[HN Gopher] GraphCast: AI model for weather forecasting ___________________________________________________________________ GraphCast: AI model for weather forecasting Author : bretthoerner Score : 363 points Date : 2023-11-14 15:42 UTC (7 hours ago) (HTM) web link (deepmind.google) (TXT) w3m dump (deepmind.google) | xnx wrote: | I continue to be a little confused by the distinction between | Google, Google Research and DeepMind. Google Research, had made | this announcement about 24-hour forecasting just 2 weeks ago: | https://blog.research.google/2023/11/metnet-3-state-of-art-n... | (which is also mentioned in the GraphCast announcement from | today) | mukara wrote: | DeepMind recently merged with the Brain team from Google | Research to form `Google DeepMind`. It seems this was done to | have Google DeepMind focused primarily (only?) on AI research, | leaving Google Research to work on other things in more than 20 | research areas. Still, some AI research involves both orgs, | including MetNet in weather forecasting. | | In any case, GraphCast is a 10-day global model, whereas MetNet | is a 24-hour regional model, among other differences. | xnx wrote: | Good explanation. Now that both the 24-hour regional and | 10-day global models have been announced in | technical/research detail, I supposed there might still be a | general blog post about how improved forecasting is when you | search for "weather" or check the forecast on Android. | mnky9800n wrote: | That would require your local weather service to use these | models | kridsdale3 wrote: | IIRC the MetNet announcement a few weeks ago said that | their model is now used when you literally Google your | local weather. I don't think it's available yet to any API | that third party weather apps pull from, so you'll have to | keep searching "weather in Seattle" to see it. | daemonologist wrote: | It's also used, at least for the high resolution | precipitation forecast, in the default Android weather | app (which is really part of the "Google" app situation). | danielmarkbruce wrote: | Is there a colab example (and/or have they released the | models) for MetNet like they have here for GraphCast? | mukara wrote: | MetNet-3 is not open-source, and the announcement said it's | already integrated into Google products/services needing | weather info. So, I'd doubt there's anything like a colab | example. | robertlagrant wrote: | This is fascinating: | | > For inputs, GraphCast requires just two sets of data: the state | of the weather 6 hours ago, and the current state of the weather. | The model then predicts the weather 6 hours in the future. This | process can then be rolled forward in 6-hour increments to | provide state-of-the-art forecasts up to 10 days in advance. | broast wrote: | Weather is markovian | Imanari wrote: | Interesting indeed, only one lagged feature for time series | forecasting? I'd imagine that including more lagged inputs | would increase performance. Rolling the forecasts forward to | get n-step-ahead forecasts is a common approach. I'd be | interested in how they mitigated the problem of the errors | accumulating/compounding. | Al-Khwarizmi wrote: | I don't know much about weather prediction, but if a model can | improve the state of the art only with that data as input, my | conclusion is that previous models were crap... or am I missing | something? | postalrat wrote: | Read the other comments. | counters wrote: | It's worth pointing out that "state of the weather" is a little | bit hand-wavy. The GraphCast model requires a fully-assimilated | 3D atmospheric state - which means you still need to run a | full-complexity numerical weather prediction system with a | massive amount of inputs to actually get to the starting line | for using this forecast tool. | | Initializing directly from, say, geostationary and LEO | satellite data with complementary surface station observations | - skipping the assimilation step entirely - is clearly where | this revolution is headed, but it's very important to | explicitly note that we're not there yet (even in a research | capacity). | baq wrote: | Yeah current models aren't quite ready to ingest real time | noisy data like the actual weather... I hear they go off the | rails if preprocessing is skipped (outliers, etc) | lispisok wrote: | I've been following these global ML weather models. The fact they | make good forecasts at all was very impressive. What is blowing | my mind is how fast they run. It takes hours on giant super | computers for numerical weather prediction models to forecast the | entire globe. These ML models are taking minutes or seconds. This | is potentially huge for operational forecasting. | | Weather forecasting has been moving focus towards ensembles to | account for uncertainty in forecasts. I see a future of large | ensembles of ML models being ran hourly incorporating the latest | measurements | wenc wrote: | Not to take away from the excitement but ML weather prediction | builds upon the