[HN Gopher] GraphCast: AI model for weather forecasting
___________________________________________________________________
GraphCast: AI model for weather forecasting
Author : bretthoerner
Score : 596 points
Date : 2023-11-14 15:42 UTC (1 days 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).
| wenyuanyu wrote:
| Any idea why it is still showing the "weather.com" link
| next to the forecast?
| kridsdale3 wrote:
| Most likely explanation would be that Weather.com signed
| a contract with Google X years ago to have something
| placed there, and nobody wants to do the work to do
| anything about it.
| 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
| hakuseki wrote:
| That is not strictly true. The weather at time t0 may affect
| non-weather phenomena at time t1 (e.g. traffic), which in
| turn may affect weather at time t2.
|
| Furthermore, a predictive model is not working with a
| complete picture of the weather, but rather some limited-
| resolution measurements. So, even ignoring non-weather, there
| may be local weather phenomena detected at time t0, escaping
| detection at time t1, but still affecting weather at time t2.
| 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.
| boxed wrote:
| Oh yea, sure. But the article makes it seems like the
| model is trained on some predictive model, instead of a
| synthesis model. That seems weird to me.
| 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.
| _visgean wrote:
| ERA5 is based on historical data. See it for yourself https
| ://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysi...,
| https://www.ecmwf.int/en/forecasts/dataset/ecmwf-
| reanalysis-...
|
| I don't using raw historical data would work for any data
| intensive model - afaik the data is patchy - there are
| spots where we don't have that many datapoints - e.g.
| middle of ocean... Also there are new satelites that are
| only available for the last x years and you want to be able
| to use these for the new models. So you need a re-analysis
| of what it would look like if you had that data 40 years
| ago...
|
| Also its very convinient dataset because many other models
| trained on it: https://github.com/google-
| research/weatherbench2 so easy to do benchmarking..
| 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
| berniedurfee wrote:
| I've always wanted to see something like that. I always
| wonder if forecasts are a coin flip beyond a window of a few
| hours.
| CSMastermind wrote:
| I know at a minimum that hurricane forecasts have gotten
| significantly better over time. We can now
|
| https://www.nhc.noaa.gov/verification/verify5.shtml
|
| Our 96 hour projections are as accurate today as the 24
| hour projections were in 1990.
| mjs wrote:
| Looks like https://sites.research.google/weatherbench/
| attempts to "benchmark" different forecast models/systems.
|
| They're very cautious about naming a "best" model though!
|
| > Weather forecasting is a multi-faceted problem with a
| variety of use cases. No single metric fits all those use
| cases. Therefore,it is important to look at a number of
| different metrics and consider how the forecast will be
| applied.
| rrr_oh_man wrote:
| That last paragraph sounds like something ChatGPT would
| write.
| AuryGlenz wrote:
| I just quit photographing weddings (and other stuff) this
| year. It's a job where the forecast really impacts you, so
| you tend to pay attention.
|
| The amount of brides I've had to calm down when rain was
| forecast for their day is pretty high. In my experience, in
| my region, precipitation forecasts more than 3 days out are
| worthless except for when it's supposed to rain for several
| days straight. Temperature/wind is better but it can still
| swing one way or the other significantly.
|
| For other types of shoots I'd tell people that ideally we'd
| postpone on the day of, and only to start worrying about it
| the day before the shoot.
|
| I'm in Minnesota, so our weather is quite a bit more
| dynamic than many regions, for what it's worth.
| jjp wrote:
| Are you thinking something like
| https://www.forecastadvisor.com/?
| meteo-jeff wrote:
| I would like to see an independent forecast comparison tool
| similar to Forecast Advisor, which evaluates numerical
| weather models. However, getting reliable ground truth data
| on a global scale can be a challenge.
|
| Since Open-Meteo continuously downloads every weather model
| run, the resulting time series closely resembles
| assimilated gridded data. GraphCast relies on the same data
| to initialize each weather model run. By comparing past
| forecasts to future assimilated data, we can assess how
| much a weather model deviates from the "truth," eliminating
| the need for weather station data for comparison. This same
| principle is also applied to validate GraphCast.
|
| Moreover, storing past weather model runs can enhance
| forecasts. For instance, if a weather model consistently
| predicts high temperatures for a specific large-scale
| weather pattern, a machine learning model (or a simple
| multilinear regression) can be trained to mitigate such
| biases. This improvement can be done for a single location
| with minimal computational effort.
| 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!
| polygamous_bat wrote:
| Yikes, after completed three steps I was asked for my email.
| No to your bait and switch, thanks!
| Vagantem wrote:
| It can take up to 10 min to generate a report - I had a
| spinner before but people just left the page. So I
| implemented a way to send it to them instead. I've never
| used the emails for anything else than that. Try it with a
| 10 min disposable email address if you like. Thanks for
| your feedback!
| polygamous_bat wrote:
| Ok, seems like your UI is not coming from a place of
| malice. However, pulling out an email input form at the
| final step is a very widespread UI dark pattern, so if
| nothing else please let people know that you will ask
| their email before they start interacting with your
| forms.
| 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.
| ryanlitalien wrote:
| You'll need a local weather expert to assist, as terrain,
| geography and other hyper-local factors create forecasting
| unpredictability. For example, Jay Peak in VT has its own
| weather, the road in has no snow, but it's a raging snowstorm
| on the mountain.
| just_testing wrote:
| I was going to ask about air quality, but just opened the site
| and you have air quality as well! Thanks!
| _visgean wrote:
| There is also https://github.com/google-research/weatherbench2
| which has baselines of numerical weather models.
| Omnipresent wrote:
| This is great. I am very curious about the architectural
| decisions you've taken here. Is there a blog post / article
| about them? 80 yrs of historical data -- are you storing that
| somewhere in PG and the APIs are just fetching it? If so, what
| indices have you set up to make APIs fetch faster etc. I just
| fetched 1960 to 2022 in about 12 secs.
| meteo-jeff wrote:
| Traditional database systems struggle to handle gridded data
| efficiently. Using PG with time-based indices is memory and
| storage extensive. It works well for a limited number of
| locations, but global weather models at 9-12 km resolution
| have 4 to 6 million grid-cells.
