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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
For cubed sphere grid with resolution NE, take N>=NE*6 (degrees of freedom, pole to pole)
Interpolate using TR’s “highorder” algorithm.
Need instantaneous output of (U,V) or (vor,div) on the GLL grid (not the PG2) grid.
For smoother results, need spectra from ~(how many?) snapshots~60 snapshots. 2x per day for 1 month will produce nice results. What about hourly over 3 days?
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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The spectra above from single snaphots have a lot of noise which can be removed by averaging. We next analyze how much averaging is necessary…necessary from an aqua planet NE256 simulation with 1 month of daily snapshots. The legend indicates how many snapshots (spaced 1 day apart) were used when averaging E(k). With 28 snapshots the data is still a touch noisy at the lower frequencies, so we recommend at least 60 snapshots. Need to test the minimum spacing: 60 snapshots from hourly data is probably not as good as 60 snapshots from daily data.
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