PRSim (version 1.5)

weather_sim_multi_sites: Simulated temperature and precipitation for two grid cells

Description

The dataset is generated with the package own routines and represent 5 series of 38 years of meteorological data for two grid cells

Usage

data("weather_sim_multi_sites")

Format

Two lists (one per variable) of four elements (one per catchment), containing a data frame each holding information about the observed time series and the stochastic simulations

YYYY

a numeric vector, year

MM

a numeric vector, month

DD

a numeric vector, day

timestamp

POSIXct vector of the daily runoff

Prec/Temp

observed precipitation/temperature

r1,...,r5

5 simulated data series

Details

The data is included to illustrate the validation and visualization routines in demo("PRSim_weather-validate").

References

Brunner, M. I., and E. Gilleland (2021). Spatial compound hot-dry events in the United States: assessment using a multi-site multi-variable weather generator, in preparation.

Examples

oldpar <- par(mfrow = c(2, 1), mar = c(3, 3, 2, 1))
data(weather_sim_multi_sites)
sim <- weather_sim_multi_sites
### define plotting colors
col_sim <- adjustcolor("#fd8d3c",alpha=0.8)
col_sim_tran <- adjustcolor("#fd8d3c",alpha=0.2)
col_obs <- adjustcolor( "black", alpha.f = 0.2)
### greys
col_vect_obs <- c('#cccccc','#969696','#636363','#252525')
### oranges
col_vect_sim <- c('#fdbe85','#fd8d3c','#e6550d','#a63603')

### plot time series for multiple sites

### Temperature (first list entry)
par(mfrow=c(2,1),mar=c(3,3,2,1))
### determine ylim
ylim_max <- max(sim[[1]][[1]]$Temp)*1.5
### observed
plot(sim[[1]][[1]]$Temp[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[1]]$Temp[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[1]]$r1[1:1000],
ylab=expression(bold(paste("Temperature [degrees]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[1]]$r1[1:1000],col=col_vect_sim[l])
}


### precipitation (second list entry)
ylim_max <- max(sim[[1]][[2]]$Prec)*1
### observed
plot(sim[[1]][[2]]$Prec[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_obs[1],
ylim=c(0,ylim_max),main='Observations')
for(l in 2){
  lines(sim[[l]][[2]]$Prec[1:1000],col=col_vect_obs[l])
}
# legend('topleft',legend=c('Station 1','Station 2'
# ),lty=1,col=col_vect_obs[1:2])
### simulated (one run)
plot(sim[[1]][[2]]$r1[1:1000],
ylab=expression(bold(paste("Precipitation [mm/d]"))),
xlab="Time [d]",type="l",col=col_vect_sim[1],
ylim=c(0,ylim_max),main='Stochastic simulations')
for(l in 2){
  lines(sim[[l]][[2]]$r1[1:1000],col=col_vect_sim[l])
}
par(oldpar)