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> In the first effort, the team behind Pangu-Weather, trained their system on 39 years of weather data and then asked it to make predictions based on current we

> In the first effort, the team behind Pangu-Weather, trained their system on 39 years of weather data and then asked it to make predictions based on current weather patterns. They found that it was as accurate at doing so as existing systems, and did its work in just a fraction of the time. That's awesome -- years of analysis paying off in a very cool and interesting way. It says that this particular model doesn't predict precipitation -- just atmospheric conditions -- but there's a lot of really interesting potential, here.

PaulHoule 3y ago
Older books talked about two methods of predicting the weather: (1) based on atmospheric modelling, and (2) based on historical trends. The American Boy's Handy Book https://en.wikipedia.org/wiki/The_American_Boy%27s_Handy_Book https://en.wikipedia.org/wiki/The_American_Boy%27s_Handy_Boo... had a chapter about using simple heuristics that, in principle, are similar to what a machine learning algorithm can learn.
sorokod 3y ago
Must say that "as accurate at doing so as existing systems" is quite mysterious. Not as accurate as systems from previous decades, not more accurate then the existing systems. What is so special about the now?
- counters 3y ago
  It's specifically referring to these forecast models having accuracy about on par with the state-of-the-science global numerical weather forecast models. "Accuracy" here specifically means esoteric metrics like the "500mb anomaly correlation coefficient" (basically a summary statistic that tells you how well the 3D atmosphere fields predicted by the model match what we observe later on). This entire class of global numerical weather forecast models has had more-or-less a monotonic increase in forecast accuracy over the past five decades. E.g., a 72 hour forecast from the current generation of these models has similar error statistics to a 24 hour forecast from its predecessors in the early 2000's. What's special about these AI forecast models is that they are significantly cheaper to run than the existing global numerical weather forecast models. Modern meteorology involves a great deal of statistical analysis to overcome chaotic uncertainty. One way we build these statistical analyses is to run dozens of forecasts with the same model, using slightly different initial conditions, to see how the forecasts diverge. But these ensembles are very under-disperse - a few dozen members just doesn't fully sample the uncertainty. Now, if you can run 1000x the number of ensemble members, a whole new world of possibility opens up. And that's before you consider just training the AI system to directly output a posterior distribution representing this uncertainty in the first place...
  - sorokod 3y ago
    Thanks for the explanation but my question was not about the cost but about the accuracy. Or is it the case that because of the reduced cost there is an opportunity to improve accuracy because more scenarios can be executed?
    - counters 3y ago
      The problem in this world of AI/weather is that "accuracy" is an extremely fuzzy concept. The leading pack of AI-NWP models (NVIDIA's FourCastNet, DeepMind's Graphcast, Huawei PanguWeather), when compared on an apples-to-apples basis with the leading pack of traditional NWP systems (NOAA GFS and ECMWF HRES) have similar accuracy metrics. But here, "accuracy" is an esoteric term that is far removed from how an end user would perceive how "good" any of these given models are at predicting tomorrow's afternoon high temperature at their house. In the world of meteorology, the way you build an "accurate" (e.g. "user-perceived accuracy") forecast is to consume the entire previous class of forecasts and apply statistics/ML to post-process them. The greater your ability to probe uncertain in the forecasts from these models - e.g. by running larger ensembles or tailoring the ones you run to try to quantify the uncertainty more explicitly - the better your opportunity to improve those 'accuracy" metrics. So yes - the opportunity here is running larger sets of tailored forecast simulations as a way to statistically optimize forecast accuracy.
- pixl97 3y ago
  >What is so special about the now >> and did its work in just a fraction of the time.
hulitu 3y ago
I'm sure this is already used in the weather stations selled on Amazon. /s

PaulHoule 3y ago

Older books talked about two methods of predicting the weather: (1) based on atmospheric modelling, and (2) based on historical trends. The American Boy's Handy Book https://en.wikipedia.org/wiki/The_American_Boy%27s_Handy_Book https://en.wikipedia.org/wiki/The_American_Boy%27s_Handy_Boo... had a chapter about using simple heuristics that, in principle, are similar to what a machine learning algorithm can learn.

sorokod 3y ago

Must say that "as accurate at doing so as existing systems" is quite mysterious. Not as accurate as systems from previous decades, not more accurate then the existing systems. What is so special about the now?

counters 3y ago
It's specifically referring to these forecast models having accuracy about on par with the state-of-the-science global numerical weather forecast models. "Accuracy" here specifically means esoteric metrics like the "500mb anomaly correlation coefficient" (basically a summary statistic that tells you how well the 3D atmosphere fields predicted by the model match what we observe later on). This entire class of global numerical weather forecast models has had more-or-less a monotonic increase in forecast accuracy over the past five decades. E.g., a 72 hour forecast from the current generation of these models has similar error statistics to a 24 hour forecast from its predecessors in the early 2000's. What's special about these AI forecast models is that they are significantly cheaper to run than the existing global numerical weather forecast models. Modern meteorology involves a great deal of statistical analysis to overcome chaotic uncertainty. One way we build these statistical analyses is to run dozens of forecasts with the same model, using slightly different initial conditions, to see how the forecasts diverge. But these ensembles are very under-disperse - a few dozen members just doesn't fully sample the uncertainty. Now, if you can run 1000x the number of ensemble members, a whole new world of possibility opens up. And that's before you consider just training the AI system to directly output a posterior distribution representing this uncertainty in the first place...
- sorokod 3y ago
  Thanks for the explanation but my question was not about the cost but about the accuracy. Or is it the case that because of the reduced cost there is an opportunity to improve accuracy because more scenarios can be executed?
  - counters 3y ago
    The problem in this world of AI/weather is that "accuracy" is an extremely fuzzy concept. The leading pack of AI-NWP models (NVIDIA's FourCastNet, DeepMind's Graphcast, Huawei PanguWeather), when compared on an apples-to-apples basis with the leading pack of traditional NWP systems (NOAA GFS and ECMWF HRES) have similar accuracy metrics. But here, "accuracy" is an esoteric term that is far removed from how an end user would perceive how "good" any of these given models are at predicting tomorrow's afternoon high temperature at their house. In the world of meteorology, the way you build an "accurate" (e.g. "user-perceived accuracy") forecast is to consume the entire previous class of forecasts and apply statistics/ML to post-process them. The greater your ability to probe uncertain in the forecasts from these models - e.g. by running larger ensembles or tailoring the ones you run to try to quantify the uncertainty more explicitly - the better your opportunity to improve those 'accuracy" metrics. So yes - the opportunity here is running larger sets of tailored forecast simulations as a way to statistically optimize forecast accuracy.
pixl97 3y ago
>What is so special about the now >> and did its work in just a fraction of the time.

hulitu 3y ago

I'm sure this is already used in the weather stations selled on Amazon. /s