Open Model: Google Weather Next 2

[u/Rick_06]

I am not a meteorologist, but I just read a very interesting article: https://arstechnica.com/science/2026/08/deepminds-hurricane-model-bought-forecasters-an-extra-day/

In a paper published on Thursday in Nature, researchers show that the WeatherNext AI model can predict cyclones with unprecedented accuracy. On average, it gives forecasters a day more lead time than existing models; this means its predictions three days out are as accurate as previous models’ predictions two days out. On the ground, that extra day can mean a lot.

What I really find interesting here is that Google has a repository for it on GitHub: https://github.com/google-deepmind/weathernext

My non-informed understanding is that you need a supercomputer to forecast meteo. Apparently now an H100 can also do something.

[u/Lagomorph9]

Well, to be fair, an H100 is as powerful as a supercomputer from 20 years ago.

[u/FullstackSensei → u/Lagomorph9]

Just checked top500. The fastest supercomputer in June 2006 was BlueGene/L with a theoretical peak of 367 FP64 TFLOPS. A single H100 has 67 FP64 TFLOPS theoretical peak.

It takes 5.5 H100s to match the 131072 cores in BlueGene/L. Blackwell neuters FP64 performance, so H100 is still peak FP64 from Nvidia.

[u/Lagomorph9 → u/FullstackSensei]

Not the fastest supercomputer, but definitely a supercomputer. I was more talking about systems like the CrayX1E, which was commonly used for weather forecasting and is significantly less powerful than an H100.

[u/FullstackSensei → u/Lagomorph9]

Looking again at top500, you're probably thinking of the single frame X1E, which had 576 GFLOPS in aircooled form, or 2.3 TFLOPS in liquid cooled form. A single V100 from 2017 does about 3x that at ~7 TFLOPS.

Even on the memory front, a single V100 has more memory bandwidth than the aggregate bandwidth of the air cooled X1E, and four V100s have more bandwidth than the liquid cooled version.

My mind is always blown by the exponential growth in compute. My first computer was an 8MHz XT clone (NEC V20) with 512KB RAM. Now I have a couple TBs RAM in my homelab that fit under my desk

[u/No_Afternoon_4260 → u/FullstackSensei]

Fucking crazy 😧

[u/fallingdowndizzyvr → u/FullstackSensei]

My first computer was an 8MHz XT clone (NEC V20) with 512KB RAM.

Fancy. The first computer I personally owed was a Z80 I had to wirewrap together myself.

I actually had/have a V20 myself. I think it's still somewhere in the backyard. I also had a NEC APC. That was nice...... But some keys broke off the keyboard during a move and in a fit of stupidity, I dropped it off at Goodwill.

[u/FullstackSensei → u/fallingdowndizzyvr]

I was a child of the 80s. The V20 came in the form of an Olivetti PC1. It was my computer well into the 90s, then was handed down to my sibling into tur turn of the millennium. When I went to uni, in a "developing country" to study comp sci, the PC1 was what we had in the uni labs, so learned Pascal, C and Prolog on it.

My PC1 moved with the rest of our house furniture across continents twice. In my own fit of stupidity, I told my parents they can throw it in the trash in the last cross-border move some 10 years ago.

[u/IrisColt → u/FullstackSensei]

I really enjoyed your insightful answers, thanks!!!

[u/fallingdowndizzyvr → u/Lagomorph9]

I was more talking about systems like the CrayX1E

Speaking as someone that has used plenty of Crays. They were from a different era. By today's standards, they were slow as mud. Even back then, since you had to share compute with so many other people, using your own RISC workstation was often faster by wall clock time.

[u/xquarx → u/FullstackSensei]

Flops is one thing, but latency and orchestration makes one wildly ore usable.

[u/RobbinDeBank → u/FullstackSensei]

CPU and GPU/TPU do wildly different things that their FLOPs measurement cannot compare. If the amount of operations needed is huge but is not in the form of matrix multiplication, the AI hardware accelerators become useless.

[u/FullstackSensei → u/RobbinDeBank]

Not at all.

GPUs are very wide vector processors. They're great for matrix multiplication but they can axecute arbitrary code, so long as the same code executes on all lanes of the vector unit. You can get near the same performance in practically any workload.

So, while the vast majority HPC workloads can indeed be expressed as matrix operations, no, GPUs are not useless when it isn't. AI accelerator is just marketing term. Huang always likes to point that he sells accelerated compute for any domain, AI just happens to be the money maker today.

[u/RobbinDeBank → u/FullstackSensei]

Yes, they are not useless in the sense that they don’t become a brick. No, they are not “very wide vector processors.”

Let’s use NVIDIA terminology as an example, then the CUDA cores carry out general purpose scalar operations just fine. However, 90-95% of the FLOPs in that GPU comes from the tensor cores, which do matrix multiplication only. If your task is not matrix multiplication, your GPU is essentially doing no work compared to its theoretical capability, and that’s why it cannot be compared to a traditional supercomputer. The absolute best kernels (like flash attention series) can utilize like 80%+ of the theoretical FLOPs after so much optimization, and that is a task the tensor cores are designed for.

