[Fatnino]

Is there somewhere to see historical forecasts?

So not "the weather on 25 December 2022 was such and such" but rather "on 20 December 2022 the forecast for 25 December 2022 was such and such"

[meteo-jeff]

Not yet, but I am working towards it: https://github.com/open-meteo/open-meteo/issues/206

[berniedurfee]

I’ve always wanted to see something like that. I always wonder if forecasts are a coin flip beyond a window of a few hours.

[Auracle]

I just quit photographing weddings (and other stuff) this year. It's a job where the forecast really impacts you, so you tend to pay attention.

The amount of brides I've had to calm down when rain was forecast for their day is pretty high. In my experience, in my region, precipitation forecasts more than 3 days out are worthless except for when it's supposed to rain for several days straight. Temperature/wind is better but it can still swing one way or the other significantly.

For other types of shoots I'd tell people that ideally we'd postpone on the day of, and only to start worrying about it the day before the shoot.

I'm in Minnesota, so our weather is quite a bit more dynamic than many regions, for what it's worth.

[CSMastermind]

I know at a minimum that hurricane forecasts have gotten significantly better over time. We can now

https://www.nhc.noaa.gov/verification/verify5.shtml

Our 96 hour projections are as accurate today as the 24 hour projections were in 1990.

[mjs]

Looks like https://sites.research.google/weatherbench/ attempts to "benchmark" different forecast models/systems.

They're very cautious about naming a "best" model though!

> Weather forecasting is a multi-faceted problem with a variety of use cases. No single metric fits all those use cases. Therefore,it is important to look at a number of different metrics and consider how the forecast will be applied.

[rrr_oh_man]

That last paragraph sounds like something ChatGPT would write.

[jjp]

Are you thinking something like https://www.forecastadvisor.com/?

[meteo-jeff]

I would like to see an independent forecast comparison tool similar to Forecast Advisor, which evaluates numerical weather models. However, getting reliable ground truth data on a global scale can be a challenge.

Since Open-Meteo continuously downloads every weather model run, the resulting time series closely resembles assimilated gridded data. GraphCast relies on the same data to initialize each weather model run. By comparing past forecasts to future assimilated data, we can assess how much a weather model deviates from the "truth," eliminating the need for weather station data for comparison. This same principle is also applied to validate GraphCast.

Moreover, storing past weather model runs can enhance forecasts. For instance, if a weather model consistently predicts high temperatures for a specific large-scale weather pattern, a machine learning model (or a simple multilinear regression) can be trained to mitigate such biases. This improvement can be done for a single location with minimal computational effort.