Fran Bartolić

Predicting the weather @salientpredict

[Fran Bartolić @fbartolic]

Weather forecasts are ultimately used to make decisions, but most NWP and AI weather models predict the atmospheric state at discrete timesteps, while real-world decisions depend on decision-centric targets that are often nontrivial to recover from state snapshots.

Our proposal is straightforward: learn the joint distribution over the atmospheric state and decision-centric targets directly, while evolving only the atmospheric state autoregressively. We illustrate the framework by modelling true daily temperature extremes despite using a 24h timestep for the state variables, and it generalizes to a broad class of decision-centric targets.

We also show you can obtain near-SOTA state forecasts with off-the-shelf vision transformers on a lat/lon grid, with 1–2 orders of magnitude cheaper training and inference than competing approaches, including recent work from @GoogleDeepMind. Finally, we obtain stable S2S and long-range forecasts at daily resolution that smoothly converge to climatology, despite operating on a rectangular grid.

Preprint now out on arXiv: https://t.co/MVAetVEadp

[Fran Bartolić @fbartolic]

I’m excited to share @salientpredict’s newest generative AI weather model, GemAI v2.

It delivers 200-member ensemble forecasts with a 126-day horizon and beats @ECMWF’s IFS ENS by a substantial margin across multiple variables and timescales.

[Fran Bartolić @fbartolic]

Gemini 3 is the only frontier model that still regularly gets stuck in infinite token generation loops....

[Fran Bartolić @fbartolic]

Google's @antigravity has become completely broken recently. Not that it every worked well though. The only reason I keep using it is because of cheap Opus 4.5 credits.

[Fran Bartolić @fbartolic]

The main contributions of the paper are neatly summarized in Figure 1:

[Fran Bartolić @fbartolic]

Weather forecasts are ultimately used to make decisions, but most NWP and AI weather models predict the atmospheric state at discrete timesteps, while real-world decisions depend on decision-centric targets that are often nontrivial to recover from state snapshots.

Our proposal is straightforward: learn the joint distribution over the atmospheric state and decision-centric targets directly, while evolving only the atmospheric state autoregressively. We illustrate the framework by modelling true daily temperature extremes despite using a 24h timestep for the state variables, and it generalizes to a broad class of decision-centric targets.

We also show you can obtain near-SOTA state forecasts with off-the-shelf vision transformers on a lat/lon grid, with 1–2 orders of magnitude cheaper training and inference than competing approaches, including recent work from @GoogleDeepMind. Finally, we obtain stable S2S and long-range forecasts at daily resolution that smoothly converge to climatology, despite operating on a rectangular grid.

Preprint now out on arXiv: https://t.co/MVAetVEadp

[Fran Bartolić @fbartolic]

I’m excited to share @salientpredict’s newest generative AI weather model, GemAI v2.

It delivers 200-member ensemble forecasts with a 126-day horizon and beats @ECMWF’s IFS ENS by a substantial margin across multiple variables and timescales.

[Coddled Affluent Professional @feelsdesperate]

The reason people hate Zohran is because he’s a spoiled retarded communist.

His parents are privileged, placeless global citizens and Zohran was able to float along without real jobs in NYC taking a swing at a ‘rap career’ while living in a Chelsea apartment owned by his mother.

Despite this he plays the victim and cries about being persecuted and is a vector for every bad idea that afflicts downwardly mobile creative affluents.

As his next act, he’s putting on, ‘Socialism: the Musical,’ with a bunch of other failed theater kids with a $100 billion annual budget in the US’s greatest city.

It’s the stupidest timeline, he’s giving normal people all sorts of things to worry about (public safety, schools, etc.) that they shouldn’t have to, and it’s all in service of the grievances of a class of losers perpetually bitter that they can’t have the lifestyle in NYC that their income affords them.

[Tropical Cowboy of Danger @FlynonymousWX]

Third pass through Melissa. GoPro in side window as different camera looking forward shooting in ultra high res 8k. Not sure when that might get processed as the file turned out ridiculous. Barely had HD space for it and MacBook Pro promptly chocked when I tried to edit it

[Fran Bartolić @fbartolic]

@EdsenTheWeather @GoogleDeepMind @ViktorCikojevic @LevangSam @PauliusRauba Thanks! We're hoping to publish something in a month or so.

[Fran Bartolić @fbartolic]

I’m excited to share @salientpredict’s newest generative AI weather model, GemAI v2.

It delivers 200-member ensemble forecasts with a 126-day horizon and beats @ECMWF’s IFS ENS by a substantial margin across multiple variables and timescales.

[Fran Bartolić @fbartolic]

@WeatherMatrix @burgwx Here's a similar animation showing the probability of maximum daily wind gust (rather than mean surface level winds) above a 98th historical percentile threshold.

[Fran Bartolić @fbartolic]

@WeatherMatrix @burgwx Here's a similar animation showing the probability of maximum daily wind gust (rather than mean surface level winds) above a 98th historical percentile threshold.

[Fran Bartolić @fbartolic]

@WeatherMatrix @burgwx Salient forecasts aren't publicly available but here's a short animation I've made showing the probability of extreme winds from today's GEFS and GemAI forecasts.

[Fran Bartolić @fbartolic]

@benln Where is it? I'm on the waitlist :/

[Fran Bartolić @fbartolic]

@aakaran31 I'm curious, why did you use M-H instead of a gradient-based sampler such as HMC?

[Fran Bartolić @fbartolic]

@GoogleDeepMind This was a long time in the making. Shoutout to @ViktorCikojevic, @LevangSam, @PauliusRauba, and everyone else who contributed to this release!

[Fran Bartolić @fbartolic]

There’s a lot more to say about GemAI v2. The model is already operational and available through our GUI and API.

We are planning to submit a paper on arXiv soon, along with a benchmark comparison against @GoogleDeepMind’s latest GenCast and FGN models