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Google says AI weather model masters 15-day forecast

286 points by lemonberry 2y ago

BrawnyBadger53 2y ago
I thought I remembered this model being released many months ago? Or is this a new update to the model?
- xnx 2y ago
  New model. Much better than the last one.
  - dylan604 2y ago
    It's so new that it has no long term track record. I want to see how its record looks after say 1/3/5 years. Any 15 day forecast is just too far into the future for me to take seriously. Even the 3 day forecast is loose at best. By the time it reaches 7-10 days, some of the forecast is completely off when it reaches the 3 day window. "But DeepMind said GenCast surpassed the precision of the center's forecasts in more than 97 percent of the 1,320 real-world scenarios from 2019 which they were both tested on." Great, so it's using historical data to make a prediction about what has already happened. I want to see it on current data where it is truly a prediction with no data to make that prediction. I'm not convinced the results haven't been juiced/salted
    - deleted 2y ago
      [deleted]
    - giarc 2y ago
      >Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
      - IshKebab 2y ago
        It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
        zactato 2y ago
        yeah, but weather patterns data isn't different now than 10 years ago, right? right? ...
        staunton 2y ago
        Overfitting is always bad by definition. The model learns meaningless noise that happens to help getting good results when applied to old data (be it due to trained weights, hyperparameters, whatever) but doesn't help at all on new data.
        gpm 2y ago
        In principle they trained on data up to 2017, validated (tried different hyper parameters) on data from 2018, and published results on data from 2019...
      - dagw 2y ago
        I wish these sort of papers would focus more on the 3 percent that it got wrong. Is it wrong by saying that a day would have a slight drizzle but it was actually sunny all day, or was it wrong by missing a catastrophic rain storm that devastated a region? I've worked on several projects trying to use AI to model various CFD calculations. We could trivially get 90+ percent accuracy on a bunch of metrics, the problem is that its almost always the really important and critical cases that end up being wrong in the AI model.
        gyrovagueGeist 2y ago
        Yep, this is also the problem that self-driving cars have with the "accidents per mile" metric.
    - zamadatix 2y ago
      2019 would already be a post-training data prediction: > GenCast is trained on 40 years of best-estimate analysis from 1979 to 2018, taken from the publicly available ERA5 (fifth generation ECMWF reanalysis) reanalysis dataset
    - deleted 2y ago
      [deleted]
- mmmrtl 2y ago
  Yeah the preprint was last year https://arxiv.org/abs/2312.15796 https://arxiv.org/abs/2312.15796
xnx 2y ago
6 days ago: https://news.ycombinator.com/item?id=42319062 https://news.ycombinator.com/item?id=42319062
bad_haircut72 2y ago
Obviously this team knows way more about this donain than me but I have to ask, wouldnt this only be able to predict weather which is in line with past weather patterns/indicators? I can imagine a weather analyst might be able to see "between the data" and recognise when some anomaly might be brewing, but an AI model would not
- mjburgess 2y ago
  Yup. There's fundamentally no way to automatically verify predictive accuracy, the assumptions in ML papers about such accuracy is (the almost always false presumption) that there are no distributional shifts in the data generating process. Here, since weather certainly changes its fundamental patterns over time, there is no way of reliably predicting out-sample performance. It's highly likely that in 1-2 years time we'd take all physics-based predictions and all predictions of current AI models, and find the AI model accuracy drops off a cliff when faced with a real out-sample.
  - wcoenen 2y ago
    Weather models can't compute some of the important physics that happens at sub-grid scale, and use various tricks to deal with that. How sure are you that these traditional models aren't also heavily tuned on past data to maximize performance? Perhaps they will also perform badly "out of distribution".
    - mjburgess 2y ago
      Causal physical models have no distributional requirements, so they are not sensitive to distribution shifts. ie., A causal model accounts for all possible distributions. The kind of risk with a causal model is that the model itself, of reality, is incorrect. This is a different kind of risk than there being 'no model at all' as in the case of curve-fitting over historical data, which is radically more fragile to expected shifts. In general, we're quite good at knowing the limitations of causal models, ie., specifying model risk here is much easier. You even, exactly, point out a known problem in the modelling which is impossible to state for an AI model. Since the AI model is just a weak induction, there are no terms/structures/etc. within that model which can be analysed to understand what parts of it are sensitive to what aspects of the system from which the data was taken. All we can say is, we know that in general, train/test distributions have to be "nearly exactly the same" for any of these methods to show anything like cross-val levels of accuracy. So we can very confidently predict when we know train/test wont be the same, that this is a mumbojumbo metric. Indeed, in the vast majority of common ML examples you can, right now, just go and look at real out-sample data collected later than the "common dataset" and you'll find the val accuracy is random or worse-than-random despite arbitarily high cross-val scores. The dataset which drives me most up-the-wall on this is house price datasets, or pricing datasets in general. Prices, generally, follow geometric brownian motion and nearly all ML models are extremelhy bad at modelling prices. So it's basically pseudoscience whenver anyone uses these datasets to demonstrate anything, esp. predictive accuracy.
atonse 2y ago
This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.
- CabSauce 2y ago
  But that's the point of a weather forecast. We don't do them for the science. (I'm not arguing against weather research.)
  - atonse 2y ago
    I would think we also use these models to run simulations. So maybe the AI models can be used to run simulations with different kinds of inputs to see if doing X in one place (like planting a lot of trees in one area) will have an outsized impact rather than doing Y in another place.
    - Retric 2y ago
      AI models rarely have that kind of predictive power because they are noticing patterns not running simulations. The model may care about trees because mountains above a specific height don’t have trees on them. The old, measures stop being useful once you start optimizing for them, AI edition.
- SpaceManNabs 2y ago
  do you want a causal weather model or one that can predict the weather well?
  - jacobgkau 2y ago
    When climate change occurs (which it is), we're going to want a causal one so we can actually make a forecast instead of predicting only based on past (unchanged) data.
Always42 2y ago
Based on the Gemini release it’s hard to take what Google claims at face value.
- petesergeant 2y ago
  Almost as bad as stuff branded IBM Watson for over promising
- amazingamazing 2y ago