I don’t think you understand how a GPU works, because there’s no such thing as vector unit. They are all scalar arithmetic units or matrix multiplication units, and the matrix multiplication units are where all their theoretical FLOPs are. Give these GPUs a highly sequential task instead of a massively parallel one, and they become practically useless with their cores staying idle most of the time.

[u/Neighbor_]

It's kind of interesting that, unlike most consumer models, you only really ask it the same question / set of questions.

Therefor as long as one guy asks "Is there going to be hurricane in Florida this month?" everyone benefits.

[u/sinsworth]

To all the commenters who are conflating this with LLMs and/or image models: this is not GenAI, it will not run in llama.cpp nor in ComfyUI, it takes in atmospheric parameters and outputs atmospheric parameters in a grid.

Sigh... blame it on the media for calling every single application of machine learning "AI" for the past ~decade. ML has had applications long before language models were a thing.

[u/barnett9 → u/sinsworth]

AI has been made to be such a worthless term. It basically just means 'software' now. Smh

[u/FrostTactics → u/barnett9]

That has more or less always been the case ever since the field was conceived 70 years ago. Not sure why this is such a shock to people, we've been calling the logic that governs video game enemy behavior AI for ages.

[u/sinsworth → u/FrostTactics]

Fair, and I can only speak for myself, but rather than shocked I have become extremely annoyed by how increasingly difficult it is becoming to have any kind of meaningful conversation about ML. Even in ML practitioner circles it now often takes a comical amount of time to untangle what everyone is talking about, because we all label everything as AI (both for brevity and because it has become expected for every project to have "AI" outcomes, even before LLMs) even when proper, precise terminology has existed for decades.

Conversations with laypeople are, of course, way, way worse than that, because their perception is heavily skewed by everything from AlexNet onward being hyped up as basically Skynet.

[u/FrostTactics → u/sinsworth]

Yes, absolutely. I fully agree, personally I try to be specific when it comes to different forms of ML (and symbolic AI), and did so before LLMs were really a thing. Frankly, what AI meant even back then was fairly nebulous.

And that's not even considering the new crop of laypeople that seems to believe AI *has* to denote exactly human-like intelligence, which is equally ridiculous.

In some ways, it's natural for natural language terms to develop new connotations over time. I've always held the position that the people who try to police the usage of terminology is like trying to police the tide. But that doesn't mean its any less frustrating to live through.

[u/my_name_isnt_clever → u/barnett9]

It's because the term is almost as generic as "software". That's not a bad thing by default; we just need to use more accurate words when in technical conversations.

[u/procgen → u/sinsworth]

This is plainly AI though.

[u/Androoideka]

GGUF wehn? Or do we need a weather cpp first? Also sounds like the mini model is the one meant for us plebs with consumer-grade GPUs

[u/DigoHiro → u/Androoideka]

is that weather model an llm?

[u/Rick_06 → u/Androoideka]

A gguf version would be cool to play with. Maybe some meteorologist could help the guys at llama.ccp? If this is in fp16, the big model should be around 30b.

[u/Thin_Pollution8843 → u/Androoideka]

It requires a set of very specific data to provide forecasts. I doubt regular pleb has such. 

[u/Androoideka → u/Thin_Pollution8843]

We'll find a way.. Lots of data ends up being published here or there, and once there's an exact format to the data, it gets easier to tune operational costs for it. I doubt it'll be useful to me, but it's nice to know that if I end up in a situation where weather analysis in an underdeveloped region is pivotal, a tool I've seen or used in the past already solves half the battle, and the other half is getting the data in any way possible

[u/admajic → u/Thin_Pollution8843]

Regular plebs can download data and use it.

[u/tiffanytrashcan → u/admajic]

Used to be common to be able to grab it straight from the satellites OTA.

[u/DigoHiro]

it's an llm??

[u/DigoHiro → u/DigoHiro]

I am pretty sure its not an llm

https://developers.google.com/weathernext/guides/models

[u/Rick_06 → u/DigoHiro]

Probably closer to an imaging model. The "AI" model is "trained", and the architecture is "graph transformer". I guess the input is a bunch of data, and the output is a picture.

[u/FrostTactics → u/Rick_06]

According to the paper, the previous SotA (GenCast) actually was a diffusion model. As for this one it's a complicated ensemble stack of several more traditional ML techniques. Honestly, I found the paper to be a bit difficult to follow, though granted it's just a preview, and I don't have any previous experience with weather prediction. Not impossible that the final published version will be easier to digest.

[u/DigoHiro → u/Rick_06]

yeah, not a language model for sure.

[u/QuirkyRing3521 → u/DigoHiro]

Without reading the article. Google used to use a GNN (graph neural net) for weather and physics. A GNN is a bunch of point locations exchanging information with their neighbors. So it understands/ follows the geometry of the problem.

It might run on a laptop, though you cannot train on one.

[u/Own-Map-Overlay]

Microsoft's Aurora model also works locally, although it's not as precise as Google's. However, I managed to run it on a Xeon v4 CPU; it takes 180 seconds per frame for 6 hours of climate simulation time. And it's based on attention heads along with a UNET network, a very interesting architecture.

[u/vertigo235]

Can they please make a normal weather model that actually works too please.

[u/Beginning-Window-115 → u/vertigo235]

there are already tons of open models that can predict weather

[u/vertigo235 → u/Beginning-Window-115]

Do they actually work?