temporalBehaviour

Computing and plotting time series of wind speed and direction

We compute and plot time series of the speed and direction of winds generated by the topical cyclone Pam (2015) in two of the main cities of Vanuatu, Port Vila (longitude = 168.33, latitude = -17.73) and Luganville (longitude = 167.17, latitude = -15.53). The coordinates of the two locations of interest are provided in a data frame as follows:

df <- data.frame(x = c(168.33, 167.17), y = c(-17.73, -15.53))
rownames(df) <- c("Port_Vila", "Luganville")

The track data for Pam nearby Vanuatu are extracted as follows:

sds <- defStormsDataset()

## Warning in checkInputsdefStormsDataset(filename, fields, basin, seasons, : No basin argument specified. StormR will work as expected
##              but cannot use basin filtering for speed-up when collecting data

## === Loading data  ===
## Open database... /private/var/folders/v1/6y176c8d2jz06qr8rd3jq_480000gn/T/RtmpxSX6hT/Rinst67575d2534f4/StormR/extdata/test_dataset.nc opened
## Collecting data ...
## === DONE ===

st <- defStormsList(sds = sds, loi = "Vanuatu", names = "PAM", verbose = 0)
plotStorms(st)
points(df$x, df$y, pch = 3, col = c("blue", "red"))
text(df$x, df$y, labels = c("Port Vila", "Luganville"), pos = 2, col = c("blue", "red"), cex = 0.8)

Then the temporalBehaviour() function with the product = "TS" argument is used to compute time series. By default the temporal resolution of is set to 1 hour but can be changed using the tempRes argument. Here we set the temporal resolution to 30 min with tempRes=0.5 as follows:

TS <- temporalBehaviour(st, points = df, product = "TS", tempRes = 0.5, verbose = 0)

With the above specification the temporalBehaviour() function returns a list of two data frames (i.e., one for each location) with the wind speed (“speed”), direction (“direction”), indices of the observations and the date and time of the observation (“isoTimes”).

str(TS)

## List of 1
##  $ PAM:List of 2
##   ..$ Port_Vila :'data.frame':   115 obs. of  4 variables:
##   .. ..$ speed    : num [1:115] NA NA NA NA NA NA NA NA NA NA ...
##   .. ..$ direction: num [1:115] NA NA NA NA NA NA NA NA NA NA ...
##   .. ..$ indices  : chr [1:115] "28" "28.1" "28.2" "28.3" ...
##   .. ..$ isoTimes : chr [1:115] "2015-03-11 21:00:00" "2015-03-11 21:30:00" "2015-03-11 22:00:00" "2015-03-11 22:30:00" ...
##   ..$ Luganville:'data.frame':   115 obs. of  4 variables:
##   .. ..$ speed    : num [1:115] NA NA NA NA NA NA NA NA NA NA ...
##   .. ..$ direction: num [1:115] NA NA NA NA NA NA NA NA NA NA ...
##   .. ..$ indices  : chr [1:115] "28" "28.1" "28.2" "28.3" ...
##   .. ..$ isoTimes : chr [1:115] "2015-03-11 21:00:00" "2015-03-11 21:30:00" "2015-03-11 22:00:00" "2015-03-11 22:30:00" ...

We use the data frame to draw time series plots for wind speed and wind direction as follows:

plot(TS$PAM$Port_Vila$speed, type = "l", ylim = c(0, 60), xlab = "", ylab = "Wind speed (m/s)", axes = FALSE, col = "blue")
points(TS$PAM$Port_Vila$speed, col = "blue", pch = 3)
lines(TS$PAM$Luganville$speed, col = "red")
points(TS$PAM$Luganville$speed, col = "red", pch = 3)
legend("topleft", pch = 3, col = c("blue", "red"), legend = c("Port Vila", "Luganville"), bty = "n")
axis(1, at = seq(1, length(TS$PAM$Port_Vila$speed)), labels = TS$PAM$Port_Vila$isoTimes, las = 2)
axis(2, at = seq(0, 60, 10), labels = seq(0, 60, 10), las = 2)

plot(TS$PAM$Port_Vila$direction, type = "l", ylim = c(0, 360), xlab = "", ylab = "Wind direction (degree north)", axes = FALSE, col = "blue")
points(TS$PAM$Port_Vila$direction, col = "blue", pch = 3)
lines(TS$PAM$Luganville$direction, col = "red")
points(TS$PAM$Luganville$direction, col = "red", pch = 3)
legend("topleft", pch = 3, col = c("blue", "red"), legend = c("Port Vila", "Luganville"), bty = "n")
axis(1, at = seq(1, length(TS$PAM$Port_Vila$speed)), labels = TS$PAM$Port_Vila$isoTimes, las = 2)
axis(2, at = seq(0, 360, 90), labels = seq(0, 360, 90), las = 2)

Maximum sustained wind speed for Port Vila and Luganville can be computed as follows:

max(TS$PAM$Port_Vila$speed, na.rm = TRUE)

## [1] 53.312

max(TS$PAM$Luganville$speed, na.rm = TRUE)

## [1] 22.112

Getting power dissipation index

The power dissipation index is computed using the product = "PDI" argument as follows:

PDI <- temporalBehaviour(st, points = df, product = "PDI", tempRes = 0.5, verbose = 0)
PDI

## $PAM
##     Port_Vila Luganville
## PDI  2101.754    335.194

Getting duration of exposure

The duration of exposure is computed using the product = "Exposure" argument as follows:

exposure_SS <- temporalBehaviour(st, points = df, product = "Exposure", tempRes = 0.5, verbose = 0)
exposure_SS

## $PAM
##                       Port_Vila Luganville
## Min threshold: 18 m/s      23.0         16
## Min threshold: 33 m/s       9.5          0
## Min threshold: 42 m/s       5.0          0
## Min threshold: 49 m/s       2.0          0
## Min threshold: 58 m/s       0.0          0
## Min threshold: 70 m/s       0.0          0

By default, the function returns the duration of exposure (in hours) to wind speeds above the thresholds used by the Saffir-Simpson hurricane wind scale (i.e., 18, 33, 42, 49, 58, and 70 \(m.s^{-1}\)). However, different thresholds can be set using the wind_threshold arguments. We can use the thresholds used by the Australian Bureau of Meteorology to rank tropical cyclones intensity as follow:

wt <- c(17.2, 24.4, 32.5, 44.2, 55.0)
exposure_BOM <- temporalBehaviour(st, points = df, product = "Exposure", tempRes = 0.5, windThreshold = wt, verbose = 0)
exposure_BOM

## $PAM
##                         Port_Vila Luganville
## Min threshold: 17.2 m/s        24       17.5
## Min threshold: 24.4 m/s        16        0.0
## Min threshold: 32.5 m/s        10        0.0
## Min threshold: 44.2 m/s         4        0.0
## Min threshold: 55 m/s           0        0.0