  Totally different constraints. It’s like saying Nobel prize winning physicists can’t be trusted because they get basic arithmetic wrong Gemini is fast food - it’s not supposed to be the best.
  - itake 2y ago
    what about Google's flu prediction model release? https://blogs.ucl.ac.uk/pcph-blog/2018/01/23/google-flu-trends-is-dead-long-live-google-trends/ https://blogs.ucl.ac.uk/pcph-blog/2018/01/23/google-flu-tren...
  - moralestapia 2y ago
    Your analogy doesn't make sense. Nobel winning physicists can definitely perform arithmetic.
    - Maxatar 2y ago
      It's trendy for accomplished people to talk down about themselves as a way to sound cute. It's similar to software developers who like to say "I don't know what I'm doing, I just Google and Stackoverflow all day." It has a certain charm to it, and certainly there are some people for whom it's true, but overall it's just a misguided attempt at being modest but ultimately a horribly misleading statement. "Do not worry about your difficulties in mathematics, I assure you that mine are greater" - Albert Einstein
    - amazingamazing 2y ago
      I didn’t say they can’t perform it. I’ve seen a laureate get plenty of trival math wrong. It doesn’t matter.
wslh 2y ago
I really wonder if, like the weather, we should see new financial prediction models, understanding that there is randomness but also patterns.
- genewitch 2y ago
  there probably already is, and it's deployed with a kill-switch one would hope at the HFT firms. I don't have any insider knowledge but i'm pretty sure that one Quant group that everyone knows about probably figured out most of the "tricks" used to get llms and SD to really take off a decade before anyone else. iirc they consistently get like 30% or greater returns, which is practically black magic and means that they have something that can see a pattern(s) in the data.
- GTP 2y ago
  I see a fundamental issue issue from systems theory here, but do note that I'm by no means an expert at that, just had a bachelor course covering it together with control theory. The issue is that you would train a model on historical data, then you would use the model's predictions to make financial decisions. But, the moment you are using those predictions, you modified the system the model learned to predict. Now, maybe we can argue that, if you only invest a little money, you aren't really influencing the stock market. But I think the case would be very different if we're talking about big investors.
  - wslh 2y ago
    Yes, it is well studied that markets are not, in general, predictable but that doesn't mean that you cannot gain an advantage if you can extract some extra meaning, even a tiny one, that is what hedge funds, and other kind of sophisticated trading firms actually do. Here we are saying that they have an extra pattern recognition tool that they can use with a probability rate.
brcmthrowaway 2y ago
Meteorologists are quaking in their boots!
- cozzyd 2y ago
  Now now, you know how nervous the AI gets on the evening news.
dumbfounder 2y ago
"More accurate forecasts of risks of extreme weather can help officials safeguard more lives, avert damage, and save money," DeepMind said. Did the DeepMind AI say this?
- magicalist 2y ago
  ...why else do you think it's important to predict hurricane paths and tornado spawning storms and flooding rains and heat waves further ahead of time?
  - deleted 2y ago
    [deleted]
- deskamess 2y ago
  Doing its own PR's as well. This model is now marketing itself.
ericra 2y ago
It does seem like this is one of those domains where new AI models could thrive. From my understanding, the amount and variety of data necessary to make these models work is huge. In addition to historical data, you've got constant satellite data, weather stations on the ground for data collection, weather balloons going high into the atmosphere multiple times daily per location, Doppler radars tracking precipitation, data from ships and other devices in the ocean measuring temps and other info, and who knows what else. It's incredible that we are able to predict anything this far into the future, but the complexity seems like it lends itself to this kind of black box approach. *This is all speculation, so I'd be grateful if someone more knowledgeable could tell if if I'm mistaken about these assumptions. It's an interesting topic.
- counters 2y ago
  That's part of the story. The real reason why this is a domain where AI models can thrive is actually because we already have extremely complex, well-developed, physics-based simulations of the atmosphere which can take all of these observations and do useful things with them. So the task we currently ask AI models to do with regards to weather forecasting is actually much, much simpler than what you're proposing - we're simply asking the AI models to emulate what these existing models are capable of doing. Actually doing useful things with the observations alone is a different story altogether. It's the fact that we've been developing computer simulations of the weather and climate since the 1950's that is the key enabler here.
BadHumans 2y ago
I'm friends with a meteorologist and the 15+ day forecast is the bane of their existence because you can't accurately forecast beyond a week so I would love to know how they are measuring accuracy. The article doesn't say and I know the paper is going to go over my head.
- n4r9 2y ago
  I would guess that everyday they're comparing the current weather against the forecast from 15 days ago. Not a lot of data points to be sure, but perhaps enough to have confidence of very high accuracy.
  - dgrin91 2y ago
    Alternatively the can do back testing - using historical data they feed a subset into the predictor, then compare it's predictions to actual history
  - diego_sandoval 2y ago
    I don't think that's necessary. You can do a backtest for any point in the past, as long as you only use the data that was available until 15 days before the day being predicted.
    - mnau 2y ago
      So do they say. I am reminded of Google Flu Trends [0]. They likely also did similar "verification" and it didn't work. > The initial Google paper stated that the Google Flu Trends predictions were 97% accurate comparing with CDC data.[4] However subsequent reports asserted that Google Flu Trends' predictions have been very inaccurate, especially in two high-profile cases. Google Flu Trends failed to predict the 2009 spring pandemic[12] and over the interval 2011–2013 it consistently overestimated relative flu incidence, [0] https://en.wikipedia.org/wiki/Google_Flu_Trends https://en.wikipedia.org/wiki/Google_Flu_Trends
      - rcpt 2y ago
choeger 2y ago
Is that a prediction in the form of "There's a 30% chance of light rain that day." or "Temperature will reach 22,5°C at 14:00."?
- superfrank 2y ago
  If they're using the same strategy they were a year or two ago when I first started hearing about Google AI making progress in this space then I think it's closer to the second thing. IIRC, they had a model that was built to predict weather 15 minutes into the future and were just recursively running that to get days worth of predictions. If that's still the case then I would assume they have predictions for every 15 minutes. Back when I first read about that they were saying the accuracy started to drop off around the 7-10 day mark though, so not sure if they improved on that or changed their strategy though.
fancyfredbot 2y ago
