This is a most excellent place for technology news and articles.
what’s perhaps most striking about GenCast is that it requires significantly less computing power than traditional physics-based ensemble forecasts like ENS. According to Google, a single one of its TPU v5 tensor processing units can produce a 15-day GenCast forecast in eight minutes. By contrast, it can take a supercomputer with tens of thousands of processors hours to produce a physics-based forecast.
If true this is extremely impressive, but this is their own evaluation, so it may be biased.
What they leave off is how much goes into training the model, but I imagine once they settle on a trained model it can carry on pretty efficiently for a long time, especially if they're baking in things like atmospheric CO2 levels to help keep forecasts in line with global warming.
Absolutely, but training is only once, being so efficient to make the actual forecast, you could have a forecast personally made for your own garden, which may be very different than a generic one covering hundreds of km². Then the about 90% accuracy will feel WAY more accurate.
I feel this personally, I live in the hills outside of a valley metro. All weather data is forecasted off of valley sensors, but shit gets weird when you suddenly climb 2000+ ft.
The best weather services in my area are those that can factor in peoples household meters into their forecasting, but those services still aren't perfect.
I'm sure the model would need to be continuously updated to take in more recent weather data.
Inputting newer weather condition data is different than changing the model. The model is the machine that does the computing, the weather data is just inputting variables. As an analogy it's like a computer - the hardware itself doesn't change, but if you do different clicks and typing input then the computer will output different things on screen. The ai model itself only changes when you train it differently.
There's a difference between the real-ish-time weather data continuously fed in to output predictions, and the decades of weather data used to build the model. The continuous feed of data is more than likely part of what Google alleges is saving significant energy.
Its the training on decades of information, and occasional updates to those trained models that take a significant amount of resources, but hopefully for relatively short bursts.
It actually makes sense if you think about it from the perspective that ML is about generalizing trends/functions. Simulating the world is hard, generalizing the world based on past observations - easy (with some lossyness).
Yeah, I've long thought that weather forecasts are a perfect use case for AI. AI is great with complicated systems that are hard to model accurately but have lots of available data.
Current weather forecasts kinda suck. I try to schedule jobs around when it's going to rain, and have to frequently reschedule because rain forecasts aren't very accurate. I really hope we can see improvements.
It would be amazing if it could have a significant impact on spatial and temporal accuracy of things like rain. I feel like for me the existing weather report is good enough for "it will probably rain tomorrow" but it's really hit-or-miss when you get to hourly resolution. A good model may be able to go so far as to say "it will probably rain between 3-4pm on the east side of town tomorrow, and 2-3pm on the west side"
That's the dream at least. With enough data and a sophisticated enough model it feels like it could be possible.
I'm not convinced you can ever get that resolution. There's a big difference between modeling the broad trends and trying to remove the uncertainty from a process that's inherently probabilistic.
Theoretically with enough data it could predict exactly what is going to happen do we have enough data currently to do that probably not but weather isn’t just completely random we just don’t understand it enough yet
My argument is that that is not the case.
There are many systems in nature that have randomness fundamentally built in. You can model the broad strokes, but the low level details are inherently unpredictable because random processes are involved at the low level. You can predict the general pattern of airflow over a jet wing, but it's not a lack of input resolution that makes it impossible to project the path of a specific molecule.
You're better off looking at QPFs than regular forecasts.
But, if you're wanting something like "will there be rain at this GPS coordinate at this time", then under some conditions that is just impossible to predict. It's not a problem with the how clever the models are or a lack of data, the physics makes it legitimately random.
Rain forecasts are mostly spot on for me. Keep in mind, %chance of rain is covering a wide area. If we want better rain forecasts we have to dial in the resolution.
I had one time a couple weeks ago where I was scheduling jobs on Monday, we were supposed to be rained out Tuesday, light/scattered showers Wednesday, and heavy rain Thursday.
Actual results was no rain Tuesday, absolute downpour on Wednesday, and sunny Thursday and Friday.
that's... actually a great use for AI. good to see something intelligent is being done after all.
Weather and other very complex and hard to predict stuff like fluid dynamics, things that don't have a human or completely random behaviour, is probably the best thing an AI could do for humanity.
Also could be good for understanding animal communication like dolphins etc.
I don't think it will really be able to do much actual human work, though. Maybe management....
Implying management is human work?
Many fields of science are only as far as they are because of AI being able to analyse Big data fast. Weather surly is not the only one. To name some examples: Astrophysics, geophysics, psychology (crowd behaviour), biology, Farming (optimising), and many more
Indeed, what’s perhaps most striking about GenCast is that it requires significantly less computing power than traditional physics-based ensemble forecasts like ENS. According to Google, a single one of its TPU v5 tensor processing units can produce a 15-day GenCast forecast in eight minutes. By contrast, it can take a supercomputer with tens of thousands of processors hours to produce a physics-based forecast.
So it's more accurate and uses significantly less computing power than current systems. Nice!
Is there any apps running this yet? The weather underground app sucks, big time.
I miss Dark Sky.
About 4 years ago, this video showed that a ML model can be used to cut costs on physics simulations. It’s about time we did that with weather too.
It's not just about cutting costs, but also improving accuracy. Physical simulations factor in a dozen or so weather conditions to predict outcomes. Machine learning can track thousands of conditions, drawing connections not realized in physical models, leading to much more accurate statistical models.
Yeah, that’s pretty impressive. I wonder if you could apply the same philosophy in other areas too. Instead of training the model with data produced in a simulation, you could just feed it real world data instead. Like, if you gave a bunch of stress-strain data to a model, could you make better predictions about the behavior of physical structures, such as bridges and towers.
As much data as they have, it seems like they could use more data. Just as an example, I have two weather apps on my phone. And for the same city, they will give me two different temperatures. Checked back to back. And those temperatures will both be different than the temperature of my thermometer at my house. What if each city had say like 50 sensors all over the city that would report in and then they would take the average of all 50 of those sensors in order to get a more accurate number? And that's just for temperature.
