61–70 of 310 posts
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.
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 i…
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.
So there's radial-coordinate voxels that represent a low resolution of the physical state of the entire atmosphere.
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 tempera…
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?
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.
Earlier quoted context omitted.
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…
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...
Any way to run this at even higher resolution, like 1 km? Could this resolve terrain forced effects like lenticular clouds on mountain tops?
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.
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.
Earlier quoted context omitted.
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 almo…
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 sa…
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.
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…