| AbsoluteTemperature | R Documentation |
It is a subset from the data set which was used for publication [1], i.e. the Global Absolute Temperature for Northern Hemisphere (1800-2013) with only complete yearly observations included. Here we have kept the years 1969-2013.
data("AbsoluteTemperature")
A data frame with 155862 observations on the following 18 variables.
Yearan integer vector of observation years from 1969 to 2013
Jannumeric vector of monthly average temperature for January
Febnumeric vector of monthly average temperature for February
Marnumeric vector of monthly average temperature for March
Aprnumeric vector of monthly average temperature for April
Maynumeric vector of monthly average temperature for May
Junnumeric vector of monthly average temperature for June
Julnumeric vector of monthly average temperature for July
Augnumeric vector of monthly average temperature for August
Sepnumeric vector of monthly average temperature for September
Octnumeric vector of monthly average temperature for October
Novnumeric vector of monthly average temperature for November
Decnumeric vector of monthly average temperature for December
longa numeric vector for the geographical longitude: positive values for eastings
lata numeric vector for the geographical latitude: positive values for northings
ha numeric vector for the altitude in metrs
stidan integer vector with the station identity number
zan integer vector with the relevant climate zone:
1, Tropical Zone
2, Subtropics
3, Temperate zone
4, Cold Zone
That data set was the output of the procedure described in [1]. Initial data set was downloaded from [2] at 2014-12-17.
[1] Demetris T. Christopoulos. Extraction of the global absolute temperature for Northern Hemisphere using a set of 6190 meteorological stations from 1800 to 2013. Journal of Atmospheric and Solar-Terrestrial Physics, 128:70 - 83, 3 2015. doi:10.1016/j.jastp.2015.03.009
[2] Met Office Hadley Centre observations datasets, station data sets,
http:///www.metoffice.gov.uk/hadobs/crutem4/data/station_files/CRUTEM.4.2.0.0.station_files.zip
(last visited 17.12.14)
#
######################################
## Load absolute temperature data set:
######################################
#
data("AbsoluteTemperature")
df=AbsoluteTemperature
## Find proportions for climate zones
pcs=table(df$z)/dim(df)[1]
## Choose an approximate size of the new sample and compute resample sizes
N=1000
resamplesizes=as.integer(round(N*pcs))
sum(resamplesizes)
## Create the grouping matrix
groupmat=data.frame("Group_ID"=1:4,"Resample_Size"=resamplesizes)
groupmat
## Simple resampling:
resample_simple <- grouped_resample(in_data = df,grp_vector = "z",
grp_matrix = groupmat,replace = FALSE, option = "Simple", rseed = 20191119)
cat(dim(resample_simple),"\n")
## Dirichlet resampling:
resample_dirichlet <- grouped_resample(in_data = df,grp_vector = "z",
grp_matrix = groupmat, replace = FALSE, option = "Dirichlet", rseed = 20191119)
cat(dim(resample_dirichlet),"\n")
#
#########################################
## Reproduce the results of 2015 article
#########################################
##
data("AbsoluteTemperature")
dh=AbsoluteTemperature
## Create yearly averages for every station
dh$avg = rowMeans(df[,month.abb[1:12]])
head(dh)
## Compute mean average of every year for all Northern Hemisphere
dagg=data.frame(aggregate(avg~Year,dh,function(x){c(mean(x),sd(x))}))
## Find used stations per year
daggn=aggregate(stid ~ Year,dh,length)
head(daggn)
tail(daggn)
## Combine all in a data frame
dagyears=data.frame(dagg$Year,daggn$stid,dagg$avg[,1],dagg$avg[,2])
colnames(dagyears)=c("Year","Nv","mu","Smu")
head(dagyears)
tail(dagyears)
#
## Compare with Table 7 (Columns: Year, Nv, mu_bar, Smu_bar), page 77 of article
## Extraction of the global absolute temperature for Northern Hemisphere
## using a set of 6190 meteorological stations from 1800 to 2013
## https://doi.org/10.1016/j.jastp.2015.03.009
## and specifically the years 1969--2013
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