Where can I use this? I’ve been trying to find hyperlocal forecasts like darksky used to be.
The HRRR is VERY good in my opinion. It updates hourly with a 15-minute resolution 18 hours out and hourly 48 hours out.
WeatherNext 2: Our most advanced weather forecasting model
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Re: WeatherNext 2: Our most advanced weather forecasting model
#52Is 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.
HRRR is 15 min res updated hourly. It's not that resolution all the way out only 18 hours I think.
Re: WeatherNext 2: Our most advanced weather forecasting model
#53Where can I use this? I’ve been trying to find hyperlocal forecasts like darksky used to be.
Precip.ai or go grab the MRMS data yourself
Re: WeatherNext 2: Our most advanced weather forecasting model
#54Reminds 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."
Re: WeatherNext 2: Our most advanced weather forecasting model
#55[dead]
Re: WeatherNext 2: Our most advanced weather forecasting model
#56Im 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…
> 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 2 members with different random noise initialisations. I wonder if this will be applied to more traditional genai at some point.
We recently had a situation where we specifically wanted to generate 2 "different" outputs from an optimization task and struggled to come up with a good heuristic for doing so. Not at all a GenAI task, but this technique probably would have helped us.
Re: WeatherNext 2: Our most advanced weather forecasting model
#57Im 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?
The goal of using CRPS is to produce an ensemble that is a good probabilistic forecast without needing calibration/post processing.
[edit: "without", not "with"]
Re: WeatherNext 2: Our most advanced weather forecasting model
#58Earlier quoted context omitted.
I've found this to be more related to poor representation of the data than inaccurate data. For example on Apple's Weather app, a "rainy" day means a high chance of rain at any point during the day. If it's 80% chance of rain at 5am and sunny the rest of the day– that counts as rainy. You can see an hourly report for more info, and generally this is pretty accurate. You have to learn how to find the right data, know…
In Norway people are extremely weather-focused, and the national weather service delivers quite advanced graphics for people to understand what is going on. The standard graph that most people look at to get an idea about today and tomorrow: https://www.yr.no/en/forecast/graph/1-72837/Norway/Oslo/Oslo... The live weather radar which shows where it is raining right now and prediction/history for rain +/- 90 minutes. T…
The United States (National Weather Service) has these too:
https://www.weather.gov/forecastmaps/
I use these and Windy: https://www.windy.com/
In my experience, these forecasts are really good 5-7 days out, and then degrade in reliability (as you would expect from predictions of chaotic systems). The apps that show you a rain cloud and a percentage number are always terrible in my experience for some reason, even if the origin of the data is the same. I'm not sure why that might be.
Re: WeatherNext 2: Our most advanced weather forecasting model
#59Where can I use this? I’ve been trying to find hyperlocal forecasts like darksky used to be.
Look out the window? Works as well as anything else for me.
Re: WeatherNext 2: Our most advanced weather forecasting model
#60I 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 to hijack you: I have some questions regarding current weather models:
I am personally not interested in predicting the weather as end users expect it, rather I am interested in representative evolutions of wind patterns. I.e. specify some location (say somewhere in the North Sea, or perhaps on mainland Western Europe), and a date (say Nov 12) without specifying a year, and would like to have the wind patterns at different heights for that location say for half an hour. Basically running with different seeds, I want to have representative evolutions of the wind vector field (without specifying starting conditions, other than location and date, i.e. NO prior weather).
Are there any ML models capable of delivering realistic and representative wind gust models?
(The context is structural stability analysis of hypothetical megastructures)