The stormwindmodel package was created to allow users to model wind
speeds at grid points in the United States based on “best tracks”
hurricane tracking data, using a model for wind speed developed by
Willoughby and coauthors (2006). The package includes functions for
interpolating hurricane tracks and for modeling and mapping wind speeds
during the storm. It includes population mean center locations for all
U.S. counties, which can be used to map winds by county; however, other
grid point locations can also be input for modeling. Full details on how
this model is fit are provided in the “Details” vignette of the
stormwindmodel package.
This package is currently in development on GitHub. You can install it
using the install_github function from the devtools package using:
devtools::install_github("geanders/stormwindmodel", build_vignettes = TRUE)For examples, the package includes data on the tracks of Hurricane Floyd in 1999 and Hurricane Katrina in 2005. You can load these example best tracks data sets using:
library(stormwindmodel)
data("floyd_tracks")
head(floyd_tracks)
#> # A tibble: 6 x 4
#> date latitude longitude wind
#> <chr> <dbl> <dbl> <dbl>
#> 1 199909071800 14.6 -45.6 25
#> 2 199909080000 15 -46.9 30
#> 3 199909080600 15.3 -48.2 35
#> 4 199909081200 15.8 -49.6 40
#> 5 199909081800 16.3 -51.1 45
#> 6 199909090000 16.7 -52.6 45
data("katrina_tracks")
head(katrina_tracks)
#> # A tibble: 6 x 4
#> date latitude longitude wind
#> <chr> <dbl> <dbl> <dbl>
#> 1 200508231800 23.1 -75.1 30
#> 2 200508240000 23.4 -75.7 30
#> 3 200508240600 23.8 -76.2 30
#> 4 200508241200 24.5 -76.5 35
#> 5 200508241800 25.4 -76.9 40
#> 6 200508250000 26 -77.7 45This example data includes the following columns:
date: Date and time of the observation (in UTC)latitude,longitude: Location of the storm at that timewind: Maximum wind speed at that time (knots)
You can input other storm tracks into the wind modeling functions in the
stormwindmodel package, but you must have your storm tracks in the
same format as these example dataframes and with these columns names to
input the tracks to the functions in stormwindmodel. If necessary, use
rename from dplyr to rename columns and convert_wind_speed from
weathermetrics to convert windspeed into knots.
The stormwindmodel package also includes a dataset with the location
of the population mean center of each U.S. county (county_points).
This dataset can be used as the grid point inputs if you want to model
storm-related winds for counties. These counties are listed by Federal
Information Processing Standard (FIPS) number, which uniquely identifies
each U.S. county. This dataset comes from the US Census file of county
population mean center
locations,
as of the 2010 Census.
data(county_points)
head(county_points)
#> gridid glat glon
#> 1 01001 32.50039 -86.49416
#> 2 01003 30.54892 -87.76238
#> 3 01005 31.84404 -85.31004
#> 4 01007 33.03092 -87.12766
#> 5 01009 33.95524 -86.59149
#> 6 01011 32.11633 -85.70119You can use a different dataset of grid points to model winds at other
U.S. locations, including across evenly spaced grid points. However, you
will need to include these grid points in a dataframe with a similar
format to this example dataframe, with columns for each grid point id
(gridid— these IDs can be random but should be unique across grid
points), and glat and glon for latitude and longitude of each grid
point.
The main function of this package is get_grid_winds. It inputs storm
tracks for a tropical cyclone (hurr_track) and a dataframe with grid
point locations (grid_df). It models winds during the tropical storm
at each grid point and outputs summaries of wind during the storm at
each grid point from the storm. The wind measurements generated for each
grid point are:
vmax_gust: Maximum 10-m 1-minute gust wind experienced at the grid point during the stormvmax_sust: Maximum 10-m 1-minute sustained wind experienced at the grid point during the stormgust_dur: Duration gust wind was at or above a specified speed (default is 20 m/s), in minutessust_dur: Duration sustained wind was at or above a specified speed (default is 20 m/s), in minutes
To get modeled winds for Hurricane Floyd at U.S. county centers, you can run:
floyd_winds <- get_grid_winds(hurr_track = floyd_tracks,
grid_df = county_points)
#> Warning: `mutate_()` is deprecated as of dplyr 0.7.0.
