Dear Colleagues,

According to the complexity of the mesoscale forecasting models often used in the weather predictions, and to the input data used and the globality of their forecasts, the solutions indicated by them (the intensity of an expected phenomena) sometimes can differ significantly from the values punctually measured at meteorological stations.

In fact, the highest resolutions of the mesoscale model do not exceed 1 km and it is well known how meteorological variables (for example rain or wind) can vary considerably between two close grid points.

This consequence is caused by the influence that the surrounding environment may have on these variables in addition to their very nature.

Mesoscale models cannot take into account the environment in its total complexity for each single point of the globe. For activities that affect human life, and not only, it is necessary to make accurate predictions on the occurrence of a certain meteorological phenomenon in a certain area with high vulnerability in order to reduce material damage or human life loss as much as possible. Our main objective is to collect the methods that are already being used or to find new approaches useful to improve the forecasts predictions “at a specific point”, making them more accurate. The methods could include, but are not limited to, interventions on the models themselves and on post-processing techniques, such as machine learning techniques.

Dr. Andrea Tateo
Guest Editor

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• mesoscale model
• error correction
• machine learning
• forecast error reduction
• weather alert