tracks the positions of players in games. In the MLB
| app they show animations of good outfield plays with "catch
| difficulty" scores assigned, based (in part) on the straight-
| line distance from the fielder's initial position to the
| position of the catch. The "routes" on the best catches are
| always nearly-straight lines, which suggests that high-level
| players have developed exactly this intuitive sense.
|
| Certainly what I was coached to do, what outfielders say they
| do, and what I see watching the game, is to "read" the ball,
| run towards where you think the ball is going, and _then_
| track the ball on the way. I was and am a shitty outfielder,
| in part because I never developed a fast-enough intuitive
| sense of where the ball is going (and because, well, I 'm
| damn slow), but watch the most famous Catch[1] caught on
| film, and it sure looks like Mays knew right away that ball
| was hit over his head.
|
| [1] https://m.youtube.com/watch?v=7bLt2xKaNH0
| spiderfarmer wrote:
| Humans are definitely able to predict where a ball wil land.
| https://www.youtube.com/watch?v=aoScYO2osb0
| pests wrote:
| Agreed. Reminds me of juggling, while learning I noticed
| that as long as I could see each ball for at least a split
| second on its upwards trajectory I could "tell" if it would
| be a good throw or not. In order to keep both hands/paths
| in my view I would stare basically straight forward and not
| look at the top of the arc and could do it at any height.
| Now I can do it much more with feel and the motion is
| muscle memory but the visual cues were my main teacher.
| aredox wrote:
| It makes sense that there are several heuristics. After
| all, "Thinking: Fast and Slow" already makes the point that
| human brains have several layers of processing with
| different advantages and drawbacks depending on situations.
| jampekka wrote:
| There are a few that kind of theories. You are probably
| referring to the Optical Acceleration Cancellation theory[1].
| There are some similar later so called "direct perception"
| theories too.
|
| The problem with these is that they don't really work, often
| even in theory. People do seem to predict at least some
| aspects of the trajectory, although not necessarily the whole
| trajectory [2].
|
| [1] https://pubs.aip.org/aapt/ajp/article-
| abstract/36/10/868/104...
|
| [2]
| https://royalsocietypublishing.org/doi/10.1098/rsos.241291
| nsm wrote:
| To quote Iain M. Banks, probably not :)
|
| > "Sma," the ship said finally, with a hint of what might have
| been frustration in its voice, "I'm the smartest thing for a
| hundred light years radius, and by a factor of about a million
| ... but even I can't predict where a snooker ball's going to
| end up after more than six collisions." [GCU Arbitrary in "The
| State of the Art"]
| Vecr wrote:
| 6? That can't be right. I don't know how big a GCU is, so the
| scale could be up to 1 OOM off, but a full redirection of all
| simulation capacity should let it integrate out further than
| that.
| Fuzzwah wrote:
| The target precision wasn't specified.
| myrmidon wrote:
| For ball-to-ball collisions, 6 is already a _highly_
| conservative estimate-- this is basically a chaotic system
| (outcome after a few iterations, while deterministic, is
| _extremely_ sensitive to exact starting conditions).
|
| The error scales up exponentially with the number of (ball-
| to-ball) collisions.
|
| So if the initial ball position is off by "half a pixel"
| (=> always non-zero) this gets amplified extremely quickly.
|
| Your intuition about the problem is probably distorted by
| considering/having experienced (less sensitive) ball/wall
| collisions.
|
| See: https://www.lesswrong.com/posts/JehyrC6W3YTtdxw6S/a-pr
| imer-o...
| namaria wrote:
| That's assuming we are actually tracking all the relevant
| indicators.
| mmazing wrote:
| > Would some hypothetical future AI just "know" that tomorrow
| it's going to be 79 with 7 mph winds, without understanding
| exactly how that knowledge was arrived at?
|
| I think a consciousness with access to a stream of information
| tends to drown out the noise to see signal, so in those terms,
| being able to "experience" real-time climate data and
| "instinctively know" what variable is headed in what direction
| by filtering out the noise would come naturally.
|
| So, personally, I think the answer is yes. :)
|
| To elaborate a little more - when you think of a typical LLM
| the answer is definitely no. But, if an AGI is likely comprised
| of something akin to "many component LLMs", then one part might
| very well likely have no idea how the information it is
| receiving was actually determined.
|
| Our brains have MANY substructures in between neuron -> "I",
| and I think we're going to start seeing/studying a lot of
| similarities with how our brains are structured at a higher
| level and where we get real value out of multiple LLM systems
| working in concert.
