calc_grid_winds | R Documentation |
This function inputs a storm track and a dataset of locations and calculates the
full time series of windspeeds over the course of the storm at each location.
It also returns the distance of the storm from the location at each time
point, as well as the surface wind direction at each time point that the storm
is within the distance specified by max_dist
. It is assumed that the
track data entered has been measured in knots, at 10 m above surface level, and with
1-minute averaging period. The dataset of locations can either be a regularly-spaced
grid or can be the central points of locations, like counties or cities. For counties
in the eastern half of the United States, the county_points
dataset that
comes with the package can be used as the grid_point
input.
calc_grid_winds( hurr_track = stormwindmodel::floyd_tracks, grid_df = stormwindmodel::county_points, tint = 0.25, max_dist = 2222.4 )
hurr_track |
Dataframe with hurricane track data for a single
storm. The dataframe must include columns for date-time (year, month, day,
hour, minute; e.g., "198808051800" for August 5, 1988, 18:00 UTC),
latitude, longitude (in degrees East), and wind speed (in knots). The column
names for each of these must be |
grid_df |
A dataframe of locations at which to calculate wind characteristics.
This dataframe must include columns for latitude and longitude for each
point, and these columns must be named |
tint |
Interval (in hours) to which to interpolate the tracks. The default is 0.25 (i.e., 15 minutes). |
max_dist |
A numeric value giving (in kilometers) the maximum distance from the storm's center to model storm-associated winds. Any value beyond this distance will be automatically set to 0 m / s for storm-associated winds. The default value is 2222.4 km (1200 nautical miles). |
An array with three elements, the first with modeled wind speeds, the second with distance of the storm from the location, at the third with the angle of surface winds. Each of the elements is a matrix, where the rows give time points over the course of the storm and the columns give each of the locations. By extracting a column from one of the matrices, you can get the time series at that location over the course of the storm (for example, by extracting a column from the first element, you can get a time series of windspeed at that location over the course of the storm).
This function can take a few minutes to run, depending on the number of locations that are being modeled.
library(tibble) library(ggplot2) library(lubridate) data("floyd_tracks") data("county_points")
floyd_winds <- calc_grid_winds(hurr_track = floyd_tracks, grid_df = county_points)
dare_county_fips <- "37055" dare_floyd_winds <- floyd_winds[["vmax_sust"]][ , dare_county_fips] enframe(name = "timepoint", value = "sustained_wind") ggplot(dare_floyd_winds, aes(x = ymd_hms(timepoint), y = sustained_wind)) + geom_line()
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