|
| I am exploiting on the homogeneity of gridded data. In a 2D
| field, calculating the data position for a graphical
| coordinate is straightforward. Once you add time as a third
| dimension, you can pick any timestamp at any point on earth.
| To optimize read speed, all time steps are stored
| sequentially on disk in a rotated/transposed OLAP cube.
|
| Although the data now consists of millions of floating-point
| values without accompanying attributes like timestamps or
| geographical coordinates, the storage requirements are still
| high. Open-Meteo chunks data into small portions, each
| covering 10 locations and 2 weeks of data. Each block is
| individually compressed using an optimized compression
| scheme.
|
| While this process isn't groundbreaking and is supported by
| file systems like NetCDF, Zarr, or HDF5, the challenge lies
| in efficiently working with multiple weather models and
| updating data with each new weather model run every few
| hours.
|
| You can find more information here:
| https://openmeteo.substack.com/i/64601201/how-data-are-
| store...
| caseyf7 wrote:
| How did you handle missing data? I've used NOAA data a few
| times and I'm always surprised at how many days of historical
| data are missing. They have also stopped recording in certain
| locations and then start in new locations over time making it
| hard to get solid historical weather information.
| Guestmodinfo wrote:
| I always suspect that they don't tell me the actual
| temperature. Maybe I am totally wrong but I suspect. I need to
| get my own physical thermometer not the digital one in my room
| and outside my house and have a camera focussed on it. So that
| later I can speed up the video and see how much the weather
| varied the previous night.
| kubiton wrote:
| What? Why?
| 3abiton wrote:
| Are multiple data sources supported?
| brna wrote:
| Hi Jeff, Great work, Respect!
|
| I just hit the daily limit on the second request at
| https://climate-api.open-meteo.com/v1/climate
|
| I see the limit for non-commercial use should be "less than
| 10.000 daily API calls". Technically 2 is less than 10.000, I
| know, but still I decided to drop you a comment. :)
| wodenokoto wrote:
| 10.000 requests / (24 hours * 60 minutes * 60 seconds) = 0.11
| requests / second
|
| or 1 request every ~9 seconds.
|
| Maybe you just didn't space them enough.
| brna wrote:
| Maybe, that would be funny. ~7 requests per minute would be
| a more dev-friendly way of enforcing the same quota.
| tomaskafka wrote:
| I confirm, open-meteo is awesome and has a great API (and API
| playground!). And is the only source I know to offer 2 weeks of
| hourly forecasts (I understand at that point they are more
| likely to just show a general trend, but it still looks
| spectacular).
|
| It's a pleasure being able to use it in
| https://weathergraph.app
| brahbrah wrote:
| > And is the only source I know to offer 2 weeks of hourly
| forecasts
|
| Enjoy the data directly from the source producing them.
|
| American weather agency:
| https://www.nco.ncep.noaa.gov/pmb/products/gfs/
|
| European weather agency:
| https://www.ecmwf.int/en/forecasts/datasets/open-data
|
| The data's not necessarily east to work with, but it's all
| there, and you get all the forecast ensembles (potential
| forecasted weather paths) too
| tomaskafka wrote:
| Thank you, I didn't know! I'd love to, but I'd need another
| 24 hours in a day to also process the data - I'm glad I can
| build on a work of others and use the friendly APIs :).
| aaarrm wrote:
| This is awesome. I was trying to do a weather project a while
| ago, but couldn't find an API to suit my needs for the life of
| me. It looks like yours still doesn't have exactly everything
| I'd want but it still has _plenty_. Mainly UV index is
| something I 've been trying to find wide historical data for,
| but it seems like it just might not be out there. I do see you
| have solar radiation, so I wonder if I could calculate it using
| that data. But I believe UV index also takes into account
| things like local air pollution and ozone forecast as well.
| 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.
| amluto wrote:
| See page three, table 1 of the paper. The model has 48 2D
| fields, on a grid, where the grid is a spherical thing
| wrapped around the surface of the Earth.
|
| There is not what I would call a 3D spherical coordinate
| system. There's no field f defined as f(theta, phi, r) --
| ther are 48 fields that are functions of theta and phi.
| 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.
| amluto wrote:
| I read a decent amount of the paper, although not the
| specific details of the model they used. And when I say I
| "never studied" it, I mean that I never took a class or read
| a textbook. I do, in fact, know something about physics and
| fluids, and I have even personally done some fluid simulation
| work.
|
| There are perfectly good models for weather in an abstract
| sense: Navier-Stokes plus various chemical models plus heat
| transfer plus radiation plus however you feel like modeling
| the effect of the ground and the ocean surface. (Or use
| Navier-Stokes for the ocean too!)
|
| But this is _wildly_ impractical. The Earth is too big. The
| relevant distance and time scales are pretty short, and the
| resulting grid would be too large. Not to mention that we
| have no way of actually measuring the whole atmosphere or
| even large sections of it in its full 3D glory in anything
| remotely close to the necessary amount of detail.
|
| Go read the Wikipedia article, and contemplate the
| "Computation" and "Parameterization" sections. This works,
| but it's horrible. It's doing something akin to making an
| effective theory (the model actually solved) out of a larger
| theory (Navier-Stokes+), but we can't even measure the fields
| in the effective theory. We might want to model a handful of
| fields at 0.25 degrees (of lat/long) resolution, but we're
| getting the data from a detailed vertical slice every time
| someone launches a weather balloon. Which happens quite
| frequently, but not continuously and not at 0.25 degree
| spatial increments.
|
| Hence my point: Google's model is sort of _learning_ an
| effective theory instead of developing one from first
| principles based on the laws of physics and chemistry.
|
| edit: I once worked in a fluid dynamics lab on something that
| was a bit analogous. My part of the lab was characterizing
| actual experiments (burning liquids and mixing of gas jets).
| Another group was trying to simulate related systems on
| supercomputers. (This was a while ago. The supercomputers
| were not very capable by modern standards.)
|
| The simulation side used a 3D grid fine enough (hopefully) to
| capture the relevant dynamics but not so fine that the
| simulation would never finish. Meanwhile, we measured
| everything in 1D 2D! We took pictures and videos with cameras
| at various wavelengths. We injected things into the fluids
| for better visualization. We measured the actual velocity at
| _one_ location (with decent temporal resolution) and hoped
| our instrumentation for that didn't mess up the experiment
| too much. We tried to arrange to know the pressure field in
| the experiment by setting it up right.
