NordklimData: The Nordklim Dataset

Description Usage Format Details Source References Examples

Description

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

Usage

1

Format

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

Details

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

Source

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

References

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

Examples

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## 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)

Example output

'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 ...

nordklimdata1 documentation built on May 29, 2017, 4:46 p.m.