hovmoller-methods: Hovmoller plots

Description

Hovmoller plots of Raster objects.

Usage

# S4 method for RasterStackBrick
hovmoller(object, dirXY=y, FUN=mean, 
digits=2, xlab='Direction', ylab='Time',
par.settings=rasterTheme(), xscale.components=xscale.raster,
add.contour=FALSE, labels=FALSE, region=TRUE, ...)
# S4 method for SpatRaster
hovmoller(object, dirXY=y, FUN=mean, 
digits=2, xlab='Direction', ylab='Time',
par.settings=rasterTheme(), xscale.components=xscale.raster,
add.contour=FALSE, labels=FALSE, region=TRUE, ...)

Arguments

object: A RasterStackBrick with a non-empty z slot, or a SpatRaster with a time index.
dirXY: A direction as a function of the coordinates (see xyLayer).
FUN: A function to be applied to the zones calculated with dirXY and zonal.
digits: An integer, number of digits for zonal.
xlab, ylab: Labels of the axis.
par.settings: Customization of lattice. See levelplot and rasterTheme for details.
xscale.components: See xscale.raster.
labels, region: Customization of contourplot when add.contour is TRUE.
add.contour: Logical, if TRUE a contourplot with filled regions is drawn.
...: Additional arguments for the contourplot and levelplot functions.

Author

Oscar Perpiñán Lamigueiro

Details

Extracted from wikipedia: "A Hovmöller diagram is a commonly used way of plotting meteorological data to highlight the role of waves. The axes of a Hovmöller diagram are typically longitude or latitude (abscissa or x-axis) and time (ordinate or y-axis) with the value of some field represented through color or shading." The direction defined by dirXY and the function FUN allows for a variety of diagrams with this method.

References

Hovmoller, E. 1949. The trough and ridge diagram. Tellus 1, 62–66.
https://www2.mmm.ucar.edu/episodes/Hovmoller/noJS/hovm200707.htm
https://psl.noaa.gov/map/clim/sst.shtml

Examples

Run this code

if (FALSE) {
library(raster)
library(terra)

dataURL <- "https://raw.github.com/oscarperpinan/bookvis/master/data/"

##Solar irradiation data from CMSAF http://dx.doi.org/10.5676/EUM_SAF_CM/RAD_MVIRI/V001
old <- setwd(tempdir())
download.file(paste0(dataURL, "SISmm2008_CMSAF.zip"),
   "SISmm2008_CMSAF.zip", method='wget')
unzip("SISmm2008_CMSAF.zip")

listFich <- dir(pattern='\\.nc')
stackSIS <- stack(listFich)
stackSIS <- stackSIS*24 ##from irradiance (W/m2) to irradiation Wh/m2

idx <- seq(as.Date('2008-01-15'), as.Date('2008-12-15'), 'month')

SISmm <- setZ(stackSIS, idx)
names(SISmm) <- month.abb

SISmmt <- rast(SISmm)
time(SISmmt) <- getZ(SISmm)

## Latitude as default
hovmoller(SISmm, xlab='Latitude')
hovmoller(SISmmt, xlab = 'Latitude')

## With contour lines and labels
hovmoller(SISmm, labels=TRUE, add.contour=TRUE,
          xlab='Latitude')

## Smooth color regions with latticeExtra::panel.2dsmoother
library(latticeExtra)

hovmoller(SISmm, panel=panel.2dsmoother, n=1000,
          labels=FALSE, add.contour=TRUE,
          xlab='Latitude')

## Using a function of coordinates
hovmoller(SISmm, dirXY=sqrt(x^2+y^2))
}

if (FALSE) {
library(zoo)

## DESCRIPTION: http://iridl.ldeo.columbia.edu/SOURCES/.CAC/.sst/
setwd(tempdir())
download.file('http://iridl.ldeo.columbia.edu/SOURCES/.CAC/.sst/data.nc', destfile = 'SST.nc')
SST <- stack('SST.nc')
idx <- seq(as.Date('1970-01-01'), as.Date('2003-03-01'), by='month')
tt <- as.yearmon(idx)
SST <- setZ(SST, tt)
names(SST) <- as.character(tt)

## Extract month value from a Date or yearmon object
month <- function(x)format(x, '%m')
## Compute anomaly using monthly grouping with ave  
anomaly <- function(x){
    ## Monthly means
    mm <- ave(x, month(tt), FUN = mean)
    ## Monthly standard deviation
    msd <- ave(x, month(tt), FUN = sd)
    ## anomaly
    (x - mm)/msd
}
## Use anomaly with calc
SSTanom <- calc(SST, anomaly)
SSTanom <- setZ(SSTanom, tt)

## Ok, let's see the result
hovmoller(SSTanom,
          at = seq(-3, 3, .25),
          panel = panel.levelplot.raster,
          interpolate = TRUE,
          yscale.components = yscale.raster.subticks,
          par.settings = BuRdTheme)
}

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