This package can be used to explore and map data from NOAA’s Storm Events Database. This storm event database is maintained by NOAA’s National Centers for Environmental Information and aims to provide information, including estimates of damage and human health impacts, for severe storm events that affect the U.S. It has aggregated storm event listings for tornadoes since the 1950s and for a broad range of event types (e.g., snow storms, heat waves, droughts, wildfires, floods) since 1996. This database has been used either alone or in conjunction with other data for a number of scientific studies. It is available for downloading at https://www.ncdc.noaa.gov/stormevents/ftp.jsp, with three files (one with event details, one with fatality details, and with with location details) available per year.
While the online database does not have a structured API, this package uses regular expressions to search the listings of available files to identify the filename for a queried year and download that year’s data to a user’s R session. The package functions then filter the downloaded storm event listings based to the dates, locations, event types, and other search limitations specified by the user. In particular, this package can be used to identify storm event listings that were close in location and time to Atlantic basin tropical storm tracks.
The package has two main functions:
find_events: Create a dataframe with event listings by county based on user-specified parameters (e.g., date range, specific tropical storm, type of event) from the NOAA Storm Events databasemap_events: Create a map showing the geographic distribution by county of certain features of an event’s dataframe (e.g., whether or not a county had an event, number of events per county, total property damage in a county across event listings).
While this package aims to create and map events data by county, some events are listed in the original database by forecast zone rather than county. For these observations, the package functions attempt to match the observation with the appropriate county. However, there are some events listed by forecast zone that cannot be matched to county, and so are excluded from results returned by this package. Further, there may be occasional errors in this matching, so we include a marker returned datasets indicating which events were listed by forecast zone rather than county, to allow further quality control checks by the user for his or her specific applications of the package.
The noaastormevents package includes options that allow users to find
events based on proximity to a tropical storm. To use this
functionality, the user must have the hurricaneexposuredata package,
available from a drat repository, installed locally. This package can be
installed by running:
library(drat)
addRepo("geanders")
install.packages("hurricaneexposuredata")It is important for users to note that there are limitations to this
storm events database. In particular, listings can be somewhat
subjective. A lack of event listing in the database should not be
considered definitive proof that storm conditions did not exist at a
location at a certain time. Further, the database has changed over time
in terms of which types of events are included. R users should review
the Storm Event Database’s documentation, which is available at the
database’s website, to be sure
they understand how to use and interpret event listings from the
database. Further, the noaastormevents package includes a “Details”
vignette with more details on this storm events data and how it is
processed by functions in this package.
The package has two main functions. First, the find_events function
can be used to create a dataframe with all storm event listings within a
specified time frame. For example, the following code creates a
dataframe with all events from the NOAA Storm Events Database listed as
beginning between September 14 and 18, 1999 (a time window relevant for
Hurricane Floyd, which caused extensive damage, especially from
flooding):
sept_1999_events <- find_events(date_range = c("1999-09-14", "1999-09-18"))
head(sept_1999_events)
#> # A tibble: 6 x 14
#> begin_date end_date state cz_type cz_name event_type source injuries_direct
#> <date> <date> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1999-09-14 1999-09-14 Flor… C Duval Thunderst… TRAIN… 0
#> 2 1999-09-14 1999-09-14 Flor… C St. Jo… Thunderst… TRAIN… 0
#> 3 1999-09-14 1999-09-14 Ariz… C Marico… Hail OFFIC… 0
#> 4 1999-09-14 1999-09-14 Ariz… C Marico… Hail TRAIN… 0
#> 5 1999-09-14 1999-09-14 Ariz… C Pinal Thunderst… NEWSP… 0
#> 6 1999-09-14 1999-09-14 Ariz… C Marico… Lightning EMERG… 1
#> # … with 6 more variables: injuries_indirect <int>, deaths_direct <int>,
#> # deaths_indirect <int>, damage_property <dbl>, damage_crops <dbl>,
#> # fips <dbl>The code call returns a data frame with a subset of data from the Storm Events Database for 1999. Each event listing with a start data between September 14 and 18 is included. The data frame has the following columns:
begin_date: The date the event beganend_date: The date the event endedstate: The state in which the event occurredcz_type: Whether the event was listed by county or by forecast zone (Where possible, events that are listed by forecast zone are linked to the appropriate county FIPS code and therefore not excluded from this returned dataframe. However, this column is included to allow users to perform quality control on events listed by forecast zone (CZ_TYPEof “Z”).)cz_name: The name of the county (or other area name) in which the event occurred.event_type: Event type (e.g., “Flood”, “Lightning”, “Tornado”, “Wildfire”). See the NOAA Storm Events documentation for definitions of these event typessource: The source of the storm event listing (e.g., trained spotter, emergency manager, general public, law enforcement)injuries_direct,injuries_indirect,deaths_direct,deaths_indirect,damage_property,damage_crops: Estimates of damage from the event to human health, property, and crops. For damages, initial values in the database (e.g.,"5K") have been converted where possible to numeric values (e.g.,5000). See the “Details” vignette for more details on this process.fips: Five-digit county (Federal Information Processing Standard) FIPS code. This code uniquely identifies each U.S. county. If the event was reported by forecast zone (cz_typeofZ), code within the package has used regular expressions to try to correctly match the area name to a county FIPS (see the “Details” vignette for more details on this process).
In some cases, a user may wish to identify all storm events listings
that were close in time and place to a tropical storm track, which can
be done using the storm option of the find_events function. To do
this, the package draws on data and functions in the packages
hurricaneexposure (available on CRAN) and hurricaneexposuredata
(available through a drat repository) to match storm event listings
against tropical storm “best tracks” data through the
hurricaneexposuredata and hurricaneexposure packages.
Event listings are pulled for all events that occurred within a five-day
window of the day the storm was closest to each county and that were in
counties within a user-specified distance of the storm track. For
example, to create a dataset with all event listings for counties within
300 kilometers (dist_limit option) of the path of Hurricane Floyd
(storm = "Floyd-1999") for a five-day window of the storm’s closest
approach to each county, the user can run:
floyd_events <- find_events(storm = "Floyd-1999", dist_limit = 300)
head(floyd_events)
#> # A tibble: 6 x 16
#> begin_date end_date state cz_type cz_name event_type source injuries_direct
#> <date> <date> <chr> <chr> <chr> <chr> <chr> <int>
#> 1 1999-09-13 1999-09-14 Flor… Z Coasta… Hurricane… NEWSP… 0
#> 2 1999-09-13 1999-09-14 Flor… Z Coasta… Hurricane… NEWSP… 0
#> 3 1999-09-14 1999-09-14 Flor… C Duval Thunderst… TRAIN… 0
#> 4 1999-09-14 1999-09-14 Flor… C St. Jo… Thunderst… TRAIN… 0
#> 5 1999-09-14 1999-09-14 Flor… C Palm B… Tornado EMERG… 0
#> 6 1999-09-14 1999-09-14 Nort… C Martin Flash Flo… EMERG… 0
#> # … with 8 more variables: injuries_indirect <int>, deaths_direct <int>,
#> # deaths_indirect <int>, damage_property <dbl>, damage_crops <dbl>,
#> # fips <dbl>, storm_id <chr>, usa_atcf_id <chr>Note that the storm ID includes a storm name (“Floyd”) and year
(“1999”). Both must be specified, as storm names are not retired until
they are used for a very severe storm. This functionality will only work
for storms included in the hurricaneexposuredata package. These
currently include:
| 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 |
Once find_events has been used to create a dataset of storm event
listings, the dataset can be explored. The user can do things like
determine the number of events of each type that occurred near in time
and location to a storm’s track. For example, here is a summary of
numbers of different types of events for Hurricane Floyd, created using
dplyr tools:
library(dplyr)
floyd_events %>%
group_by(event_type) %>%
summarize(n = n()) %>%
arrange(desc(n)) %>%
knitr::kable(col.names = c("Event type", "Number of events"),
caption = "NOAA Storm Events within 200 km and within a 5-day window of Hurricane Floyd.")| Event type | Number of events |
|---|---|
| Flash Flood | 232 |
| High Wind | 157 |
| Hurricane (Typhoon) | 118 |
| Heavy Rain | 28 |
| Strong Wind | 21 |
| Thunderstorm Wind | 19 |
| Tornado | 18 |
| Tropical Storm | 16 |
| Flood | 14 |
| Coastal Flood | 10 |
| Storm Surge/Tide | 5 |
| Funnel Cloud | 2 |
| Waterspout | 1 |
NOAA Storm Events within 200 km and within a 5-day window of Hurricane Floyd.
Similarly, you could create a summary with the states in which the most events were listed and give the number and type of events in each of those counties:
floyd_events %>%
group_by(state, event_type) %>%
summarize(n = n()) %>%
ungroup() %>%
arrange(state, desc(n)) %>%
mutate(event_type = paste0(event_type, " (", n, ")")) %>%
group_by(state) %>%
summarize(Total = sum(n),
Events = paste(event_type, collapse = ", ")) %>%
ungroup() %>%
arrange(desc(Total)) %>%
knitr::kable()
#> `summarise()` regrouping output by 'state' (override with `.groups` argument)
#> `summarise()` ungrouping output (override with `.groups` argument)| state | Total | Events |
|---|---|---|
| North Carolina | 146 | Flash Flood (58), Hurricane (Typhoon) (58), Tornado (17), High Wind (9), Funnel Cloud (2), Thunderstorm Wind (1), Waterspout (1) |
| Virginia | 97 | Flash Flood (57), High Wind (16), Hurricane (Typhoon) (16), Tropical Storm (5), Flood (2), Heavy Rain (1) |
| Pennsylvania | 58 | Flash Flood (28), High Wind (27), Coastal Flood (3) |
| New York | 52 | High Wind (27), Flash Flood (24), Flood (1) |
| New Jersey | 51 | Flash Flood (18), High Wind (18), Coastal Flood (5), Tropical Storm (4), Heavy Rain (3), Hurricane (Typhoon) (3) |
| Maryland | 43 | Flash Flood (15), High Wind (11), Tropical Storm (6), Storm Surge/Tide (5), Hurricane (Typhoon) (4), Heavy Rain (2) |
| Massachusetts | 37 | Strong Wind (12), Heavy Rain (11), High Wind (11), Flash Flood (2), Flood (1) |
| South Carolina | 33 | Thunderstorm Wind (13), Hurricane (Typhoon) (11), High Wind (5), Flash Flood (4) |
| Maine | 23 | High Wind (12), Flash Flood (6), Flood (5) |
| Florida | 21 | Hurricane (Typhoon) (13), Thunderstorm Wind (5), Flash Flood (2), Tornado (1) |
| Connecticut | 18 | Flash Flood (7), Heavy Rain (4), Strong Wind (3), Flood (2), High Wind (2) |
| Vermont | 17 | High Wind (14), Flash Flood (3) |
| Rhode Island | 14 | Heavy Rain (5), Strong Wind (5), Flash Flood (2), High Wind (2) |
| Georgia | 13 | Hurricane (Typhoon) (13) |
| Delaware | 8 | Flash Flood (3), High Wind (3), Coastal Flood (2) |
| New Hampshire | 8 | Flood (3), Flash Flood (2), Heavy Rain (2), Strong Wind (1) |
| District Of Columbia | 2 | Flash Flood (1), Tropical Storm (1) |
The package also has a function, map_events, for mapping several
different features from the storm event dataframes returned by
find_events. This function is meant to provide reasonable defaults for
many maps a user might want to create with this data, but in some cases
might not provide the exact map a user would like to create. In that
case, we recommend the user try the choroplethr package, which can be
used to map US county data as long as the data includes county FIPS
codes, as the dataframes returned by find_events do.
The map_events function allows you to create maps of several features
of the dataframe returned by find_events:
- “any events”: Map whether or not a county had any listed events
- “number of events”: Map the number of events listed for a county
- “direct deaths”: Map the total count of direct deaths across all events listed for a county
- “direct injuries”: Map the total count of direct injuries across all events listed for a county
- “indirect deaths”: Map the total count of direct deaths across all events listed for a county
- “indirect injuries”: Map the total count of direct injuries across all events listed for a county
- “property damage”: Map the total property damage across all events listed for a county
- “crop damage”: Map the total crop damage across all events listed for a county
The maps for property and crop damage use a logarithmic scale, while others use an untransformed scale. Listings for all events within a county in the input dataframe are summed to create county-level values to map. While the function allows mapping indirect injuries and deaths, in practice is seems these are rarely listed in recent Storm Events data, so these may not create informative maps.
The map_events function inputs a dataframe as created by
find_events. For example, which counties had events listed that
started between September 14 and 18, 2009, you can run:
event_data <- find_events(date_range = c("1999-09-14", "1999-09-18"))
map_events(event_data)
Because the first argument of map_events is a dataframe, you can also
use the pipe operator (%>%) to pipe the results of a call to
find_events directly into map_events, as shown in the next example.
The map_events function includes a states option, to allow the user
to limit the map to a subset of states (only states in the continental
US can be included). This option also allows the inputs of “east” (the
default, maps only states in the eastern half of the United States) and
“all” (maps all states in the continental US). To create a map of all
events within 300 km of the track of Hurricane Floyd with start dates in
a five-day window of the storm’s closest date to each county, and to map
all continental states, you can run:
floyd_events <- find_events(storm = "Floyd-1999", dist_limit = 300)
floyd_events %>%
map_events(states = "all")
If you instead would like to limit the map to states along the East Coast and plot the number of reported events per county, you could instead run:
floyd_events %>%
map_events(plot_type = "number of events",
states = c("florida", "georgia", "south carolina", "north carolina",
"virginia", "maryland", "delaware", "pennsylvania",
"new jersey", "new york", "connecticut", "rhode island",
"massachusetts", "vermont", "new hampshire", "maine",
"west virginia"))
The maps for crop and property damage use a logarithmic scale, which typically shows differences in these measurements better than an untransformed scale. For example, here is the call and resulting map to map crop damage associated with Hurricane Floyd in North Carolina, Virginia, and Maryland:
floyd_events %>%
map_events(plot_type = "crop damage",
states = c("north carolina", "virginia", "maryland"))
Property damage can be mapped in a similar way:
floyd_events %>%
map_events(plot_type = "property damage",
states = c("florida", "georgia", "south carolina", "north carolina",
"virginia", "maryland", "delaware", "pennsylvania",
"new jersey", "new york", "connecticut", "rhode island",
"massachusetts", "vermont", "new hampshire", "maine",
"west virginia"))
The track of a tropical storm can be added to a map by specifying the
name of the storm with the storm argument and setting add_tracks to
TRUE. For example, the track of Hurricane Floyd can be added to a map
of crop damage in North Carolina, Virginia, and Maryland from the
example dataframe with the call:
floyd_events %>%
map_events(plot_type = "crop damage",
states = c("north carolina", "virginia", "maryland"),
storm = "Floyd-1999", add_tracks = TRUE)
There are many more details describing how this package works, as well as details on the Storm Events data, in the “Details” vignette that also comes with this package.