WG: Weather generators for conditioned on simulated climate...

In metno/esd: Climate analysis and empirical-statistical downscaling (ESD) package for monthly and daily data

Weather generators for conditioned on simulated climate aggregated statistics.

Description

Weather generators for conditional simulation of daily temperature and/or precipitation, given mean and/or standard deviation. The family of WG functions procude stochastic time series with similar characteristics as the station series provided (if none if provided, it will use either ferder or bjornholt provided by the esd-package). Here characteristics means similar mean value, standard deviation, and spectral properties. FTscramble takes the Fourier components (doing a Fourier Transform - FT) of a series and reassigns random phase to each frequency and then returns a new series through an inverse FT. The FT scrambling is used for temperature, but not for precipitation that is non-Gaussian and involves sporadic events with rain. For precipitation, a different approach is used, taking the wet-day frequency of each year and using the wet-day mean and ranomly generated exponentially distributed numbers to provide similar aggregated annual statistics as the station or predicted though downscaling. The precipitation WG can also take into account the number of consequtive number-of-dry-days statistics, using either a Poisson or a gemoetric distribution.

Usage

WG(x, ...)

Arguments

x	station object
...	additional arguments
option	Define the type of WG
amean	annual mean values. If NULL, use those estimated from x; if NA, estimate using DSensemble.t2m, or if provided, assume a 'dsensemble' object.
asd	annual standard deviation. If NULL, use those estimated from x; if NA, estimate using DSensemble.t2m, or if provided, assume a 'dsensemble' object.
t	Time axis. If null, use the same as x or the last interval of same length as x from downscaled results.
ip	passed on to DSensemble.t2m
select	passed on to DSensemble.t2m
lon	passed on to DSensemble.t2m
lat	passed on to DSensemble.t2m
plot	if TRUE, plot results
biascorrect	passed on to DSensemble.t2m
verbose	passed on to DSensemble.t2m
mu	annual wet-mean values. If NULL, use those estimated from x; if NA, estimate using DSensemble.t2m, or if provided, assume a 'dsensemble' object.
fw	annual wet-day frequency. If NULL, use those estimated from x; if NA, estimate using DSensemble.t2m, or if provided, assume a 'dsensemble' object.
ndd	annual mean dry spell length. If NULL, use those estimated from x; if NA, estimate using DSensemble.t2m, or if provided, assume a 'dsensemble' object.
threshold	Definition of a rainy day.
method	Assume a gemoetric or a poisson distribution. Can also define ownth methods.
t2m	station object with temperature
precip	station object with precipitation.

Details

The weather generater produces a series with similar length as the provided sample data, but with shifted dates according to specified scenarios for annual mean mean/standard deviation/wet-day mean/wet-day frequency.

WG.FT.day.t2m generates daily temperature from seasonal means and standard deviations. It is given a sample station series, and uses FTscramble to generate a series with random phase but similar (or predicted - in the future) spectral characteristics. It then uses a quantile transform to prescribe predicted mean and standard deviation, assuming the distributions are normal. The temperal structure (power spectrum) is therefore similar as the sample provided.

WG.fw.day.precip uses the annual wet-day mean and the wet-day frequency as input, and takes a sample station of daily values to stochastically simulate number consequtive wet days based on its annual mean number. If not specified, it is taken from the sample data after being phase scrambeled (FTscramble) The number of wet-days per year is estimated from the wed-day frequency, it too taken to be phase scrambled estimates from the sample data unless specifically specified. The daily amount is taken from stochastic values generated with rexp. The number of consequtive wet days can be approximated by a geometric distribution (rgeom), and the annual mean number was estimated from the sample series.

Author(s)

R.E. Benestad

Examples

data(ferder)
t2m <- WG(ferder)
data(bjornholt)
pr <- WG(bjornholt)

metno/esd documentation built on April 24, 2024, 9:19 p.m.