|
| With the goal of _understanding_ the phenomena, I think this
| was the right approach. But if we just wanted to predict
| future frames of video from past frames, I would expect a
| nice ML model to work better. (Well, I would expect it to
| work better _now_. The state of the art was not so great at
| the time.)
| counters wrote:
| Weather models are routinely run at resolutions as fine as
| 1-3 km - fine enough that we do not parameterize things
| like convection and allow the model to resolve these
| motions on its native grid. We typically do this over
| limited areas (e.g. domain the size of a continent), but
| plenty of groups have such simulations globally. It's just
| not practical (cost for compute and resulting data) to do
| this regularly, and it offers little by way of direct
| improvement in forecast quality.
|
| Furthermore, we don't have to necessarily measure the whole
| atmosphere in 3D; physical constraints arising from Navier-
| Stokes still apply, and we use them in conjunction with the
| data we _do_ have to estimate a full 3D atmospheric state
| complete with uncertainties.
| cryptoz wrote:
| Again haha! Still no mention of using barometers in phones. Maybe
| some day.
| EricLeer wrote:
| The weather company claims to do this (they are also the main
| provider of weather data for apple).
| 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.
| hokkos wrote:
| The API is unusably slow, the only way is to use the AWS, GCP
| or Azure mirrors, but they miss a lot of variables and are
| updated sparingly or with a delay.
| jauntywundrkind wrote:
| I created an account on ECMWF but I still dont have access to
| the ERA5 page, just a big permissions denied message. :/
|
| Any pointers?
| _visgean wrote:
| You can get some of the historical data also from here:
| https://cloud.google.com/storage/docs/public-datasets/era5 (if
| the official API is too slow. )
|
| To use the data in live fashion I think you would need to get
| license from ECMWF...
| 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?
| EricLeer wrote:
| See for instance the pytorch geometric [1] package, which
| is the main implementation in pytorch. They also link to
| some papers there that might explain you more.
|
| [1] https://pytorch-geometric.readthedocs.io/en/latest/
| 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
| counters wrote:
| GraphCast does predict rainfall - see
| https://charts.ecmwf.int/products/graphcast_medium-rain-
| acc?... for example.
| _visgean wrote:
| It will get adopted, eventually we will have more accurate
| weather forecasts. Thats good for anything that depends on
| weather - e.g. energy consumption and production,
| transportation costs...
| 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.
| nl wrote:
| Every measurement has inherent errors in it - and those
| errors are large if the task is to measure the location
| and velocity of every molecule in the atmosphere.
|
| You also need to measure the exact amount of solar
| radiation before it hits these molecules (which is
| impossible, so we assume this is constant depending on
| latitude and time)
|
| These errors compound (the butterfly effect) which is why
| we can't get perfect predictions.
|
| This is a limit inherent in physical systems because of
| physics, not really a data problem.
| 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.
| crazygringo wrote:
| Because there are tons of parts of weather where chaos _isn 't_
| the limiting factor currently.
|
| There are a limited number of weather stations producing
| measurements, and a limited "cell size" for being able to
| calculate forecasts quickly enough, and geographical factors
| that aren't perfectly accounted for in models.
|
| AI is able to help substantially with all of these -- from
| interpolation to computational complexity to geography effects.
| 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?
| bobviolier wrote:
| I don't know how or why, but yes, it has become less accurate
| over at least the last year or so.
| 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
| je42 wrote:
| However, tools like buienrader seem to have trouble in the
| recent months/years to accurately predict local weather.
| 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?
| counters wrote:
| Yes, this is effectively what 4DVar data assimilation is
| [1]. But it's very, very expensive to continually run new
| forecasts with re-assimilated state estimates. Actually,
| one of the _biggest_ impacts that models like GraphCast
| might have is providing a way to do exactly this - rapidly
| re-running the forecast in response to updated initial
| conditions. By tracking changes in the model evolution over
| subsequent re-initializations like this, one could might be
| able to better quantify expected forecast uncertainty, even
| moreso than just by running large ensembles.
|
| Expect lots of R&D in this area over the next two years...
|
| [1]: https://www.ecmwf.int/en/about/media-
| centre/news/2022/25-yea...
| brap wrote:
| So isn't it just a problem of measurement then?
|
| Say you had a massive array of billions of perfect sensors in
| different locations, and had all the computing power to
| process this data, would an N year daily forecast then be a
| solved problem?
|
| For the sake of the argument I'm ignoring "external" factors
| that could affect the weather (e.g meteors hitting earth,
| changes in man-made pollution, etc)
| counters wrote:
| At that point you're slipping into Laplace's Demon.
|
| In practical terms, we see predictability horizons get
| _shorter_ when we increase observation density and spatial
| resolution of our models, because more, small errors from
| slightly imperfect observations and models still cascade to
| larger scales.
| _visgean wrote:
| See https://en.wikipedia.org/wiki/Numerical_weather_prediction
|
| > Present understanding is that this chaotic behavior limits
| accurate forecasts to about 14 days even with accurate input
| data and a flawless model. In addition, the partial
| differential equations used in the model need to be
| supplemented with parameterizations for solar radiation, moist
| processes (clouds and precipitation), heat exchange, soil,
| vegetation, surface water, and the effects of terrain.
|
| I think there is a hope that DL models wont have this problem.
| 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
| helloplanets wrote:
| Was interested to check this out for Helsinki, but site loads
| blank on Safari :(
| Vagantem wrote:
| Oh, yea spotted now - I'll have a look as soon as I'm at my
| computer, will fix. Until then, I think you'll have to use it
| on a desktop - thanks for spotting!
| 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.
| shmageggy wrote:
| I think the paper has what you are looking for. Several figures
| comparing performance to HRES, and "GraphCast... took roughly
| four weeks on 32 Cloud TPU v4 devices using batch parallelism.
| See supplementary materials section 4 for further training
| details."
| alxmrs wrote:
| I'm so happy you asked about this! Check out
| https://sites.research.google/weatherbench/
| crazygringo wrote:
| Making progress on weather forecasting is amazing, and it's been
| interesting to see the big tech companies get into this space.
|
| Apple moved from using The Weather Channel to their own
| forecasting a year ago [1].
