fields (version 8.4-1)

CO2: 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, 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)