It takes 8 minutes to produce a 15 day forecast. That's actually quite a long time for an AI model. I should probably read the paper to find out why but does anyone know? Is the model predicting the weather in 10 minutes time and just run iteratively 2000 times for a 14 day forecast?
- counters 2y ago
  > That's actually quite a long time for an AI model. Sure, but it's several orders of magnitudes smaller than the parent, physics-based weather model it's emulating, which may take 2-3 hours of wallclock time on an HPC system with a few thousand cores allocated to running it. > I should probably read the paper to find out why but does anyone know? Is the model predicting the weather in 10 minutes time and just run iteratively 2000 times for a 14 day forecast? Basically, yes. It's run autoregressively but in 6-hour forecast intervals. The added complexity is the iterative nature of the diffusion process used to generate each of those forecast intervals.
YeGoblynQueenne 2y ago
The catch: the model is predicting the past: >> But DeepMind said GenCast surpassed the precision of the center's forecasts in more than 97 percent of the 1,320 real-world scenarios from 2019 which they were both tested on.
- az226 2y ago
  Yes, but it was trained on 2018 data and earlier.
  - YeGoblynQueenne 2y ago
    After 2019. That's "the past" they predicted. Here's a slightly longer answer. When training machine learning models it's the done thing to test them on held-out data, and use the error on the held-out test data to estimate the accuracy of a model on truly unseen data that we really don't have- such as observations that are still in the future, like the weather tomorrow (as in 12/12/24) [1]. The problem is that held-out test data is not really unseen and when a model doesn't perform very well on it, it is common to tweak the model, tweak hyperparameters, tweak initialisation etc etc, until the model performs well on the held-out test data [2]; which ends up optimising the model on the held-out test data and therefore destroying any ability to estimate the accuracy of the model when predicting truly unseen data [3]. You can check Deepmind's paper and see if you can find where in the description of their methodology they explain what they did to mitigate this effect. You won't find it. This is enough of a problem when the model is, say, an image classifier, but when it's a model that's supposed to predict the weather 15 days from now, the best you can say when you look at the results on held-out test data is: the model is doing fine when predicting the weather we had in the past. ____________ [1] Yes, that's why we test models on held-out data. Not so we can brag about their "accuracy", or so we can put a little leaderboard in our papers (a little table with all the datasets on one side, all the systems on the other side, and all our entries in bold or else we don't submit the paper) and brag about that. We're trying to estimate generlisation error. [2] "The first iteration of my model cost hundreds of man-hours and thousands of dollars to code and train but if it doesn't perform well on the first try on my held-out test data I'm going to scrap it and start all over again from scratch". Yeah. Right. [3] Even worse: everyone splits their experimental dataset to 80/20% partitions, 80% for training and 20% for testing, and that already screws over the accuracy of any error estimates. Not only we're predicting 20% of our data from 80% of it, but we're predicting a tiny amount of data in absolute terms, compared to the true distribution.
queuebert 2y ago
They can say what they want, but I get rained on by surprise rain more than I ever have in my life, now that I'm practically forced to use the built-in Google weather due to them and Apple catching and killing all the good weather apps.
- acomjean 2y ago
  https://www.weather.gov/ https://www.weather.gov/ It you are in the US and while it still exists. (I found out about it when private weather companies were mad they were giving out forecasts for free.. )
  - istjohn 2y ago
    Even better: www.weather.gov/{zip_code}
- daemonologist 2y ago
  Apple killing Dark Sky is one of my major peeves.
- Mashimo 2y ago
  My 3rd party weather app still works. What did google do?
mattxxx 2y ago
Can we extract the latent understanding it has? Would be really cool to convert it's predictive model into a computer program that predicts written in like python/C/rust/whatever, and I think that would better serve our ability to understand the world.
- counters 2y ago
  We don't need to; dynamical meteorology is an incredibly mature field and our understanding of the fluid dynamics of the atmosphere grossly exceeds the resolutions and limitations of coarse, 0.25 degree global numerical models.
bilsbie 2y ago
Could they train this as a multi model model with language so you could actually ask it questions about its predictions and such?
encoderer 2y ago
I recently read that the 10 day forecast now is as accurate as the 5 day forecast from 30 years ago when I was a kid. This surprised me. I grew up in Ohio and now I live in the Bay Area and the forecast here seems to be accurate only 2-3 days out. It would be so helpful to have an accurate 10 day forecast.
- memco 2y ago
  This also surprises me. The weather app on my iphone has been atrocious with local same-day forecasts lately. It will say that the forecast for noon is sunny when it is 11:50 and dumping rain. It sometimes gets the overall idea that warmer or colder weather is coming sometime soon but it’s off by up to 2 days. I haven’t bothered to look for better sources, but it might be worth doing some research and comparison.
  - genewitch 2y ago
    as mentioned elsewhere, weather.gov has accurate enough predictions for stuff like rain. It's good enough that i can confidently claim on a wednesday that i need to leave some place before 16:30 because there's a storm coming that i saw on weather.gov the preceeding sunday or monday. and there's a 75% chance that it will be raining at 16:30. This may not sound impressive, but in Louisiana knowing when a storm system is coming as opposed to some rain is probably pretty taxing on weather models; and is nearly universally good information to have. I find i can't rely on the temperature and humidity readings, the "local prediction center" is in a concrete jungle (near an "international" airport, to boot), and "town" is consistently 7 degrees warmer than where i reside. Other than that, if it says temp below 32, it will freeze. if there's a heat advisory, we feel it too, just less so than town 20 miles away. Hope this helps, and i hope other countries/regions do this as well as weather.gov - it just works! I do use ventusky for tropical system paths and the like, if the gov site allcaps weather briefing doesn't give me enough detail my android phones always say "30c", no matter what settings i change - the built in weather thing is just broken. As far as phone apps go, i don't know any good weather ones. I've never used ventusky on a cellphone, either.
- chris_va 2y ago
Ankaios 2y ago
Turns out the Google AI's 15-day forecasts are for the weather in Mountain View. Which I can forecast 15 days out, too.
- gcanyon 2y ago
  Lemme guess: Night and morning low clouds, burning off in the afternoon. Source: lived in Los Angeles for a dozen years, saw the weather forecast a few times
- genewitch 2y ago
