# Sample Of Experimental And Observational Climate Data In Function Of Longitudes And Latitudes with Start This sample data set contains gridded seasonal forecast and corresponding observational data from the Copernicus Climate Change ECMWF-System 5 forecast system, and from the Copernicus Climate Change ERA-5 reconstruction. Specifically, for the 'tas' (2-meter temperature) variable, for the 15 first forecast ensemble members, monthly averaged, for the 3 first forecast time steps (lead months 1 to 4) of the November start dates of 2000 to 2005, for the Mediterranean region (27N-48N, 12W-40E). The data was generated on (or interpolated onto, for the reconstruction) a rectangular regular grid of size 360 by 181. data ## Returns sample s2dv_cube object ``` lonlat_temp_st <- NULL repos_exp <- paste0('/esarchive/exp/ecmwf/system5c3s/monthly_mean/', '$var$_f6h/$var$_$sdate$.nc') sdates <- sapply(2000:2005, function(x) paste0(x, '1101')) lonmax <- 40 lonmin <- -12 latmax <- 48 latmin <- 27 lonlat_temp_st$exp <- CST_Start(dataset = repos_exp, var = 'tas', member = startR::indices(1:15), sdate = sdates, ftime = startR::indices(1:3), lat = startR::values(list(latmin, latmax)), lat_reorder = startR::Sort(decreasing = TRUE), lon = startR::values(list(lonmin, lonmax)), lon_reorder = startR::CircularSort(0, 360), synonims = list(lon = c('lon', 'longitude'), lat = c('lat', 'latitude'), member = c('member', 'ensemble'), ftime = c('ftime', 'time')), return_vars = list(lat = NULL, lon = NULL, ftime = 'sdate'), retrieve = TRUE) dates <- c(paste0(2000, c(11, 12)), paste0(2001, c('01', 11, 12)), paste0(2002, c('01', 11, 12)), paste0(2003, c('01', 11, 12)), paste0(2004, c('01', 11, 12)), paste0(2005, c('01', 11, 12)), 200601) dates <- sapply(dates, function(x) {paste0(x, '01')}) dates <- as.POSIXct(dates, format = ' dim(dates) <- c(ftime = 3, sdate = 6) dates <- t(dates) names(dim(dates)) <- c('sdate', 'ftime') path.obs <- '/esarchive/recon/ecmwf/era5/monthly_mean/$var$_f1h-r1440x721cds/$var$_$date$.nc' lonlat_temp_st$obs <- CST_Start(dataset = path.obs, var = 'tas', date = unique(format(dates, ' ftime = startR::values(dates), ftime_across = 'date', ftime_var = 'ftime', merge_across_dims = TRUE, split_multiselected_dims = TRUE, lat = startR::values(list(latmin, latmax)), lat_reorder = startR::Sort(decreasing = TRUE), lon = startR::values(list(lonmin, lonmax)), lon_reorder = startR::CircularSort(0, 360), synonims = list(lon = c('lon', 'longitude'), lat = c('lat', 'latitude'), ftime = c('ftime', 'time')), transform = startR::CDORemapper, transform_extra_cells = 2, transform_params = list(grid = 'r360x181', method = 'conservative'), transform_vars = c('lat', 'lon'), return_vars = list(lon = NULL, lat = NULL, ftime = 'date'), retrieve = TRUE) library(lubridate) dates_exp <- lonlat_temp_st$exp$attrs$Dates lonlat_temp_st$exp$attrs$Dates <- floor_date(ymd_hms(dates_exp), unit = "months") dim(lonlat_temp_st$exp$attrs$Dates) <- dim(dates_exp) dates_obs <- lonlat_temp_st$obs$attrs$Dates lonlat_temp_st$obs$attrs$Dates <- floor_date(ymd_hms(dates_obs), unit = "months") dim(lonlat_temp_st$obs$attrs$Dates) <- dim(dates_obs) ``` ## Details The `CST_Start` call used to generate the data set in the infrastructure of the Earth Sciences Department of the Barcelona Supercomputing Center is shown next. Note that `CST_Start` internally calls `startR::Start` and then uses `as.s2dv_cube` that converts the `startR_array` into `s2dv_cube`. ## Author(s) Nicolau Manubens nicolau.manubens@bsc.es

lonlat_temp_st dataset