years of data produced by numerical models on | supercomputers. It cannot do anything without that computation | and its forecasts are dependent on the quality of that | computation. Ensemble models are already used to quantify | uncertainty (it's referenced in their paper). | | But it is exciting that they are able to recognize patterns in | multi year and produce medium term forecasts. | | Some comments here suggest this replaces supercomputers models. | This would a wrong conclusion.It does not (the paper explicitly | states this). It uses their output as input data. | boxed wrote: | I don't get this. Surely past and real weather should be the | input training data, not the output of numerical modeling? | counters wrote: | Well, what is "real weather data?" | | We have dozens of complementary and contradictory sources | of weather information. Different types of satellites | measuring EM radiation in different bands, weather | stations, terrestrial weather radars, buoys, weather | balloons... it's a massive hodge-podge of different systems | measuring different things in an uncoordinated fashion. | | Today, it's not really practical to assemble that data and | directly feed it into an AI system. So the state-of-the-art | in AI weather forecasting involves using an intermediate | representation - "reanalysis" datasets which apply a | sophisticated physics based weather model to assimilate all | of these data sets into a single, self-consistent 3D and | time-varying record of the state of the atmosphere. This | data is the unsung hero of the weather revolution - just as | the WMO's coordinated synoptic time observations for | weather balloons catalyzed effective early numerical | weather prediction in the 50's and 60's, accessible re- | analysis data - and the computational tools and platforms | to actually work with these peta-scale datasets - has | catalyzed the advent of "pure AI" weather forecasting | systems. | goosinmouse wrote: | Great comment, thank you for sharing your insights. I | don't think many people truly understand just how massive | these weather models are and the sheer volume of data | assimilation work that's been done for decades to get us | to this point today. | | I always have a lot of ideas about using AI to solve very | small scale weather forecasting issues, but there's just | so much to it. It's always a learning experience for | sure. | mnky9800n wrote: | It uses era5 data which is reanalysis. These models will always | need the numerical training data. What's impressive is how well | the emulate the physics in those models so cheaply. But since | the climate changes there will eventually be different weather | in different places. | | https://www.ecmwf.int/en/forecasts/documentation-and-support | counters wrote: | Absolutely - but large ensembles are just the tip of the | iceberg. Why bother producing an ensemble when you could just | output the posterior distribution of many forecast predictands | on a dense grid? One could generate the entire ensemble-derived | probabilities from a single forward model run. | | Another very cool application could incorporate generative | modeling. Inject a bit of uncertainty in a some observations | and study how the manifold of forecast outputs changes... | ultimately, you could tackle things like studying the | sensitivity of forecast uncertainty for, say, a tropical | cyclone or nor'easter relative to targeted observations. | Imagine a tool where you could optimize where a Global Hawk | should drop rawindsondes over the Pacific Ocean to maximally | decrease forecast uncertainty for a big winter storm impacting | New England... | | We may not be able to engineer the weather anytime soon, but in | the next few years we may have a new type of crystal ball for | anticipating its nuances with far more fidelity than ever | before. | kridsdale3 wrote: | This is basically equivalent to NVIDIA's DLSS machine learning | running on Tensor Cores to "up-res" or "frame-interpolate" the | extremely computationally intensive job the traditional GPU | rasterizer does to simulate a world. | | You could numerically render a 4k scene at 120FPS at extreme | cost, or you could render a 2k scene at 60FPS, then feed that | to DLSS to get a close-enough approximation of the former at | enormous energy and hardware savings. | Gys wrote: | I live in an area which regularly has a climate differently then | forecasted: often less rain and more sunny. Would be great if I | can connect my local weather station (and/or its history) to some | model and have more accurate forecasts. | speps wrote: | Because weather data is interpolated between multiple stations, | you wouldn't even need the local station position, your own | position would be more accurate as it'd take a lot more | parameters into account. | tash9 wrote: | One piece of context to note here is that models like ECMWF are | used by forecasters as a tool to make predictions - they aren't | taken as gospel, just another input. | | The