Extract Storms
The defStormsList() function allows to extract
tropical cyclone track data for a given tropical cyclone or set of
tropical cyclones nearby a given location of interest
(loi). The loi can be defined using a country
name, a specific point (defined by its longitude and latitude
coordinates), or any user imported or defined spatial polygon
shapefiles. By default only observations located within 300 km around
the loi are extracted but this can be changed using the
max_dist argument. Users can also extract tropical cyclones
using the name of the storm or the season
during which it occurred. If both the name and the
season arguments are not filled then the
defStormsList() function extracts all tropical cyclones
since the first cyclonic season in the database. Once the data are
extracted, the plotStorms() function can be used to
visualize the trajectories and points of observation of extracted
tropical cyclones on a map.
In the following example we use the test_dataset
provided with the package to illustrate how cyclone track data can be
extracted and visualised using country and cyclone names, specific point
locations, and polygon shapefiles, as described below.
Getting and ploting tropical cyclone track data
Using country names
We extract data on the tropical cyclone Pam (2015) nearby Vanuatu as follows:
## Warning in checkInputsdefStormsDataset(filename, sep, fields, basin, seasons, : No basin argument specified. StormR will work as expected
## but cannot use basin filtering for speed-up when collecting dataThe defStormsList() function returns a
stormsList object in which the first slot
@data contains a list of Storm objects. With
the above specification the stormsList contains only one
Storm object corresponding to cyclone PAM and the track
data can be obtained using the getObs() function as
follows:
## iso.time lon lat msw scale rmw pres poci
## 1 2015-03-08 12:00:00 168.9000 -7.500000 13 0 93 100400 100500
## 2 2015-03-08 15:00:00 169.0425 -7.652509 14 0 93 100200 100200
## 3 2015-03-08 18:00:00 169.2000 -7.800000 15 0 93 100000 100000
## 4 2015-03-08 21:00:00 169.3850 -7.942489 15 0 93 100000 100000
## 5 2015-03-09 00:00:00 169.6000 -8.100000 15 0 93 100000 100100
## 6 2015-03-09 03:00:00 169.8425 -8.284999 16 0 93 99800 100100The number of observation and the indices of the observations can be
obtained using the getNbObs() and getInObs()
as follows:
## [1] 57## [1] 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47The data can be visualised on a map as follows:
Using a specified point location
We can extract all tropical cyclones near Nouméa (longitude = 166.45, latitude = -22.27) between 2015 and 2021 as follows:
pt <- c(166.45, -22.27)
st <- defStormsList(sds = sds, loi = pt, seasons = c(2015, 2021), verbose = 0)The number, the names, and the season of occurrence of the storms in
the returned stormsList object can be obtained using the
getNbStorms(), getNames(), and
getSeasons() functions as follows:
## [1] 4## [1] "SOLO" "GRETEL" "LUCAS" "NIRAN"## SOLO GRETEL LUCAS NIRAN
## 2015 2020 2021 2021We can plot track data for the topical cyclone Niran only using the
names argument of the plotStorms() function as
follows:
The track data for Niran can also be extracted and stored in a new
object using the getStorm() function as follows:
## [1] "NIRAN"Using a user defined spatial polygon shapefile
We can extract all tropical cyclones that occurred between 2015 and
2021 near the New Caledonia exclusive economic zone using the
eezNC shapefile provided with the StormR
package as follows:
Information about the spatial extent of the track data exaction can
be obtained using the getLOI(), getBuffer(),
and getBufferSize() functions as follows:
LOI <- getLOI(st)
Buffer <- getBuffer(st)
BufferSize <- getBufferSize(st)
terra::plot(Buffer, lty = 3, main = paste(BufferSize, "km buffer arround New Caledonian EEZ", sep = " "))
terra::plot(LOI, add = TRUE)
terra::plot(countriesHigh, add = TRUE)
Using different wind scale
By default the Saffir-Simpson hurricane wind scale (SSHS) is used in
defStormsList() to assign level to storms.
The maximum level reached in the scale for each cyclone can then be
obtained using the getScale() function as follows:
## PAM SOLO ULA WINSTON ZENA UESI GRETEL LUCAS NIRAN
## 6 1 5 6 3 2 2 2 6In this case, the SSHS scale is composed of 6 thresholds resulting in 6 levels spanning from level 0 to level 6.
We can only plot cyclones that reached level 5 and 6 using the
category argument of the plotStorms() function
as follows:
Finally, the user can choose his own scale and associated palette, by
setting the scale and scalePalette inputs in
defStormsList(). In the following example, we use the
Tokyo’s tropical cyclone intensity scale to analyse tropical storm
PAM.
StormR provides default palette and category names:
# Tokyo's tropical cyclone intensity scale
RSMCScale <- c(16.94, 24.44, 32.5, 43.33, 53.61)
sts_jpn <- defStormsList(sds = sds,
loi = "Vanuatu",
names = "PAM",
scale = RSMCScale,
verbose = 0)
plotStorms(sts_jpn)
But you can also easily customize them:
RSMCPalette <- c("#6ec1ea", "#4dffff", "#c0ffc0", "#ffd98c", "#ff738a", "#a188fc")
names(RSMCPalette) <- c("Tropical depression",
"Tropical storm",
"Severe tropical storm",
"Typhoon",
"Very strong typhoon",
"Violent typhoon")
sts_jpn <- defStormsList(sds = sds,
loi = "Vanuatu",
names = "PAM",
scale = RSMCScale,
scalePalette = RSMCPalette,
verbose = 0)
plotStorms(sts_jpn)
Dynamic plot
plotStorms allows the user to dynamically plot tracks
within an interactive map using leaflet library by setting
dynamicPlot to TRUE. Doing so, the user can
explore the map the way he wants and click and each dotted colored
observations to see there informations.