|
| Using AI to produce better weather forecasts is _exactly_ the
| kind of thing that is right up Google 's alley -- I'm very happy
| to see this, and can't wait for this to get built into our
| weather apps.
|
| [1] https://en.wikipedia.org/wiki/Weather_(Apple)
| blacksmith_tb wrote:
| Well, Apple acquired Dark Sky and then shut it down for Android
| users[1], and then eventually for iOS users as well (but rolled
| it into the built in weather app, I think).
|
| 1: https://www.theverge.com/2020/3/31/21201666/apple-
| acquires-w...
| _visgean wrote:
| > Apple moved from using The Weather Channel to their own
| forecasting a year ago [1].
|
| AFAIK they don't have their own forecasting models, they use
| same data sources as everyone else:
| https://support.apple.com/en-us/HT211777
| crazygringo wrote:
| Your linked article says they use their own, if you're on a
| version later than iOS 15.2.
| _visgean wrote:
| No it does not. Read the secion "Data sources", they list
| all the usual regional providers.
| joegibbs wrote:
| When will we have enough data that we will be able to apply this
| to everything? Imagine a model that can predict all kinds of
| trends - what new consumer good will be the most likely to
| succeed, where the next war is most likely to break out, who will
| win the next election, which stocks are going to break out. One
| gigantic black box with a massive state, with input from
| everything - planning approvals, social media posts, solar
| activity, air travel numbers, seismic readings, TV feeds.
| drakenot wrote:
| Sounds a bit like the premise for the Asimov series, "The
| Foundation"
| hammad93 wrote:
| I think it's irresponsible to call first on this because it will
| hinder scientific collaboration. I appreciate this contribution
| but the journalism was sloppy.
| whoislewys_1 wrote:
| Predicting weather and stock prices don't seem too far apart.
|
| Is it inevitable that all market alpha gets mined by AI?
| HereBePandas wrote:
| I'd be shocked - given the incentives - if it hasn't already
| happened to a great extent. Many of the types of people Google
| DeepMind hires are also the types of people hedge funds hire.
| rottc0dd wrote:
| How long does this forecasting hold, given butterfly effect et
| al?
| EricLeer wrote:
| I am in the power forecasting domain, where weather forecasts are
| one of the most important inputs. What I find surprising is that
| with all the papers and publications from google in the past
| years, there seems to be no way to get access to these forecasts!
| We've now evaluated numerous of the ai weather forecasting
| startups that are popping up everywhere and so far for all of
| them their claims fall flat on their face when you actually start
| comparing their quality in a production setting next to the HRES
| model from ECMWF.
| scellus wrote:
| GraphCast, Pangu-Weather from Huawei, FourCastNet and EC's own
| AIFS are available on the ECMWF chart website
| https://charts.ecmwf.int, click "Machine learning models" on
| the left tab. (Clicking anything makes the URL very long.)
|
| Some of these forecasts are also downloadable as data, but I
| don't know whether GraphCast is. Alternatively, if forecasts
| have a big economic value to you, loading latest ERA5 and the
| model code, and running it yourself should be relatively
| trivial? (I'm no expert on this, but I think that is ECMWF's
| aim, to distribute some of the models and initial states as
| easily runnable.)
| isaacfrond wrote:
| I find this quite surprising actually.
|
| You'd think predicting the weather is mostly a matter of fast
| computation. The physical rules are well understood, so to get a
| better estimate use a finer mesh in your finite element
| computation and use a smaller time scale in estimating your
| differential equations.
|
| Neural networks are notoriously bad at exact approximation. I
| mean you can never beat a calculator when the issue is doing
| calculations.
|
| So apparently the AI found some shortcut for doing the actual
| computational work. That is also surprising as weather is a
| chaotic system. Shortcuts should not exist.
|
| Long story short, I don't get what's going on here.
| uoaei wrote:
| > The physical rules are well understood
|
| Nope. They're constantly updating these models with really
| finnicky things like cloud nucleation rates that differ
| depending on which tree species's pollen is in the air. They've
| gotten a lot better (~2 day to ~7 day hi-res forecasts) but
| they're still wrong a lot of the time. The reason is the chaos
| as you say, however, chaos is deterministic, so, that a
| deterministic method can approximate a deterministic system is
| really not the surprising part.
|
| You don't get what's going on here because your baseline
| understanding is a lot worse than you think it is.
|
| What they're doing is skipping literal numerical simulation in
| favor of graph- (attention-) based approaches. Typical weather
| models simulate pretty fine resolution and return hourly
| forecasts. Google's new approach is learning an approximate
| Markov model at 6 hours resolution directly so they don't need
| to run on massive supercomputers.
| flir wrote:
| It's a model of a model?
|
| And it turns out to be better?
|
| That's so counter-intuitive I'm kinda amazed anyone even
| bothered to research it, let alone that it worked.
|
| Uh..... now do horse racing.
| uoaei wrote:
| "All models are wrong, some models are useful." Some are
| more wrong and more useful simultaneously ;) This is
| actually the typical state of things in numerical
| simulation: we have infinite-resolution differential
| equations modeling such physical systems, but to implement
| them _in silico_ we need to discretize and approximate
| various aspects of those models to achieve usefulness re:
| time and accuracy. Google has merely gone one level further
| in the tradeoff.
|
| For more info on Google's approach, look into surrogate
| models. It's becoming more common especially in things like
| weather and geology.
| karaterobot wrote:
| I used to think so too, but evidently weather forecasting is a
| much harder problem than it seems from the outside. I was
| talking to a physicist who told me who had first wanted to get
| into weather modeling, but that it was too hard. I think his
| quote was something like: "those guys are hard. core."
| boxed wrote:
| My grandfather specifically chose meteorology as a field
| because it had the most numbers heh.
| SoftTalker wrote:
| Too many variables. The combinatorial complexity exceeds what
| any computational model can deal with.
| staunton wrote:
| It's a chaotic system, one could equally well wonder how
| it's possible at all, even given insane amounts of compute,
| especially forcasting days and weeks ahead...
| giovannibonetti wrote:
| AI/ML's bitter lesson [1] applies again. In this case, the AI
| model may have learned a more practical model than the one
| human researchers painstakingly came up with by applying piles
| and piles of physics research.
