R: Simulated global CO2 observations

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, 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)

[Package fields version 9.6.1 Index]