| interludead wrote:
| I think that's actually a pretty good way to frame how these
| deep learning-based forecasting models might evolve
| IncreasePosts wrote:
| Why would we need weather models on our desktop, instead of just
| consuming the output of a far more powerful model?
|
| Unless people start interacting with weather forecast differently
| in the future it doesn't seem like something that needs
| personalization
| rafram wrote:
| Read the article. The point is that it can run on sub-
| supercomputer hardware, not specifically that it can run on
| consumer desktops.
| avianlyric wrote:
| The title has been editorialised. The paper is about building
| AI weather models on par with current state-of-the-art weather
| models that require super computers to run. But without the
| need for the super computers, just a normal desktop computer is
| enough.
|
| That represents a huge step-change in need compute for accurate
| weather models, and opens up the possibility of even more
| accurate models, if the accuracy of these techniques scales
| with available compute. If you can get state-of-the-art
| accuracy with a desktop computer, something that normally
| requires a super-computer, what happens if you run those
| techniques on a super-computer?
| benob wrote:
| Didn't read the paper, but it would be cool to apply
| distillation to these big physics-driven models, in order to
| simulate their outcome on much smaller models.
| brigandish wrote:
| Additionally (to the other replies to your question), asking
| for the weather is just another data point in the huge amount
| of surveillance occurring. If I can get the weather for my
| location without accessing a remote server where someone else
| gets to see my query and probably location, all the better.
| This provides that possibility.
| rkagerer wrote:
| Are all the inputs (from buoys, weather balloons, stations, etc)
| from decades of history stored, as well as past daily forecasts
| of existing weather models, so that this AI algorithm (and any
| future new ones) can be run across historic data and compared in
| terms of performance to existing methods?
|
| Is there a big Clearinghouse for this data?
|
| Kind of like how fintech algos can be run against historic stock
| market data to evaluate them.
| lgeorget wrote:
| The World Meteorological Organization has data that national
| weather services exchange to make forecast. Apart from that,
| its on a country-by-country basis.
|
| In France for instance Meteo-France has released all of its
| historical data in January 2024:
| https://www.data.gouv.fr/fr/organizations/meteo-france/#/dat...
| graemep wrote:
| But national models go beyond there borders and usually have
| some modelling outside so the exchanges will cover quite a
| lot of the world.
|
| For example, the UK's metoffice has a low resolution medium
| term global model:
| https://www.metoffice.gov.uk/research/approach/modelling-
| sys...
| cship2 wrote:
| How does windy do it? Did they just scrap all the sites or
| api. Would be great to have global dump every 1 minute of all
| the countries. Or have it have in a radio broadcast.
| jcd000 wrote:
| Windy uses openmeteo, which in turn aggregates many weather
| providers under one roof/api
| mschuster91 wrote:
| > Is there a big Clearinghouse for this data?
|
| Many commercial and even third party governments rely on the
| data from NOAA and its archives, on top of that the EU runs its
| own EUMETSAT fleet of data, plus a ton of national services -
| unfortunately, the result is there's a looooot of datasets.
|
| NOAA's dataset is public domain [1], EUMETSAT only requires
| attribution for most of its data [2]. On top of that you got
| the EU's Climate Data store [3], ECMWF [4], and ECA&D [5].
|
| The service that many private weather services provide is to
| aggregate and weigh all of the publicly available datasets, and
| some also add in data from their own ground stations,
| commercially licensed "realtime" data from governmental
| services, and their own models as well.
|
| The interesting question is what DOGE will do regarding NOAA -
| it is increasingly possible that NOAA will shut down, either to
| be turned into a pay-for-play model, replaced by private
| services, or just carelessly dropped in its entirety.
|
| [1] https://www.ncei.noaa.gov/archive
|
| [2] https://user.eumetsat.int/resources/user-guides/data-
| registr...
|
| [3] https://cds.climate.copernicus.eu/
|
| [4] https://www.ecmwf.int/en/forecasts/datasets
|
| [5] https://www.ecad.eu/dailydata/
| Loughla wrote:
| NOAA saves lives. Cutting that organization would be an
| absolute slap in the face to rural trump voters who rely on
| that system for weather alerts.
| sunshinesnacks wrote:
| > The service that many private weather services provide is
| to aggregate and weigh all of the publicly available datasets
|
| Yes, and more people need to understand this. Too many people
| seem to think that commercial services won't be impacted if
| NOAA stops doing what they do.
| K0balt wrote:
| NOAA marine forecasts and FAA integration is critical
| infrastructure for marine transportation and aviation. If
| they shut that down it will result in direct losses much
| larger than NOAAs budget, and thousands of lives lost through
| disaster and workplace accidents.
