Luka78

Thank you for your comments! We will improve some of the points mentioned.

Meteosource learns from historical data and forecasts from many different models (including those from NOAA), compares the data from different places and meteorological situations, and based on this machine learning creates a single output that minimises individual models' biases and local inaccuracies. This is where it should add value.

Thank you for your suggestion - is it mainly the FAQ answer which you find a bit puzzling or anything else we should improve on the website?

Regarding forecasts.cloud - this website will focus on our applied modelling (renewable generation etc.) and will serve as an offer of models for specialised industries, not weather API. It is not fully operational yet (the English version will be launched later this year) - I am impressed you have been able to find it :)

It is originally a European-based company founded in 2007 and started as a website, mobile app, and data provider serving mainly corporate clients. Meteosource as a service (low-cost weather API) started at the end of 2020 and this is just a new service operated by the same company, not rebranded company or anything else... I don't think there is anything sketchy in this.

The machine learning model combines more weather models as mentioned and this feeds proprietary local models that compute the weather exactly for your location. If you request a specific GPS, the chance of rain will be computed for the exact point based on the orography and nearby grid points.

Well, most available weather APIs just download data from NOAA and "repackage" them to JSON format and do not add much value.

Thank you for your comment! We are going to extend the historical weather to 20 years soon. At the moment it is possible to prepare a customised dataset based on your needs - please get in touch at support(at)meteosource.com and we will be happy to help with your specific case.