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Large Hurricane Analysis - Precipitation

top_15_precipitation.Rmd

Program Background

  • Script name: Large Hurricane Analysis - Precipitation

  • Project name: ADRD and Hurricane Exposure

  • Program description:

    Amount of precipitation was calculated for every day specified within the HURDAT2 plus an additional 2 days after. nClimGrid raster data was used to calculate an areal mean precipitation for all tracts/zctas.

  • Data:

  • Restrictions:

  • Outline of program/analysis:

    • Specify all storms
    • Download and process hurdat2 tracks
    • Download nClimGrid data for duration of storm plus 2 days after
    • Calculate areal mean precipitation

  • Author: Ryan Zomorrodi

  • Revisions

  • Input files

    • All files are downloaded using R functions
    • Cache is stored in raw-data\nClimGrid

  • Output files

    • tract/{storm id}/daily_prcp.csv

    • zcta/{storm id}/daily_prcp.csv

      Column Description
      geoid 2010 geographic identifier
      date Date in UTC
      mean_prcp

      Mean areal precipitation (mm) for the specified geoid and tract

      Note: there may be NAs for a small number of coastline tract which were not entirely covered by nClimGrid

The following hurricanes were selected.

hur_info <- readRDS("raw-data/RF1_Hurricanes_Final.rds")
# get fips codes for each state
fips_codes <- distinct(tigris::fips_codes, state, state_code)
hur_info$fips <- pmap(hur_info, function(states, ...) {
  fips_codes$state_code[match(states, fips_codes$state)]
})

Get hurdat2 info for each storm 

exposhur:::download_newest_hurdat2(path = "raw-data/hurdat2")

hur_info$hurdat2 <- pmap(hur_info, function(hurricane, ...){
  hurdat  <- get_hurdat2(
    storm_id = hurricane,
    path = "raw-data/hurdat2"
  )
  
  list(
    start_date = min(hurdat$datetime),
    end_date = max(hurdat$datetime),
    hurdat = hurdat
  )
})
hur_info <- unnest_wider(hur_info, hurdat2)

Get states 

hur_info$tracts_sf <- pmap(hur_info, function(start_date, fips, ...) {
  tracts_list <- map(fips, 
    ~ tracts(state = .x, year = 2010, cb = TRUE)
  )
  bind_rows(tracts_list) %>%
    mutate(GEOID = str_sub(GEO_ID, -11)) %>% # get tract geoids
    filter(!st_is_empty(.)) %>% # remove tracts with empty geographies
    st_transform(4326) %>% # transform to WGS 84
    rename(geoid = GEOID)
})

hur_info$zctas_sf <- pmap(hur_info, function(start_date, fips, ...) {
  zctas_list <- map(fips, 
    ~ zctas(state = .x, year = 2010, cb = FALSE)
  )
  bind_rows(zctas_list) %>%
    filter(!st_is_empty(.)) %>% # remove zctas with empty geographies
    st_transform(4326) %>%
    rename(geoid = GEOID10)
})

Get nClimGrid 

hur_info$nClimGrid <- pmap(hur_info, function(start_date, end_date, ...) {
  get_nClimGrid(
    interval = interval(start_date, end_date + days(2)),
    path = "raw-data/nClimGrid"
  )["prcp"]
})

Calculate daily precipitation 

hur_info$tract_prcp_daily_avg <- pmap(hur_info, function(nClimGrid, tracts_sf, ...) {
  calc_daily_avgs(
    nClimGrid = nClimGrid,
    geography = tracts_sf,
    geoid = "geoid"
  )
})

pwalk(hur_info, function(tract_prcp_daily_avg, hurricane, ...) {
  write_csv(tract_prcp_daily_avg, str_c("tract/", hurricane, "/daily_prcp.csv"))
})

hur_info$zcta_prcp_daily_avg <- pmap(hur_info, function(nClimGrid, zctas_sf, ...) {
  calc_daily_avgs(
    nClimGrid = nClimGrid,
    geography = zctas_sf,
    geoid = "geoid"
  )
})

pwalk(hur_info, function(zcta_prcp_daily_avg, hurricane, ...) {
  write_csv(zcta_prcp_daily_avg, str_c("zcta/", hurricane, "/daily_prcp.csv"))
})

End Time

end_time <- Sys.time()
cat("End time: ", end_time, "\n")
cat("Duration: ", start_time - end_time, "\n")

Visualizations

Estimated Precipitation (mm)
Using Anderson Definitions (2 days before and 1 day after nearest approach)
Storm Mean SD Min 25% Quantile Median 75% Quantile Max Proportion 75+ mm Distribution
ALLISON-2001 35.554 50.258 0.000 0.000 7.942 62.555 266.911 0.178
CHARLEY-2004 55.196 33.810 0.000 19.547 54.864 70.348 204.354 0.212
FLORENCE-2018 92.329 73.997 6.286 34.434 72.593 127.828 414.180 0.491
FRANCES-2004 101.366 57.784 0.000 59.478 98.321 135.248 277.157 0.683
HARVEY-2017 154.009 203.869 0.000 10.032 78.329 187.278 706.293 0.511
IKE-2008 43.605 72.696 0.000 2.153 13.393 35.817 290.219 0.158
IRENE-2011 121.648 82.651 0.000 46.219 127.422 198.781 367.668 0.665
IRMA-2017 144.078 71.503 3.887 86.598 136.486 192.758 396.242 0.819
IVAN-2004 60.214 49.524 0.000 21.803 42.498 97.653 250.620 0.329
KATRINA-2005 59.749 47.379 0.000 23.230 42.142 90.638 350.554 0.289
MATTHEW-2016 70.584 78.303 0.000 8.935 35.734 109.224 367.656 0.363
MICHAEL-2018 48.546 39.067 0.346 14.675 34.607 82.957 193.468 0.293
RITA-2005 35.737 59.568 0.000 0.000 0.000 45.985 276.335 0.173
SANDY-2012 67.584 47.566 1.198 26.250 56.852 92.555 232.859 0.365
WILMA-2005 54.684 36.029 0.000 27.131 48.037 82.331 160.694 0.281