Downscaling

[uganzedo1974]

Hello, Thanks for your support. Now I have some more questions
I am using ECCORestoring both in the initial conditions and in the forcing for salinity, temperature and u and v. This is the domain
λ1₁, λ1₂ = ( -16, -13) #( 0, 42)
λ₁, λ₂ = ( 344, 347)
φ₁, φ₂ = (27, 30)
and a rat=0.008
My only interest is the most realistic behavior possible near the islands, that is why the resolution is high.
I understand that the sponge_layer would be the most appropriate at the margins of the domain but I have two questions.

1. How can I implement sponge layer in ECCORestoring in the border areas.
2. I understand that the mask behaves the same as the land area.

[glwagner]

To use ECCORestoring, you need to create a mask that returns a number between 0 and 1. It should return "1" in the regions where you would like restoring to occur. Here is the ECCORestoring docstring:

"""
    ECCORestoring([arch=CPU(),]
                  variable_name::Symbol;
                  version=ECCO4Monthly(),
                  dates=all_ECCO_dates(version),
                  dates = all_ECCO_dates(version), 
                  time_indices_in_memory = 2, 
                  time_indexing = Cyclical(),
                  mask = 1,
                  rate = 1,
                  grid = nothing,
                  inpainting = NearestNeighborInpainting(prod(size(metadata))),
                  cache_inpainted_data = true)

Create a forcing term that restores to values stored in an ECCO field time series.
The restoring is applied as a forcing on the right hand side of the evolution equations calculated as

F = mask ⋅ rate ⋅ (ECCO_variable - simulation_variable[i, j, k])

where `ECCO_variable` is linearly interpolated in space and time from the ECCO dataset of choice to the 
simulation grid and time.

Arguments
=========

- `arch`: The architecture. Typically `CPU()` or `GPU()`. Default: `CPU()`.

- `variable_name`: The name of the variable to restore. Choices include:
  * `:temperature`,
  * `:salinity`,
  * `:u_velocity`,
  * `:v_velocity`,
  * `:sea_ice_thickness`,
  * `:sea_ice_area_fraction`.

Keyword Arguments
=================

- `version`: The version of the ECCO dataset. Default: `ECCO4Monthly()`.

- `dates`: The dates to use for the ECCO dataset. Default: `all_ECCO_dates(version)`.

- `time_indices_in_memory`: The number of time indices to keep in memory; trade-off between performance
                            and memory footprint.    

- `time_indexing`: The time indexing scheme for the field time series≥

- `mask`: The mask value. Can be a function of `(x, y, z, time)`, an array, or a number.

- `rate`: The restoring rate, i.e., the inverse of the restoring timescale (in s⁻¹).

- `time_indices_in_memory:` how many time instances are loaded in memory; the remaining are loaded lazily.

- `inpainting`: inpainting algorithm, see [`inpaint_mask!`](@ref). Default: `NearestNeighborInpainting(Inf)`.

- `grid`: If `isnothing(grid)`, ECCO data is interpolated on-the-fly to the simulation grid.
          If `!isnothing(grid)`, ECCO data is pre-interpolated to `grid`.
          Default: nothing.

- `cache_inpainted_data`: If `true`, the data is cached to disk after inpainting for later retrieving. 
                          Default: `true`.
                          
It is possible to also pass an `ECCOMetadata` type as the first argument without the need for the 
`variable_name` argument and the `version` and `dates` keyword arguments.
"""

I understand that the mask behaves the same as the land area.

This is not a question --- can you clarify?