|
| [1] http://www.incompleteideas.net/IncIdeas/BitterLesson.html
| savanaly wrote:
| That's only superficially similar to ai's bitter lesson. The
| bitter lesson is about methods to achieve results in AI, not
| about comparing AI methods to non-AI methods.
| uoaei wrote:
| Hardly. They trained on the output of numerical simulations,
| so it's basically a method for summarizing approximate
| dynamics of numerical simulations themselves.
| amelius wrote:
| > by applying piles and piles of physics research.
|
| You mean by remembering piles and piles of example data and
| interpolating between it.
| 3cats-in-a-coat wrote:
| This is essentially the same exact problem as a classic chess
| playing program, recursively computing all possibilities N
| moves ahead, and an AI which "groks" the game's patterns and
| knows where to focus fewer resources with greater success.
|
| This translates especially well to games like Go, where
| computing all moves is not even pragmatically possible the
| classic way. But AI beats the best Go players.
|
| Raw models are excellent for establishing the theory, and for
| training the AI. But... the AI is better at figuring out more
| effective, precise, and efficient model within itself, based on
| both synthetic (based on models) and real data (actual weather
| patterns).
|
| EDIT: And just to point out, this is not just an AI phenomenon.
| You are a neural network. And "intuition" is the sense of
| predicting outcomes you develop, without knowing how and why
| precisely. This is why I frown upon people with academic
| knowledge who dismiss people with say engineering or other
| practical experience in a field. A farmer may not tell you why
| doing things a weird way results in amazing crop yields, but he
| gets the gains, and when theory doesn't correlate with reality,
| it's not reality that's wrong, but the theory.
|
| To recap, nothing beats "learning by example". And AI learns by
| example. Of course, the formal theoretic models that we can
| collectively share, explain, and evolve over time have their
| own strong benefits and have allowed us to grow as a
| civilization. Computers are in effect "formal computation
| machines". I don't think we'll run AI for long on digital
| circuits and it's a clumsy workaround. Computers will have
| analog processing units for AI and digital processing units for
| cold, hard logic and data. And the combination is the most
| powerful approach of all.
| sheepshear wrote:
| The accuracy improvement boils down to representing more
| salient features in the model. The humans got a head start
| figuring out what to model, but the machine figures it out
| faster, so it caught up and surpassed them. Now it models more
| important stuff.
|
| The speed difference is a side effect of completely different
| implementations. One is a step-by-step simulator, the other is
| an input/output pattern matcher.
| mnw21cam wrote:
| I did my doctorate in the Met Office.
|
| Weather forecasting is two separate problems. The first of
| these is physics - given the state of the atmosphere right now,
| what will it do. And this is hard, because there are so many
| different effects, combined with the fact that our
| computational models have a limited resolution. There's a huge
| amount of work that goes into making the simulation behave like
| a real atmosphere does, and a lot of that is faking what is
| going on at a smaller scale than the model grid.
|
| The second part is to work out what the current state of the
| atmosphere is. This is what takes vast amounts of computing
| power. We don't have an observation station at every grid point
| and at every altitude in the atmospheric model, so we need to
| find some other way to infer what the atmospheric state is from
| the observations that we can from it. Many of these
| observations are limited in locality, like weather stations, or
| are a complex function of the atmospheric state, like satellite
| imagery. The light reaching a satellite has been affected by
| all the layers of the atmosphere it passes through, and
| sometimes in a highly nonlinear way. In order to calculate the
| atmospheric state, we need to take the previous forecast of the
| current atmospheric state, compare it to the observations, then
| find the first derivative (as in calculus) of the observation
| function so that we can adjust the atmospheric state estimate
| to the new best estimate. This is then complicated by the fact
| that the observations were not all taken at a single time
| snapshot - for instance polar orbiting satellites will be
| taking observations spread out in time. So, we need to use the
| physics model to wind the atmospheric state back in time to
| when the observation was taken, find the first derivative of
| _that_ too, and use it to reconcile the observations with the
| atmospheric state.
|
| It's a massive minimisation/optimisation problem with millions
| of free variables, and in some cases we need the second
| derivative of all these functions too in order to make the
| whole thing converge correctly and within a reasonable amount
| of time. It takes a reasonable number of iterations of the
| minimisation algorithm to get it settle on a solution. The
| problem is that these minimisation methods often assume that
| the function being minimised is reasonably linear, which
| certain atmospheric phenomena are not (such as clouds), so
| certain observations have to be left out of the analysis to
| avoid the whole thing blowing up.
|
| My doctorate was looking to see if the nonlinearity involved in
| a cloud forming as air was moving upwards could be used to
| translate a time-series of satellite infra-red observations
| into a measurement of vertical air velocity. The answer was
| that this single form of nonlinearity made the whole
| minimisation process fairly dire. I implemented a fairly simple
| not-quite-machine-learning approach, and it was able to find a
| solution that was almost as accurate but much more reliable
| than the traditional minimisation method.
|
| Also, to answer the dead sibling comment asking whether weather
| is really a chaotic system - yes it is. The definition of a
| chaotic system is that a small change in current state results
| in a very large change in outcome, and that's definitely the
| case. The improvements in weather forecasting over the last few
| decades have been due to improvements in solving both of the
| above problems - the physics has been pinned down better, but
| we're also better as working out the current atmospheric state
| fairly accurately, and that has added something like a day of
| forecasting accuracy each decade we have been working on it.
| londons_explore wrote:
| Shortcuts 100% exist.
|
| Imagine another physical problem. Simulating a sand grain and
| how it bounces off other sand grains or lodges against them. If
| you wanted to simulate a sand mountain, you could use a massive
| amount of compute and predict the location and behaviour of
| every single grain.
|
| Or, you could take a bunch of well-known shortcuts and just
| know that sand sits in a heap at the angle-of-repose. That
| angle decides how steep the mountain will be. any steeper and
| it will tumble till it's at that angle.
|
| Suddenly, the computation is dramatically reduced, and you get
| pretty much the same result.
| tovej wrote:
| The point was that weather, unlike a sandheap, is a chaotic
| hydrodynamic system with turbulent flows, that means it's
| computationally intractable to do exactly, which is why
| weather forecasts are only good for a few days anyway.
|
| The example you gave does not really explain anything.