global models tend to consistently miss in places that have | local weather "quirks" - which is why local forecasters tend to | do better than, say, accuweather, where it just posts what the | models say. | | Local forecasters might have learned over time that, in early | Autumn, the models tend to overpredict rain, and so when they | give their forecasts, they'll tweak the predictions based on | the model tendencies. | dist-epoch wrote: | There are models which take as input both global forecasts and | local ones, and which then can transpose a global forecast into | a local one. | | National weather institutions sometimes do this, since they | don't have the resources to run a massive supercomputer model. | Gys wrote: | Interesting. So what I am looking for is probably an even | more scaled down version? Or something that runs in the cloud | with an api to upload my local measurements. | supdudesupdude wrote: | Hate to break it but one weather station wont improve a | forecast? What are they supposed to do? Ignore the output | of our state of the art forecast models and add an if | statement for your specific weather station?? | freedomben wrote: | weather prediction seems to me like a terrific use of machine | learning aka statistics. The challenge I suppose is in the data. | To get perfect predictions you'd need to have a mapping of what | conditions were like 6 hours, 12 hours, etc before, and what the | various outcomes were, which butterflies flapped their wings and | where (this last one is a joke about how hard this data would | be). Hard but not impossible. Maybe impossible. I know very | little about weather data though. Is there already such a format? | tash9 wrote: | It's been a while since I was a grad student but I think the | raw station/radiosonde data is interpolated into a grid format | before it's put into the standard models. | kridsdale3 wrote: | This was also in the article. It splits the sphere surface in | to 1M grids (not actually grids in the cartesian sense of a | plane, these are radial units). Then there's 37 altitude | layers. | | So there's radial-coordinate voxels that represent a low | resolution of the physical state of the entire atmosphere. | serjester wrote: | To call this impressive is an understatement. Using a single GPU, | outperforms models that run on the world's largest super | computers. Completely open sourced - not just model weights. And | fairly simple training / input data. | | > ... with the current version being the largest we can | practically fit under current engineering constraints, but which | have potential to scale much further in the future with greater | compute resources and higher resolution data. | | I can't wait to see how far other people take this. | thatguysaguy wrote: | They said single TPU machine to be fair, which means like 8 | TPUs (still impressive) | wenc wrote: | It builds on top of supercomputer model output and does better | at the specific task of medium term forecasts. | | It is a kind of iterative refinement on the data that | supercomputers produce -- it doesn't supplant supercomputers. | In fact the paper calls out that it has a hard dependency on | the output produced by supercomputers. | carbocation wrote: | I don't understand why this is downvoted. This is a classic | thing to do with deep learning: take something that has a | solution that is expensive to compute, and then train a deep | learning model from that. And along the way, your model might | yield improvements, too, and you can layer in additional | features, interpolate at finer-grained resolution, etc. If | nothing else, the forward pass in a deep learning model is | almost certainly way faster than simulating the next step in | a numerical simulation, but there is room for improvement as | they show here. Doesn't invalidate the input data! | danielmarkbruce wrote: | Because "iterative refinement" is sort of wrong. It's not a | refinement and it's not iterative. It's an entirely | different model to physical simulation which works entirely | differently and the speed up is order of magnitude. | | Building a statistical model to approximate a physical | process isn't a new idea for sure.. there are literally | dozens of them for weather.. the idea itself isn't really | even iterative, it's the same idea... but it's all in the | execution. If you built a model to predict stock prices | tomorrow and it generated 1000% pa, it wouldn't be | reasonable for me to call it iterative. | kridsdale3 wrote: | It is iterative when you look at the scope of "humans | trying to solve things over time". | danielmarkbruce wrote: | lol, touche. | andbberger wrote: | "amortized inference" is a better name for it | borg16 wrote: | > the forward pass in a deep learning model is almost | certainly way faster than simulating the next step in a | numerical simulation | | Is this the case in most of such refinements (architecture | wise)? | danielmarkbruce wrote: | Practically speaking yes. You'd not