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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.

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 3y ago
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 3y ago
  I don't get this. Surely past and real weather should be the input training data, not the output of numerical modeling?
  - counters 3y ago
    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 3y ago
      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 3y ago
      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 3y ago
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 https://www.ecmwf.int/en/forecasts/documentation-and-support
counters 3y ago
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 3y ago
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.

wenc 3y ago

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 3y ago
I don't get this. Surely past and real weather should be the input training data, not the output of numerical modeling?
- counters 3y ago
  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 3y ago
    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 3y ago
    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 3y ago

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 https://www.ecmwf.int/en/forecasts/documentation-and-support

counters 3y ago

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 3y ago

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.