In timtrice/Hurricanes: Web Scraper for Atlantic and East Pacific Hurricanes and Tropical Storms

knitr::opts_chunk$set(
    echo = TRUE, 
    fig.width = 7, 
    fig.asp = 1, 
    fig.align = "center"
)

library(dplyr)
library(ggplot2)
library(rrricanes)
#library(sp)

key <- "AL092008"
adv <- 42

fstadv <- fstadv |> filter(StormKey == key, Adv <= adv)

GIS Advisory Forecast Track, Cone of Uncertainty, and Watches/Warnings

gis_adv <- gis_advisory(key = key, advisory = adv) |> 
          gis_download()

Get bounding box of the forecast polygon.

bbox <- bbox(gis_adv$al092008.042_5day_pgn)

Generate a base plot of the Atlantic ocean.

(bp <- al_tracking_chart(color = "black", fill = "white", size = 0.1, res = 50))

I like to add a little cushion for the map inset and forecast cone data.

lat_min <- bbox[2,1] - 5
lat_max <- bbox[2,2] + 5
lon_min <- bbox[1,1] - 10
lon_max <- bbox[1,2] + 10

Build a thin tracking map for the inset.

bp_inset <- ggplotGrob(bp +
            geom_rect(mapping = aes(xmin = lon_min, xmax = lon_max,
       ymin = lat_min, ymax = lat_max),
              color = "red", alpha = 0) +
                           theme_bw() +
                           theme(axis.title = element_blank(),
                                 axis.ticks = element_blank(),
                                 axis.text.x = element_blank(),
                                 axis.text.y = element_blank(),
                                 plot.margin = margin(0, 0, 0, 0, "pt")))

Modify original bp zoomed in on our area of interest.

(bp <- bp +
     coord_equal(xlim = c(lon_min, lon_max),
                 ylim = c(lat_min, lat_max)) +
     scale_x_continuous(expand = c(0, 0)) +
     scale_y_continuous(expand = c(0, 0)) +
     labs(x = "Lon",
          y = "Lat",
          caption = sprintf("rrricanes %s", packageVersion("rrricanes"))))

Combine bp and bp_inset to finalize initial base plot. bp will be a base plot without the inset. bpi will have the inset.

(bpi <- bp + annotation_custom(grob = bp_inset, xmin = lon_max - 5,
                               xmax = lon_max - 1, ymin = -Inf,
                               ymax = lat_min + 5))

Current Advisory Details

Lines and Polygons spatial dataframes can be helpfully converted using shp_to_df. The original spatial dataframes can be plotted directly in ggplot2 but, to my understanding, access to the other variables are not available.

# Convert object SpatialLinesDataframe to dataframe
shp_storm_lin <- shp_to_df(gis_adv$al092008.042_5day_lin)
shp_storm_ww <- shp_to_df(gis_adv$al092008.042_ww_wwlin)
# Convert object SpatialPolygonsDataframe to dataframe
shp_storm_pgn <- shp_to_df(gis_adv$al092008.042_5day_pgn)

Points dataframes can just be converted with tibble::as_data_frame.

# Convert object SpatialPointsDataframe to dataframe
shp_storm_pts <- as_data_frame(gis_adv$al092008.042_5day_pts)

Modify shp_storm_pts$DVLBL with full strings and ordered factor.

shp_storm_pts$DVLBL <- factor(shp_storm_pts$DVLBL, 
                              levels = c("D", "S", "H"), 
                              labels = c("Tropical Depression", 
                                         "Tropical Storm", 
                                         "Hurricane"))

Same with shp_storm_pts$TCWW:

shp_storm_ww$TCWW <- factor(shp_storm_ww$TCWW, 
                            levels = c("TWA", "TWR", "HWA", "HWR"), 
                            labels = c("Tropical Storm Watch", 
                                       "Tropical Storm Warning", 
                                       "Hurricane Watch", 
                                       "Hurricane Warning"))

bpi + geom_polygon(data = shp_storm_pgn, 
                   aes(x = long, y = lat, group = group),
                   alpha = 0.15, fill = "orange") + 
    geom_path(data = shp_storm_lin, aes(x = long, y = lat, group = group)) + 
    geom_point(data = shp_storm_pts, aes(x = LON, y = LAT, fill = DVLBL,
                                         shape = DVLBL, size = MAXWIND)) + 
    geom_path(data = shp_storm_ww, aes(x = long, y = lat, color = TCWW, 
                                       group = group), size = 1) + 
    scale_shape_manual(values = c(21, 21, 21, 21)) + 
    guides(shape = guide_legend(override.aes = list(size = 3)), 
           size = guide_legend(nrow = 1)) + 
    theme(legend.position = "bottom", 
          legend.box = "vertical")

Very often, areas that are under a hurricane watch may also be under a tropical storm warning. The chart above does not show the hurricane watch area.

timtrice/Hurricanes documentation built on Oct. 10, 2023, 8:15 p.m.