CO2: Simulated global CO2 observations

Description Usage Format Details Examples

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

1

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

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
## 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, 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$lon.lat, CO2$y, lon.lat=TRUE,lambda=1.5, theta=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 take a while)
look<- predictSurface( out2, grid.list=list( x=CO2.true$x, y=CO2.true$y))
image.plot(look)


## End(Not run)


Search within the fields package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.