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The FOSCAT method, the Copernicus Marine data sources, and the FOSCAT hyperparameters (NORIENT, KERNELSZ, optimiser, iterations) are identical to chain #3; the new variables are (a) the resolution (nside=128 vs nside=32), and (b) the explicit two-arm geometry comparison enabled by the healpix-resample package\'s ellipsoid parameter. As with chain #3, FOSCAT was used via the annefou/FOSCAT@v0.1.0-cpu fork for CPU execution; that CPU patch has since been merged upstream and is included in foscat>=2026.4.1 on PyPI." } } } rows { name { value: "hasDiscipline" } } rows { name { value: "Q1348989" } } rows { quad { p_iri { } o_iri { prefix_id: 12 } } } rows { name { value: "hasMethodologyDescription" } } rows { quad { p_iri { prefix_id: 9 } o_literal { lex: "The pipeline uses Copernicus Marine L3S PMW SST (cmems_obs-sst_glo_phy_l3s_pmw_P1D-m, with cloud gaps) and L4 SST analysis (cmems_obs-sst_glo_phy-temp_nrt_P1D-m, gap-free reference) for 2026-04-01, both at 0.25\302\260 native resolution. The L3S product is regridded onto the L4 grid via nearest-neighbour interpolation. The two products are then independently resampled to HEALPix nside=128 (level=7) under each geometry: standard perfect-sphere (the healpy default) and WGS84 oblate ellipsoid. WGS84 resampling is performed via healpix_resample.GroupByResampler(level=7, reduce=\'mean\', ellipsoid=\'WGS84\'); sphere resampling uses the same package with ellipsoid=\'sphere\' to ensure the implementation difference between the two runs is exactly the geometry argument. Cloud-gap pixels are identified as ocean cells (defined by the L4 mask) without L3S observations. A spherical-harmonic baseline (lmax=60) on the L4 product provides an initial guess for the gaps. FOSCAT scattering synthesis (scat_cov.funct(NORIENT=4, KERNELSZ=3), 300 L-BFGS iterations, CPU) is then run separately under each geometry, using L4\'s scattering coefficients as the statistical reference and a cloud-only mask for gradient updates. 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