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The present work was realized within the frame of the EU HORIZON 2020 HYPERION project ( http://www.hyperion-project.eu/ ). The main aim of the work was to develop an innovative methodology for the continuous update and refinement of numerical modelling results based on in-situ measurements. This method is based on a 3-D data assimilation protocol which supports fusion of data-streams from micro weather stations installed at a number of selected demonstration pilots. More specifically, the data aggregated from the sensor network and delivered through the monitoring system are initially engaged with the simulated data and as a second step they are analysed and fused from a fully interoperable data management platform. This process includes the aggregation, synchronization, calibration, and assimilation steps. In this way, local-scale atmospheric and climate which are in principle not resolved are explicitly introduced into a high-resolution operational mesoscale system (OMS). Preliminary results show a clear upgrading in the accuracy of the produces simulations, which can be directly attributed to the fact that this newly developed function of the OMS enhances its performance in the microscale.

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Dynamic Data Assimilation of Meteorological and Climate Data from Sensors

  • 会议论文

  • 发表日期：

    2025年

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  • Eleftherios Chourdakis,
  • George Tsegas,
  • Fotios Barmpas,
  • Nicolas Moussiopoulos,
  • Christos Vlachokostas

摘要

The present work was realized within the frame of the EU HORIZON 2020 HYPERION project ( http://www.hyperion-project.eu/ ). The main aim of the work was to develop an innovative methodology for the continuous update and refinement of numerical modelling results based on in-situ measurements. This method is based on a 3-D data assimilation protocol which supports fusion of data-streams from micro weather stations installed at a number of selected demonstration pilots. More specifically, the data aggregated from the sensor network and delivered through the monitoring system are initially engaged with the simulated data and as a second step they are analysed and fused from a fully interoperable data management platform. This process includes the aggregation, synchronization, calibration, and assimilation steps. In this way, local-scale atmospheric and climate which are in principle not resolved are explicitly introduced into a high-resolution operational mesoscale system (OMS). Preliminary results show a clear upgrading in the accuracy of the produces simulations, which can be directly attributed to the fact that this newly developed function of the OMS enhances its performance in the microscale.