StatSeasAtlHurr <- function(atlano=NULL, tropano=NULL, hrvar='HR') { # # Verify that variables are either TC, HR or PDI. # ----------------------------------------------- # if (hrvar != "HR" && hrvar != "TC" && hrvar != "PDI") { stop("Hurricane variable not recognized.") } # # Verify that both Atl and Trop SSTA are present. # ----------------------------------------------- # if (is.null(atlano)) { stop("Atlantic SST missing.") } if (is.null(tropano)) { stop("Tropical SST missing.") } # # Verify that Atl and Trop SSTA are of the same dimensions. # --------------------------------------------------------- # if (length(dim(atlano)) != length(dim(tropano))) { stop("Input arrays are of different dimensions.") } else { for (i in 1:length(dim(atlano))) { if (dim(atlano)[i] != dim(tropano)[i]) { stop("Input arrays are of different sizes.") } } } # # Get the values of the betas according to the hurricane activity measure we specified. # ------------------------------------------------------------------------------------- # if (hrvar == 'HR') { # beta's are derived from Villarini et al. (2012), Mon Wea Rev, 140, 44-65. # beta's are for corrected hurricane data + ERSST with SBC criteria (table 2) beta0 <- 1.85 betaAtl <- 1.05 betaTrop <- -1.17 } else if (hrvar == 'TC') { # beta's are from Villarini et al. (2010), Mon Wea Rev, 138, 2681-2705. # beta's are for corrected TC data (lifetime >= 48h) + ERSST (table 5) beta0 <- 2.10 betaAtl <- 1.02 betaTrop <- -1.05 } else if (hrvar == 'PDI') { # beta's are from Villarini et al. (2012), J Clim, 25, 625-637. # beta's are from ERSST, with SBC penalty criterion (table 1) beta0 <- 0.76 betaAtl <- 1.94 betaTrop <- -1.78 } # # Create matrix of similar dimension as atlano for beta0. # ------------------------------------------------------- # intercept <- array(beta0, dim(atlano)) # # Compute statistical relationship b/w SSTAs and mean hurricane activity. # ----------------------------------------------------------------------- # atl <- betaAtl * atlano trop <- betaTrop * tropano # temp <- intercept + atl + trop # statval <- list(mean=array(NaN,dim(atl)),var=array(NaN,dim(atl))) statval$mean[] <- vapply(X=temp, FUN=exp, numeric(1)) # # Compute the variance of the distribution. # TC and HR follow a Poisson distribution, so the variance is equal to the mean. # PDI follows a gamma distribution, with sigma = -0.57. (variance = sigma^2 * mean^2). # ---------------------------------------------------------------------------------- # if (hrvar == "HR" && hrvar == "TC") { statval$var <- statval$mean } else { sigma <- -0.57 statval$var[] <- sigma^2 * vapply(X=statval$mean, FUN=function(x) x^2, numeric(1)) } # # Output # ~~~~~~~~ # statval }