  haha, one of the few times my gut reaction to something on HN was dead on. "The easiest job in the world is the weatherperson in San Diego... And now, Ryan, what's the weather going to be like today?' 'uh, nice. back to you!'"
  - space_oddity 2y ago
    San Diego weather forecasting is the meteorological equivalent of being on cruise control
    - thephyber 2y ago
      > 80% of the time, the WeatherMan is right every time!
  - drexlspivey 2y ago
    Thanks Ollie
  - deleted 2y ago
    [deleted]
- pharrington 2y ago
  Nope. An excerpt from https://www.nature.com/articles/s41586-024-08252-9 https://www.nature.com/articles/s41586-024-08252-9: >Figure 2 shows some of the forecast samples of GenCast for Typhoon Hagibis, shortly before it made landfall in Japan on 12 October 2019. Figure 2b–e,g,h–k,m shows that GenCast forecasts are sharp and have spherical harmonic power spectra that closely match the ERA5 ground truth at both 1- and 15-day lead times
space_oddity 2y ago
How long before models like this are reliably part of decision-making for disaster response or everyday weather planning?
urbandw311er 2y ago
As long as the AI in charge of weather forecasting never ever comes within the control of the AI charged with fixing climate change. GOAL: Fix world’s problems. OBSERVED CAUSE: Too many humans. DECISION: Tell humans about impending hurricane? Yes/No
- ClassyJacket 2y ago
  I think it'll reach the conclusion it should kill us all regardless
ovj129 2y ago
This a great use case for AI to be used
latexr 2y ago
> A new artificial intelligence-based weather model can deliver 15-day forecasts with unrivaled accuracy and speed, a Google lab said, with potentially life-saving applications as climate change ramps up. How hilariously dystopian. Instead of spending energy/power/influence to mitigate/solve/revert the problem, they’re actively making it worse to in the name of predicting how bad it’s going to be (to try and save themselves, which will ultimately fail).
- whamlastxmas 2y ago
  Blaming climate change on AI seems like a stretch
  - latexr 2y ago
    That’s not what I said, and taking the argument in good faith would make it obvious. Of course AI isn’t to blame for climate change, it started way before this craze and there isn’t one single culprit. But the energy consumption doesn’t help, and burning more fossil fuels to power this energy-hungry systems makes it worse, that’s the point. https://www.computerweekly.com/news/366592778/Microsoft-and-Googles-GHG-emissions-gains-call-viability-of-net-zero-targets-into-question https://www.computerweekly.com/news/366592778/Microsoft-and-...
    - 93po 2y ago
      i think the point is that we have to pick and choose our battles, everything has an impact, but i think the impact of AI in terms of positives has incredible, massive upside given the negative externalities of the power generation used to power it. sure, bitcoin is stupid, let's not do that. on the list of common sense things we should do to combat climate change, energy used for AI is like #100, especially considering the big names are trying to use nuclear ASAP to power it in a clean way
- 123yawaworht456 2y ago
  each time you use your computing device to make a comment on the internet, you're emitting CO2 and accelerating climate change.
motoboi 2y ago
This is from a model from 2019! Published now on nature. Imagine what they are cooking now.
amelius 2y ago
Can't we let prediction markets solve the weather forecasting problem?
wiz21c 2y ago
FTA: > But he said these forecasting systems are reliant on the weather prediction models that are already running, such as that operated by ECMWF. et voilà.
mnky9800n 2y ago
I don’t really understand why Google and other companies making similar models are able to train on existing modelled or reanalysis data sets and then claim further accuracy than the originals. Sure, stacks of convolutions with multimodal attention blocks should be able to tease apart all the of idiosyncratic correlations that the original models may not have seen. But it’s unclear to me that better models is the direction to go in as opposed to better data.
- gwbas1c 2y ago
  > The model was trained on four decades of temperature, wind speed and air pressure data from 1979 to 2018 and can produce a 15-day forecast in just eight minutes—compared to the hours it currently takes. Basically, they trained the model on old observations, not old predictions. IE, imagine someone created a giant spreadsheet of every temperature observation, windspeed observation, precipitation observation, ect, and then told the computer to learn how to predict a column.
  - mnky9800n 2y ago
    From the paper: > GenCast is trained on 40 years of best-estimate analysis from 1979 to 2018, taken from the publicly available ERA5 (fifth generation ECMWF reanalysis) reanalysis dataset https://www.nature.com/articles/s41586-024-08252-9 https://www.nature.com/articles/s41586-024-08252-9 They say they trained on era5 data. That is modeled data not only direct observations. They do it this way to have a complete global data set that is consistent in time and space. https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview https://cds.climate.copernicus.eu/datasets/reanalysis-era5-s...
jdlyga 2y ago
Yeah, but can their AI weather model show up in a cute outfit and point to a map too
- bamboozled 2y ago
  Yes.
smartmic 2y ago
Discussed 3 days ago: https://news.ycombinator.com/item?id=42351698 https://news.ycombinator.com/item?id=42351698
helsinki 2y ago
On a related note, the company I work for is hiring a meteorologist for a commodities trading desk in NYC and London. Send me an e-mail if you would like a referral: https://mlp.eightfold.ai/careers/job?domain=mlp.com&pid=755939667061&domain=mlp.com&sort_by=relevance&job_index=84 https://mlp.eightfold.ai/careers/job?domain=mlp.com&pid=7559...
tokai 2y ago
Will never trust Googles claims about forecasting since the google flu debacle. They'll fudge the numbers until they suit them.
gwbas1c 2y ago
This makes me wonder: How far back can weather be reliably predicted?
- vasco 2y ago
  Does anyone have a nice website for looking at past weather? I often could use it.
- hwillis 2y ago
  1: Previous conditions in fluids cannot be determined with any kind of accuracy except for really obvious cases like "did this wet ground come from a cloud?" or laminar flows (https://www.youtube.com/watch?v=p08_KlTKP50 https://www.youtube.com/watch?v=p08_KlTKP50). Since weather is fluid mixing at high speeds with low viscosity, there is a huge amount of turbulence and entropy. Entropy means systems are not time-reversible. 2: Can we predict weather 2000 or 1M years ago based on estimates of temperature and geography? Yes, pretty reliably. Vegetation and albedo are some of the biggest variables- plant transpiration puts huge amounts of water into the air and changes surface temperatures. But we have a pretty good idea of the geography, and relatively small changes don't have a huge impact on general weather trends. 3: Can we predict the exact weather on a day 100 years ago, given available measurements? No, not really. Without satellites you need radar, and without radar you need weather balloons. Coal burning also had impacts on weather. Low pressures at the surface can tell you that the weather may get worse, but it doesn't tell you where its coming from or what the higher airflows are doing.
butlike 2y ago
Why not 16?
Aeroi 2y ago
Does anyone know if this works for marine forecasts? specifically weather gribs?
Aeroi 2y ago
Sweet, how will this help for commodities futures trading?

