Spatial interpolation techniques for a near real-time mapping of pressure and temperature data
- Published
- Accepted
- Subject Areas
- Spatial and Geographic Information Systems
- Keywords
- Environmental data interpolation, Meteorological re-analysis, Regression kriging
- Copyright
- © 2016 Ferrando et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Spatial interpolation techniques for a near real-time mapping of pressure and temperature data. PeerJ Preprints 4:e2223v2 https://doi.org/10.7287/peerj.preprints.2223v2
Abstract
Among the different techniques for atmosphere monitoring, the GNSS (Global Navigation Satellite System) can provide an innovative contribution (Bevis et al.,1992; Crespi et al., 2004; Sguerso et al., 2013, 2015). The Laboratory of Geomatics, Geodesy and GIS of the University of Genoa has identified a GIS procedure and a simplified physical model to monitor the Precipitable Water Vapour (PWV) content, using data measured by existing infrastructures. The starting points are local estimations of Zenith Total Delay (ZTD) from a GNSS Permanent Stations (PSs) network, a Digital Terrain Model (DTM) and local Pressure (P) and Temperature (T) measurements (Sguerso et al., 2014; Ferrando et al., 2016). The present paper shows the study of the most appropriate interpolation technique for P and T data to create PWV maps in a quick, stable and automatic way, to support the monitoring of intense meteorological events for both a posteriori and near real-time applications. The resulting P and T maps were compared to meteorological re-analysis, to check the reliability of the simplified physical model. Additionally, the Regression Kriging (RK) was employed to evaluate the data correlation with elevation and to study the applicability of the technique.
Author Comment
The following changes were made with respect to the previous version of the short paper (also considering the comments of the reviewer):
- improvement of Figure 1
- some references added
- minor revisions and formatting to the suggested template