| londons_explore wrote:
| The sandheap is chaotic too - just one sand grain tumbling
| can be enough to start a landslip. But the end result tends
| not to depend on the minute details - if sand grain A
| didn't cause the landslip, then a few seconds later sand
| grain B would have.
| tovej wrote:
| That's not chaos, the outcome is the same even if the
| input varies.
|
| Chaos is when the outcome differs greatly with small
| changes in input.
| twayt wrote:
| You get the same result in a short span of time, heck you may
| even get a reliable error bound.
|
| Where this falls apart is that error accumulates over time
| and not just for one heap of sand but for many such heaps of
| sand that also interact with other heaps of sand.
|
| Predicting weather for the next hour is trivial. Aviation
| runs on the fact that you can forecast fairly accurately into
| the next hour most of the time.
|
| The difficulty scales superlinearly over time due to the
| error accumulation over predictions
| empath-nirvana wrote:
| > Neural networks are notoriously bad at exact approximation.
|
| Neural networks can compute pretty much anything. There's no
| reason, given the same inputs and with enough trainining data
| that it shouldn't be able to discover the same physical laws
| that were hard-coded previously.
| T-A wrote:
| https://www.science.org/content/article/models-galaxies-atom...
|
| https://towardsdatascience.com/physics-informed-neural-netwo...
|
| https://maziarraissi.github.io/PINNs/
|
| https://arxiv.org/abs/2001.08055
|
| https://arxiv.org/abs/2009.11990
| amelius wrote:
| It's more like table lookup or interpolation than actual
| computation.
| cowboysauce wrote:
| > So apparently the AI found some shortcut for doing the actual
| computational work. That is also surprising as weather is a
| chaotic system. Shortcuts should not exist.
|
| Why do you say that shortcuts should not exist? Even very basic
| statements like "falling pressure and increasing humidity
| indicate a storm is coming" are generally valid. I've done a
| little bit of storm-chasing and I'm able to point out areas
| that are likely to experience severe thunderstorms based on a
| few values (CAPE, dew point, wind shear, etc). I'm sure
| forecast meteorologists have even better skills. Are those not
| shortcuts?
| greatpostman wrote:
| OpenAI is releasing legitimate AGI, google puts out a weather
| prediction model lol.
| dnlkwk wrote:
| I do love Google Maps more than any other product they have lol
| lambda_garden wrote:
| OpenAI has not released AGI.
| postexitus wrote:
| You assuming OpenAI's models are AGI tells more about you than
| anything else.
| rational_indian wrote:
| If Alan Turing says ChatGPT is an AGI, it's good enough for
| me.
| piyh wrote:
| Not quite
|
| https://arxiv.org/abs/2310.20216
| GaggiX wrote:
| The Turing test is there only to test a machine's ability
| to exhibit intelligent behaviour, not to test if it's an
| AGI or not.
| debugnik wrote:
| I doubt he would've, half the point of Turing's paper was
| to stop people from debating what is or isn't "thinking"
| and to focus on the actual capabilities instead (like
| passing the test). He specifically wrote:
|
| > "Can machines think?" I believe to be too meaningless to
| deserve discussion.
|
| So I don't think he would've appreciated such a fuzzy
| concept as AGI.
| tantalor wrote:
| One of these things is useful.
| mg wrote:
| It's interesting, that Google keeps publishing AI research
| papers. Is there a business rationale behind it?
|
| OpenAI has become one of the fastest growing companies of all
| time. And much of it is based on Google's "Attention is all you
| need" and other papers.
|
| Since Microsoft added the Dall-E 3 image creator to Bing, Bing
| saw a huge inflow of new users. Dall-E is also a technology
| rooted in Google papers.
|
| I wonder how Google thinks about this internally.
| ArnoVW wrote:
| It's difficult to retain top talent if you do not allow them to
| publish.
| DaiPlusPlus wrote:
| How does Apple do it, if anyone knows? Apple is so loathe to
| keep their potential product plans hidden that AAPL employees
| aren't even allowed to have GitHub accounts without mgr
| approval... but they _have_ to be employing serious
| researchers, but they'll never get to publish on volition.
| rational_indian wrote:
| How does Apple do what? AFAICT Apple does not do research,
| at least at the same level or on the same topics as Google
| or Microsoft.
| Someone wrote:
| > they have to be employing serious researchers, but
| they'll never get to publish on volition.
|
| That's not true. I wouldn't know how free they are to
| publish but they _do_ publish stuf. See
| https://machinelearning.apple.com/
| og_kalu wrote:
| Apple does publish some stuff. But anyway it's a balance
| between publishing and shipping products. The researcher
| wants to get some credit for his/her work. If you ship a
| lot of products he can put his/her name on then publishing
| research isn't quite as important and vice versa.
| crazygringo wrote:
| _Does_ Apple do it?
|
| They don't seem to be on the forefront of the AI train at
| all. They haven't been building AI products the way Google
| and Microsoft have been. Siri has been stuck for a long
| time.
|
| When I think of Apple, I think of a lot of things, but AI
| is not on that list.
| jedberg wrote:
| For every paper they publish, they have three others that they
| are keeping to themselves. Publishing papers is a recruiting
| technique.
| matsemann wrote:
| How's the distribution of the errors? For instance I don't care
| if it's better on average by 1 Celsius each day for normal
| weather, if it once every month is off by 10 Celsius when there
| is a drastic weather event, for instance.
|
| I'm all for better weather data, it's quite critical up in the
| mountains, so that's why my question about how reliable it is in
| life&death situations.
| CorrectHorseBat wrote:
| https://www.science.org/doi/10.1126/science.adi2336
|
| Seems like it's better at predicting extreme weather events
| tony_cannistra wrote:
| Similar methodologies are being applied to climate modeling, too.
|
| The Allen Institute has worked on it for a while, and has hired
| quite a few PhDs (https://allenai.org/climate-modeling).
| lainga wrote:
| How long? The cloud microparameterisation looks really
| exciting, but 10-year stability for a GCM (and "nearly
| conserving" water) is not great
| tony_cannistra wrote:
| I'm not sure. NVIDIA is also working on it (with,
| interestingly, some of the original AI2 folks).
|
| Similar to the DeepMind effort, the ACE ML model that
| AI2+others developed is really just looking for parity with
| physical models at this stage. It looks like they've almost
| achieved this, with similar massive improvements in compute
| time + resource needs.