likely build a | statistical model when you could build a good simulation | of the underlying process if the simulation was already | really fast and accurate. | silveraxe93 wrote: | Could you point me to the part where it says it depends on | supercomputer output? | | I didn't read the paper but the linked post seems to say | otherwise? It mentions it used the supercomputer output to | impute data during training. But for prediction it just | needs: | | > For inputs, GraphCast requires just two sets of data: the | state of the weather 6 hours ago, and the current state of | the weather. The model then predicts the weather 6 hours in | the future. This process can then be rolled forward in 6-hour | increments to provide state-of-the-art forecasts up to 10 | days in advance. | serjester wrote: | You can read about it more in their paper. Specifically | page 36. Their dataset, ERA5, is created using a process | called reanalysis. It combines historical weather | observations with modern weather models to create a | consistent record of past weather conditions. | | https://storage.googleapis.com/deepmind- | media/DeepMind.com/B... | silveraxe93 wrote: | Ah nice. Thanks! | dekhn wrote: | I can't find the details, but if the supercomputer job | only had to run once, or a few times, while this model | can make accurate predictions repeatedly on unique | situations, then it doesn't matter as much that a | supercomputer was required. The goal is to use the | supercomputer once, to create a high value simulated | dataset, then repeatedly make predictions from the lower- | cost models. | whatever1 wrote: | So best case scenario we can avoid some computation for | inference, assuming that historical system dynamics are still | valid. This model needs to be constantly monitored by full | scale simulations and rectified over time. | westurner wrote: | "BLD,ENH: Dask-scheduler (SLURM,)," https://github.com/NOAA- | EMC/global-workflow/issues/796 | | Dask-jobqueue https://jobqueue.dask.org/ : | | > _provides cluster managers for PBS, SLURM, LSF, SGE and | other [HPC supercomputer] resource managers_ | | Helpful tools for this work: Dask-labextension, DaskML, CuPY, | SymPy's lambdify(), Parquet, Arrow | | GFS: Global Forecast System: | https://en.wikipedia.org/wiki/Global_Forecast_System | | TIL about Raspberry-NOAA and pywws in researching and | summarizing for a comment on "Nrsc5: Receive NRSC-5 digital | radio stations using an RTL-SDR dongle" (2023) | https://news.ycombinator.com/item?id=38158091 | pkulak wrote: | Why can't they just train on historical data? | xapata wrote: | We don't have enough data. There's only one universe, and | it's helpful to train on counter-factual events. | meteo-jeff wrote: | In case someone is looking for historical weather data for ML | training and prediction, I created an open-source weather API | which continuously archives weather data. | | Using past and forecast data from multiple numerical weather | models can be combined using ML to achieve better forecast skill | than any individual model. Because each model is physically | bound, the resulting ML model should be stable. | | See: https://open-meteo.com | boxed wrote: | Open-Meteo has a great API too. I used it to build my iOS | weather app Frej (open source and free: | https://github.com/boxed/frej) | | It was super easy and the responses are very fast. | mdbmdb wrote: | Is it able to provide data on extreme events. Say, the current | and potential path of a hurricane? similar to .kml that NOAA | provides | meteo-jeff wrote: | Extreme weather is predicted by numerical weather models. | Correctly representing hurricanes has driven development on | the NOAA GFS model for centuries. | | Open-Meteo focuses on providing access to weather data for | single locations or small areas. If you look at data for | coastal areas, forecast and past weather data will show | severe winds. Storm tracks or maps are not available, but | might be implemented in the future. | mdbmdb wrote: | Appreciate the response. Do you know of any services that | provide what I described in the previous comments? I'm | specifically interested in extreme weather conditions and | their visual representation (hurricanes, tornados, hails | etc.) with API capabilities | swells34 wrote: | Go to: nhc.noaa.gov/gis There's a list of data and | products with kmls and kmzs and geojsons and all sorts of | stuff. I haven't actually used the API for retrieving | these, but NOAA has a pretty solid track record with data | dissemination. | dmd wrote: | I would love to hear about this centuries-old NOAA GFS | model. The one I know about definitely doesn't have that | kind of history behind it. | K2h wrote: | Some of the oldest data may come from ships logs back to | 1836 | | https://www.reuters.com/graphics/CLIMATE-CHANGE-ICE- | SHIPLOGS... | meteo-jeff wrote: | Sorry, decades. | | KML files for storm tracks are still the best way to go. | You could calculate storm tracks yourself for other weather | models like DWD ICON, ECMWF IFS or MeteoFrance ARPEGE, but | storm tracks based on GFS ensembles are easy to use with | sufficient accuracy | comment_ran wrote: | How about https://pirateweather.net/en/latest/ ? | | Does anyone have a compare this API with the latest API we have | here? | meteo-jeff wrote: | Both APIs use weather models from NOAA GFS and HRRR, | providing accurate forecasts in North America. HRRR updates | every hour, capturing recent showers and storms in the | upcoming hours. PirateWeather gained popularity last year as | a replacement for the Dark Sky API when Dark Sky servers were | shut down. | | With Open-Meteo, I'm working to integrate more weather | models, offering access not only to current forecasts but | also past data. For Europe and South-East Asia, high- | resolution models from 7 different weather services improve | forecast accuracy compared to global models. The data covers | not only common weather variables like temperature, wind, and | precipitation but also includes information on wind at higher | altitudes, solar radiation forecasts, and soil properties. | | Using custom compression methods, large historical weather | datasets like ERA5 are compressed from 20 TB to 4 TB, making | them accessible through a time-series API. All data is stored | in local files; no database set-up required. If you're | interested in creating your own weather API, Docker images | are provided, and you can download open data from NOAA GFS or | other weather models. | Fatnino wrote: | Is there somewhere to see historical forecasts? | | So not "the weather on 25 December 2022 was such and such" but | rather "on 20 December 2022 the forecast for 25 December 2022 | was such and such" | meteo-jeff wrote: | Not yet, but I am working towards it: | https://github.com/open-meteo/open-meteo/issues/206 | Vagantem wrote: | That's awesome! I've hooked something similar up to my service | - https://dropory.com which predicts which day it will rain the | least for any location | | Based on historical data! | willsmith72 wrote: | this is really cool, I've been looking for good snow-related | weather APIs for my business. I tried looking on the site, but | how does it work, being coordinates-based? | | I'm used to working with different weather stations, e.g. | seeing different snowfall prediction at the bottom of a | mountain, halfway up, and at the top, where the coordinates are | quite similar. | amluto wrote: | I've never studied weather forecasting, but I can't say I'm | surprised. All of these models, AFAICT, are based on the "state" | of the weather, but "state" deserves massive scare quotes: it's a | bunch of 2D fields (wind speed, pressure, etc) -- note the _2D_. | Actual weather dynamics happen in three dimensions, and three | dimensional land features, buildings, etc as well as gnarly 2D | surface phenomena (ocean surface temperature, ground surface | temperature, etc) surely have strong effects. | | On top of this, surely the actual observations that feed into the | model are terrible -- they come from weather stations, sounding | rockets, balloons, radar, etc, none of which seem likely to be | especially accurate in all locations. Except that, where a | weather station exists, the output of that station _is_ the | observation that people care about -- unless you 're in an | airplane, you don't personally care about the geopotential, but | you do care about how windy it is, what the temperature and | humidity are, and how much precipitation there is. | | ISTM these dynamics ought to be better captured by learning them | from actual observations than from trying to map physics both | ways onto the rather limited datasets that are available. And a | trained model could also learn about the idiosyncrasies of the | observation and the extra bits of forcing (buildings, etc) that | simply are not captured by the inputs. | | (Heck, my personal in-my-head neural network can learn a mapping | from NWS forecasts to NWS observations later in the same day that | seems better than what the NWS itself produces. Surely someone | could train a very simple model that takes NWS forecasts as | inputs and produces its estimates of NWS observations during the | forecast period as outputs, thus handling things like "the NWS | consistently underestimates the daily high temperature at such- | and-such location during a summer heat wave.") | Difwif wrote: | I'm not sure why you're emphasizing that weather forecasting is | just 2D fields. Even in the article they mention GraphCast | predicts multiple data points at each global location across a | variety of altitudes. All existing global computational | forecast models work the same way. They're all 3d spherical | coordinate systems. | WhitneyLand wrote: | How does it make sense to say this is something you've "never | studied", followed by how they "ought to be" doing it better? | | It also seems like some of your facts differ from theirs, may I | ask how far you read into the paper? | kridsdale3 