map_temp: Sample Of Experimental And Observational Climate Spatial Data

In esviz: Plotting Functions for Climate Science and Services

Sample Of Experimental And Observational Climate Spatial Data

Description

This sample data contain gridded seasonal forecast and corresponding observational data from ECMWF-System 5 forecast system and ERA-5 reconstruction. Specifically, for the monthly mean 2-meter temperature ("tas") variable, the first 3 ensemble members, the first 5 forecast time steps from November initial month, year 2000 to 2005, the Iberian Peninsula region (35N-45N, 10W-5E).

Details

The package "startR" is used to load the data from the data esarchive in the Earth Sciences Department of Barcelona Supercomputing Center.

 # 1. Load libraries and define common variables
 library(startR)
 library(CSTools)

 sdates <- sapply(2000:2005, function(x) paste0(x, '1101'))
 lonmax <- 5
 lonmin <- -10
 latmax <- 45
 latmin <- 35

 # 2. Load exp

 repos_exp <- paste0('/esarchive/exp/ecmwf/system5c3s/monthly_mean/',
                     '$var$_f6h/$var$_$sdate$.nc')

 exp <- Start(dat = repos_exp,
              var = 'tas',
              member = indices(1:3),
              sdate = sdates,
              time = indices(1:5),
              lat = values(list(latmin, latmax)),
              lat_reorder = Sort(decreasing = FALSE), 
              lon = values(list(lonmin, lonmax)),
              lon_reorder = CircularSort(-180, 180),
              synonims = list(lon = c('lon', 'longitude'),
                              lat = c('lat', 'latitude'),
                              member = c('member', 'ensemble')),
              return_vars = list(lat = NULL, lon = NULL, time = 'sdate'),
              retrieve = TRUE)


 # 3. Load obs

 exp_time <- attr(exp, "Variables")$common$time
 obs_date <- array(format(exp_time, "

 path_obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$date$.nc'
 obs <- Start(dat = path_obs,
              var = 'tas',
              date = obs_date,
              split_multiselected_dims = TRUE,
              lat = values(list(latmin, latmax)),
              lat_reorder = Sort(decreasing = FALSE),
              lon = values(list(lonmin, lonmax)),
              lon_reorder = CircularSort(-180, 180),
              synonims = list(lon = c('lon', 'longitude'),
                              lat = c('lat', 'latitude')),
              transform = CDORemapper,
              transform_extra_cells = 2,
              transform_params = list(grid = 'r360x181',
                                      method = 'conservative'),
              transform_vars = c('lat', 'lon'),
              return_vars = list(lon = NULL, lat = NULL, time = 'date'),
              retrieve = TRUE)


 # 4. Check data

 obs_time <- attr(obs,"Variables")$common$time

 identical(format(obs_time, "
#[1] TRUE
 exp_lat <- attr(exp, "Variables")$common$lat
 exp_lon <- attr(exp, "Variables")$common$lon
 obs_lat <- attr(obs, "Variables")$common$lat
 obs_lon <- attr(obs, "Variables")$common$lon

 all.equal(exp_lat, obs_lat, check.attributes = F)
#[1] TRUE
 all.equal(exp_lon, obs_lon, check.attributes = F)
#[1] TRUE


# 5. Combine into one object

 map_temp <- list()
 map_temp$exp <- exp
 map_temp$obs <- obs

esviz documentation built on Feb. 4, 2026, 5:13 p.m.