286 points by lemonberry 2y ago

BrawnyBadger53 2y ago

I thought I remembered this model being released many months ago? Or is this a new update to the model?

xnx 2y ago
New model. Much better than the last one.
- dylan604 2y ago
  It's so new that it has no long term track record. I want to see how its record looks after say 1/3/5 years. Any 15 day forecast is just too far into the future for me to take seriously. Even the 3 day forecast is loose at best. By the time it reaches 7-10 days, some of the forecast is completely off when it reaches the 3 day window. "But DeepMind said GenCast surpassed the precision of the center's forecasts in more than 97 percent of the 1,320 real-world scenarios from 2019 which they were both tested on." Great, so it's using historical data to make a prediction about what has already happened. I want to see it on current data where it is truly a prediction with no data to make that prediction. I'm not convinced the results haven't been juiced/salted
  - deleted 2y ago
    [deleted]
  - giarc 2y ago
    >Great, so it's using historical data to make a prediction about what has already happened. That's ok though because it doesn't know it's acting on old data. Researchers would give it data and ask for the 15 day forecast, the researchers then compare against that real world data. And as noted, "it surpassed the centres forecast in more than 97 percent" of those tests. This is all referred to as backtesting.
    - IshKebab 2y ago
      It's still not as good as actually new data. The individual model may not overfit the existing data, but the whole system of researchers trying lots of models, choosing the best ones, trying different hyperparameters etc. easily can.
      - zactato 2y ago
        yeah, but weather patterns data isn't different now than 10 years ago, right? right? ...
        staunton 2y ago
        Overfitting is always bad by definition. The model learns meaningless noise that happens to help getting good results when applied to old data (be it due to trained weights, hyperparameters, whatever) but doesn't help at all on new data.
      - gpm 2y ago
        In principle they trained on data up to 2017, validated (tried different hyper parameters) on data from 2018, and published results on data from 2019...
    - dagw 2y ago
      I wish these sort of papers would focus more on the 3 percent that it got wrong. Is it wrong by saying that a day would have a slight drizzle but it was actually sunny all day, or was it wrong by missing a catastrophic rain storm that devastated a region? I've worked on several projects trying to use AI to model various CFD calculations. We could trivially get 90+ percent accuracy on a bunch of metrics, the problem is that its almost always the really important and critical cases that end up being wrong in the AI model.
      - gyrovagueGeist 2y ago
        Yep, this is also the problem that self-driving cars have with the "accidents per mile" metric.
  - zamadatix 2y ago
    2019 would already be a post-training data prediction: > GenCast is trained on 40 years of best-estimate analysis from 1979 to 2018, taken from the publicly available ERA5 (fifth generation ECMWF reanalysis) reanalysis dataset
  - deleted 2y ago
    [deleted]
mmmrtl 2y ago
Yeah the preprint was last year https://arxiv.org/abs/2312.15796 https://arxiv.org/abs/2312.15796

xnx 2y ago

6 days ago: https://news.ycombinator.com/item?id=42319062 https://news.ycombinator.com/item?id=42319062

bad_haircut72 2y ago

Obviously this team knows way more about this donain than me but I have to ask, wouldnt this only be able to predict weather which is in line with past weather patterns/indicators? I can imagine a weather analyst might be able to see "between the data" and recognise when some anomaly might be brewing, but an AI model would not

mjburgess 2y ago
Yup. There's fundamentally no way to automatically verify predictive accuracy, the assumptions in ML papers about such accuracy is (the almost always false presumption) that there are no distributional shifts in the data generating process. Here, since weather certainly changes its fundamental patterns over time, there is no way of reliably predicting out-sample performance. It's highly likely that in 1-2 years time we'd take all physics-based predictions and all predictions of current AI models, and find the AI model accuracy drops off a cliff when faced with a real out-sample.
- wcoenen 2y ago
  Weather models can't compute some of the important physics that happens at sub-grid scale, and use various tricks to deal with that. How sure are you that these traditional models aren't also heavily tuned on past data to maximize performance? Perhaps they will also perform badly "out of distribution".
  - mjburgess 2y ago
    Causal physical models have no distributional requirements, so they are not sensitive to distribution shifts. ie., A causal model accounts for all possible distributions. The kind of risk with a causal model is that the model itself, of reality, is incorrect. This is a different kind of risk than there being 'no model at all' as in the case of curve-fitting over historical data, which is radically more fragile to expected shifts. In general, we're quite good at knowing the limitations of causal models, ie., specifying model risk here is much easier. You even, exactly, point out a known problem in the modelling which is impossible to state for an AI model. Since the AI model is just a weak induction, there are no terms/structures/etc. within that model which can be analysed to understand what parts of it are sensitive to what aspects of the system from which the data was taken. All we can say is, we know that in general, train/test distributions have to be "nearly exactly the same" for any of these methods to show anything like cross-val levels of accuracy. So we can very confidently predict when we know train/test wont be the same, that this is a mumbojumbo metric. Indeed, in the vast majority of common ML examples you can, right now, just go and look at real out-sample data collected later than the "common dataset" and you'll find the val accuracy is random or worse-than-random despite arbitarily high cross-val scores. The dataset which drives me most up-the-wall on this is house price datasets, or pricing datasets in general. Prices, generally, follow geometric brownian motion and nearly all ML models are extremelhy bad at modelling prices. So it's basically pseudoscience whenver anyone uses these datasets to demonstrate anything, esp. predictive accuracy.

atonse 2y ago

This is great from a practical standpoint (being able to predict weather), but does it actually improve our understanding of the weather, or WHY those predictions are better? That is my issue with some of these AI advances. With these, we won't have actually gotten better at understanding the weather patterns, since it's all just a bunch of weights which nobody really understands.

CabSauce 2y ago
But that's the point of a weather forecast. We don't do them for the science. (I'm not arguing against weather research.)
- atonse 2y ago
  I would think we also use these models to run simulations. So maybe the AI models can be used to run simulations with different kinds of inputs to see if doing X in one place (like planting a lot of trees in one area) will have an outsized impact rather than doing Y in another place.
  - Retric 2y ago
    AI models rarely have that kind of predictive power because they are noticing patterns not running simulations. The model may care about trees because mountains above a specific height don’t have trees on them. The old, measures stop being useful once you start optimizing for them, AI edition.
SpaceManNabs 2y ago
do you want a causal weather model or one that can predict the weather well?
- jacobgkau 2y ago
  When climate change occurs (which it is), we're going to want a causal one so we can actually make a forecast instead of predicting only based on past (unchanged) data.

