UM Geospatial Digital Special Collections

Citation: NOAA - National Hurricane Center (2014). Miami-Dade SLOSH Cat 4 [gis dataset]. University of Miami GDSC. http://journals.ametsoc.org/doi/abs/10.1175/WCAS-D-14-00049.1. last updated: 2024-10-28 (update frequency: As Needed). Abstract: A storm surge risk map created by NOAA utilizing the Sea, Lake, and Overland Surges from hurricanes (SLOSH) model. Storm surge composites – Maximum Envelopes of Water (MEOWs) and Maximum of MEOWs (MOMs) – are created to assess and visualize storm surge risk under varying conditions. Maximum of MEOWs (MOMs) in the attribute table represents the Maximum Envelopes of Water (MEOWs). MEOWs are created by computing the maximum storm surge resulting from up to 100,000 hypothetical storms simulated through each SLOSH grid of varying forward speed, radius of maximum wind, intensity (Categories 1-5), landfall location, tide level, and storm direction. This SLOSH model is based on category 4 storms and has been clipped to Miami-Dade County. Key words: Florida, Miami-Dade County, MDC, Water, SLOSH, Cat4 Open Geospatial Consortium Standards: raster tiles | raster tile url: https://gdsc.idsc.miami.edu/rastertiles/mdc_2022_slosh_cat4/{z}/{x}/{y}.png | WMS (coming soon) | WFS (coming soon) Analytical Functions: gdsc_zonal_statistics(table=mdc_2022_slosh_cat4,lat=25.718,lng=-80.279,radius=500) ETL documentation

ETL for mdc_2022_slosh_cat4

######### # GDSC ETL is performed in two steps: shell script to ETL data into postgres # and then additional transformations with SQL in postGIS (if needed). #### ######### # Step 1 - bash script (pseudo code) #### # create directory structure and move into it mkdir -p /data/mdc_2022_slosh_cat4/{download,etl} && cd /data/mdc_2022_slosh_cat4 # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # load sea_2022_slosh_cat4 as dependency (see sea_2022_slosh_cat4 for details). # clip raster with geometry as: gdalwarp -cutline PG:"" -csql "SELECT geom FROM fl_2022_tl_cousub WHERE countyfp='086'" -crop_to_cutline -s_srs EPSG:4269 -t_srs EPSG:4269 /data/sea_2022_noaa_slosh/download/US_SLOSH_MOM_Inundation_v3/us_Category4_MOM_Inundation_HIGH.tif download/mdc_2022_slosh_cat4.tif ######### # Step 2 - SQL script (pseudo code): #### # load raster into postGIS with: raster2pgsql -s 4269 -d -C -I mdc_2022_slosh_cat4.tif -F -t 256x256 mdc_2022_slosh_cat4 > load_raster.sql psql < load_raster.sql rm load_raster.sql # render raster tiles mkdir -p /tiles/raster/mdc_2022_slosh_cat4 cd /data/mdc_2022_slosh_cat4/ cat > mdc_2022_slosh_cat4_color_ramp.txt <<_EOF 0 254 252 254 255 10% 234 236 249 255 20% 211 214 245 255 30% 186 192 240 255 40% 153 161 233 255 50% 130 140 227 255 60% 98 111 217 255 70% 70 85 210 255 80% 45 63 207 255 90% 18 38 197 255 100% 6 23 198 255 nv 0 0 0 0 _EOF gdaldem color-relief download/mdc_2022_slosh_cat4.tif mdc_2022_slosh_cat4_color_ramp.txt download/mdc_2022_slosh_cat4_color.tif -alpha gdal2tiles.py -ex --xyz -z 8-16 download/mdc_2022_slosh_cat4_color.tif /tiles/raster/mdc_2022_slosh_cat4/