| hackernewds wrote:
| why is hiring phds a measure?
| Lacerda69 wrote:
| if you don't hire phds you're not serious about it
| tony_cannistra wrote:
| in this particular case, most of the important/needle-moving
| work being done in climate modeling is done with a hell of a
| lot of context about prior work. PhDs have that, by
| necessity.
|
| They're also good at prioritizing outcomes, rather than other
| stuff.
| devindotcom wrote:
| yep I just talked to one of the climsim guys and included the
| project in my writeup of this news:
|
| https://techcrunch.com/2023/11/14/courtesy-of-ai-weather-for...
| tokai wrote:
| Just like their flu modelling outperformed conventional models
| right?
| Anon84 wrote:
| Humm... are you referring to Google Flu? [1]
|
| That was a very different beast. It relied on using Google
| searches to infer the prevalence of various Influenza Like
| Illnesses in real time, while the CDC reports data with a
| 2-week lag. Notably, some of the queries they found to be
| correlated were... strange... like NBA results.
|
| Not unsurprisingly (in hindsight, at least) [2], this
| eventually broke down when epidemics and flu symptoms got in
| the news and completely changed what people were searching for.
|
| [1] https://www.nature.com/articles/nature07634
|
| [2] https://www.science.org/doi/10.1126/science.1248506
| tokai wrote:
| Yeah I know its way different methods. Sorry for being
| disingenuous. The point of my snarking was that google made a
| lot of noise about Google Flu but then quietly got rid of it
| when it didn't work. To me Googles research has a tendency to
| be more about headlines than actually solving problems.
| Anon84 wrote:
| No worries, Google does tend to do a good job of
| monopolizing attention in whatever they do and Epidemic
| Modeling is... complicated. Probably much more complicated
| than pretty much any other kind of modeling since people
| have the bad habit of thinking and acting in whatever way
| they want (sometimes with the explicit purpose of breaking
| your model :).
|
| Now, if you want to see the real-world state-of-the-art
| epidemic modeling on a global scale, checkout
| GLEaM/GLEaMViz https://www.gleamviz.org/ (full disclaimer,
| in a previous life I was the lead developer).
|
| And if you're interested in a basic intro, you can also
| checkout my (somewhat neglected) series of blog posts from
| the pandemic days:
| https://github.com/DataForScience/Epidemiology101
| </ShamelessSelfPromotion>
| lesuorac wrote:
| > Notably, some of the queries they found to be correlated
| were... strange... like NBA results.
|
| Doesn't seem that strange to me.
|
| The presence of a professional sports team in your area is
| correlated with an increase in flu rates. Getting an ice
| hockey (NHL) is pretty much the worst.
|
| https://www.upi.com/Health_News/2023/08/08/flu-deaths-
| sports...
| Anon84 wrote:
| That's definitely one factor, but from what I recall (it's
| been a while) the connection was slightly more subtle. The
| NBA season (Oct-Apr) overlaps the flu season (Dec-Feb) so
| if people are googling NBA results you're in either in or
| close to the typical flu season. If the NBA decided to
| change their schedule, the correlation would go away.
| acolderentity wrote:
| How could an ai, programmed with the bias of people that already
| suck at predicting the weather, even get close to being accurate?
| david-gpu wrote:
| You don't train the AI with the forecasts made by other
| systems. You train the AI with the actual weather that was
| measured hours/days later.
| jvalencia wrote:
| Weather is a complex mix of many systems. The traditional
| approach is to understand all the systems and add them
| together. Since we don't understand them all fully, we get a
| lot of chaos.
|
| The ML algorithm doesn't care about the science, the agendas,
| the theories, nothing. It just looks for patterns in the data.
| Instead of an exact calculation it's more akin to numerical
| analysis. Turns out that looking at the whole in this case, is
| better than the sum of the parts.
| tchvil wrote:
| windguru which is in part or fully based on crowd-sourced weather
| stations is already surprisingly accurate few days in advance, in
| many regions I tried. For a few hours forecast nothing beats the
| rain radar. I wonder if they have already or will put some AI in
| their models.
| hackernewds wrote:
| anecdata does not equal data?
| hexo wrote:
| "AI" aka machine learning
| hackitup7 wrote:
| I've been really impressed at how much better weather forecasting
| has become already. I remember weather forecasts feeling like a
| total crapshoot as recently as 15 years ago or so.
| patall wrote:
| Isn't that highly subjective to where you live? Because I moved
| to Scandinavia and the forecast here is so incredibly bad,
| compared to central europe.
| tomesco wrote:
| Yes, driven by local data collection. More tightly packed
| ground stations and the availability of atmospheric
| measurement at various altitudes will improve accuracy.
| mike-cardwell wrote:
| Also, the weather is just a lot more predictable in some
| areas than others.
| londons_explore wrote:
| I think it's mostly this. If you look at a weather radar
| map, sometimes you see a speckled pattern of rain where
| there is heavy rain in places, and 100 yards away there
| is no rain at all. No way you can predict that multiple
| days out.
| obscurette wrote:
| This. Just some days ago I had a conversation with
| meteorologist who said exactly this - the weather has
| never been easy to predict in northen Europe and it has
| become even less predictable with climate change and
| global warming.
| Kye wrote:
| I feel this living in the path of moisture coming from
| the Gulf of Mexico. My phone has gotten good at letting
| me know when the rain will start and stop to within a few
| minutes, but whatever data source Apple uses still
| struggles with near-term prediction (day+) in the summer
| when there are random popup storms all the time.
| MichaelNolan wrote:
| Moving from Phoenix to Austin was a bit of a shock.
| Weather prediction in Phoenix is essentially perfect. In
| Austin the forecast seems much less accurate.
| hutzlibu wrote:
| Central europe (minus the alps) is way easier to predict. You
| can just look at the clouds on a satelite and see how they
| move, usually west to east, and then extrapolate linearily.
|
| All the fjords and mountains and lakes in norway really make
| it hard, to precicesly model it. And I think they strongly
| and chaotically influence the weather in sweden as well.
|
| Also, there are way more people living in central europe, so
| probably more effort is spend on them.
