iki.dataclim-package: Consistency, homogeneity, summary statistics and indices of...

In iki.dataclim: Consistency, Homogeneity and Summary Statistics of Climatological Data

Description

The package offers an S4 infrastructure to store climatological station data of various temporal aggregation scales. In-built quality control and homogeneity tests follow the methodology from the European Climate Assessment & Dataset project. Wrappers for climate indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), a quick summary of important climate statistics and climate diagram plots provide a fast overview of climatological characteristics of the station.

Details

Package:	iki.dataclim
Type:	Package
Version:	1.0
Date:	2014-07-18
License:	GPL-3
Depends:	methods

This package bundles part of the code developed for the dataclim-project, a collaboration between the German "Gesellschaft fuer Internationale Zusammenarbeit (GIZ)" and the Indonesian weather service BMKG, funded by the German environmental ministery under the "Internationale Klimaschutz Initiative (iki.)". Hence the name.

Author(s)

Author: Boris Orlowsky <[e-mail]>

References

ECA&D: http://eca.knmi.nl/documents/atbd.pdf

ETCCDI climate indices: http://cccma.seos.uvic.ca/ETCCDMI/list_27_indices.shtml,

dataclim project: http://www.giz.de/en/worldwide/16743.html

Examples

## A typical work flow could be:

## load the package
library(iki.dataclim)

## load example data and create vector of class Date
data(potsdam)
date <- as.Date(potsdam$date)

## create a dataclim object
myDataclim <- createDataclim(date=date, tmin=potsdam$tmin, tmax=potsdam$tmax,
                          prec=potsdam$prec, basePeriod=1981:2010)

## look at the days with quality issues
slot(myDataclim, "flaggedData")

## evaluate homogeneity of temperature and precipitation
evalHomogeneity(myDataclim)

## look at summary climate statistics for the period 1980-2000
summary(myDataclim, 1980:2000)

## create a Walter-Lieth climate diagram
plotWalterLieth(myDataclim)

## convert the dataclim object to a climdexInput object (from package
## climdex.pcic) and compute a comprehensive set of ETCCDI climate
## indices
myClimdex <- createClimdex(myDataclim, basePeriod=1981:2010)
climdexIndices <- computeClimdex(myClimdex)

## plot the annual timeseries of maximum daily maximum temperature
plot(climdexIndices$annual[, "txx"])

Example output

           tmin tmax prec
1979-11-14  3.6  1.8  0.5
1983-10-07 85.8 15.8  0.2
1985-05-03  2.2 74.8  2.7
$tests
    DTR vDTR RR1
SNH  NS   p5  NS
BHR  NS   p5  NS
PET  NS   NS  NS
VON  NS   NS  NS

$breaks
     DTR vDTR  RR1
SNH 1981 2003 1981
BHR 2003 2004 1989
PET 2002 2003 1988
VON   NA   NA   NA

$classes
    temp     prec 
"useful" "useful" 

$means
     tmin      tmax      prec 
 5.263338 13.571717  1.571438 

$stdDevs
    tmin     tmax     prec 
6.931498 9.127379 3.665477 

$annualCycleAvg
         tmin      tmax     prec
Jan -2.554531  2.690476 43.44286
Feb -2.259911  4.141145 38.74762
Mar  0.718894  8.662366 43.84762
Apr  3.894286 14.263651 39.83333
May  8.446083 19.750082 50.44286
Jun 11.394603 21.755556 69.27143
Jul 13.520737 24.104916 53.87143
Aug 13.320891 24.001229 59.12857
Sep 10.061429 19.134921 42.07619
Oct  5.956544 13.667030 35.38095
Nov  1.390635  6.754603 41.88571
Dec -1.209677  3.364363 56.09524

$annualCycleMin
     tmin  tmax prec
Jan -20.8 -15.3    0
Feb -19.9  -8.6    0
Mar -14.0  -4.6    0
Apr  -5.8   0.8    0
May  -1.7   7.0    0
Jun   3.1  10.7    0
Jul   6.9  13.2    0
Aug   5.4  12.8    0
Sep   2.4   9.5    0
Oct  -4.4   2.5    0
Nov -10.9  -6.0    0
Dec -17.7 -10.7    0

$annualCycleMax
    tmin tmax   prec
Jan  9.5 14.5  601.4
Feb  9.8 18.6  708.4
Mar 10.1 22.8  719.2
Apr 14.7 30.0 1011.0
May 16.2 31.5 1227.6
Jun 20.8 36.6 1839.0
Jul 20.4 36.7 1298.9
Aug 21.0 38.6 2108.0
Sep 16.9 30.5 1029.0
Oct 15.9 27.2  682.0
Nov 11.6 16.6  738.0
Dec 10.9 14.6  678.9

$annualTrends
            tmin       tmax       prec
trend 0.05896554 0.05337003 -4.9920779
pval  0.03710054 0.15329303  0.2179758

$monthlyTrends
$monthlyTrends$trend
            tmin          tmax        prec
Jan  0.149530792  0.1368077084 -1.01818182
Feb  0.202135820  0.2103264346  0.55844156
Mar  0.046036866  0.0350397989  0.14116883
Apr  0.100640693  0.1275367965 -1.08311688
May  0.063058232  0.0423495322  0.03064935
Jun  0.030640693  0.0502077922 -2.11766234
Jul  0.039928781  0.0300879765 -0.27909091
Aug  0.051633850  0.0850858819  0.64519481
Sep  0.024069264 -0.0231688312  0.45922078
Oct  0.001249686 -0.0002327887 -0.58662338
Nov -0.029406926 -0.0583506494 -0.86714286
Dec  0.028068705  0.0047507331 -0.87493506

$monthlyTrends$pval
          tmin       tmax      prec
Jan 0.22900336 0.24767117 0.2021569
Feb 0.10145316 0.08401350 0.4393946
Mar 0.51327817 0.66820984 0.8949141
Apr 0.01369894 0.01819664 0.1763919
May 0.20207809 0.60300934 0.9785904
Jun 0.34826842 0.43521084 0.1224350
Jul 0.35685778 0.74658436 0.7958892
Aug 0.13536020 0.17737912 0.5255615
Sep 0.55090461 0.78281983 0.5660502
Oct 0.98094185 0.99669573 0.5604885
Nov 0.64614751 0.43638052 0.1430193
Dec 0.69752869 0.95233978 0.3462273


$basePeriod
 [1] 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994
[16] 1995 1996 1997 1998 1999 2000

iki.dataclim documentation built on May 2, 2019, 2:38 a.m.