CO2 {fields}R Documentation

Simulated global CO2 observations


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.




The format of CO2 is a list with two components:

The format of CO2.true is a list in "image" format with components:


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.


## Not run: 

# A quick look at the observations with world map
quilt.plot( CO2$, CO2$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$, CO2$y, theta=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$, CO2$y,,lambda=1.5, theta=4*68) 
#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 take a while)
look<- predictSurface( out2, grid.list=list( x=CO2.true$x, y=CO2.true$y))

## End(Not run)

[Package fields version 8.4-1 Index]