| CO2 {fields} | R Documentation |
Simulated global CO2 observations
Description
This is an example of moderately large spatial data set and consists of simulated CO2 concentrations that are irregularly sampled from a lon/lat grid. Also included is the complete CO2 field (CO2.true) used to generate the synthetic observations.
Usage
data(CO2) Format
The format of CO2 is a list with two components:
lon.lat: 26633x2 matrix of the longitude/latitude locations. These are a subset of a larger lon/lat grid (see example below).
y: 26633 CO2 concentrations in parts per million.
The format of CO2.true
is a list in "image" format with components:
x longitude grid values.
y latitude grid values.
z an image matrix with CO2 concentration in parts per million
mask a logical image that indicates with grid locations were selected for the synthetic data set
CO2.
Details
This data was generously provided by Dorit Hammerling and Randy Kawa as a test example for the spatial analysis of remotely sensed (i.e. satellite) and irregular observations. The synthetic data is based on a true CO2 field simulated from a geophysical, numerical model.
Examples
## Not run:
data(CO2)
#
# A quick look at the observations with world map
quilt.plot( CO2$lon.lat, CO2$y)
world( add=TRUE)
# Note high concentrations in Borneo (biomass burning), Amazonia and
# ... Michigan (???).
# spatial smoothing using the wendland compactly supported covariance
# see help( fastTps) for details
# First smooth using locations and Euclidean distances
# note taper is in units of degrees
out<-fastTps( CO2$lon.lat, CO2$y, aRange=4, lambda=2.0)
#summary of fit note about 7300 degrees of freedom
# associated with fitted surface
print( out)
# image plot on a grid (this takes a while)
surface( out, type="I", nx=300, ny=150)
# smooth with respect to great circle distance
out2<-fastTps( CO2$lon.lat, CO2$y, lon.lat=TRUE,lambda=1.5, aRange=4*68)
print(out2)
#surface( out2, type="I", nx=300, ny=150)
# these data are actually subsampled from a grid.
# create the image object that holds the data
#
temp<- matrix( NA, ncol=ncol(CO2.true$z), nrow=nrow(CO2.true$z))
temp[ CO2.true$mask] <- CO2$y
# look at gridded object.
image.plot(CO2.true$x,CO2.true$y, temp)
# to predict _exactly_ on this grid for the second fit;
# (this takes a while)
look<- predictSurface( out2, list( x=CO2.true$x, y=CO2.true$y) )
image.plot(look)
## End(Not run)