CanadianWeather: Canadian average annual weather cycle
In Data2LD: Functional Data Analysis with Linear Differential Equations

Description Usage Format Source Examples

Daily temperature and precipitation at 35 different locations in Canada averaged over 1960 to 1994.

1	CanadianWeather

'CanadianWeather' and 'daily' are lists containing essentially the same data. 'CanadianWeather' may be preferred for most purposes; 'daily' is included primarily for compatability with scripts written before the other format became available and for compatability with the Matlab 'fda' code. 'CanadianWeather' contains the following objects:

dailyAv: a three dimensional array c(365, 35, 3) summarizing data collected at 35 different weather stations in Canada on the following: [,,1] = [,, 'Temperature.C']: average daily temperature for each day of the year [,,2] = [,, 'Precipitation.mm']: average daily rainfall for each day of the year rounded to 0.1 mm. [,,3] = [,, 'log10precip']: base 10 logarithm of Precipitation.mm after first replacing 27 zeros by 0.05 mm (Ramsay and Silverman 2005, p. 248).
place: Names of the 35 different weather stations in Canada whose data are summarized in 'dailyAv'. These names vary between 6 and 11 characters in length. By contrast, daily[["place"]] which are all 11 characters, with names having fewer characters being extended with trailing blanks.
province: names of the Canadian province containing each place
coordinates: a numeric matrix giving 'N.latitude' and 'W.longitude' for each place.
region: Which of 4 climate zones contain each place: Atlantic, Pacific, Continental, Arctic.
monthlyTemp: A matrix of dimensions (12, 35) giving the average temperature in degrees Celcius for each month of the year.
monthlyPrecip: A matrix of dimensions (12, 35) giving the average daily precipitation in milimeters for each month of the year.
geogindex: Order the weather stations from East to West to North

Ramsay, James O., and Silverman, Bernard W. (2005), Functional Data Analysis, 2nd ed., Springer, New York. Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York

##
## 1.  Plot (latitude & longitude) of stations by region
##
with(CanadianWeather, plot(-coordinates[, 2], coordinates[, 1], type='n',
                           xlab="West Longitude", ylab="North Latitude",
                           axes=FALSE) )
Wlon <- pretty(CanadianWeather$coordinates[, 2])
axis(1, -Wlon, Wlon)
axis(2)

rgns <- 1:4
names(rgns) <- c('Arctic', 'Atlantic', 'Continental', 'Pacific')
Rgns <- rgns[CanadianWeather$region]
with(CanadianWeather, points(-coordinates[, 2], coordinates[, 1],
                             col=Rgns, pch=Rgns) )
legend('topright', legend=names(rgns), col=rgns, pch=rgns)

##
## 2.  Plot dailyAv[, 'Temperature.C'] for 4 stations
##
data(CanadianWeather)
# Expand the left margin to allow space for place names
op <- par(mar=c(5, 4, 4, 5)+.1)
# Plot
stations <- c("Pr. Rupert", "Montreal", "Edmonton", "Resolute")
matplot(day.5, CanadianWeather$dailyAv[, stations, "Temperature.C"],
        type="l", axes=FALSE, xlab="", ylab="Mean Temperature (deg C)")
axis(2, las=1)
# Label the horizontal axis with the month names
axis(1, monthBegin.5, labels=FALSE)
axis(1, monthEnd.5, labels=FALSE)
axis(1, monthMid, monthLetters, tick=FALSE)
# Add the monthly averages
matpoints(monthMid, CanadianWeather$monthlyTemp[, stations])
# Add the names of the weather stations
mtext(stations, side=4,
      at=CanadianWeather$dailyAv[365, stations, "Temperature.C"],
     las=1)
# clean up
par(op)