wind: Ireland wind data, 1961-1978
In gstat: Spatial and Spatio-Temporal Geostatistical Modelling, Prediction and Simulation

Description Usage Format Note Author(s) References Examples

Daily average wind speeds for 1961-1978 at 12 synoptic meteorological stations in the Republic of Ireland (Haslett and raftery 1989). Wind speeds are in knots (1 knot = 0.5418 m/s), at each of the stations in the order given in Fig.4 of Haslett and Raftery (1989, see below)

1	data(wind)

data.frame wind contains the following columns:

year: year, minus 1900
month: month (number) of the year
day: day
RPT: average wind speed in knots at station RPT
VAL: average wind speed in knots at station VAL
ROS: average wind speed in knots at station ROS
KIL: average wind speed in knots at station KIL
SHA: average wind speed in knots at station SHA
BIR: average wind speed in knots at station BIR
DUB: average wind speed in knots at station DUB
CLA: average wind speed in knots at station CLA
MUL: average wind speed in knots at station MUL
CLO: average wind speed in knots at station CLO
BEL: average wind speed in knots at station BEL
MAL: average wind speed in knots at station MAL

data.frame wind.loc contains the following columns:

Station: Station name
Code: Station code
Latitude: Latitude, in DMS, see examples below
Longitude: Longitude, in DMS, see examples below
MeanWind: mean wind for each station, metres per second

This data set comes with the following message: “Be aware that the dataset is 532494 bytes long (thats over half a Megabyte). Please be sure you want the data before you request it.”

The data were obtained on Oct 12, 2008, from: http://www.stat.washington.edu/raftery/software.html The data are also available from statlib.

Locations of 11 of the stations (ROS, Rosslare has been thrown out because it fits poorly the spatial correlations of the other stations) were obtained from: http://www.stat.washington.edu/research/reports/2005/tr475.pdf

Roslare lat/lon was obtained from google maps, location Roslare. The mean wind value for Roslare comes from Fig. 1 in the original paper.

Haslett and Raftery proposed to use a sqrt-transform to stabilize the variance.

Adrian Raftery; imported to R by Edzer Pebesma

These data were analyzed in detail in the following article:

Haslett, J. and Raftery, A. E. (1989). Space-time Modelling with Long-memory Dependence: Assessing Ireland's Wind Power Resource (with Discussion). Applied Statistics 38, 1-50.

and in many later papers on space-time analysis, for example:

Tilmann Gneiting, Marc G. Genton, Peter Guttorp: Geostatistical Space-Time Models, Stationarity, Separability and Full symmetry. Ch. 4 in: B. Finkenstaedt, L. Held, V. Isham, Statistical Methods for Spatio-Temporal Systems.

data(wind)
summary(wind)
wind.loc
library(sp) # char2dms
wind.loc$y = as.numeric(char2dms(as.character(wind.loc[["Latitude"]])))
wind.loc$x = as.numeric(char2dms(as.character(wind.loc[["Longitude"]])))
coordinates(wind.loc) = ~x+y
# fig 1:
if (require(mapdata)) {
    map("worldHires", xlim = c(-11,-5.4), ylim = c(51,55.5))
    plot(wind.loc, add=TRUE, pch=16)
    text(coordinates(wind.loc), pos=1, label=wind.loc$Station)
}

wind$time = ISOdate(wind$year+1900, wind$month, wind$day)
# time series of e.g. Dublin data:
plot(DUB~time, wind, type= 'l', ylab = "windspeed (knots)", main = "Dublin")

# fig 2:
#wind = wind[!(wind$month == 2 & wind$day == 29),]
wind$jday = as.numeric(format(wind$time, '%j'))
windsqrt = sqrt(0.5148 * as.matrix(wind[4:15]))
Jday = 1:366
windsqrt = windsqrt - mean(windsqrt)
daymeans = sapply(split(windsqrt, wind$jday), mean)
plot(daymeans ~ Jday)
lines(lowess(daymeans ~ Jday, f = 0.1))

# subtract the trend:
meanwind = lowess(daymeans ~ Jday, f = 0.1)$y[wind$jday]
velocity = apply(windsqrt, 2, function(x) { x - meanwind })

# match order of columns in wind to Code in wind.loc:
pts = coordinates(wind.loc[match(names(wind[4:15]), wind.loc$Code),])

