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Spherical harmonic transform available in NCL and pyNGL for lat/lon data.
Interpolate EAM native grid output to a (N+1)x2N lat/lon cap grid with TempestRemap’s “highorder” (native SE shape function) algorithm.
For cubed sphere grid with resolution NE, take N>=NE*6 (degrees of freedom, pole to pole)
Need instantaneous vorticity, divergence and PS on the GLL grid (not the PG2) grid. PS is needed if interpolating to pressure levels
in EAM, use: ‘VOR:I','DIV:I','DYN_PS:I'
Need ‘DYN_PS’ instead of ‘PS’ to get PS on the GLL grid instead of the PG2 grid.
For smooth results, average over at least 1 months month (post spinup) with 12h snapshots. Running longer rather than with higher frequency I/O results in smoother spectra in Aqua planet. Are there seasonal effects in Real planet?
At NE256, interpolating and computing the spectra for each snapshot takes about 5min and 30GB of memory. At NE1024 this will probably require ~500GB of memory and could take 320min per snapshot.
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Computing the KE spectra requires performing spherical harmonic transforms. This can be done via the long standing SPHEREPACK Fortran package ( https://www2.cisl.ucar.edu/resources/legacy/spherepack ), which has an easy to use interface in NCL (and pyNGL?as of 2020/7, not available in pyNGL).
SPHEREPACK requires gridded data on either a Gaussian grid or an equal angle lat/lon “cap” grid which includes points at the poles. It relies on a combination of associated Legendre transforms and FFTs. For efficiency the grid should have dimensions N x 2N (Gaussian), or (N+1) x 2N (equal angle cap) with N the product of powers of small primes. The most common lat/lon data is an equal angle offset grid, which avoids pole points (see. CAM-FV Grid Overview ). Data on this offset grid would have to first be interpolated to a cap grid before computing spherical harmonic transforms.
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