wrote: | No need, they're a software engineer (presumably). That just | means they're better than everyone. | cryptoz wrote: | Again haha! Still no mention of using barometers in phones. Maybe | some day. | user_7832 wrote: | (If someone with knowledge or experience can chime in, please | feel free.) | | To the best of my knowledge, poor weather (especially wind | shear/microbursts) are one of the most dangerous things possible | in aviation. Is there any chance, or plans, to implement this in | the current weather radars in planes? | tash9 wrote: | If you're talking about small scale phenomena (less than 1km), | then this wouldn't help other than to be able to signal when | the conditions are such that these phenomena are more likely to | happen. | jauntywundrkind wrote: | From what I can tell from reading & based off | https://colab.research.google.com/github/deepmind/graphcast/... , | one needs access to ECMWF Era5 or HRES data-sets or something | similar to be able to run and use this model. | | Unknown what licensing options ECMWF offers for Era5, but to use | this model in any live fashion, I think one is probably going to | need a small fortune. Maybe some other dataset can be adapted | (likely at great pain)... | sunshinesnacks wrote: | ERA5 is free. The API is a bit slow. | | I think that only some variables from the HRES are free, but | not 100% sure. | sagarpatil wrote: | How does one go about hosting this and using this as an API? | syntaxing wrote: | Maybe I missed it but does anyone know what it will take to run | this model? Seems something fun to try out but not sure if 24GB | of VRAM is suffice. | kridsdale3 wrote: | It says in the article that it runs on Google's tensor units. | So, go down to your nearest Google data center, dodge security, | and grab one. Then escape the cops. | azeirah wrote: | You could also just buy a very large amount of their coral | consumer TPUs :D | comment_ran wrote: | So for a daily user, to make it a practical usage, let's say if I | have a local measurement of X, I can predict, let's say, 10 days | later, or even just tomorrow, or the day after tomorrow, let's | say the wind direction, is it possible to do that? | | If it is possible, then I will try using the sensor to measure my | velocity at some place where I live, and I can run the model and | see how the results look like. I don't know if it's going to | accurately predict the future or within a 10% error bar range. | dist-epoch wrote: | No, this model uses as input the current state of the weather | across the whole planet. | carabiner wrote: | > GraphCast makes forecasts at the high resolution of 0.25 | degrees longitude/latitude (28km x 28km at the equator). | | Any way to run this at even higher resolution, like 1 km? Could | this resolve terrain forced effects like lenticular clouds on | mountain tops? | dist-epoch wrote: | One big problem is input weather data. It's resolution is poor. | carabiner wrote: | Yeah, not to mention trying to validate results. Unless we | grid install weather stations every 200 m on a mountain | top... | max_ wrote: | I have far more respect for the AI team at DeepMind even thou | they may be less popular than say OpenAI or "Grok". | | Why? Other AI studios seem to work on gimmicks while DeepMind | seems to work on genuinely useful AI applications [0]. | | Thanks for the good work! | | [0] Not to say that Chat GPT & Midjourney are not useful, I just | find DeepMinds quality of research more interesting. | max_ wrote: | Has anyone here heard of "Numerical Forecasting" models for | weather? I heard they "work so well". | | Does GraphCast come close to them? | max_ wrote: | What's the difference between a "Graph Neural Network" and a deep | neural network? | dil8 wrote: | Graph neural networks are deep learning models that trained on | graph data. | RandomWorker wrote: | Do you have any resources where I could learn more about | these networks? | haolez wrote: | Are there any experts around that can chime in on the possible | impacts of this technology if widely adopted? | supdudesupdude wrote: | It doesnt predict rainfall so i doubt most of us will actually | care about it until then. Still it depends on input data (the | current state of weather etc). How are we supposed to | accurately model the weather at every point in the world? | Especially when tech bro Joe living in San Fran expects things | to be accurate to a meter within his doorstep | miserableuse wrote: | Does anybody know if its possible to initialize the model using | GFS initial conditions used for the GFS HRES model? If so, where | can I find this file and how can I use it? Any help would be | greatly appreciated! | counters wrote: | You can try, but other models in this class have struggled when | initialized using model states pulled from other analysis | systems. | | ECMWF publishes a tool that can help bootstrap simple inference | runs with different AI models [1] (they have plugins for | several). You could write