#> Please use `mutate()` instead.
#> See vignette('programming') for more help
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `select_()` is deprecated as of dplyr 0.7.0.
#> Please use `select()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> Warning: `summarise_()` is deprecated as of dplyr 0.7.0.
#> Please use `summarise()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
floyd_winds %>%
dplyr::select(gridid, vmax_gust, vmax_sust, gust_dur, sust_dur) %>%
slice(1:6)
#> gridid vmax_gust vmax_sust gust_dur sust_dur
#> 1 01001 2.971364 1.994204 0 0
#> 2 01003 1.958180 1.314215 0 0
#> 3 01005 4.806562 3.225880 0 0
#> 4 01007 2.309274 1.549848 0 0
#> 5 01009 2.600039 1.744992 0 0
#> 6 01011 4.077514 2.736587 0 0If you use the coutny_points data that comes with the package for the
grid_df argument, you will model winds for county centers. In this
case, the gridid is a county FIPS, and the stormwindmodel package
has a function called map_wind for mapping the estimated winds for
each county. By default, it maps the maximum sustained wind in each
county during the storm in meters per second.
map_wind(floyd_winds)
You can input the track for any Atlantic Basin tropical storm into
get_grid_winds, as long as you convert it to meet the following format
requirements:
- Is a dataframe of class
tbl_df(you can use thetbl_dffunction fromdplyrto do this) - Has the following columns:
date: A character vector with date and time (in UTC), expressed as YYYYMMDDHHMM.latitude: A numeric vector with latitude in decimal degrees.longitude: A numeric vector with longitude in decimal degrees.wind: A numeric vector with maximum storm wind speed in knots
For the grid point locations at which to model, you can input a
dataframe with grid points anywhere in the eastern half of the United
States. For example, you may want to map wind speeds for Hurricane
Katrina by census tract in Orleans Parish, LA. The following code shows
how a user could do that with the stormwindmodel package.
First, the tigris package can be used to pull US Census tract
shapefiles for a county. You can use the following code to pull these
census tract file shapefiles for Orleans Parish in Louisiana:
library(tigris)
new_orleans <- tracts(state = "LA", county = c("Orleans"),
class = "sp") This shapefile gives the polygon for each census tract. You can use the
gCentroid function from the rgeos package to determine the location
of the center of each census tract:
library(rgeos)
new_orleans_tract_centers <- gCentroid(new_orleans, byid = TRUE)@coords
head(new_orleans_tract_centers)
#> x y
#> 1 -89.95393 30.04011
#> 2 -89.91693 30.03769
#> 30 -90.01988 29.95959
#> 31 -90.07362 29.97811
#> 32 -90.12008 29.91933
#> 46 -90.08967 29.94482With some cleaning, you can get this data to the format required for the
get_grid_winds function. In particular, you should add the tract id
from the original shapefiles as the grid id, as this will help you map
the modeled wind results:
new_orleans_tract_centers <- new_orleans_tract_centers %>%
tbl_df() %>%
mutate(gridid = unique(new_orleans@data$TRACTCE)) %>%
dplyr::rename(glat = y,
glon = x)
#> Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
head(new_orleans_tract_centers)
#> # A tibble: 6 x 3
#> glon glat gridid
#> <dbl> <dbl> <chr>
#> 1 -90.0 30.0 001747
#> 2 -89.9 30.0 001750
#> 3 -90.0 30.0 000800
#> 4 -90.1 30.0 003600
#> 5 -90.1 29.9 011400
#> 6 -90.1 29.9 008600Here is a map of the census tracts, with the center point of each shown
with a red dot (note that an area over water is also included– this is