|
| I sure hope those "smart" people are capable of understanding
| that.
| mschuster91 wrote:
| > I sure hope those "smart" people are capable of
| understanding that.
|
| Well, NOAA can be privatized, sold off to the highest
| bidder and be fed money from the annual government to
| provide said critical infrastructure.
|
| In the end, it's always one giant ass grift.
| _joel wrote:
| > Kind of like how fintech algos can be run against historic
| stock market data to evaluate them.
|
| Backtesting, that's called.
| axismundi wrote:
| In weather it's called hindcast
| trillic wrote:
| afttest
| NitpickLawyer wrote:
| > Are all the inputs (from buoys, weather balloons, stations,
| etc) from decades of history stored
|
| I was also thinking about smartphones. They have barometric
| data, and while it might vary from phone to phone, I'm sure
| something like a kalman filter + historic data could do
| something there.
|
| Think about gathering all the data from "stationary" phones,
| correlate that with weather sat data, and with real "ground
| truth" weather stations, and then go back 30 - 60 min / a day,
| and see what comes out.
| Havoc wrote:
| >smartphones. They have barometric data
|
| For anyone else having a TIL moment: It's apparently for
| vertical position and supposedly sub meter accurate. o_O
| p_l wrote:
| Depends on smartphone (and smartwatches). Not all have it,
| at times it disappeared from brands that had it earlier.
|
| The smartwatch series I use explicitly include it because
| it's essentially the "all in one" version in a series that
| had smartwatches designed for aircraft use (including
| military versions where they serve as _backup cabin
| pressure warning_ , apparently)
| usrusr wrote:
| Smartwatches include barometers because they enable
| elevation measurement far more accurate than than GNSS
| (GPS and GPS-alikes, they tend to be noticeably less
| accurate in the vertical than in the horizontal). In
| particular when both are combined: barometry for high
| frequency changes (going up a hill) continuously
| calibrated against a long term average of the GNSS
| elevation to filter out low frequency changes in
| barometry (weather influence).
|
| But back to device could weather observation, this would
| require continuous GNSS, devices don't do that (network
| location would not be good enough).
| orion138 wrote:
| Dark Sky used that data for hyper local forecasting...
|
| https://news.ycombinator.com/item?id=22740466
|
| https://www.theverge.com/2015/6/22/8822767/dark-sky-
| weather-...
| xgulfie wrote:
| I'm still grumpy about apple buying and shuttering darksky
| thatcat wrote:
| They stopped using it during later years.
| counters wrote:
| Not really; it was a gimmick. They used standard forecast
| post-processing techniques to bias correct global/regional
| weather models. There is virtually no evidence they
| actually used device data in this process.
| phillipseamore wrote:
| "Collecting and processing of barometric data from
| smartphones for potential use in numerical weather prediction
| data assimilation"
|
| https://rmets.onlinelibrary.wiley.com/doi/10.1002/met.1805
|
| https://github.com/dmidk/smaps
| TheJoeMan wrote:
| It would also be nice to have a historical store of the weather
| predictions and not just instantaneous parameters. But I'm not
| aware of such record, perhaps because weathermen don't want a
| record of their (mis)predictions...
| ano-ther wrote:
| Oh they absolutely do track their predictions and how the
| different models perform. That's how they keep improving.
|
| This is a lay-person overview which cites some of the in-
| depth studies: https://ourworldindata.org/weather-forecasts
| interludead wrote:
| Getting high-quality, harmonized data at global scale can still
| be a challenge, especially when it comes to observational
| coverage in developing regions
| wewxjfq wrote:
| There's climate reanalysis, which combines historical
| observations with weather models to get clean data of past
| weather conditions, which is then used by researchers for
| various purposes. Most notably is ERA5 by ECMWF.
|
| [0] https://en.wikipedia.org/wiki/ECMWF_re-analysis
| serialdev wrote:
| Is there an equivalent modelling approach for earthquake
| prediction? A data repository for it widely shared on the same
| line as the top comment would work
| roelschroeven wrote:
| What data is used to train this AI? The article doesn't say
| anything about that (tough I have to admit I didn't read it
| super carefully). My first thought would be exactly all this
| historical data, but then you can't use that same data to test
| the AI's performance. Are different subsets of the available
| historical data used for training vs testing?
| axismundi wrote:
| It's most likely ERA5:
| https://cds.climate.copernicus.eu/datasets/reanalysis-
| era5-s...
