Probabilistic Storm Surge

library(dplyr)
library(ggplot2)
library(rrricanes)
library(rrricanesdata)
library(sp)

Data

key <- "AL092008"
adv <- 42

fstadv <- fstadv %>% filter(Key == key, Adv <= adv)

Calculate the last date/time of forecast/advisory product and subtract 3 hrs

dt <- (last(fstadv$Date) - (60 * 60 * 3)) %>% 
    strftime(format = "%Y%m%d%H", tz = "UTC")

Wrap gis_download in safely. Not all products will exist for every storm or even every advisory.

I’m downloading the psurge products for 5 feet since there are no other storm surge products available for Ike.

dl <- purrr::safely(.f = gis_download)
gis_surge <- gis_prob_storm_surge(key, products = list("psurge" = c(5)), 
                                  datetime = dt) %>% dl()

## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS
## = dumpSRS, : Discarded ellps NCEP_SPHERE in Proj4 definition: +proj=longlat
## +R=6371200 +no_defs

## Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS
## = dumpSRS, : Discarded datum NCEP_SPHERE in Proj4 definition: +proj=longlat
## +R=6371200 +no_defs

## Warning in showSRID(wkt2, "PROJ"): Discarded ellps NCEP_SPHERE in Proj4
## definition: +proj=longlat +R=6371200 +no_defs +type=crs

## Warning in showSRID(wkt2, "PROJ"): Discarded datum NCEP_SPHERE in Proj4
## definition

## OGR data source with driver: ESRI Shapefile 
## Source: "/tmp/RtmpavP8B6", layer: "al092008_2008091112_gt5"
## with 83 features
## It has 2 fields

if (!is.null(gis_surge$error))
    message(gis_surge$error)

Generate a base plot of the Atlantic ocean.

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

## Regions defined for each Polygons
## Regions defined for each Polygons

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

Since we’re dealing with a polygon shapefile, we can get the bounding box of the dataset.

bbox <- bbox(gis_surge$result$al092008_2008091112_gt5)

Add a little cushion for the map inset.

lat_min <- bbox[2,1] - 2
lat_max <- bbox[2,2] + 2
lon_min <- bbox[1,1] - 4
lon_max <- bbox[1,2] + 4

Build a map 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")))

## Coordinate system already present. Adding new coordinate system, which will replace the existing one.

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)

Convert the SpatialPolygonsDataframe to a dataframe.

shp_storm_surge <- shp_to_df(gis_surge$result$al092008_2008091112_gt5)

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.737571720000005 30.046955109999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.719192500000005 30.234281540000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -96.348724369999999 28.765779500000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.253189090000006 29.984903339999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -90.783256530000003 29.898960110000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.629417419999996 29.864345549999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -90.294624330000005 29.640205380000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -89.751045230000003 30.516399379999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -90.7856369 29.466335300000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -90.656616209999996 29.417226790000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -96.302360530000001 28.686407089999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.505218510000006 29.683414460000002

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -92.575874330000005 29.868747710000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -92.616615300000007 29.801092149999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.460922240000002 29.631181720000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -92.579154970000005 29.709463119999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.626701350000005 29.78854179

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -90.710487369999996 29.014230730000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -95.98847198 28.432285310000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -89.794616700000006 29.835002899999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -91.866325380000006 29.9016552

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -92.386306759999997 29.541282649999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.230262760000002 29.709266660000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.597785950000002 29.522840500000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.368270870000003 29.630216600000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -93.938018799999995 29.422967910000001

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.696296689999997 29.369365689999999

## Warning in RGEOSUnaryPredFunc(spgeom, byid, "rgeos_isvalid"): Ring Self-
## intersection at or near point -94.250823969999999 29.567092899999999

## SpP is invalid

## Warning in rgeos::gUnaryUnion(spgeom = SpP, id = IDs): Invalid objects found;
## consider using set_RGEOS_CheckValidity(2L)

Probability of storm surge greater than five feet.

bpi + geom_point(data = shp_storm_surge, 
                aes(x = long, y = lat, color = ProbSurge05), size = 1) + 
    theme(legend.position = "bottom", 
          legend.box = "vertical") +
    labs(title = "Probabilistic Storm Surge > 5ft", 
         caption = sprintf("rrricanes %s", packageVersion("rrricanes")))

Probabilistic Storm Surge

Tim Trice

June 11, 2017

Data

Probabilistic Storm Surge

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