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%#
%# fields is a package for analysis of spatial data written for
%# the R software environment.
%# Copyright (C) 2024 Colorado School of Mines
%# 1500 Illinois St., Golden, CO 80401
%# Contact: Douglas Nychka, douglasnychka@gmail.edu,
%#
%# This program is free software; you can redistribute it and/or modify
%# it under the terms of the GNU General Public License as published by
%# the Free Software Foundation; either version 2 of the License, or
%# (at your option) any later version.
%# This program is distributed in the hope that it will be useful,
%# but WITHOUT ANY WARRANTY; without even the implied warranty of
%# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
%# GNU General Public License for more details.
%#
%# You should have received a copy of the GNU General Public License
%# along with the R software environment if not, write to the Free Software
%# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
%# or see http://www.r-project.org/Licenses/GPL-2
%##END HEADER
%##END HEADER
\name{CO2}
\alias{CO2}
\alias{CO2.true}
\docType{data}
\title{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 \code{CO2} is a list with two components:
\itemize{
\item lon.lat: 26633x2 matrix of the longitude/latitude locations.
These are a subset of a larger lon/lat grid (see example below).
\item y: 26633 CO2 concentrations in parts per million.
}
The format of \code{CO2.true}
is a list in "image" format with components:
\itemize{
\item x longitude grid values.
\item y latitude grid values.
\item z an image matrix with CO2 concentration in parts per million
\item mask a logical image that indicates with grid locations were
selected for the synthetic data set \code{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{
\dontrun{
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)
}
}
\keyword{datasets}
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