In mid-2023, the TR “intbilin” algorithm was failing when the source grid was not global (i.e. MPAS ocean grids). This has been resolved in 2024. Here are some tests comparing ESMF’s bilin, TR’s bilin and TR’s intbilin:
maping error for the Y16_32 test function, oEC60to30v3 → ne30pg2 | ||
|---|---|---|
Algorithm | L2 Error | Max Error |
ESMF bilin | 9.698e-04 | 1.931e-03 |
TR bilin | 2.022e-03 | 2.338e-02 |
TR intbilin | 1.334e-02 | 1.206e-01 |
TR aave | 1.260e-02 | 1.189e-01 |
Initially Mark Taylor was concerned that the ESMF bilin had significantly lower errors than TR bilin. But this is due to the fact that ESMF bilin map was produced with the “-i” option, which tells ESMF to ignore cells on the atmosphere grid which are not fully covered by cells on the ocean grid. Without the “-i” option, ESMF 8.2.0 would crash with a segfault. ( ESMF can be run with an extrapolation option, which produces results over the entire atmosphere grid, including all land - not looked at here.). Below is a close up of the error (around the UK) when mapping the Y16_32 test function. Examining individual cells, it appears that for cells where ESMF and TR both produce data, the errors are identical.
Plot of the difference between mapped Y16_32 an exact Y16_32 testfield, on atmosphere grid | |
|---|---|
ESMF bilin Y16_32 error plot |  |
TR bilin Errors away from coastline identical to ESMF bilin errors Large errors are at coastlines, where TR bilin is mapping data to cells ignored by ESMF bilin |  |
TR intbilin Note that TR’s intbilin does have significantly larger errors as compared to bilin intbilin is of interest since it will produce good results when downsampling and upsampling. intbilin appears to have similar accuracy as ‘aave’ for this mesh, but the error appears slightly smoother. |  |
TR aave monotone and conservative 1st order flux map |  |