UM Geospatial Digital Special Collections

Citation: NOAA - National Hurricane Center (2014). Miami-Dade SLOSH Cat 1 Vector [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 1 storms and has been clipped to Miami-Dade County. This a vector (polygon) version of the original raster model. Key words: Florida, Miami-Dade County, MDC, Water, SLOSH, Cat1, Vector Open Geospatial Consortium Standards: dynamic vector tiles | | WMS (coming soon) | WFS (coming soon) Analytical Functions: gdsc_get_attributes(tablename=mdc_2022_slosh_cat1_vector,lat=25.718,lng=-80.279,identifier=max_meows,attrs=[max_meows],normal='') gdsc_nearest_neighbor(table=mdc_2022_slosh_cat1_vector,lat=25.718,lng=-80.279,id=max_meows) ETL documentation

ETL for mdc_2022_slosh_cat1_vector

######### # 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_cat1_vector/{download,etl} && cd /data/mdc_2022_slosh_cat1_vector # set update flag based on last update and update frequency do_update=0 if date() < last_update + update_frequency else do_update = 1 # polygonize raster with: gdal_polygonize.py /data/mdc_2022_slosh_cat1/download/mdc_2022_slosh_cat1.tif download/mdc_2022_slosh_cat1_vector.shp Value max_meows ######### # Step 2 - SQL script (pseudo code): #### # load into postGIS with: ogr2ogr -lco GEOMETRY_NAME=geom -f PostgreSQL PG:"" download/mdc_2022_slosh_cat1_vector.shp -nlt multipolygon -nln mdc_2022_slosh_cat1_vector # add local geometry column and reproject existing geometries into local EPSG: SELECT AddGeometryColumn ( 'mdc_2022_slosh_cat1_vector', 'geom_local',2236,'multipolygon',2 ); UPDATE mdc_2022_slosh_cat1_vector SET geom_local=ST_MakeValid(ST_RemoveRepeatedPoints(ST_Transform(ST_Multi(geom),2236))); CREATE INDEX mdc_2022_slosh_cat1_vector_geom_local_idx ON mdc_2022_slosh_cat1_vector USING GIST (geom_local); NOTIFY pgrst, 'reload schema';"