Comparison using the MOST model setup

Comparison using the MOST model setup#

From the GeoClaw Tsunami Tutorial

See CopalisBeach examples for more about the Copalis Beach location and a list of other examples and tutorials based on this location.

This directory $GTT/CopalisBeach/MOST_comparison contains code to perform a GeoClaw simulation that mimics the setup of the MOST model used by the NOAA Center for Tsunami Research (NCTR), which uses fixed nested grids at 3 resolutions, called the A, B, and C grids.

The Copalis Beach example used in this tutorial has also been explored by the NCTR group using the MOST model, and they provided the grid data that is in the subdirectory MOST_data. Note that these grids are used both as the topography DEMs and as the computational grids in MOST. (Use the fetch_MOST_data.py script to fetch this data.)

The script convert_most_to_asc.py converts these into a standard GeoClaw topofile format for use as topofiles in this example. The setrun.py file specifies 3 levels of fixed refinement with no regridding, and covering the same regions as the A, B, and C grids.

Changes to setrun.py#

The file $GTT/CopalisBeach/exercise1/setrun.py was modified to create the setrun.py file in this directory.

Below are the changes needed to force fixed resolution on the A, B, and C grids.

First we set the domain extent and resolution to match the A grid:

clawdata.lower[0] = -126.       # west longitude
clawdata.upper[0] = -123.75     # east longitude

clawdata.lower[1] = 45.         # south latitude
clawdata.upper[1] = 48.5        # north latitude

# Number of grid cells: Coarsest grid is 15 arcsec (1/240 degree)
clawdata.num_cells[0] =  540
clawdata.num_cells[1] =  840

Next we specify three levels of AMR with the resolutions of the A, B, and C grids:

# max number of refinement levels:
amrdata.amr_levels_max = 3

# List of refinement ratios at each level (length at least mxnest-1)

# Set up for 8 levels here, possibly using fewer:
# dx = dy = 15", 3", 1/3"
refinement_ratios = [5,9]
amrdata.refinement_ratios_x = refinement_ratios
amrdata.refinement_ratios_y = refinement_ratios
amrdata.refinement_ratios_t = refinement_ratios

We also specify that the initial grid refinement determined at time t0 should never be modified (no regridding is done). We also specify that the points flagged for refinement by the flagregions specified below should not be buffered by additional points, so the refinement patches at Levels 2 and 3 should identically agree with the B and C grids at all times:

# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 100000000  # NEVER REGRID

# width of buffer zone around flagged points:
# (typically the same as regrid_interval so waves don't escape):
amrdata.regrid_buffer_width  = 0   # NO BUFFER around Bgrid, Cgrid

# clustering alg. cutoff for (# flagged pts) / (total # of cells refined)
# (closer to 1.0 => more small grids may be needed to cover flagged cells)
amrdata.clustering_cutoff = 0.7 # SHOULDN'T MATTER (only flag in rectangles)

Finally, the flagregions below specify three regions with each spatial_region specified based on the extent of the A, B and C grids:

# ---------------
# REGIONS:
# ---------------

flagregions = rundata.flagregiondata.flagregions  # empty list initially

# MOST A grid: (full computational domain)
flagregion = FlagRegion(num_dim=2)
flagregion.name = 'Region_Agrid'
flagregion.minlevel = 1
flagregion.maxlevel = 1
flagregion.t1 = 0.
flagregion.t2 = 1e9
flagregion.spatial_region_type = 1  # Rectangle
x1,y1 = clawdata.lower
x2,y2 = clawdata.upper
flagregion.spatial_region = [x1,x2,y1,y2]
flagregions.append(flagregion)


# MOST B grid
flagregion = FlagRegion(num_dim=2)
flagregion.name = 'Region_Bgrid'
flagregion.minlevel = 2
flagregion.maxlevel = 2
flagregion.t1 = 0.
flagregion.t2 = 1e9
flagregion.spatial_region_type = 1  # Rectangle
flagregion.spatial_region = [-126.5, -124.1, 47.0, 47.4]
flagregions.append(flagregion)

# MOST C grid
flagregion = FlagRegion(num_dim=2)
flagregion.name = 'Region_Cgrid'
flagregion.minlevel = 3
flagregion.maxlevel = 3
flagregion.t1 = 0.
flagregion.t2 = 1e9
flagregion.spatial_region_type = 1  # Rectangle
flagregion.spatial_region = [-124.25, -124.14, 47.1, 47.18]
flagregions.append(flagregion)
Contents