included as one of the census tract shapefiles pulled by tigris for
Orleans Parish):
library(sf)
new_orleans <- new_orleans %>%
st_as_sf()
new_orleans_centers <- new_orleans_tract_centers %>%
st_as_sf(coords = c("glon", "glat")) %>%
st_set_crs(4269)
library(ggplot2)
ggplot() +
geom_sf(data = new_orleans) +
geom_sf(data = new_orleans_centers, color = "red", size = 0.6)
Since the new_orleans_tract_centers is now in the appropriate format
to use with the stormwindmodel functions, you can input it directly
into get_grid_winds to model the winds from Hurricane Katrina at each
census tract center:
new_orleans_tracts_katrina <- get_grid_winds(hurr_track = katrina_tracks,
grid_df = new_orleans_tract_centers)
head(new_orleans_tracts_katrina)
#> glon glat gridid vmax_gust vmax_sust gust_dur sust_dur
#> 1 -89.95393 30.04011 001747 62.91971 42.22799 1110 690
#> 2 -89.91693 30.03769 001750 65.34514 43.85580 1110 690
#> 3 -90.01988 29.95959 000800 59.41499 39.87583 1110 675
#> 4 -90.07362 29.97811 003600 56.61081 37.99383 1110 675
#> 5 -90.12008 29.91933 011400 54.80164 36.77962 1110 690
#> 6 -90.08967 29.94482 008600 55.98677 37.57502 1095 675To plot these modeled winds, you can merge this modeled data back into the “sf” version of the census tract shapefile data, joining by census tract identification, and then add to the map. You can show wind speed in this map with color.
new_orleans <- new_orleans %>%
left_join(new_orleans_tracts_katrina, by = c("TRACTCE" = "gridid"))library(viridis)
ggplot() +
geom_sf(data = new_orleans, aes(fill = vmax_sust)) +
geom_sf(data = new_orleans_centers, color = "red", size = 0.6) +
scale_fill_viridis(name = "Maximum\nsustained\nwinds (m/s)")
There are also functions in this package that you can use to create a time series of all modeled winds at a specific grid point throughout the storm. For example, here is the code to calculate modeled wind at the population mean center of Dare County, NC (FIPS: 37055) throughout Hurricane Floyd:
dare_county <- county_points %>% # Get grid point information for Dare County
filter(gridid == "37055")
with_wind_radii <- floyd_tracks %>%
create_full_track() %>% # Interpolate tracks to every 15 minutes
add_wind_radii() # Calculate required inputs for Willoughby wind model
dare_winds <- calc_grid_wind(grid_point = dare_county, # Model winds at one grid point
with_wind_radii = with_wind_radii)
ggplot(dare_winds, aes(x = date, y = windspeed)) +
geom_line() +
xlab("Observation time (UTC)") +
ylab("Modeled surface wind (m / s)") 
For more details, see the “Details” vignette, which walks through all steps of the modeling process.
There are a number of options when mapping wind speeds using map_wind.
First, you can use the add_storm_track function to add the storm track
to the map. This function inputs one dataframe with tracking data (the
floyd_tracks example data that comes with the package in this case) as
well as the plot object created using map_wind, which is input using
the plot_object argument. In this example code, we’ve first created
the base map of winds by county using map_wind and then input that,
along with Floyd’s track data, into add_storm_track to create a map
with both winds and the storm tracks:
floyd_map <- map_wind(floyd_winds)
add_storm_track(floyd_tracks, plot_object = floyd_map)
You can also choose whether to map sustained or gust winds (value,
which can take “vmax_gust” or “vmax_sust”), as well as the unit to use
for wind speed (wind_metric, which can take values of “mps” [the
default] or “knots”).
