This function calculates the anomaly (number of standard deviations from the mean climatology) of a forecast layer.
anomaly(r, b, asEFFIS = FALSE)is the RasterLayer to compare to the climatology.
RasterBrick/Stack containing the historical observations or a proxy (typically a reanalysis) that is used to derive the climatological information.
Logical, if TRUE the anomalies are categorised as in EFFIS. If FALSE (default), the returned anomalies are continuous variables.
The function returns a RasterLayer with extent, resolution and
land-sea mask matching those of r. Values are the number standard
deviations from the historical mean values.
The objects r and b should be comparable: same
resolution and extent.
More information on anomaly is available here:
https://bit.ly/2Qvekz4. To estimate fire climatology one can use hindcast or
reanalysis data. Examples of the latter are available from Zenodo:
https://zenodo.org/communities/wildfire.
if (FALSE) {
# Generate dummy RasterLayer
r <- raster(nrows = 1, ncols = 1,
xmn = 0, xmx = 360, ymn = -90, ymx = 90, vals = 0.3)
raster::setZ(r) <- as.Date("2018-01-01")
# Generate dummy RasterBrick
b <- raster::brick(lapply(1:(365 * 3),
function(i) raster::setValues(r, runif(raster::ncell(r)))))
raster::setZ(b) <- seq.Date(from = as.Date("1993-01-01"),
to = as.Date("1995-12-31"),
by = "day")
# Compute anomaly
x <- anomaly(r, b)
# This plots nicely using rasterVis::levelplot(), see example on GWIS
# (\url{https://gwis.jrc.ec.europa.eu}
rasterVis::levelplot(x, col.regions = colorRamps::matlab.like(n = 11))
}