NordklimData: The Nordklim Dataset
In nordklimdata1: Dataset for Climate Analysis with Data from the Nordic Region

Description Usage Format Details Source References Examples

The NORDKLIM data set - monthly data for 7 climatic elements from 114 stations in 5 Nordic countries.

1	data(NordklimData)

A data frame with 71329 observations on the following 16 variables.

NordklimNumber: Nordklim number identifier
ClimateElement: Climate element identifier
FirstYear: First year of the dataset
January: Readings for January
February: Readings for February
March: Readings for March
April: Readings for April
May: Readings for May
June: Readings for June
July: Readings for July
August: Readings for August
September: Readings for September
October: Readings for October
November: Readings for November
December: Readings for December
CountryCode: Country code

The NORDKLIM data set has 16 columns, the first three columns are the Nordklim number, climate element number and first year of the dataset, the next 12 columns are twelve months of readings, from January to December and the last column is the country code. Monthly climatic elements in the NORDKLIM data set:

Element number	Climatic element	Unit	Abbreviation
101	Mean temperature	0.1 C	T
111	Mean maximum temperature	0.1 C	Tx
112	Highest maximum temperature	0.1 C	Th
113	Day of Th	date	Thd
121	Mean minimum temperature	0.1 C	Tn
122	Lowest minimum temperature	0.1 C	Tl
123	Day of Tl	date	Tld
401	Mean Pressure	0.1 hPa	P
601	Precipitation Sum	0.1 mm	R
602	Maximum 1-day precipitation	0.1 mm	Rx
701	Number of days with snow cover (> 50% covered)	days	dsc
801	Mean cloud cover	%	N

http://www.smhi.se/hfa_coord/nordklim

Nordklim dataset 1.0 - description and illustrations Norwegian meteorological institute, 08/01 KLIMA, 2001

## Not run: 
data(NordklimData)
str(NordklimData)
# get all the country codes
countries <- unique(NordklimData$CountryCode)
# earliest and latest year of data collection
minFirstYear<- min(NordklimData$FirstYear)
maxFirstYear<- max(NordklimData$FirstYear)
allyears <- min(NordklimData$FirstYear):max(NordklimData$FirstYear)
# get the yearly average of all records
avgNordk <- cbind(NordklimData[,c('CountryCode','ClimateElement','FirstYear',
'NordklimNumber')], 
YrAvg=apply(NordklimData[,c('January','February','March','April','May','June',
'July','August','September', 'October','November','December')],1,function(x) 
{x[x==-9999]<-NA;mean(x,na.rm = TRUE)}))
str(avgNordk)
# plot the Danish mean temperatures for its 5 stations (for a quick visual 
# inspection, no need for labels or legends)
DanavgNordk <- avgNordk[which(avgNordk$CountryCode=='DK' & 
avgNordk$ClimateElement==101),c('FirstYear','YrAvg','NordklimNumber')]
p <- unique(DanavgNordk$NordklimNumber)
for (Dp in p) { plot(DanavgNordk[which(DanavgNordk$NordklimNumber==Dp),
c('FirstYear','YrAvg')],type='l',col=( which(Dp==p)),
xlim=c(min(DanavgNordk$FirstYear), max(DanavgNordk$FirstYear)),
ylim=c(60,120)); if (Dp != p[length(p)]) par(new=T)}
# average each country
avgNordkCountry=aggregate(YrAvg ~ CountryCode+ClimateElement+FirstYear , 
data = avgNordk, function(x) {x[x==-9999]<-NA;mean(x,na.rm = TRUE)})
str(avgNordkCountry)
# plot the temperatures (mean of all stations) for each country
for (country in countries) { plot(avgNordkCountry[
which(avgNordkCountry$CountryCode==country & avgNordkCountry$ClimateElement==101),
c('FirstYear','YrAvg')],type='l',col=( which(country==countries)),
xlim=c(minFirstYear, maxFirstYear),ylim=c(0,120),
main='Mean of yearly means of all stations for each country',
xlab='Years',ylab='Mean temperature'); 
if (country != countries[length(countries)]) par(new=T)}
legend('topleft', legend = countries, col=1:5, pch=1, lty=1, merge=TRUE)

## End(Not run)

'data.frame':	71329 obs. of  16 variables:
 $ NordklimNumber: int  6193 6193 6193 6193 6193 6193 6193 6193 6193 6193 ...
 $ ClimateElement: int  101 101 101 101 101 101 101 101 101 101 ...
 $ FirstYear     : int  1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 ...
 $ January       : int  -12 0 -50 -2 0 6 -17 29 20 3 ...
 $ February      : int  1 0 -21 14 -33 7 -12 15 22 -1 ...
 $ March         : int  9 4 12 34 3 30 21 20 11 1 ...
 $ April         : int  35 41 49 57 49 46 46 38 58 43 ...
 $ May           : int  85 93 79 93 100 96 92 86 88 81 ...
 $ June          : int  116 129 136 140 141 169 148 136 119 133 ...
 $ July          : int  169 142 173 171 158 174 160 136 175 171 ...
 $ August        : int  152 161 166 159 159 160 179 159 158 166 ...
 $ September     : int  141 144 127 114 142 132 129 132 132 135 ...
 $ October       : int  113 90 101 82 84 104 89 87 93 91 ...
 $ November      : int  45 43 44 67 51 36 50 63 78 58 ...
 $ December      : int  35 1 32 35 11 10 30 47 3 39 ...
 $ CountryCode   : chr  "DK" "DK" "DK" "DK" ...
'data.frame':	71329 obs. of  5 variables:
 $ CountryCode   : chr  "DK" "DK" "DK" "DK" ...
 $ ClimateElement: int  101 101 101 101 101 101 101 101 101 101 ...
 $ FirstYear     : int  1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 ...
 $ NordklimNumber: int  6193 6193 6193 6193 6193 6193 6193 6193 6193 6193 ...
 $ YrAvg         : num  74.1 70.7 70.7 80.3 72.1 ...
'data.frame':	8111 obs. of  4 variables:
 $ CountryCode   : chr  "IS" "IS" "IS" "IS" ...
 $ ClimateElement: int  101 101 101 101 111 111 112 121 121 122 ...
 $ FirstYear     : int  1870 1871 1872 1873 1873 1873 1873 1873 1873 1873 ...
 $ YrAvg         : num  41.7 36.1 41.8 36 89.2 ...