# Changes between Version 6 and Version 7 of udg/ecoms/RPackage/homogeneization

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Feb 14, 2014 1:22:07 PM (8 years ago)
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 v6 The different nature of the different climate products, models and variables, and the idiosyncratic naming and storage conventions often applied by the various modelling centres, makes necessary a previous homogeneization across datasets in order to implement a truly user-friendly toolbox for data access. The meteor package achieves this aim by defining a common ''vocabulary'' to all climate datasets. The particular variables of each dataset are translated -and transformed if necessary- into the common vocabulary by means of a ''dictionary''. Both features are next described. The different nature of the different climate products, models and variables, and the idiosyncratic naming and storage conventions often applied by the various modelling centres, makes necessary a previous homogeneization across datasets in order to implement a truly user-friendly toolbox for data access. The ecomsUDG.Raccess package achieves this aim by defining a common ''vocabulary'' to all climate datasets. The particular variables of each dataset are translated -and transformed if necessary- into the common vocabulary by means of a ''dictionary''. Both features are next described: == Vocabulary definition In order to set a common framework with a precise definition of the variables, the meteoR package is based on the use of a vocabulary. In essence, the vocabulary is a table containing the standard names of a number of variables commonly used in impact studies and downscaling applications, subject to permanent revision or addition of new standard variables. The naming conventions and the units are based on the standard name [http://www.specs-fp7.eu/wiki/index.php/Data#SPECS_convention:_Standard_output_and_data_management_description table] provided in the frame of the SPECS project, although in case of conflict, and in order to maximize the inter-operability of the vocabulary, the nomenclature is also compliant with the [http://cf-pcmdi.llnl.gov/documents/cf-standard-names/standard-name-table/16/cf-standard-name-table.html/ NetCDF Climate and Forecast Metadata Convention]. In order to set a common framework with a precise definition of the variables, the ecomsUDG.Raccess package is based on the use of a vocabulary. In essence, the vocabulary is a table containing the standard names of a number of variables commonly used in impact studies and downscaling applications, subject to permanent revision or addition of new standard variables. The naming conventions and the units are based on the standard name [http://www.specs-fp7.eu/wiki/index.php/Data#SPECS_convention:_Standard_output_and_data_management_description table] provided in the frame of the SPECS project, although in case of conflict, and in order to maximize the inter-operability of the vocabulary, the nomenclature is also compliant with the [http://cf-pcmdi.llnl.gov/documents/cf-standard-names/standard-name-table/16/cf-standard-name-table.html/ NetCDF Climate and Forecast Metadata Convention]. * Identifier: this is the standard name that the loading functions require as argument when we set the standard.vars argument to TRUE (see the [wiki:../Functions defined functions]). * Standard_name: standard name of the variable as defined by the CF convention. * Units: units in which the standard variable is returned * identifier: this is the standard name that the loadSeasonalForecast function will recognize automatically. * standard_name: standard name of the variable as defined by the CF convention. * units: units in which the standard variable is returned. The vocabulary has been included as a is a built-in dataset of the ecomsUDG.Raccess package in order to provide the user a reference of the standard variables. {{{ "identifier","standard_name","units" "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" "zg","geopotential_height","m" "plev","air_pressure","Pa" "psl","air_pressure_at_sea_level","Pa" "ps","surface_air_pressure","Pa" "hus","specific_humidity","kg kg-1" "hur","relative_humidity","1" "ua","eastward_wind","m s-1" "va","northward_wind","m s-1" > library(ecomsUDG.Raccess) Loading required package: rJava Loading required package: sp > data(vocabulary) > print(vocabulary) identifier              standard_name           units 1        tas        2-meter temperature degrees Celsius 2     tasmax    maximum 2-m temperature degrees Celsius 3     tasmin    minimum 2-m temperature degrees Celsius 4         tp Total precipitation amount              mm 5        psl  air pressure at sea level              Pa }}} 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. In some particular cases (e.g. the precipitation provided by the System4 model outputs), the variables are also deaccumulated. 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 (this is the variable name returned by the datasetInventory function) and the corresponding name of the standard variable as defined in the vocabulary (i.e., the identifier), and by applying the corresponding transformation to the native variable in order to match the standard units by means of an offset and a scale factor. In some particular cases (e.g. the precipitation provided by the System4 model outputs), the variables are also deaccumulated. 2. The dictionary also 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 by the argument dictionary. 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 [wiki:../Functions#dataInventory dataInventory]. The columns of the dictionary are next described: In essence, the dictionary is a comma-sepparated text file (csv), that by default is identified with the same name than the argument dataset of the loadSeasonalForecast function. The dictionaries for the currently available datasets are included in the ecomsUDG.Raccess package, within the ''dictionaries'' folder. The dictionaries are read internally by loadSeasonalForecast to undertake the conversions needed for returning the standard variables, so by default, the user does not need to worry about it. In case an interested user wants to inspect a particular dictionary, he/she can proceed as follows: {{{ > ip <- installed.packages() > # Path to the installed library > libPath <- ip[grep("ecomsUDG.Raccess", ip[ ,1]), 2] > # Path to the dictionaries folder > dicPath <- paste(libPath, "/ecomsUDG.Raccess/dictionaries", sep = "") > list.files(dicPath) [1] "CFSv2_seasonal_16.csv"   "System4_annual_15.csv"   "System4_seasonal_15.csv" [4] "System4_seasonal_51.csv" > dic <- read.csv(list.files(dicPath, full = TRUE)[grep("CFSv2", list.files(dicPath))]) > str(dic.cfs) 'data.frame':      5 obs. of  9 variables: $identifier : Factor w/ 5 levels "psl","tas","tasmax",..: 3 4 2 5 1$ short_name      : Factor w/ 5 levels "Maximum_temperature_height_above_ground" ... $time_step : Factor w/ 1 level "6h": 1 1 1 1 1$ lower_time_bound: int  0 0 0 0 0 $upper_time_bound: int 6 6 6 6 0$ aggr_fun        : Factor w/ 5 levels "max","mean","min",..: 1 3 2 5 4 $offset : num -273 -273 -273 0 0$ scale           : int  1 1 1 21600 1 \$ deaccum         : int  0 0 0 0 0 }}} The latest version of the dictionaries can be checked-out in the development version of the package at the [https://github.com/SantanderMetGroup/ecomsUDG.Raccess/tree/master/inst/dictionaries GitHub repository]. The columns of the dictionary are next described: * identifier: this is the name of the standard variable, as defined in the vocabulary * 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: {{{ identifier,short_name,time_step,lower_time_bound,upper_time_bound,aggr_fun,offset,scale,deaccum tasmax,Maximum_temperature_at_2_metres_since_last_24_hours_surface,24h,0,24,max,-273.15,1,0 tasmin,Minimum_temperature_at_2_metres_since_last_24_hours_surface,24h,0,24,min,-273.15,1,0 tas,Mean_temperature_at_2_metres_since_last_24_hours_surface,24h,0,24,mean,-273.15,1,0 pr,Total_precipitation_surface,24h,0,24,sum,0,1000,1 psl,Mean_sea_level_pressure_surface,6h,0,0,none,0,1,0 }}} Note that column names matter (not so their relative order), because the data load functions will perform the conversion of the variables to the standard format by finding the corresponding values by the name of the columns. The [https://www.meteo.unican.es/trac/meteo/attachment/wiki/EcomsUdg/meteoR_v1_0.zip meteoR] package includes some dictionaries, and specific examples are given in the [wiki:../Examples Examples section]