Always42 2y ago

Based on the Gemini release it’s hard to take what Google claims at face value.

petesergeant 2y ago
Almost as bad as stuff branded IBM Watson for over promising
amazingamazing 2y ago
Totally different constraints. It’s like saying Nobel prize winning physicists can’t be trusted because they get basic arithmetic wrong Gemini is fast food - it’s not supposed to be the best.
- itake 2y ago
  what about Google's flu prediction model release? https://blogs.ucl.ac.uk/pcph-blog/2018/01/23/google-flu-trends-is-dead-long-live-google-trends/ https://blogs.ucl.ac.uk/pcph-blog/2018/01/23/google-flu-tren...
- moralestapia 2y ago
  Your analogy doesn't make sense. Nobel winning physicists can definitely perform arithmetic.
  - Maxatar 2y ago
    It's trendy for accomplished people to talk down about themselves as a way to sound cute. It's similar to software developers who like to say "I don't know what I'm doing, I just Google and Stackoverflow all day." It has a certain charm to it, and certainly there are some people for whom it's true, but overall it's just a misguided attempt at being modest but ultimately a horribly misleading statement. "Do not worry about your difficulties in mathematics, I assure you that mine are greater" - Albert Einstein
  - amazingamazing 2y ago
    I didn’t say they can’t perform it. I’ve seen a laureate get plenty of trival math wrong. It doesn’t matter.

wslh 2y ago

I really wonder if, like the weather, we should see new financial prediction models, understanding that there is randomness but also patterns.

genewitch 2y ago
there probably already is, and it's deployed with a kill-switch one would hope at the HFT firms. I don't have any insider knowledge but i'm pretty sure that one Quant group that everyone knows about probably figured out most of the "tricks" used to get llms and SD to really take off a decade before anyone else. iirc they consistently get like 30% or greater returns, which is practically black magic and means that they have something that can see a pattern(s) in the data.
GTP 2y ago
I see a fundamental issue issue from systems theory here, but do note that I'm by no means an expert at that, just had a bachelor course covering it together with control theory. The issue is that you would train a model on historical data, then you would use the model's predictions to make financial decisions. But, the moment you are using those predictions, you modified the system the model learned to predict. Now, maybe we can argue that, if you only invest a little money, you aren't really influencing the stock market. But I think the case would be very different if we're talking about big investors.
- wslh 2y ago
  Yes, it is well studied that markets are not, in general, predictable but that doesn't mean that you cannot gain an advantage if you can extract some extra meaning, even a tiny one, that is what hedge funds, and other kind of sophisticated trading firms actually do. Here we are saying that they have an extra pattern recognition tool that they can use with a probability rate.

brcmthrowaway 2y ago

Meteorologists are quaking in their boots!

cozzyd 2y ago
Now now, you know how nervous the AI gets on the evening news.

dumbfounder 2y ago

"More accurate forecasts of risks of extreme weather can help officials safeguard more lives, avert damage, and save money," DeepMind said. Did the DeepMind AI say this?

magicalist 2y ago
...why else do you think it's important to predict hurricane paths and tornado spawning storms and flooding rains and heat waves further ahead of time?
- deleted 2y ago
  [deleted]
deskamess 2y ago
Doing its own PR's as well. This model is now marketing itself.

ericra 2y ago

It does seem like this is one of those domains where new AI models could thrive. From my understanding, the amount and variety of data necessary to make these models work is huge. In addition to historical data, you've got constant satellite data, weather stations on the ground for data collection, weather balloons going high into the atmosphere multiple times daily per location, Doppler radars tracking precipitation, data from ships and other devices in the ocean measuring temps and other info, and who knows what else. It's incredible that we are able to predict anything this far into the future, but the complexity seems like it lends itself to this kind of black box approach. *This is all speculation, so I'd be grateful if someone more knowledgeable could tell if if I'm mistaken about these assumptions. It's an interesting topic.

counters 2y ago
That's part of the story. The real reason why this is a domain where AI models can thrive is actually because we already have extremely complex, well-developed, physics-based simulations of the atmosphere which can take all of these observations and do useful things with them. So the task we currently ask AI models to do with regards to weather forecasting is actually much, much simpler than what you're proposing - we're simply asking the AI models to emulate what these existing models are capable of doing. Actually doing useful things with the observations alone is a different story altogether. It's the fact that we've been developing computer simulations of the weather and climate since the 1950's that is the key enabler here.

BadHumans 2y ago

I'm friends with a meteorologist and the 15+ day forecast is the bane of their existence because you can't accurately forecast beyond a week so I would love to know how they are measuring accuracy. The article doesn't say and I know the paper is going to go over my head.

n4r9 2y ago
I would guess that everyday they're comparing the current weather against the forecast from 15 days ago. Not a lot of data points to be sure, but perhaps enough to have confidence of very high accuracy.
- dgrin91 2y ago
  Alternatively the can do back testing - using historical data they feed a subset into the predictor, then compare it's predictions to actual history
- diego_sandoval 2y ago
  I don't think that's necessary. You can do a backtest for any point in the past, as long as you only use the data that was available until 15 days before the day being predicted.
  - mnau 2y ago
    So do they say. I am reminded of Google Flu Trends [0]. They likely also did similar "verification" and it didn't work. > The initial Google paper stated that the Google Flu Trends predictions were 97% accurate comparing with CDC data.[4] However subsequent reports asserted that Google Flu Trends' predictions have been very inaccurate, especially in two high-profile cases. Google Flu Trends failed to predict the 2009 spring pandemic[12] and over the interval 2011–2013 it consistently overestimated relative flu incidence, [0] https://en.wikipedia.org/wiki/Google_Flu_Trends https://en.wikipedia.org/wiki/Google_Flu_Trends
    - rcpt 2y ago

choeger 2y ago

Is that a prediction in the form of "There's a 30% chance of light rain that day." or "Temperature will reach 22,5°C at 14:00."?

superfrank 2y ago
If they're using the same strategy they were a year or two ago when I first started hearing about Google AI making progress in this space then I think it's closer to the second thing. IIRC, they had a model that was built to predict weather 15 minutes into the future and were just recursively running that to get days worth of predictions. If that's still the case then I would assume they have predictions for every 15 minutes. Back when I first read about that they were saying the accuracy started to drop off around the 7-10 day mark though, so not sure if they improved on that or changed their strategy though.

fancyfredbot 2y ago

It takes 8 minutes to produce a 15 day forecast. That's actually quite a long time for an AI model. I should probably read the paper to find out why but does anyone know? Is the model predicting the weather in 10 minutes time and just run iteratively 2000 times for a 14 day forecast?

counters 2y ago
> That's actually quite a long time for an AI model. Sure, but it's several orders of magnitudes smaller than the parent, physics-based weather model it's emulating, which may take 2-3 hours of wallclock time on an HPC system with a few thousand cores allocated to running it. > I should probably read the paper to find out why but does anyone know? Is the model predicting the weather in 10 minutes time and just run iteratively 2000 times for a 14 day forecast? Basically, yes. It's run autoregressively but in 6-hour forecast intervals. The added complexity is the iterative nature of the diffusion process used to generate each of those forecast intervals.

