Consistency, homogeneity, summary statistics and indices of climate 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 <boris@climate-babel.org>

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

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## 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"])