WikiPrint - from Polar Technologies

The ?SPECS-EUPORIAS Data Portal does not intent to provide full access to the different seasonal forecasting datasets, but only to a reduced number of variables (typically at surface; e.g. precipitation and temperature) required by the different impact applications. Thus, a key task for the development of the portal is identifying those variables relevant for the different activities, as well as the particular temporal frequencies (e.g. daily or monthly) and aggregation (e.g. instantaneous, daily means, daily maximum, etc.) needed.

In order to identify the variables needed for both EUPORIAS and SPECS projects, several cross-sectional actions are being conducted in order to obtain the required feedback from end users (either by contacting the different work packages or by consulting project documents). Moreover, the precise definition and homogeneization of these variables across different datasets is of utmost importance for the development of a truly user-friendly tool for end users.

The different activities under development towards an integrated, user-friendly framework for data access are summarized in the following sections:

  1. Identification of the variables needed by impact applications: The following WPs of EUPORIAS and SPECS provide relevant feedback for the identification of the variables required by end user's:
  1. Identification of the predictors needed for statistical downscaling tasks: The variables required (to be used as predictors) for the following WPs are being collected.
  1. Data Homogeneization: The different nature of the various climate products, models and variables, and the idiosyncratic naming and storage conventions often applied by the modelling centres, makes necessary a previous homogeneization across datasets in order to implement a truly user-friendly toolbox for data access. To this aim, a R package for data access is currently under development. Data homogeneization is achieved through the creation of a common vocabulary. The particular variables of each dataset are then translated -and transformed if necessary- into the common vocabulary by means of a dictionary. Both features -vocabulary and dictionary- are described here?.