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Hello,
My research group is interested in using this method for our ICON (Icosahedral Nonhydrostatic Weather and Climate Model) dataset. If there is an interest, I would like to help out with this process, and we have contacted the MPI ICON developer for additional support. For now, this link shows the license agreement needed to access the source code: https://code.mpimet.mpg.de/projects/iconpublic/wiki/How%20to%20obtain%20the%20model%20code
The text was updated successfully, but these errors were encountered:
Great! To use gcm-filters with output from the ICON model, you'll need to code up a Laplacian kernel for the ICON grid. I recommend porting the Laplacian that exists within ICON rather than trying to develop a new discretization from scratch.
Does ICON use an unstructured grid? If so, we will have to think a little bit about how to integrate it into gcm-filters.
So far all grids are rectangular, locally orthogonal grids, and we leverage xarray which is really great for those kind of grids. I wonder if UXarray could be a way forward, but as far as I know that package is still under development.
The grids in netcdf format store various quantities about the nodes(vertices) and edges, as well as the parent/child relationship of the vertices between grids, and neighboring nodes for a grid.
The laplacian kernel is located in the source code here: [dir]/src/shr_horizontal/mo_math_laplace.f90
Hello,
My research group is interested in using this method for our ICON (Icosahedral Nonhydrostatic Weather and Climate Model) dataset. If there is an interest, I would like to help out with this process, and we have contacted the MPI ICON developer for additional support. For now, this link shows the license agreement needed to access the source code:
https://code.mpimet.mpg.de/projects/iconpublic/wiki/How%20to%20obtain%20the%20model%20code
The text was updated successfully, but these errors were encountered: