Stormwindmodel

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Overview

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)

Package example data

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    45

This example data includes the following columns:

• date: Date and time of the observation (in UTC)
• latitude, longitude: Location of the storm at that time
• wind: 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 -85.70119

You 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.

Basic example

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 storm
• vmax_sust: Maximum 10-m 1-minute sustained wind experienced at the grid point during the storm
• gust_dur: Duration gust wind was at or above a specified speed (default is 20 m/s), in minutes
• sust_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        0

If 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)

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Further functionality

Options for modeling 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 the tbl_df function from dplyr to 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 29.94482

With 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 008600

Here 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)

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Sinc