Servizi meteo previsionali sul Web: una opportunità per la ricerca e la modellistica.

Weather forecasting is a very important issue not only for agriculture but also for tourism, and particularly for the management of extreme events. Web-services and apps for mobile devices are becoming increasingly popular, providing weather forecast related to the user location or to points of interest selected interactively on the map or from destinations lists. There are both free and paid services available, and some of them follow open-data policy. Services that provide the ability to perform requests via API are of particular interest, since they allow machine-to-machine calls, by automating the population of local databases, and, finally, developing and implementing new services. In the present work, we developed and implemented Python-scripts and libraries to obtain a fully automated data flow from the daily download of data from two weather forecast web-services, to the population of a PostgreSQL geo-database. The accuracy assessment of the forecasting time-series of mean daily air temperature is presented and discussed.