StatSeasAtlHurr.R

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 <- 2.1
    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
}