Weather data by Meteosource

Most weather services around the world prepare their forecasts based on a single global model and then apply one of the existing local models to the data. No matter how accurate the local model, its limits are determined by the accuracy of the global model. We have chosen a different approach to eliminate most of the potential errors.

First, Meteosource uses our vast database of historical weather and all past forecasts of available global weather models to create a single output that minimises the errors and biases of individual models. This is achieved using machine learning algorithms that evaluate past errors in different meteorological situations and locations. As a result, we obtain a global forecast of the most probable weather scenario based on all the models available.

Using such blended output, we make use of Meteosource’s regional models which increase resolution and take account of local conditions and geography.

Weather is constantly changing and even the best model outputs might need to be adjusted before the next run of global models is available. Using all the available measurements (from stations, radar, etc.) for a given location, Meteosource updates its forecasts in real-time by adjusting the outputs based on the latest information.