CO2: Simulated global CO2 observations

In fields: Tools for Spatial Data

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)

fields documentation built on Aug. 18, 2023, 1:06 a.m.