stcfclayton {CoSMoS}

Clayton SpatioTemporal Correlation Structure

Description

Provides spatiotemporal correlation structure function based on Clayton copula. For more details on the parametric spatiotemporal correlation structures see section 2.3 and 2.4 in Papalexiou and Serinaldi (2020).

Usage

stcfclayton(t, s, scfid, tcfid, copulaarg, scfarg, tcfarg)

Arguments

t

time lag

s

spatial lag (distance)

scfid

ID of the spatial (marginal) correlation structure (e.g. weibull)

tcfid

ID of the temporal (marginal) correlation structure (e.g. weibull)

copulaarg

parameter of the Clayton copula linking the marginal correlation structures

scfarg

parameters of spatial (marginal) correlation structure

tcfarg

parameters of temporal (marginal) correlation structure

References

Papalexiou, S.M., Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2), e2019WR026331, doi: 10.1029/2019WR026331

Papalexiou, S.M., Serinaldi, F., Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57, e2020WR029466, doi: 10.1029/2020WR029466

Examples

library(plot3D)

## specify grid of spatial and temporal lags
d <- 31
st <- expand.grid(0:(d - 1),
                  0:(d - 1))

## get the STCS
wc <- stcfclayton(t = st[, 1],
                  s = st[, 2],
                  scfid = 'weibull',
                  tcfid = 'weibull',
                  copulaarg = 2,
                  scfarg = list(scale = 20,
                                shape = 0.7),
                  tcfarg = list(scale = 1.1,
                                shape = 0.8))

## visualize the STCS
wc.m <- matrix(wc,
               nrow = d)

persp3D(z = wc.m, x = 1: nrow(wc.m), y = 1:ncol(wc.m),
        expand = 1, main = "", scale = TRUE, facets = TRUE,
        xlab="Time lag", ylab = "Distance", zlab = "STCF",
        colkey = list(side = 4, length = 0.5), phi = 20, theta = 120,
        resfac = 5,  col= gg2.col(100))