YeGoblynQueenne 2y ago

The catch: the model is predicting the past: >> But DeepMind said GenCast surpassed the precision of the center's forecasts in more than 97 percent of the 1,320 real-world scenarios from 2019 which they were both tested on.

az226 2y ago
Yes, but it was trained on 2018 data and earlier.
- YeGoblynQueenne 2y ago
  After 2019. That's "the past" they predicted. Here's a slightly longer answer. When training machine learning models it's the done thing to test them on held-out data, and use the error on the held-out test data to estimate the accuracy of a model on truly unseen data that we really don't have- such as observations that are still in the future, like the weather tomorrow (as in 12/12/24) [1]. The problem is that held-out test data is not really unseen and when a model doesn't perform very well on it, it is common to tweak the model, tweak hyperparameters, tweak initialisation etc etc, until the model performs well on the held-out test data [2]; which ends up optimising the model on the held-out test data and therefore destroying any ability to estimate the accuracy of the model when predicting truly unseen data [3]. You can check Deepmind's paper and see if you can find where in the description of their methodology they explain what they did to mitigate this effect. You won't find it. This is enough of a problem when the model is, say, an image classifier, but when it's a model that's supposed to predict the weather 15 days from now, the best you can say when you look at the results on held-out test data is: the model is doing fine when predicting the weather we had in the past. ____________ [1] Yes, that's why we test models on held-out data. Not so we can brag about their "accuracy", or so we can put a little leaderboard in our papers (a little table with all the datasets on one side, all the systems on the other side, and all our entries in bold or else we don't submit the paper) and brag about that. We're trying to estimate generlisation error. [2] "The first iteration of my model cost hundreds of man-hours and thousands of dollars to code and train but if it doesn't perform well on the first try on my held-out test data I'm going to scrap it and start all over again from scratch". Yeah. Right. [3] Even worse: everyone splits their experimental dataset to 80/20% partitions, 80% for training and 20% for testing, and that already screws over the accuracy of any error estimates. Not only we're predicting 20% of our data from 80% of it, but we're predicting a tiny amount of data in absolute terms, compared to the true distribution.

queuebert 2y ago

They can say what they want, but I get rained on by surprise rain more than I ever have in my life, now that I'm practically forced to use the built-in Google weather due to them and Apple catching and killing all the good weather apps.

acomjean 2y ago
https://www.weather.gov/ https://www.weather.gov/ It you are in the US and while it still exists. (I found out about it when private weather companies were mad they were giving out forecasts for free.. )
- istjohn 2y ago
  Even better: www.weather.gov/{zip_code}
daemonologist 2y ago
Apple killing Dark Sky is one of my major peeves.
Mashimo 2y ago
My 3rd party weather app still works. What did google do?

mattxxx 2y ago

Can we extract the latent understanding it has? Would be really cool to convert it's predictive model into a computer program that predicts written in like python/C/rust/whatever, and I think that would better serve our ability to understand the world.

counters 2y ago
We don't need to; dynamical meteorology is an incredibly mature field and our understanding of the fluid dynamics of the atmosphere grossly exceeds the resolutions and limitations of coarse, 0.25 degree global numerical models.

bilsbie 2y ago

Could they train this as a multi model model with language so you could actually ask it questions about its predictions and such?

encoderer 2y ago

I recently read that the 10 day forecast now is as accurate as the 5 day forecast from 30 years ago when I was a kid. This surprised me. I grew up in Ohio and now I live in the Bay Area and the forecast here seems to be accurate only 2-3 days out. It would be so helpful to have an accurate 10 day forecast.

memco 2y ago
This also surprises me. The weather app on my iphone has been atrocious with local same-day forecasts lately. It will say that the forecast for noon is sunny when it is 11:50 and dumping rain. It sometimes gets the overall idea that warmer or colder weather is coming sometime soon but it’s off by up to 2 days. I haven’t bothered to look for better sources, but it might be worth doing some research and comparison.
- genewitch 2y ago
  as mentioned elsewhere, weather.gov has accurate enough predictions for stuff like rain. It's good enough that i can confidently claim on a wednesday that i need to leave some place before 16:30 because there's a storm coming that i saw on weather.gov the preceeding sunday or monday. and there's a 75% chance that it will be raining at 16:30. This may not sound impressive, but in Louisiana knowing when a storm system is coming as opposed to some rain is probably pretty taxing on weather models; and is nearly universally good information to have. I find i can't rely on the temperature and humidity readings, the "local prediction center" is in a concrete jungle (near an "international" airport, to boot), and "town" is consistently 7 degrees warmer than where i reside. Other than that, if it says temp below 32, it will freeze. if there's a heat advisory, we feel it too, just less so than town 20 miles away. Hope this helps, and i hope other countries/regions do this as well as weather.gov - it just works! I do use ventusky for tropical system paths and the like, if the gov site allcaps weather briefing doesn't give me enough detail my android phones always say "30c", no matter what settings i change - the built in weather thing is just broken. As far as phone apps go, i don't know any good weather ones. I've never used ventusky on a cellphone, either.
chris_va 2y ago

Ankaios 2y ago

Turns out the Google AI's 15-day forecasts are for the weather in Mountain View. Which I can forecast 15 days out, too.

