NCEP.vis.points: Visualize Weather Data Interpolated to a Point on a Map
In RNCEP: Obtain, Organize, and Visualize NCEP Weather Data

Description Usage Arguments Details Value Author(s) References Examples

This function creates a map with points. The color of the points indicates the value of some variable at that point. These values can e.g. be obtained by applying the function NCEP.interp.

NCEP.vis.points(wx, lats, lons, cols=heat.colors(64),
    transparency=.5, connect=TRUE, axis.args=NULL, 
    points.args=NULL, map.args=NULL, grid.args=NULL, 
    title.args=NULL, image.plot.args=NULL, lines.args=NULL)

`wx`	A vector of weather data as returned by `NCEP.interp`
`lats`	A vector of latitudes in decimal degrees indicating the locations of the points
`lons`	A vector of longitudes in decimal degrees indicating the locations of the points
`cols`	A vector of colors such as that generated by `rainbow`, `heat.colors`, `topo.colors`, `terrain.colors`, or similar functions
`transparency`	A numeric value between 0 and 1 indicating the transparency of the filled points on the map.
`connect`	Logical. Should a line be drawn connecting the points?
`axis.args`	A list of arguments controlling the drawing of axes. See `axis` for acceptable arguments and the examples below for a demonstration.
`points.args`	A list of arguments controlling the drawing of points. See `points` for acceptable arguments and the examples below for a demonstration.
`map.args`	A list of arguments controlling the drawing of the map. See `map` for acceptable arguments and the examples below for a demonstration.
`grid.args`	A list of arguments controlling the drawing of the lat/long grid lines. See `abline` for acceptable arguments and the examples below for a demonstration.
`title.args`	A list of arguments controlling the how titles and axis lables are written. See `title` for acceptable arguments and the examples below for a demonstration.
`image.plot.args`	A list of arguments controlling the plotting of the color-bar legend and the legend axis and labels. See `image.plot` for acceptable arguments and the examples below for a demonstration.
`lines.args`	A list of arguments controlling the drawing of the line connecting the points. See `lines` for acceptable arguments and the examples below for a demonstration.

Most of the components of a plot produced by this function can be controlled by supplying a list of arguments to the embedded function that produces the particular component of the plot. For example, the text and size of the plot's title can be controlled by specifying a list of acceptable arguments to title.args. Similarly, the axes, map, and grid lines are controlled by specifying a list of acceptable arguements to axis.args, map.args, and grid.args, respectively. Through the argument image.plot.args the user can control the plotting of the color-bar legend and the color-bar's title and axis labels. See the examples below for a demonstration of how to apply these different arguments.

A plot is produced. No data are returned.

Michael U. Kemp mukemp+RNCEP@gmail.com

To cite package 'RNCEP' in publications use:

Kemp, M. U., van Loon, E. E., Shamoun-Baranes, J., and Bouten, W. 2011. RNCEP:global weather and climate data at your fingertips. – Methods in Ecology and Evolution. DOI:10.1111/j.2041-210X.2011.00138.x.

## Not run: 
library(RNCEP)
## In this example, we use datetime and locational data
## obtained from a GPS device attached to a lesser 
## black-backed gull. 
data(gull, package='RNCEP')

## First, visualize the entire track representing altitude
## with the point colors ##
## Note the specification of the title
## Also, note the specification of the legend label
## and adjustment of its placement
NCEP.vis.points(wx=gull$altitude, lats=gull$latitude, 
    lons=gull$longitude, cols=topo.colors(64),
    title.args=list(main='Lesser black-backed gull'),
    image.plot.args=list(legend.args=list(text='Altitude',
    adj=-1, cex=1.25)))

## Take a subset of the data based on the datetime of 
## the measurement ##
ss <- subset(gull, format(gull$datetime, "%Y-%m-%d %H:%M:%S") >=
    "2008-09-19 16:00:00" & format(gull$datetime, 
    "%Y-%m-%d %H:%M:%S") <= "2008-09-19 19:30:00")


## Now collect cloud cover, temperature, and wind
## information for each point in the subset ##
cloud <- NCEP.interp(variable='tcdc.eatm', level='gaussian', 
    lat=ss$latitude, lon=ss$longitude, dt=ss$datetime, 
    reanalysis2=TRUE, keep.unpacking.info=TRUE)
temp <- NCEP.interp(variable='air.sig995', level='surface', 
    lat=ss$latitude, lon=ss$longitude, dt=ss$datetime,
    reanalysis2=FALSE, keep.unpacking.info=TRUE)
uwind <- NCEP.interp(variable='uwnd', level=925, 
    lat=ss$latitude, lon=ss$longitude, dt=ss$datetime,
    reanalysis2=TRUE, keep.unpacking.info=TRUE)
vwind <- NCEP.interp(variable='vwnd', level=925, 
    lat=ss$latitude, lon=ss$longitude, dt=ss$datetime, 
    reanalysis2=TRUE, keep.unpacking.info=TRUE)	

## Now visualize the subset of the GPS track using color
## to indicate the cloud cover ##
## Note the adjustment to the color of the basemap
## And the setting of the map range ##
## And the explicit placement of the colorbar legend
## using the smallplot argument
NCEP.vis.points(wx=cloud, lats=ss$latitude, lons=ss$longitude,
    cols=rev(heat.colors(64)),
    map.args=list(col='darkgreen',xlim=c(-7,4), ylim=c(40,50)),
    title.args=list(main='Lesser black-backed gull'),
    image.plot.args=list(legend.args=list(text='Cloud Cover %',
        adj=-.1, padj=-.5, cex=1),
    smallplot=c(.83,.86,.15,.85)))

## Now visualize the subset of the GPS track using color
## to indicate the temperature ##
## Note the adjustment of point size
NCEP.vis.points(wx=temp, lats=ss$latitude, lons=ss$longitude,
    cols=rev(heat.colors(64)),
    points.args=list(cex=1.25),
    title.args=list(main='Lesser black-backed gull'),
    image.plot.args=list(legend.args=list(text='Kelvin',
        adj=-.4, padj=-.5, cex=1.15)),
    map.args=list(xlim=c(-7,4), ylim=c(40,50)))

## Now calculate the tailwind component from the U and V
## wind components assuming that the bird's preferred 
## direction is 225 degrees
tailwind <- (sqrt(uwind^2 + vwind^2)*cos(((atan2(uwind,vwind)*
    (180/pi))-225)*(pi/180)))

## Now visualize the subset of the GPS track using color
## to indicate the tailwind speed ##
## Note the adjustment of grid and axis properties
NCEP.vis.points(wx=tailwind, lats=ss$latitude, lons=ss$longitude,
    cols=rev(heat.colors(64)),
    axis.args=list(las=2), grid.args=list(lty=1),
    title.args=list(main='Lesser black-backed gull'),
    image.plot.args=list(legend.args=list(text='Tailwind m/s',
        adj=0, padj=-2, cex=1.15)),
    map.args=list(xlim=c(-7,4), ylim=c(40,50)))

## End(Not run)