Plot s2dv verification skill scores

Description

Plots s2dv_cube verification skill scores for gridded, polygon and point data generated by c4h_verify . It is used for the following statistics: BSS, RPSS, CRPSS, AbsBiasSS, MSSS, RMSSS and ROCSS. For MSE and RMSE, please use the function c4h_plot .

c4h_plotskill(
  skill,
  sign = NULL,
  time = NULL,
  boundaries = NULL,
  mask_boundaries = FALSE,
  title = NULL,
  legend = NULL,
  palette = "SunsetDark",
  noskillcol = "#b7d6ff",
  coordgrid = FALSE,
  ...
)

Arguments

  • skill: s2dv_cube object with the skill data to be plotted.
  • sign: An additional optional binary s2dv_cube indicating significance.
  • time: Numerical index of the time to be plotted. Defaults to NULL (all times).
  • boundaries: Polygon boundaries to add to the map. Set to NULL for no boundaries (default), otherwise supply a polygon sf object or set to “countries” for coarse country boundaries.
  • mask_boundaries: If TRUE (default), mask the data to the provided boundaries.
  • title: Optional plot title.
  • legend: Optional legend title. If NULL (default), it uses the variable names.
  • palette: GHR, RColorBrewer or colorspace palette. Use “-” before the palette name (e.g., “-Reds”) to reverse it. Alternatively, a vector of hex codes to create a continuous interpolated palette.
  • noskillcol: Colour for skill scores < 0 depicting a skill lower than climatologies.
  • coordgrid: If TRUE, display coordinate grids on the map.
  • ...: Extra arguments indicating fixed aesthetics to be passed to stars::geom_stars, including lwd, alpha, size and colour.

Returns

A ggplot2 plot with the verification skill scores.

See Also

c4h_verify for calculating forecast skill and c4h_plot

for general plotting of s2dv objects.

Examples

# DISCLAIMER: The purpose of these examples is to show the functionality
# of the different functions. To perform a meaningful verification, a much
# larger time series is required.
# Load data
hcst <- c4h_load(
  system.file("extdata", "hindcast", package = "clim4health"),
  variable = "t2m",
  year = 2010:2012,
  month = 1,
  leadtime_month = 1:3,
  ext = "nc"
)
lats <- hcst$coords$latitude
lons <- hcst$coords$longitude
obs <- c4h_load(
  system.file("extdata", "reanalysis", package = "clim4health"),
  variable = "t2m",
  year = 2010:2012,
  month = 1,
  leadtime_month = 1:3,
  bbox = c(max(lats), min(lons), min(lats), max(lons)),
  ext = "nc"
)

# Example with gridded data
dwn <- c4h_downscale(
  "Interpolation",
  exp = hcst,
  obs = obs,
  method_remap = "bilinear",
  target_grid = system.file(
    "extdata",
    "reanalysis/t2m_201001.nc",
    package = "clim4health"
  )
)
hcst_dwn <- dwn$exp
skill_grid <- c4h_verify(exp = hcst_dwn, obs = obs, metrics = "BSS")
c4h_plotskill(skill_grid$BSS10$bss, skill_grid$BSS10$sign)
# Make some artificial significance for testing
skill_grid$BSS10$sign$data[,,,,, 3:8, 15:25] <- TRUE
c4h_plotskill(skill = skill_grid$BSS10$bss, sign = skill_grid$BSS10$sign)

# Example with polygon data
areas <- sf::read_sf(system.file("extdata", "areas", "munip_vallecauca.gpkg",
                                 package = "clim4health"))
obs_area <- c4h_space(obs,
                      areas = areas,
                      fun = "mean",
                      areas_id = "munip_code")
hcst_area <- c4h_space(hcst,
                       areas = areas,
                       fun = "mean",
                       areas_id = "munip_code")
skill_area <- c4h_verify(exp = hcst_area,
                         obs = obs_area,
                         metrics = "CRPSS",
                         brier_thresholds = c(0.1, 0.9))
c4h_plotskill(skill_area$CRPSS$crpss, skill_area$CRPSS$sign)
# Make some artificial significance for testing
skill_area$CRPSS$sign$data[1, 1, 1, 1:3, 1, sample(1:42, 10)] <- TRUE
c4h_plotskill(skill = skill_area$CRPSS$crpss, sign = skill_area$CRPSS$sign)

# Example with station data
point_locs <- clim4health::c4h_load(system.file("extdata",
                                               "stations",
                                               package = "clim4health"),
                                    ext = "csv",
                                    year = 2010:2012,
                                    month = 1,
                                    leadtime_month = 1:3,
                                    variable = "temp_mean")
points <- list(latitude = point_locs$attrs$location$latitude,
               longitude = point_locs$attrs$location$longitude)
point_locs <- c4h_time(point_locs,
                       time_aggregation = "monthly",
                       dim_aggregation = "sdate",
                       fun = "mean")
dwn <- c4h_downscale("Interpolation", exp = hcst,
                     points = points, method_point_interp = "bilinear")
hcst_dwn <- dwn$exp
skill_point <- c4h_verify(exp = hcst_dwn,
                          obs = point_locs,
                          metrics = "BSS")
c4h_plotskill(skill = skill_point$BSS10$bss, boundaries = areas)
# Make some artificial significance for testing
skill_point$BSS10$sign$data[1, 1, 1, 1:3, 1, 4:6] <- TRUE
c4h_plotskill(skill = skill_point$BSS10$bss,
              sign = skill_point$BSS10$sign,
              boundaries = areas)