gcanyon 2y ago
Lemme guess: Night and morning low clouds, burning off in the afternoon. Source: lived in Los Angeles for a dozen years, saw the weather forecast a few times
genewitch 2y ago
haha, one of the few times my gut reaction to something on HN was dead on. "The easiest job in the world is the weatherperson in San Diego... And now, Ryan, what's the weather going to be like today?' 'uh, nice. back to you!'"
- space_oddity 2y ago
  San Diego weather forecasting is the meteorological equivalent of being on cruise control
  - thephyber 2y ago
    > 80% of the time, the WeatherMan is right every time!
- drexlspivey 2y ago
  Thanks Ollie
- deleted 2y ago
  [deleted]
pharrington 2y ago
Nope. An excerpt from https://www.nature.com/articles/s41586-024-08252-9 https://www.nature.com/articles/s41586-024-08252-9: >Figure 2 shows some of the forecast samples of GenCast for Typhoon Hagibis, shortly before it made landfall in Japan on 12 October 2019. Figure 2b–e,g,h–k,m shows that GenCast forecasts are sharp and have spherical harmonic power spectra that closely match the ERA5 ground truth at both 1- and 15-day lead times

space_oddity 2y ago

How long before models like this are reliably part of decision-making for disaster response or everyday weather planning?

urbandw311er 2y ago

As long as the AI in charge of weather forecasting never ever comes within the control of the AI charged with fixing climate change. GOAL: Fix world’s problems. OBSERVED CAUSE: Too many humans. DECISION: Tell humans about impending hurricane? Yes/No

ClassyJacket 2y ago
I think it'll reach the conclusion it should kill us all regardless

ovj129 2y ago

This a great use case for AI to be used

latexr 2y ago

> A new artificial intelligence-based weather model can deliver 15-day forecasts with unrivaled accuracy and speed, a Google lab said, with potentially life-saving applications as climate change ramps up. How hilariously dystopian. Instead of spending energy/power/influence to mitigate/solve/revert the problem, they’re actively making it worse to in the name of predicting how bad it’s going to be (to try and save themselves, which will ultimately fail).

whamlastxmas 2y ago
Blaming climate change on AI seems like a stretch
- latexr 2y ago
  That’s not what I said, and taking the argument in good faith would make it obvious. Of course AI isn’t to blame for climate change, it started way before this craze and there isn’t one single culprit. But the energy consumption doesn’t help, and burning more fossil fuels to power this energy-hungry systems makes it worse, that’s the point. https://www.computerweekly.com/news/366592778/Microsoft-and-Googles-GHG-emissions-gains-call-viability-of-net-zero-targets-into-question https://www.computerweekly.com/news/366592778/Microsoft-and-...
  - 93po 2y ago
    i think the point is that we have to pick and choose our battles, everything has an impact, but i think the impact of AI in terms of positives has incredible, massive upside given the negative externalities of the power generation used to power it. sure, bitcoin is stupid, let's not do that. on the list of common sense things we should do to combat climate change, energy used for AI is like #100, especially considering the big names are trying to use nuclear ASAP to power it in a clean way
123yawaworht456 2y ago
each time you use your computing device to make a comment on the internet, you're emitting CO2 and accelerating climate change.

motoboi 2y ago

This is from a model from 2019! Published now on nature. Imagine what they are cooking now.

amelius 2y ago

Can't we let prediction markets solve the weather forecasting problem?

wiz21c 2y ago

FTA: > But he said these forecasting systems are reliant on the weather prediction models that are already running, such as that operated by ECMWF. et voilà.

mnky9800n 2y ago

I don’t really understand why Google and other companies making similar models are able to train on existing modelled or reanalysis data sets and then claim further accuracy than the originals. Sure, stacks of convolutions with multimodal attention blocks should be able to tease apart all the of idiosyncratic correlations that the original models may not have seen. But it’s unclear to me that better models is the direction to go in as opposed to better data.

gwbas1c 2y ago
> The model was trained on four decades of temperature, wind speed and air pressure data from 1979 to 2018 and can produce a 15-day forecast in just eight minutes—compared to the hours it currently takes. Basically, they trained the model on old observations, not old predictions. IE, imagine someone created a giant spreadsheet of every temperature observation, windspeed observation, precipitation observation, ect, and then told the computer to learn how to predict a column.
- mnky9800n 2y ago
  From the paper: > GenCast is trained on 40 years of best-estimate analysis from 1979 to 2018, taken from the publicly available ERA5 (fifth generation ECMWF reanalysis) reanalysis dataset https://www.nature.com/articles/s41586-024-08252-9 https://www.nature.com/articles/s41586-024-08252-9 They say they trained on era5 data. That is modeled data not only direct observations. They do it this way to have a complete global data set that is consistent in time and space. https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview https://cds.climate.copernicus.eu/datasets/reanalysis-era5-s...

jdlyga 2y ago

Yeah, but can their AI weather model show up in a cute outfit and point to a map too

bamboozled 2y ago
Yes.

smartmic 2y ago

Discussed 3 days ago: https://news.ycombinator.com/item?id=42351698 https://news.ycombinator.com/item?id=42351698

helsinki 2y ago

On a related note, the company I work for is hiring a meteorologist for a commodities trading desk in NYC and London. Send me an e-mail if you would like a referral: https://mlp.eightfold.ai/careers/job?domain=mlp.com&pid=755939667061&domain=mlp.com&sort_by=relevance&job_index=84 https://mlp.eightfold.ai/careers/job?domain=mlp.com&pid=7559...

tokai 2y ago

Will never trust Googles claims about forecasting since the google flu debacle. They'll fudge the numbers until they suit them.

gwbas1c 2y ago

This makes me wonder: How far back can weather be reliably predicted?

vasco 2y ago
Does anyone have a nice website for looking at past weather? I often could use it.
hwillis 2y ago
1: Previous conditions in fluids cannot be determined with any kind of accuracy except for really obvious cases like "did this wet ground come from a cloud?" or laminar flows (https://www.youtube.com/watch?v=p08_KlTKP50 https://www.youtube.com/watch?v=p08_KlTKP50). Since weather is fluid mixing at high speeds with low viscosity, there is a huge amount of turbulence and entropy. Entropy means systems are not time-reversible. 2: Can we predict weather 2000 or 1M years ago based on estimates of temperature and geography? Yes, pretty reliably. Vegetation and albedo are some of the biggest variables- plant transpiration puts huge amounts of water into the air and changes surface temperatures. But we have a pretty good idea of the geography, and relatively small changes don't have a huge impact on general weather trends. 3: Can we predict the exact weather on a day 100 years ago, given available measurements? No, not really. Without satellites you need radar, and without radar you need weather balloons. Coal burning also had impacts on weather. Low pressures at the surface can tell you that the weather may get worse, but it doesn't tell you where its coming from or what the higher airflows are doing.

butlike 2y ago

Why not 16?

Aeroi 2y ago

Does anyone know if this works for marine forecasts? specifically weather gribs?

Aeroi 2y ago

Sweet, how will this help for commodities futures trading?