R/StatSeasAtlHurr.R

In s2dverification: Set of Common Tools for Forecast Verification

#'Compute estimate of seasonal mean of Atlantic hurricane activity
#'
#'Compute one of G. Villarini's statistically downscaled measure of mean 
#'Atlantic hurricane activity and its variance. The hurricane activity is 
#'estimated using seasonal averages of sea surface temperature anomalies over 
#'the tropical Atlantic (bounded by 10N-25N and 80W-20W) and the tropics at 
#'large (bounded by 30N-30S). The anomalies are for the JJASON season.\cr
#'The estimated seasonal average is either 1) number of hurricanes, 2) number 
#'of tropical cyclones with lifetime >=48h or 3) power dissipation index 
#'(PDI; in 10^11 m^3 s^{-2}).\cr
#'The statistical models used in this function are described in\cr
#'
#'@param atlano Array of Atlantic sea surface temperature anomalies. 
#'  Must have the same dimension as tropano.
#'@param tropano Array of tropical sea surface temperature anomalies. 
#'  Must have the same dimension as atlano.
#'@param hrvar The seasonal average to be estimated. The options are either\cr
#'  "HR" (hurricanes) \cr
#'  "TC" (tropical cyclones with lifetime >=48h) \cr
#'  "PDI" (power dissipation index) \cr
#'
#'@return A list composed of two matrices:\cr
#'\enumerate{
#'  \item{
#'    A matrix (mean) with the seasonal average values of the desired quantity.\cr 
#'  }
#'  \item{
#'    A matrix (var) of the variance of that quantity.\cr
#'  }
#'}
#'The dimensions of the two matrices are the same as the dimensions of 
#'  atlano/tropano.
#'
#'@keywords datagen
#'@references 
#'Villarini et al. (2010) Mon Wea Rev, 138, 2681-2705.\cr
#'Villarini et al. (2012) Mon Wea Rev, 140, 44-65.\cr
#'Villarini et al. (2012) J Clim, 25, 625-637.\cr
#'An example of how the function can be used in hurricane forecast studies 
#'  is given in\cr
#'Caron, L.-P. et al. (2014) Multi-year prediction skill of Atlantic hurricane 
#'  activity in CMIP5 decadal hindcasts. Climate Dynamics, 42, 2675-2690. 
#'  doi:10.1007/s00382-013-1773-1.
#'@author History:\cr
#'0.1  -  2015-11  (Louis-Philippe Caron)  -  Original code 
#'@examples
#'# Let AtlAno represents 5 different 5-year forecasts of seasonally averaged 
#'# Atlantic sea surface temperature anomalies.
#'AtlAno <- matrix(c(-0.31, -0.36, 0.26, -0.16, -0.16, 
#'                   -0.06, -0.22, -0.31, -0.36, -0.39, 
#'                    0.20, -0.14, 0.12, 0.22, 0.02,
#'                   -0.28, 0.26, -0.10, 0.18, 0.33, 
#'                    0.45, 0.46, 0.04, 0.12, 0.21), 
#'                    nrow = 5, ncol = 5)
#'# Let TropAno represents 5 corresponding 5-year forecasts of seasonally averaged 
#'# tropical sea surface temperature anomalies.
#'TropAno <- matrix(c(-0.22, -.13, 0.07, -0.16, -0.15,
#'                     0.00,  -0.03, -0.22, -0.13, -0.10,
#'                     0.07, -0.07, 0.17, 0.10, -0.15,
#'                    -0.01, 0.08, 0.07, 0.17, 0.13,
#'                     0.16, 0.15, -0.09, 0.03, 0.27),
#'                     nrow = 5, ncol = 5)
#'# The seasonal average of hurricanes for each of the five forecasted years, 
#'# for each forecast, would then be given by
#'hr_count <- StatSeasAtlHurr(atlano = AtlAno, 
#'                            tropano = TropAno, 
#'                            hrvar = 'HR')
#'print(hr_count$mean)
#'
#'@export
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(NA, dim(atl)), var = array(NA, 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
}