# fig 3, but not really yet...
dists = spDists(pts, longlat=TRUE)
corv = cor(velocity)
sel = !(as.vector(dists) == 0)
plot(as.vector(corv[sel]) ~ as.vector(dists[sel]),
	xlim = c(0,500), ylim = c(.4, 1), xlab = "distance (km.)", 
	ylab = "correlation") 
# plots all points twice, ignores zero distance 

# now really get fig 3:
ros = rownames(corv) == "ROS"
dists.nr = dists[!ros,!ros]
corv.nr = corv[!ros,!ros]
sel = !(as.vector(dists.nr) == 0)
plot(as.vector(corv.nr[sel]) ~ as.vector(dists.nr[sel]), pch = 3,
	xlim = c(0,500), ylim = c(.4, 1), xlab = "distance (km.)", 
	ylab = "correlation") 
# add outlier:
points(corv[ros,!ros] ~ dists[ros,!ros], pch=16, cex=.5)
xdiscr = 1:500
# add correlation model:
lines(xdiscr, .968 * exp(- .00134 * xdiscr))

      year          month             day             RPT       
 Min.   :61.0   Min.   : 1.000   Min.   : 1.00   Min.   : 0.67  
 1st Qu.:65.0   1st Qu.: 4.000   1st Qu.: 8.00   1st Qu.: 8.12  
 Median :69.5   Median : 7.000   Median :16.00   Median :11.71  
 Mean   :69.5   Mean   : 6.523   Mean   :15.73   Mean   :12.36  
 3rd Qu.:74.0   3rd Qu.:10.000   3rd Qu.:23.00   3rd Qu.:15.92  
 Max.   :78.0   Max.   :12.000   Max.   :31.00   Max.   :35.80  
      VAL             ROS             KIL              SHA       
 Min.   : 0.21   Min.   : 1.50   Min.   : 0.000   Min.   : 0.13  
 1st Qu.: 6.67   1st Qu.: 8.00   1st Qu.: 3.580   1st Qu.: 6.75  
 Median :10.17   Median :10.92   Median : 5.750   Median : 9.96  
 Mean   :10.65   Mean   :11.66   Mean   : 6.306   Mean   :10.46  
 3rd Qu.:14.04   3rd Qu.:14.67   3rd Qu.: 8.420   3rd Qu.:13.54  
 Max.   :33.37   Max.   :33.84   Max.   :28.460   Max.   :37.54  
      BIR              DUB              CLA              MUL        
 Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
 1st Qu.: 4.000   1st Qu.: 6.000   1st Qu.: 5.090   1st Qu.: 5.370  
 Median : 6.830   Median : 9.210   Median : 8.080   Median : 8.170  
 Mean   : 7.092   Mean   : 9.797   Mean   : 8.494   Mean   : 8.496  
 3rd Qu.: 9.670   3rd Qu.:12.960   3rd Qu.:11.420   3rd Qu.:11.210  
 Max.   :26.160   Max.   :30.370   Max.   :31.080   Max.   :25.880  
      CLO              BEL             MAL       
 Min.   : 0.040   Min.   : 0.13   Min.   : 0.67  
 1st Qu.: 5.330   1st Qu.: 8.71   1st Qu.:10.71  
 Median : 8.290   Median :12.50   Median :15.00  
 Mean   : 8.707   Mean   :13.12   Mean   :15.60  
 3rd Qu.:11.630   3rd Qu.:16.88   3rd Qu.:19.83  
 Max.   :28.210   Max.   :42.38   Max.   :42.54  
         Station Code       Latitude     Longitude MeanWind
1       Valentia  VAL        51d56'N       10d15'W     5.48
2      Belmullet  BEL        54d14'N       10d00'W     6.75
3    Claremorris  CLA        53d43'N        8d59'W     4.32
4        Shannon  SHA        52d42'N        8d55'W     5.38
5  Roche's Point  RPT        51d48'N        8d15'W     6.36
6           Birr  BIR        53d05'N        7d53'W     3.65
7      Mullingar  MUL        53d32'N        7d22'W     4.38
8     Malin Head  MAL        55d22'N        7d20'W     8.03
9       Kilkenny  KIL        52d40'N        7d16'W     3.25
10        Clones  CLO        54d11'N        7d14'W     4.48
11        Dublin  DUB        53d26'N        6d15'W     5.05
12       Roslare  ROS 52d16'56.791"N 6d21'25.056"W     6.00
Loading required package: mapdata
Loading required package: maps