Barrier island / Palmico Sound in MOM6

[JessicaMeixner-NOAA]

Description

To address SST issues in the Palmico Sound in MOM6, turn on MOM_porous_barriers in the 1/4 degree MOM6 input.

Solution

See suggestion below.

Alternatives

Also looking into a fix on the DA side.

Related to

Copying relevant parts from @g uillaumevernieres NOAA-EMC/GDASApp#1506 which is addressing issues on the DA side.

MOM6 implemenation (LLM produced, take it with a grain of Salt)

Steps to Configure MOM_porous_barriers in MOM6

Enable the Porous Barrier Module

• Ensure that MOM_porous_barriers is included in your MOM6 configuration. The parameter file for MOM6, typically MOM_input, should have:

USE_POROUS_BARRIERS = True

• This activates the porous barrier code.

Define the Barrier Properties

The porous barriers are usually specified in the porous_barrier_table. This is either included directly in MOM_input or in an external file referenced there.

• Example configuration in MOM_input:

POROUS_BARRIER_TABLE = porous_barrier_table.nc

• This points to a NetCDF file that defines the porous barriers.

Create or Modify the NetCDF Barrier File

• The NetCDF file (porous_barrier_table.nc) contains information such as:
  • Location of the barriers (grid indices)
  • Permeability coefficient (how much flow is allowed through)
  • Orientation (zonal or meridional)
  • Depth dependence

Example Structure of a Porous Barrier File

• The dataset typically contains variables such as:
  • barrier_x, barrier_y → Grid cell indices
  • permeability → Values between 0 (solid barrier) and 1 (fully open)
  • depth_range → Depth range of the porous barrier
• Example using Python (xarray):

import xarray as xr
import numpy as np

# Define barrier properties
barriers = {
    "barrier_x": np.array([30, 31, 32]),  # X-indices of barrier
    "barrier_y": np.array([50, 50, 50]),  # Y-indices
    "permeability": np.array([0.2, 0.5, 0.8]),  # Partial permeability
    "depth_range": np.array([500, 1000, 1500]),  # Depth of influence
}

# Create dataset
ds = xr.Dataset(
    {key: (("barrier",), value) for key, value in barriers.items()},
    coords={"barrier": np.arange(len(barriers["barrier_x"]))},
)

# Save to NetCDF
ds.to_netcdf("porous_barrier_table.nc")