A useful contribution to atmosphere monitoring may be provided by the analysis of GNSS (Global Navigation Satellite System) signals. The authors have identified a procedure to monitor in space and time the Precipitable Water Vapor (PWV) content on regionally extended and orographically complex area. The starting point of the procedure is the local estimation of Zenith Total Delay (ZTD) on a GNSS Permanent Stations network, observed from existing infrastructures and integrated with Pressure and Temperature data to produce PWV maps. The present paper deals with the identification of the most appropriate technique to interpolate ZTD, to create maps in quick and automatic way for near realtime application, in order to support the monitoring of intense meteorological events. The main difficulties are due to the sparse distribution of data, combined with high resolution and wide computational region. ZTD has been interpolated through the methods implemented in GRASS GIS: Inverse Distance Weighted, Regularized Spline with Tension, Ordinary Kriging and Triangulated Irregular Network.

Zenith total delay interpolation to support GNSS monitoring of potential precipitations

Ferrando, I.;Federici, B.;Sguerso, D.
2017

Abstract

A useful contribution to atmosphere monitoring may be provided by the analysis of GNSS (Global Navigation Satellite System) signals. The authors have identified a procedure to monitor in space and time the Precipitable Water Vapor (PWV) content on regionally extended and orographically complex area. The starting point of the procedure is the local estimation of Zenith Total Delay (ZTD) on a GNSS Permanent Stations network, observed from existing infrastructures and integrated with Pressure and Temperature data to produce PWV maps. The present paper deals with the identification of the most appropriate technique to interpolate ZTD, to create maps in quick and automatic way for near realtime application, in order to support the monitoring of intense meteorological events. The main difficulties are due to the sparse distribution of data, combined with high resolution and wide computational region. ZTD has been interpolated through the methods implemented in GRASS GIS: Inverse Distance Weighted, Regularized Spline with Tension, Ordinary Kriging and Triangulated Irregular Network.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/888405
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