map_wind(floyd_winds, value = "vmax_gust", wind_metric = "knots")
Finally, you can map a binary classification of counties with winds at or above a certain break point. For example, to map counties with sustained wind at or above 34 knots during the storm, you can run:
map_wind(floyd_winds, value = "vmax_sust", wind_metric = "knots",
break_point = 34)
You can get an R version of best tracks data for Atlantic basin storms
from 1988 to 2015 through the hurricaneexposuredata package (also in
development on GitHub):
devtools::install_github("geanders/hurricaneexposuredata")Here are all the storms currently included in that dataset:
library(hurricaneexposuredata)
data("hurr_tracks")
hurr_tracks %>%
tidyr::separate(storm_id, c("storm", "year")) %>%
dplyr::select(storm, year) %>%
dplyr::distinct() %>%
dplyr::group_by(year) %>%
dplyr::summarize(storms = paste(storm, collapse = ", ")) %>%
knitr::kable()
#> Warning: Expected 2 pieces. Additional pieces discarded in 27 rows [3313, 3314,
#> 3315, 3316, 3317, 3318, 3319, 3320, 3321, 3322, 3323, 3324, 3325, 3326, 3327,
#> 3328, 3329, 3330, 3331, 3332, ...].
#> `summarise()` ungrouping output (override with `.groups` argument)| year | storms |
|---|---|
| 1988 | Alberto, Beryl, Chris, Florence, Gilbert, Keith, AL13, AL14, AL17 |
| 1989 | Allison, Chantal, Hugo, Jerry |
| 1990 | AL01, Bertha, Marco |
| 1991 | Ana, Bob, Fabian, AL12 |
| 1992 | AL02, Andrew, Danielle, Earl |
| 1993 | AL01, Arlene, Emily |
| 1994 | Alberto, AL02, Beryl, Gordon |
| 1995 | Allison, Dean, Erin, Gabrielle, Jerry, Opal |
| 1996 | Arthur, Bertha, Edouard, Fran, Josephine |
| 1997 | AL01, Ana, Danny |
| 1998 | Bonnie, Charley, Earl, Frances, Georges, Hermine, Mitch |
| 1999 | Bret, Dennis, AL07, Floyd, Harvey, Irene |
| 2000 | AL04, Beryl, AL09, Gordon, Helene, Leslie |
| 2001 | Allison, Barry, Gabrielle, Karen, Michelle |
| 2002 | Arthur, Bertha, Cristobal, Edouard, Fay, Gustav, Hanna, Isidore, Kyle, Lili |
| 2003 | Bill, Claudette, AL07, Erika, Grace, Henri, Isabel |
| 2004 | Alex, Bonnie, Charley, Frances, Gaston, Hermine, Ivan, Jeanne, Matthew |
| 2005 | Arlene, Cindy, Dennis, Emily, Katrina, Ophelia, Rita, Tammy, Wilma |
| 2006 | Alberto, Beryl, Chris, Ernesto |
| 2007 | Andrea, Barry, Erin, Gabrielle, Humberto, Ten, Noel |
| 2008 | Cristobal, Dolly, Edouard, Fay, Gustav, Hanna, Ike, Kyle, Paloma |
| 2009 | One, Claudette, Ida |
| 2010 | Alex, Two, Bonnie, Five, Earl, Hermine, Nicole, Paula |
| 2011 | Bret, Don, Emily, Irene, Lee |
| 2012 | Alberto, Beryl, Debby, Isaac, Sandy |
| 2013 | Andrea, Dorian, Karen |
| 2014 | Arthur |
| 2015 | Ana, Bill, Claudette |
| 2016 | Bonnie, Colin, Eight, Hermine, Julia, Matthew |
| 2017 | Cindy, Emily, Harvey, Irma, Jose, Nate, Philippe |
| 2018 | Alberto, Chris, Florence, Gordon, Michael |
| Two | Twenty |
Willoughby, HE, RWR Darling, and ME Rahn. 2006. “Parametric Representation of the Primary Hurricane Vortex. Part II: A New Family of Sectionally Continuous Profiles.” Monthly Weather Review 134 (4): 1102–20.