| vodou wrote:
| This is partly due to less satellite coverage, something this
| project is trying to fix: https://www.esa.int/Applications/Ob
| serving_the_Earth/Meteoro...
| cameronh90 wrote:
| The accuracy is definitely location dependent, but I
| anecdotally agree with the GP that the accuracy has improved
| substantially, at least for the UK where I am.
|
| Ten years ago, the weather forecast was so unreliable that I
| just assumed anything could happen on a given day, no matter
| the season. Frequently it would be unable to even tell you
| whether it was currently raining, and my heuristic for next
| day forecast instead was to just assume the weather would be
| the same as today.
|
| Nowadays I find the next day forecasts are nearly always
| accurate and hourly precipitation forecasts are good enough
| that I can plan my cycles and walks around them.
| eppp wrote:
| It still is. I farm outside of my day job and trying to
| schedule time to do things like cut hay is sort of a crapshoot.
| Hay needs a 3-4 day window to dry, rake and roll. This year I
| got rained on at least twice on days where the NWS showed clear
| and sunny for 3 days on the spot forecast. 20% or 50% chance of
| rain is almost useless knowledge. We went for weeks with a 20%
| chance and it never rained. We still got everything done but it
| sticks out a lot when you are watching it closely.
| TSiege wrote:
| Well it's actually not a 20% or 50% chance of rain. It's that
| it will definitely rain but only for 20-50% of the projected
| area forecasted
| ellisv wrote:
| This is exactly wrong.
|
| > The "Probability of Precipitation" (PoP) simply describes
| the probability that the forecast grid/point in question
| will receive at least 0.01" of rain.
|
| [1] https://www.weather.gov/ffc/pop
|
| It is worth noting the estimate is an areal average.
| lettergram wrote:
| Yup, I had the same issue. Showed 4 days of clear 80-90
| degree weather. Twice in that timeframe it rained (!1 inch
| each time), ruined the cut.
| Workaccount2 wrote:
| If you live in an area with "summer storms" it's basically
| impossible to forecast anything more than a general area
| (usually thousands of square miles) that they will appear in.
|
| Its like a shotgun shooting a wall. You can pretty accurately
| predict the area of the shot, but its incredibly hard to
| place where exactly each shot in that area will land.
| eppp wrote:
| I do live in such an area and we end up just taking the
| risk a lot of the time. Most of the time it is fine.
| Sometimes disaster. It is a bit frustrating.
| ghaff wrote:
| Weird (very local in particular) stuff still happens and
| tropical weather tracks, for example, can still be pretty
| unpredictable. But, living in Massachusetts, I still remember
| how the Blizzard of '78 basically caught everyone by total
| surprise and left hundreds/thousands(?) of people stranded at
| work and on highways. Never say never, but it's pretty unlikely
| you'd see that level of surprise today.
|
| (A friend of mine who moved to the Boston area about ten years
| after the event once told me that she had never seen a northern
| city in which so many people headed home from work if they saw
| so much as a snowflake.)
| asdff wrote:
| If you live in an area with a lot of microclimates within one
| city, weather forecasting is honestly no better than astrology.
| runemadsen wrote:
| I was just telling my wife this after looking up the "no
| rain" weather report and getting absolutely showered 5
| minutes later in an hour-long rain storm. Weather reports
| suck so much.
| drcongo wrote:
| Is there a chance that it just made something up and got lucky
| like ChatGPT?
| Kuinox wrote:
| Is there a chance you made something up and got lucky ?
| alberth wrote:
| Is this really an "AI" story?
|
| Aren't existing weather forecasting _models_ , already a form of
| "AI"?
|
| I'm no AI/ML expert, but isn't the real story here is that a new
| model (like GPT-4.0) is better than the previous/existing model
| (GPT-3.5).
|
| It's just grabs way more attention calling the new model "AI" (vs
| not referring to the old as such).
| mdpye wrote:
| It's an ML story. The article specifies that the current (now
| previous?) state of the art models are numerical, crunching
| vast equations representing atmospheric physics.
| surfmike wrote:
| No, existing models use more numerical methods. This is using a
| completely different approach.
|
| > GraphCast utilizes what researchers call a "graph neural
| network" machine-learning architecture, trained on over four
| decades of ECMWF's historical weather data. It processes the
| current and six-hour-old global atmospheric states, generating
| a 10-day forecast in about a minute on a Google TPU v4 cloud
| computer. Google's machine learning method contrasts with
| conventional numerical weather prediction methods that rely on
| supercomputers to process equations based on atmospheric
| physics, consuming significantly more time and energy.
| dauertewigkeit wrote:
| The multimesh is interesting. Still, I bet the Fourier Neural
| Operator approach will prove superior. Members of the same team
| (Sanchez-Gonzales, Battaglia) have already published multiple
| variations of this model, applied to other physical scenarios and
| lots of them proved to be dead ends. My money is on the FNO
| approach, anyway, which for some reason is only given a brief
| reference. To their credit DeepMind usually publishes extensive
| comparisons with previously published models. This time such a
| comparison is conspicuously missing.
|
| Full disclosure: I think DeepMind often publish these bombastic
| headlines about their models which often don't live up to their
| hype, or at least that was my personal experience. They have a
| good PR team, anyway.
| kleiba wrote:
| How is that a disclosure?
| counters wrote:
| Pragmatically speaking, it doesn't really matter if one is
| better than the other, at least until there is a massive jump
| in forecast quality (e.g. advancing the Day 5 accuracy up to
| Day 3). In the real world, we would never take raw model
| guidance from _any_ source - the best forecasts invariably come
| from consensus systems that look across many different models.
| So it's good to have a diverse lineage of forecasting systems,
| as uncorrelated errors boost the performance of these consensus
| systems.
| thriftwy wrote:
| Yandex claims to be using AI-based weather forecasting for a good
| part of a decade and claims it as a success. It is quite good.
|
| https://meteum.ai/
| counters wrote:
| My understanding is that they just use an AI-based
| precipitation nowcast (see [1]). Very different
| forecast/modeling problem than GraphCast.
|
| [1]: https://arxiv.org/abs/1905.09932
| devit wrote:
| Seems like it would be much better to do conventional weather
| forecasting and then feed the predictions along with input data
| and other relevant information to a machine learning system.
___________________________________________________________________
(page generated 2023-11-15 23:01 UTC)