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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" deserv

by amluto 3y ago

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 3y ago
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 3y ago
  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 3y ago
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 3y ago
  No need, they're a software engineer (presumably). That just means they're better than everyone.
- amluto 3y ago
  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.)

by amluto 3y ago

Difwif 3y ago

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 3y ago
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 3y ago

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 3y ago
No need, they're a software engineer (presumably). That just means they're better than everyone.
amluto 3y ago
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.)