a tool that re-maps a GDAS analysis | to "look like" ERA-5 or IFS analysis, and then try feeding it | into GraphCast. But YMMV if the integration is stable or not - | models like PanguWx do not work off-the-shelf with this | approach. | | [1]: https://github.com/ecmwf-lab/ai-models | miserableuse wrote: | Thank you for your response. Are these ML models initialized | by gridded initial conditions measurements (such as the GDAS | pointed out) or by NWP model forecast results (such as hour- | zero forecast from the GFS)? Or are those one and the same? | counters wrote: | They're more-or-less the same thing. | pyb wrote: | Curious. How can AI/ML perform on a problem that is, as far as I | understand, inherently chaotic / unpredictable ? It sounds like a | fundamental contradiction to me. | vosper wrote: | Weather isn't fundamentally unpredictable. We predict weather | with a fairly high degree of accuracy (for most practical | uses), and the accuracy getting better all the time. | | https://scijinks.gov/forecast-reliability | sosodev wrote: | I'm kinda surprised that this government science website | doesn't seem to link sources. I'd like to read the research | to understand how they're measuring the accuracy. | keule wrote: | IMO a chaotic system will not allow for long-term forecast, but | if there is any type of pattern to recognize (and I would | assume there are plenty), an AI/ML model should be able to | create short-term prediction with high accuracy. | pyb wrote: | Not an expert, but "Up to 10 days in advance" sounds like | long-term to me ? | joaogui1 wrote: | I think 10 days is basically the normal term for weather, | in that we can get decent predictions for that span using | "classical"/non-ML methods. | pyb wrote: | IDK, I wouldn't plan a hike in the mountains based on | 10-day predictions. | keule wrote: | To be clear: With short-term I meant the mentioned 6 hours | of the article. They use those 6 hours to create forecasts | for up to 10 days. I would think that the initial | predictors for a phenomenon (like a hurricane) are well | inside that timespan. With long-term, I meant way beyond a | 14-day window. | kouru225 wrote: | But AI/ML models require good data and the issue with chaotic | systems like weather is that we don't have good enough data. | joaogui1 wrote: | The issue with chaotic systems is not data, is that the | error grows superlinearly with time, and since you always | start with some kind of error (normally due to measurement | limitations) this means that after a certain time horizon | the error becomes to significant to trust the prediction. | That hasn't a lot to do with data quality for ML models | kouru225 wrote: | That's an issue with data: If your initial conditions are | wrong (Aka your data collection has any error or isn't | thorough enough) then you get a completely different | result. | kouru225 wrote: | Yes. Very accurate as long as you don't need to predict the | unpredictable. So it's useless. | | Edit: I do see a benefit to the idea if you compare it to the | Chaos Theorists "gaining intuition" about systems. | pyb wrote: | IDK if it's useless, but it's counter-intuitive to me. | simonebrunozzi wrote: | Amazing. Is there an easy way to run this on a local laptop? | dnlkwk wrote: | Curious how this factors in long-range shifts or patterns eg el | nino. Most accurate is a bold claim | stabbles wrote: | If you live in a country where local, short-term rain / shower | forecast is essential (like [1] [2]), it's funny to see how | incredibly bad radar forecast is. | | There are really convenient apps that show an animated map with | radar data of rain, historical data + prediction (typically). | | The prediction is always completely bonkers. | | You can eyeball it better. | | No wonder "AI" can improve that. Even linear extrapolation is | better. | | Yes, local rain prediction is a different thing from global | forecasting. | | [1] https://www.buienradar.nl [2] | https://www.meteoschweiz.admin.ch/service-und-publikationen/... | bberenberg wrote: | Interesting that you say this. I spent in month in AMS 7-8 | years ago and buienradar was accurate down to the minute when I | used it. Has something changed? | supdudesupdude wrote: | Funny to mention. None of the AI forecasts can actually predict | precip. None of them mention this and i assume everyone thinks | this means the rain forecasts are better. Nope just temperature | and humidity and wind. Important but come on, it's a bunch of | shite | brap wrote: | Beyond the difficulty of running calculations (or even accurately | measuring the current state), is there a reason to believe | weather is unpredictable? | | I would imagine we probably have a solid mathematical model of | how weather behaves, so given enough resources to measure and | calculate, could you, in theory, predict the daily weather going | 10 years into the future? Or is there something inherently | "random" there? | danbrooks wrote: | Small changes in initial state can