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GraphCast: AI model for weather forecasting

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Re: GraphCast: AI model for weather forecasting

#271

Post by hutzlibu »

patall wrote: post #250
hackitup7 wrote: post #245

I've been really impressed at how much better weather forecasting has become already. I remember weather forecasts feeling like a total crapshoot as recently as 15 years ago or so.

Isn't that highly subjective to where you live? Because I moved to Scandinavia and the forecast here is so incredibly bad, compared to central europe.

Central europe (minus the alps) is way easier to predict. You can just look at the clouds on a satelite and see how they move, usually west to east, and then extrapolate linearily.

All the fjords and mountains and lakes in norway really make it hard, to precicesly model it. And I think they strongly and chaotically influence the weather in sweden as well.

Also, there are way more people living in central europe, so probably more effort is spend on them.

Re: GraphCast: AI model for weather forecasting

#272

Post by [deleted] »

ellisv wrote: post #261
TSiege wrote: post #259

Earlier quoted context omitted.

Well it's actually not a 20% or 50% chance of rain. It's that it will definitely rain but only for 20-50% of the projected area forecasted

This is exactly wrong. > The "Probability of Precipitation" (PoP) simply describes the probability that the forecast grid/point in question will receive at least 0.01" of rain. [1] https://www.weather.gov/ffc/pop It is worth noting the estimate is an areal average.

[deleted]

Re: GraphCast: AI model for weather forecasting

#273

Post by lesuorac »

Anon84 wrote: post #238
tokai wrote: post #219

Just like their flu modelling outperformed conventional models right?

Humm... are you referring to Google Flu? [1] That was a very different beast. It relied on using Google searches to infer the prevalence of various Influenza Like Illnesses in real time, while the CDC reports data with a 2-week lag. Notably, some of the queries they found to be correlated were... strange... like NBA results. Not unsurprisingly (in hindsight, at least) [2], this eventually broke down when epidemics an…

> Notably, some of the queries they found to be correlated were... strange... like NBA results.

Doesn't seem that strange to me.

The presence of a professional sports team in your area is correlated with an increase in flu rates. Getting an ice hockey (NHL) is pretty much the worst.

https://www.upi.com/Health_News/2023/08/08/flu-deaths-sports...

Re: GraphCast: AI model for weather forecasting

#274

Post by dauertewigkeit »

The multimesh is interesting. Still, I bet the Fourier Neural Operator approach will prove superior. Members of the same team (Sanchez-Gonzales, Battaglia) have already published multiple variations of this model, applied to other physical scenarios and lots of them proved to be dead ends. My money is on the FNO approach, anyway, which for some reason is only given a brief reference. To their credit DeepMind usually publishes extensive comparisons with previously published models. This time such a comparison is conspicuously missing.

Full disclosure: I think DeepMind often publish these bombastic headlines about their models which often don't live up to their hype, or at least that was my personal experience. They have a good PR team, anyway.

Re: GraphCast: AI model for weather forecasting

#275

Post by vodou »

patall wrote: post #250
hackitup7 wrote: post #245

I've been really impressed at how much better weather forecasting has become already. I remember weather forecasts feeling like a total crapshoot as recently as 15 years ago or so.

Isn't that highly subjective to where you live? Because I moved to Scandinavia and the forecast here is so incredibly bad, compared to central europe.

This is partly due to less satellite coverage, something this project is trying to fix: https://www.esa.int/Applications/Observing_the_Earth/Meteoro...

Re: GraphCast: AI model for weather forecasting

#276

Post by jauntywundrkind »

sunshinesnacks wrote: post #58
jauntywundrkind wrote: post #33

From what I can tell from reading & based off https://colab.research.google.com/github/deepmind/graphcast/... , one needs access to ECMWF Era5 or HRES data-sets or something similar to be able to run and use this model. Unknown what licensing options ECMWF offers for Era5, but to use this model in any live fashion, I think one is probably going to need a small fortune. Maybe some other dataset can be adapted (likely…

ERA5 is free. The API is a bit slow. I think that only some variables from the HRES are free, but not 100% sure.

I created an account on ECMWF but I still dont have access to the ERA5 page, just a big permissions denied message. :/

Any pointers?

Re: GraphCast: AI model for weather forecasting

#278

Post by MichaelNolan »

mike-cardwell wrote: post #262
tomesco wrote: post #253

Earlier quoted context omitted.

Yes, driven by local data collection. More tightly packed ground stations and the availability of atmospheric measurement at various altitudes will improve accuracy.

Also, the weather is just a lot more predictable in some areas than others.

Moving from Phoenix to Austin was a bit of a shock. Weather prediction in Phoenix is essentially perfect. In Austin the forecast seems much less accurate.

Re: GraphCast: AI model for weather forecasting

#279

Post by ghaff »

hackitup7 wrote: post #245

I've been really impressed at how much better weather forecasting has become already. I remember weather forecasts feeling like a total crapshoot as recently as 15 years ago or so.

Weird (very local in particular) stuff still happens and tropical weather tracks, for example, can still be pretty unpredictable. But, living in Massachusetts, I still remember how the Blizzard of '78 basically caught everyone by total surprise and left hundreds/thousands(?) of people stranded at work and on highways. Never say never, but it's pretty unlikely you'd see that level of surprise today.

(A friend of mine who moved to the Boston area about ten years after the event once told me that she had never seen a northern city in which so many people headed home from work if they saw so much as a snowflake.)

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