| scellus wrote:
| No, they say end-to-end, meaning they use raw obsevations.
| Most or all other medium-range models start with ERA5.
|
| There's a paper from Norway that tried end-to-end, but
| their results were not spectacular. That's the aim of many
| though, including ECMWF. Note that ECMWF already has their
| AIFS in production, so AI weather prediction is pretty
| mainstream nowadays.
|
| Google has a local nowcast model that uses raw
| observations, in production, but that's a different genre
| of forecasting than the medium-range models of Aardvark.
| counters wrote:
| > Google has a local nowcast model that uses raw
| observations, in production, but that's a different genre
| of forecasting than the medium-range models of Aardvark.
|
| It's very clear from the MetNet announcement blog[1] that
| they require HRRR or other NWP output at runtime.
|
| [1]: https://research.google/blog/metnet-3-a-state-of-
| the-art-neu...
| wafngar wrote:
| They train with ERA5 and observations.
| guhidalg wrote:
| There are several places you can get this data, both historical
| and in near-realtime.
|
| I work on one of them:
| https://planetarycomputer.microsoft.com/catalog?filter=weath...
| counters wrote:
| > Is there a big Clearinghouse for this data?
|
| The short answer is, "no." There are some projects like the
| "NNJA-AI" project at Brightband[1] which is attempting to
| create such a clearing house in order to focus research efforts
| across the community.
|
| [1]: https://www.brightband.com/data/nnja-ai
| westoque wrote:
| not to hijack this thread but my dad did extensive research in
| sea breeze and rainfall modeling and he would have loved to see
| these AI and machine learning advancements in weather prediction.
|
| [0]:
| https://www.revistascca.unam.mx/atm/index.php/atm/article/do...
|
| [1]: https://wmo.int/about-wmo/awards/international-
| meteorologica...
| thenthenthen wrote:
| Another challenge I would add to the list is that weather data is
| strategic data.
| politelemon wrote:
| > replaces all of the steps
|
| Unable to tell if this is an exaggeration or if I'm just missing
| nuance, how would the model replace the data gathering which is
| listed as step 1.
| sunshinesnacks wrote:
| I think it's replacing data assimilation. Ingesting observation
| data from lots of sources. Not replacing observations
| themselves.
| jedberg wrote:
| It's a shame they just cut funding for launching the weather
| balloons and other equipment needed to collect this data.
|
| https://apnews.com/article/weather-forecasts-worsen-doge-tru...
| HumblyTossed wrote:
| Let's name "they". It was Trump via DOGE.
| echelon wrote:
| Super smart to name it "Aardvark".
|
| It'll sort first in any alphabetized list. Before GFS, etc.
|
| By the same measure, it's probably smart to name your company or
| app A-something rather than Z-something.
| desas wrote:
| People were doing this in the days of printed phone books.
| There would be an "AAA Plumbers", "ABC Taxis" and so on.
|
| Reputedly, "Apple Computers" coming ahead of "Atari" (Jobs' ex-
| employer) in the phonebook was one of the reasons for Apple
| being Apple.
| Onavo wrote:
| How does it compare to Google's
| https://www.nature.com/articles/s41586-024-08252-9
| scellus wrote:
| Google's is initialized with a gridded dataset, ERA5, from
| ECMWF. Using ERA5 is the current standard here, and ECMWF
| themselves build on that mostly now. Meanwhile, Aardvark tries
| to do the same directly from observations.
| bazzargh wrote:
| When I saw this I thought... "The Turing Institute? Does that
| still exist?"
|
| https://en.wikipedia.org/wiki/Turing_Institute
|
| There was a previous Turing Institute in Glasgow doing AI
| research (meaning, back then rules-based systems, but IIRC my
| professor was doing some work with them on neural networks),
| which hit the end of the road in 1994. There was some interesting
| stuff spun out of there, but it's a whole different institute.
| interludead wrote:
| Curious to see how it handles edge cases though, like hurricanes
| or rare extreme weather events that aren't well-represented in
| historical data
| dsr_ wrote:
| I note that they don't actually say "matches the accuracy of
| current best models".
|
| Or even "almost as accurate as the current best models".
| herodoturtle wrote:
| For a second I thought this was related to that old school
| FogCreek internship programme with the same name.
|
| Fun documentary at the time. Something about 12 weeks with geeks,
| and jumping out of windows comes to mind.
| noiv wrote:
| Model data here:
| https://zenodo.org/records/13158382?token=eyJhbGciOiJIUzUxMi...
|
| ~14GB, needs login, works with some federated accounts.
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