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.

Both APIs use weather models from NOAA GFS and HRRR, providing accurate forecasts in North America. HRRR updates every hour, capturing recent showers and storms in the upcoming hours. PirateWeather gained popularity last year as a replacement for the Dark Sky API when Dark Sky servers were shut down.

With Open-Meteo, I'm working to integrate more weather models, offering access not only to current forecasts but also past data. For Europe and South-East Asia, high-resolution models from 7 different weather services improve forecast accuracy compared to global models. The data covers not only common weather variables like temperature, wind, and precipitation but also includes information on wind at higher altitudes, solar radiation forecasts, and soil properties.

Using custom compression methods, large historical weather datasets like ERA5 are compressed from 20 TB to 4 TB, making them accessible through a time-series API. All data is stored in local files; no database set-up required. If you're interested in creating your own weather API, Docker images are provided, and you can download open data from NOAA GFS or other weather models.

Sorry, decades.

KML files for storm tracks are still the best way to go. You could calculate storm tracks yourself for other weather models like DWD ICON, ECMWF IFS or MeteoFrance ARPEGE, but storm tracks based on GFS ensembles are easy to use with sufficient accuracy

Extreme weather is predicted by numerical weather models. Correctly representing hurricanes has driven development on the NOAA GFS model for centuries.

Open-Meteo focuses on providing access to weather data for single locations or small areas. If you look at data for coastal areas, forecast and past weather data will show severe winds. Storm tracks or maps are not available, but might be implemented in the future.

Snowfall is already available in the historical weather API. Because the resolution is fairly limited for long term weather reanalysis data, snow analysis for single mountains slopes/peaks may not be that accurate.

If you only want to analyse the weeks to get the date of last snowfall and how much power might be there, use the forecast API and the "past_days" parameter to get a continuous time-series of past high-resolution weather forecasts.

Hi, creator of open-meteo.com here! I am using a more wide range of weather models to better cover Europe, Northern Africa and Asia. North America is covered as well with GFS+HRRR and even weather models from the Canadian weather service.

In contrast to pirate weather, I am using compressed local files to more easily run API nodes, without getting a huge AWS bill. Compression is especially important for large historical weather datasets like ERA5 or the 10 km version ERA5-Land.

Let me know if you have any questions!