Meteorological Applications (Jul 2021)

The Italian open data meteorological portal: MISTRAL

  • Michele Bottazzi,
  • Gabriella Scipione,
  • Gian Franco Marras,
  • Giuseppe Trotta,
  • Mattia D'Antonio,
  • Beatrice Chiavarini,
  • Cinzia Caroli,
  • Margherita Montanari,
  • Sanzio Bassini,
  • Estíbaliz Gascón,
  • Timothy Hewson,
  • Andrea Montani,
  • Davide Cesari,
  • Enrico Minguzzi,
  • Tiziana Paccagnella,
  • Renata Pelosini,
  • Paolo Bertolotto,
  • Luca Monaco,
  • Martina Forconi,
  • Luca Giovannini,
  • Carlo Cacciamani,
  • Luca Delli Passeri,
  • Andrea Pieralice

DOI
https://doi.org/10.1002/met.2004
Journal volume & issue
Vol. 28, no. 4
pp. n/a – n/a

Abstract

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Abstract At the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps.

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