chillR (version 0.75)

extract_temperatures_from_grids: Extract temperature information from gridded dataset

Description

Temperature data is often available in gridded format, and records for particular points must be extracted for work on site-specific issues (such as chill calculation). This function implements this, for certain types of gridded data.

Usage

extract_temperatures_from_grids(
  coordinates,
  grid_format,
  grid_specifications,
  scenario_year = NA,
  reference_year = NA,
  scenario_type = NA,
  labels = NA,
  temperature_check_args = NULL
)

Value

temperature scenario object extracted from the grids, consisting of the following elements: 'data' = a data frame with n_intervals elements containing the absolute or relative temperature information. 'reference_year' = the year the scenario is representative of. 'scenario_type' = the scenario type ('absolute' or 'relative'); 'labels' = and elements attached to the input temperature_scenario as an element names 'labels'.

The function generates errors, when problems arise.

Arguments

coordinates

numeric vector specifying coordinates for the point location of interest. These coordinates have to use the same coordinate system as the grids, from which data are to be extracted. The elements can be named as 'longitude' and 'latitude', or provided as unnamed elements. In the latter case, the first element is interpreted as the x-coordinate (e.g. longitude or Easting) and the second element as the y-coordinate (e.g. latitude or Northing).

grid_format

character string specifying the type of raster data. See details below.

grid_specifications

list of specifications that instruct the function on where to find the temperature grids. See grid_format descriptions for what is required here.

scenario_year

year the temperature scenario is representative of, e.g. 2050, 2080. If the scenario period is an interval, this should be the median of all years in this interval.

reference_year

year of reference for the gridded climate data. This is only important for relative temperature scenarios. If the reference period is an interval, this should be the median of all years in this interval.

scenario_type

character string specifying whether the climate data contains a relative or absolute temperature sceanario. Accordingly, this should be 'relative' or 'absolute'. Can also be NA, which is the default, in which case the function makes a guess on which type applies. This guess is directed by the temperature_check_args.

labels

list of labels to be passed to the labels argument of the resulting temperature_scenario

temperature_check_args

list of arguments to be passed to the check_temperature_scenario function. Check documentation of that function for details.

Author

Eike Luedeling

Details

The following climate data formats are supported: "AFRICLIM" - data downloaded from https://www.york.ac.uk/environment/research/kite/resources/; "CCAFS" - data downloaded from http://ccafs-climate.org/data_spatial_downscaling/; "WorldClim" - data downloaded from http://www.worldclim.org/. All these databases provide separate zipped files for monthly minimum and monthly maximum temperatures, but they differ slightly in format and structure. If you want to see additional formats included, please send me a message.

Examples

  coordinates<-c(10.6082,34.9411)
 # grid_specifications<-list(base_folder="D:/DATA/AFRICLIM/GeoTIFF_30s/future_scenarios/",
 #                           minfile="tasmin_rcp45_2055_CCCma-CanESM2_CCCma-CanRCM4_wc30s.zip",
 #                           maxfile="tasmax_rcp45_2055_CCCma-CanESM2_CCCma-CanRCM4_wc30s.zip")
                            
 # extract_temperatures_from_grids(coordinates,grid_format="AFRICLIM",grid_specifications,
 #    scenario_type="relative",scenario_year=2055)
                 
 # grid_specifications<-list(base_folder="D:/DATA/CCAFS_climate/",
 #                           minfile="bcc_csm1_1_rcp2_6_2030s_tmin_30s_r1i1p1_b4_asc.zip",
 #                           maxfile="bcc_csm1_1_rcp2_6_2030s_tmax_30s_r1i1p1_b4_asc.zip")
 #temps<-extract_temperatures_from_grids(coordinates,grid_format="CCAFS",grid_specifications,
 #                                       scenario_type="relative",scenario_year=2035)