lead to huge changes down | the line. See: the butterfly effect or chaos theory. | | https://en.wikipedia.org/wiki/Chaos_theory | ethanbond wrote: | AFAIK there's nothing _random_ anywhere except near atomic | /subatomic scale. Everything else is just highly chaotic/hard- | to-forecast deterministic causal chains. | counters wrote: | What you're describing is effectively how climate models work; | we run a physical model which solves the equations that govern | how the atmosphere works out forward in time for very long time | integrations. You get "daily weather" out as far as you choose | to run the model. | | But this isn't a "weather forecast." Weather forecasting is an | initial value problem - you care a great deal about how the | weather will evolve from the current atmospheric conditions. | Precisely because weather is a result of what happens in this | complex, 3D fluid atmosphere surrounding the Earth, it happens | that small changes in those initial conditions can have a very | big impact on the forecast on relatively short time-periods - | as little as 6-12 hours. Small perturbations grow into larger | ones and feedback across spatial scales. Ultimately, by day | ~3-7, you wind up with a very different atmospheric state than | what you'd have if you undid those small changes in the initial | conditions. | | This is the essence of what "chaos" means in the context of | weather prediction; we can't perfectly know the initial | conditions we feed into the model, so over some relatively | short time, the "model world" will start to look very different | than the "real world." Even if we had perfect models - capable | of representing all the physics in the atmosphere - we'd still | have this issue as long as we had to imperfectly sample the | atmosphere for our initial conditions. | | So weather isn't inherently "unpredictable." And in fact, by | running lots of weather models simultaneously with slightly | perturbed initial conditions, we can suss out this uncertainty | and improve our estimate of the forecast weather. In fact, this | is what's so exciting to meteorologists about the new AI models | - they're so much cheaper to run that we can much more | effectively explore this uncertainty in initial conditions, | which will indirectly lead to improved forecasts. | willsmith72 wrote: | is it possible to self-correct, looking at initial value | errors in the past? Is it too hard to prescribe the error in | the initial value? | supdudesupdude wrote: | I'll be impressed when it can predict rainfall better than GFS / | HRRR / EURO etc | Vagantem wrote: | Related to this, I built a service that shows what day it has | rained the least on in the last 10 years - for any location and | month! Perfect to find your perfect wedding date. Feel free to | check out :) | | https://dropory.com | knicholes wrote: | What are the similarities between weather forecasting and | financial market forecasting? | KRAKRISMOTT wrote: | Both are complex systems traditionally modeled with | differential equations and statistics. | sonya-ai wrote: | Well it's a start, but weather forecasting is far more | predictable imo | csours wrote: | Makes me wonder how much it would take to do this for a city at | something like 100 meter resolution. | layoric wrote: | I can't see any citation to accuracy comparisons, or maybe I just | missed them? Given the amount of data, and complexity of the | domain, it would be good to see a much more detailed breakdown of | their performance vs other models. | | My experience in this space is that I was first employee at | Solcast building a live 'nowcast' system for 4+ years (left | ~2021) targeting solar radiation and cloud opacity initially, but | expanding into all aspects of weather, focusing on the use of the | newer generation of satellites, but also heavily using NWP models | like ECMWF. Last I knew,nowcasts were made in minutes on a decent | size cluster of systems, and has been shown in various studies | and comparisons to produce extremely accurate data (This article | claims 'the best' without links which is weird..), be interesting | on how many TPUsv4 were used to produce these forecasts and how | quickly? Solcast used ML as a part of their systems, but when it | comes down to it, there is a lot more operationally to producing | accurate and reliable forecasts, eg it would be arrogant to say | the least to switch from something like ECMWF to this black box | anytime soon. | | Something I said as just before I left Solcast was that their | biggest competition would come from Amazon/Google/Microsoft and | not other incumbent weather companies. They have some really | smart modelers, but its hard to compete with big tech resources. | I believe Amazon has been acquiring power usage IoT related | companies over the past few years, I can see AI heavily moving | into that space as well.. for better or worse. ___________________________________________________________________ (page generated 2023-11-14 23:00 UTC)