Version 7 (modified by juaco, 9 years ago) (diff)


Overview of the R package under development

Since the R language has been adopted for some key tasks in the EUPORIAS and SPECS projects (including the development of comprehensive validation and statistical-downscaling packages) a R package is currently under development. In the current status of this task, some functions for data exploration and access have been created. These functions allow the creation of accessible datasets from locally stored climate files, the creation of data inventories providing an overview of the characteristics of the data (variables stored, units, time resolution ...) and accessing local and remote datasets in a straightforward manner by means of simple arguments, allowing the retrieval of dimensional slices of observational, reanalysis and forecast (System4) climate data. A full R package with added capabilities (including specific plot methods) and access to new datasets will be soon released for the SPECS/EUPORIAS community, as soon as new databases are incorporated into the SPECS-EUPORIAS THREDDS Data Server and new user's needs and requirements are identified and discussed.

Vocabulary definition

In order to set a common framework with a precise definition of the variables, the R package is based on the use of a vocabulary. Essentially, the vocabulary is simply a table containing the standard names of a number of variables commonly used in impact studies and downscaling applications. The naming conventions and the units are based on the standard name table provided by the NetCDF Climate and Forecast Metadata Convention. The vocabulary consists of a table with:

  • Identifier: this is the standard name that the loading functions require as argument when we set the standard.vars argument to TRUE.
  • Standard_name: standard name of the variable as defined by the CF convention.
  • Units: units in which the standard variable is returned
"ta","temperature","degrees Celsius"
"tas","2-meter temperature","degrees Celsius"
"tasmax","maximum 2-m temperature","degrees Celsius"
"tasmin","minimum 2-m temperature","degrees Celsius"
"pr","Precipitation amount","mm"
"hus","specific_humidity","kg kg-1"
"ua","eastward_wind","m s-1"
"va","northward_wind","m s-1"


The dictionary is a table whose aim is twofold:

  1. On the one hand, the dictionary is intended for the translation of generic variables, as idiosyncratically defined in each particular dataset, to the standard variables defined in the vocabulary with their corresponding nomenclature and units. This is achieved by providing a correspondence between the name of the variable as encoded in the dataset (short_name) and the corresponding name of the standard variable as defined in the vocabulary (identifier), and by applying the corresponding transformation to the native variable in order to match the standard units by means of a scale factor and an offset.
  2. In addition, the dictionary provides additional metadata often not explicitly declared in the datasets, regarding the time aggregation of the dataset (often referred to as the cell method). This includes the fields time_step, which is merely informative, and describes the time interval between two consecutive values, and the lower_time_bound and upper_time_bound, which are the values that should be summed to each verification time to unequivocally delimit the time span encompassed by each value.

The dictionary is a comma-sepparated text file (csv), that by default is identified with the same name than the dataset, and the extension .dic, and stored in the same directory than the dataset, although its name and location can be other if adequately specified in the loading functions. The dictionary must be created "by hand" by the user, because it requires some a priori knowledge about the characteristics of the data stored in the dataset, that can be partly obtained using the function dataInventory. The columns of the dictionary are next described:

  • identifier: this is the name of the standard variable, as defined in the vocabulary
  • short_name: this is the name with which the original variable has been coded in the dataset
  • time_step: the time interval between consecutive times in the time dimension axis (in hours)
  • lower_time_bound: lower time bound of the variable
  • upper_time_bound: upper time bound of the variable. For instance, if a variable has identical lower and upper time bounds, it means that it is instantaneous.
  • aggr_fun: time aggregation function. Type of aggregation function applied to the variable between the lower and upper time bound.
  • offset: constant summed to the original variable for units conversion (e.g.: offset = -273.15 for conversion from Kelvin to Celsius)
  • scale: scale factor applied to the original variable for units conversion (e.g.: scale = 0.001 for conversion from m to mm)
  • deaccum. This is a logical flag (0 = FALSE, 1= TRUE), which indicates if the variable should be de-accumulated at each time step. Typically applied to precipitation in some forecast datasets.

In the following example, we show the characteristics of the dictionary constructed for the 15 members seasonal forecast of the ECMWF's System4 model:


Note that the names of the columns are important (not so their relative order), because the loadData and loadObservations R functions will perform the conversion of the variable to the standard format by finding the corresponding values by the name of the columns.

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