31–40 of 140 posts
It feels like real weather AI|Forecast|whatever_you_want_to_call_it is still far, far away. Maybe it's just the consumer aspect of weather apps but I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast. Still a lot of clear days when rain was predicted or the even more dreaded torrential downpour when it was supposed to be sunny and cl…
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…
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…
This is really important: You're not the end user of this product. These types of models are not built for laypeople to access them. You're an end user of a product that may use and process this data, but the CRPS scorecard, for example, should mean nothing to you. This is specifically addressing an under-dispersion problem in traditional ensemble models, due to a limited number (~50) and limited set of perturbed ini…
Sorry - not sure this is a reasonable take-away. The models here are all still initialized from analysis performed by ECMWF; Google is not running an in-house data assimilation product for this. So there's no feedback mechanism between ensemble spread/uncertainty and the observation itself in this stack. The output of this system could be interrogated using something like Ensemble Sensitivity Analysis, but there's nothing novel about that and we can do that with existing ensemble forecast systems.
It feels like real weather AI|Forecast|whatever_you_want_to_call_it is still far, far away. Maybe it's just the consumer aspect of weather apps but I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast. Still a lot of clear days when rain was predicted or the even more dreaded torrential downpour when it was supposed to be sunny and cl…
> I don't feel as if I get any more accurate data now than I did back when my parents turned to the daily weather channel for the forecast. The accuracy improvement is provable. A four-day forecast today is as accurate as a one-day forecast 30 years ago. And this is supremely impressive, because the difficulty of predicting the weather grows exponentially, not linearly, with time. You are welcome to your feelings - a…
Yes, _in aggregate_, forecasts are objectively, quantifiably better in 2025 than they were in 2005 let alone 1985. But any given, specific forecast may have unique and egregious failure modes. Look no further than the GFS' complete inability to lock on to the forecast track for Hurricane Melissa a month ago. This is dramatically compounded when you look at mesoscale forecast, where higher spatial resolution is a liability that leads to double-penalty errors (e.g. setting up a mesoscale snow squall band just slightly south of where it actually develops).
And keep in mind that the benchmarks shared from this model product are evaluating an ensemble mean, which further confounds things. Even if the ensemble mean is well-calibrated and accurate, there can be critical spread from the ensemble members themselves.
Is anyone aware of good sources of higher resolution models? Hourly resolution like this model provides doesn’t help much now that energy markets have moved to 15-min and 5-min resolution.
How does one use weather data in an energy market, if you don't mind my asking?
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…
What is the goal of doing that vs using L2 loss?
15 years later and still no word from Google if they will use the barometers in Android devices to assimilate surface pressure data. It has been shown that this can improve forecast accuracy. I think IBM may be doing it with their weather apps, but Google/Apple would have dramatically more data available. Apple even bought Dark Sky, which purported to do this but never released any information - so I doubt they reall…
Where can I use this? I’ve been trying to find hyperlocal forecasts like darksky used to be.
> By incorporating WeatherNext technology, we’ve now upgraded weather forecasts in Search, Gemini, Pixel Weather and Google Maps Platform’s Weather API. In the coming weeks, it will also help power weather information in Google Maps.
Is anyone aware of good sources of higher resolution models? Hourly resolution like this model provides doesn’t help much now that energy markets have moved to 15-min and 5-min resolution.
How does one use weather data in an energy market, if you don't mind my asking?
* 90 degree day => more air conditioning usage => power goes up
* 70 degree sunny day => that's also July 4th (holiday, not a work day when factories or heavy industry are running) => lots of people go outside + it's a holiday => power consumption goes DOWN
* 10 degree difference colder/hotter => impacts resistance of power lines => impacts transmission congestion credits => impacts power prices
It's a fascinating industry. One power trading company that I consulted for had a meteorologist who was also a trader. They literally hired the dude from a news channel if I remember it correctly.