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WeatherNext 2: Our most advanced weather forecasting model

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Re: WeatherNext 2: Our most advanced weather forecasting model

#91

Post by jowea »

cogman10 wrote: post #80
lampiaio wrote: post #54

Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."

I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…

I think false negatives (i.e. it rains when it's not supposed to) are both more bothersome and noticeable, so your weather person won't be very popular.

Re: WeatherNext 2: Our most advanced weather forecasting model

#92

Post by jasonmarks_ »

bigtones wrote: post #62

Googles weather prediction engine is already very good, and the new hurricane model was breathtakingly good this season when tested against actual hurricane paths. Meanwhile, the US Government Global Forecasting System continues to get worse. https://arstechnica.com/science/2025/11/googles-new-weather-...

> Global Forecasting System continues to get worse

What do you mean?

Re: WeatherNext 2: Our most advanced weather forecasting model

#93

Post by lucb1e »

CaliforniaKarl wrote: post #85

For folks who are interested, I suggest checking out "The weather machine: a journey inside the forecast" by Andrew Blum[0]. It's a great read into the history of weather forecasting pre-Covid. [0]: https://search.worldcat.org/title/1153659005

Sounds interesting! I was listening to the 5-minute audiobook preview and it starts right up my alley, but then devolves into meta chatter about how the author wrote this other book about the people that built the internet, how they learn the most by touching the machines and talking to the creators (as if you learn something about meteorology from walking up to a weather server). 'Must just be the intro' I thought, but then one review of the five that I found says (translated) "This book talks about people who deal with meteorology, their age, their physical appearance, their biography, their clothes, the meal he took with them. It describes places where observatories are located. It does not explain the weather phenomena or how to predict them."

Many nonfiction books have it to some extent and it's usually fine (like 5% of the content, either relevant or easy to pass into one ear and out the other), but this sounds like it takes up a good chunk of the book with who's-whos and (former) meteorological celebrities

What's your take on this? Does it spend more than, say, 20% talking about the people as compared to the content matter about weather forecast mechanisms and innovations?

Re: WeatherNext 2: Our most advanced weather forecasting model

#94

Post by schiffern »

tekkk wrote: post #82
empath75 wrote: post #65

Earlier quoted context omitted.

There is a fairly compelling argument that divination in the ancient world was not a useless waste of time, as is commonly assumed, but that having either a process or a person that can make essentially random choices for them allowed people to make hard, consequential decisions where they might otherwise be paralyzed, especially when the penalty for not acting was worse than making a mistake.

Never thought of that. Probably a bit too generous given that it could be just as well waste of time and resources, nevermind the bias of the voodoo doctor. Most of it was just weirdly provided therapy I suppose to relieve stress. But it is funny that humans put a great lot of weight on social contracts and being given explicit orders, maybe even publicly, must help pursuing action instead of rumination. Especially i…

"Evolution doesn't optimize for correctness, it optimizes for minimum error cost."

It's a subtle but important distinction.

Re: WeatherNext 2: Our most advanced weather forecasting model

#95

Post by Bjartr »

empath75 wrote: post #65
lampiaio wrote: post #54

Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."

There is a fairly compelling argument that divination in the ancient world was not a useless waste of time, as is commonly assumed, but that having either a process or a person that can make essentially random choices for them allowed people to make hard, consequential decisions where they might otherwise be paralyzed, especially when the penalty for not acting was worse than making a mistake.

IIRC, the value of randomness went even further than that. I think it was in the allocation of land for rice paddies. I-ching was used to decide if any given farmer's land was to be used that year or something like that. The benefit wasn't divination selecting better land, but by way of random selection, gave an impersonal excuse to leave fields unplanted some years, which is beneficial in the long term to overall yield.

Re: WeatherNext 2: Our most advanced weather forecasting model

#96

Post by jasonmarks_ »

lysecret wrote: post #30

Im pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction. Essentially you add random noise to the inputs and train by minimizing the regular loss (like l1) and at the same time maximizing the difference between…

> Im pretty deep into this topic and what might be interesting to an outsider is that the leading models like neuralgcm/weathernext 1 before as well as this model now are all trained with a "crps" objective which I haven't seen at all outside of ml weather prediction.

You are a bit misleading here. The model is trained on historical data but each run off of new instrument readings will be generated a few times in an ensemble.

Re: WeatherNext 2: Our most advanced weather forecasting model

#97

Post by [deleted] »

mikestorrent wrote: post #88
cogman10 wrote: post #80

Earlier quoted context omitted.

I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…

Your fairly accurate weather person is going to have to stay away from Vancouver / the Pacific Northwest ;-)

[deleted]

Re: WeatherNext 2: Our most advanced weather forecasting model

#98

Post by [deleted] »

empath75 wrote: post #65
lampiaio wrote: post #54

Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."

There is a fairly compelling argument that divination in the ancient world was not a useless waste of time, as is commonly assumed, but that having either a process or a person that can make essentially random choices for them allowed people to make hard, consequential decisions where they might otherwise be paralyzed, especially when the penalty for not acting was worse than making a mistake.

[deleted]

Re: WeatherNext 2: Our most advanced weather forecasting model

#99

Post by jasonmarks_ »

binsquare wrote: post #4

I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per: ``` WeatherNext 2 can generate forecasts 8x faster and with resolution up to 1-hour. This breakthrough is enabled by a new model that can provide hundreds of possible scenarios. ``` As an end user, all I care is that there's one accurate for…

> I find it interesting that they quantify the improvement on speed and number of forecast-ed scenarios but lack details on how it results in improved accuracy of the forecast per:

Definitely. Training on the historical data creates compelling forecasts but it comes off as a magic box. Where are the missing physics for the high performance cluster?

Re: WeatherNext 2: Our most advanced weather forecasting model

#100

Post by AYBABTME »

cogman10 wrote: post #80
lampiaio wrote: post #54

Reminds me of a funny WWII story: Kenneth Arrow and his statisticians found that their long-range forecasts were no better than numbers pulled out of a hat. The forecasters agreed and asked their superiors to be relieved of this duty. The reply was: "The Commanding General is well aware that the forecasts are no good. However he needs them for planning purposes."

I think it was a stats class where I learned this, but as it turns out bad weather is less common than good weather. To be a fairly accurate weather person, you merely need to say "there will be no precipitation" and you'll be right like 90% of the time anywhere on earth. What makes that funny is that historically, weather forecasters have been less than 90% accurate. Now, I will say that today's weather models are p…

I think this depends a lot on the region. I find that forecast quality differs widely from region to region. My guess is that it's a matter of (1) some regions have less advanced models available to them and (2) some regions have fundamentally more complex and unpredictable weather patterns.

Concrete if anecdotal example: weather forecast in SF are fairly accurate but the weather patterns are also simple to predict with the Pacific High and the simpler high level mechanics at play. Weather forecasts in Seoul are quite often completely wrong, but the weather patterns are also much more dynamics at a macro level with competing large systems in China/Gobi desert and the Western Pacific.

I'm not a meteorologist, just a sailor who likes to look at weather.

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