A web interface for gridding and visualizing oceanographic data sets

Spatial interpolation of observations on a regular grid is a common task in many oceanographic disciplines (and geosciences in general). Diva (Data-Interpolating Variational Analysis) is an analysis tool for gridding oceanographic in situ data. Diva 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 are also used to propagate the information of a given observation spatially. Diva is a command-line driven application. To make Diva easier to use, a web interface has been developed. The user can directly upload his/her data in ASCII format and enter several parameters for the analysis. The analyzed field, location of the observations, and the error mask are then directly visualized in the browser.

While this interface allows the user to create his/her own gridded field, a web interface is also developed to visualize pre-computed gridded oceanographic data sets. Those data sets are typically four-dimensional (longitude, latitude, depth and time).
The system allows to visualize horizontal sections at a given depth and time to study the horizontal distribution of a given variable. It is also possible to display the results on an arbitrary vertical section. To study the evolution of the variable in time, the horizontal and vertical sections can also be animated. The user can customize the plot by changing the color-map, the range of the color-bar, the type of the plot (linearly interpolated color, simple contours, filled contours) and download the current view as a simple image or as Keyhole Markup Language (KML) file for visualization in applications such as Google Earth.

The system is build using a client and server architecture. The server is written in Python using the Web Server Gateway Interface. The server implements version 1.1.1 and 1.3.0 of the Web Map Service (WMS) protocol of the Open Geospatial Consortium. On the server, all oceanographic data sets are stored as NetCDF files organized in folders and sub-folders allowing for a hierarchical presentation of the available variables. The client is build as a web application using the OpenLayers Javascript library. The web interface is accessible at http://gher-diva.phys.ulg.ac.be/. It is currently used for climatologies created in the frame of the SeaDataNet project and will be used for the EMODNET project (chemical lot). Thrid-party data centers can also integrate the web interface of Diva to show an interpolated field of in situ data as an additional WMS layer.

A demonstration near-real time cloud-free sea surface temperature (SST) product of the Mediterranean Sea is presented. The reconstruction of the data set missing information (due to clouds, for example) is realised using DINEOF (Data Interpolating Empirical Orthogonal Functions). DINEOF is an EOF-based technique that does no need a priori information about the data set (such as signal to noise ratio, or correlation length) and that has shown to be faster and equally reliable than other widely used techniques for reconstructing missing data, such as optimal interpolation.

Here we present a daily reconstruction of the Western Mediterranean SST. Cloudy data are downloaded from the Ifremer Medspiration ftp site. After extracting the data from the study zone, they are added to a data set containing the last 6 months of SST. A first DINEOF reconstruction is performed to identify outliers, i.e. pixels for which the analysis-observation difference (the residuals) are larger than the statistically expected misfit calculated during the analysis. Proximity to a cloud edge and deviation respect to a local median also penalize a pixel in the outlier classification. These outliers are removed from the original data set, and a second DINEOF reconstruction is performed, along with the calculation of error maps. Plots are realised, and the reconstruction of the latest 10 days is shown at http://gher-diva.phys.ulg.ac.be/DINEOF/dineof.html, together with the original data, the error maps and identified outliers. The whole procedure takes less than two hours and has been running automatically for more than 5 months. This product is intended as a demonstration of the capabilities of DINEOF as a near-real time technique to reconstruct missing data in satellite data sets. This procedure can be easily applied to other variables and other geographical zones.