Journal cover Journal topic
Advances in Geosciences An open-access journal for refereed proceedings and special publications
Journal topic

Journal metrics

CiteScore value: 2.0
SNIP value: 0.753
IPP value: 1.58
SJR value: 0.478
Scimago H <br class='widget-line-break'>index value: 37
Scimago H
h5-index value: 12
Volume 28
Adv. Geosci., 28, 29–37, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
Adv. Geosci., 28, 29–37, 2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.

  27 Sep 2010

27 Sep 2010

A web interface for griding arbitrarily distributed in situ data based on Data-Interpolating Variational Analysis (DIVA)

A. Barth2,1, A. Alvera-Azcárate2,1, C. Troupin1, M. Ouberdous1, and J.-M. Beckers2,1 A. Barth et al.
  • 1GeoHydrodynamics and Environment Research (GHER), MARE, AGO, University of Liège, Liège, Belgium
  • 2National Fund for Scientific Research, Belgium

Abstract. Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). It is often used to create climatological maps for physical, biological or chemical parameters representing e.g. monthly or seasonally averaged fields. Since instantaneous observations can not be directly related to a field representing an average, simple spatial interpolation of observations is in general not acceptable. DIVA (Data-Interpolating Variational Analysis) is an analysis tool which takes the error in the observations and the typical spatial scale of the underlying field into account. Barriers due to the coastline and the topography in general and also currents estimates (if available) are used to propagate the information of a given observation spatially.

DIVA is a command-line driven application written in Fortran and Shell Scripts. To make DIVA easier to use, a web interface has been developed ( Installation and compilation of DIVA is therefore not required. The user can directly upload the data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are presented as different layers using the Web Map Service protocol. They are visualized in the browser using the Javascript library OpenLayers allowing the user to interact with layers (for example zooming and panning). Finally, the results can be downloaded as a NetCDF file, Matlab/Octave file and Keyhole Markup Language (KML) file for visualization in applications such as Google Earth.