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Sphere vs flat matched-filter pipelines for marine-heatwave detection on synthetic global SST on HEALPix-NESTED
https://w3id.org/sciencelive/np/RAYkg28oVS9Ns_4iDocjaKYGJXQ3VifTt96CyAy56Qrko/spherical-ml-latitude-invariance-study-2026
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First end-to-end demonstration in this repository of the substrate-dependence with this matched-filter pair; no prior single-paper implementation. The within-discipline failure mode is documented qualitatively in DeepSphere and DLWP-HEALPix follow-ups but not quantified at this granularity with this minimal feature pair.
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Synthetic global SST samples are generated on a 1° lat-lon raster as a cosine-of-latitude baseline plus Gaussian noise, with optional marine-heatwave events injected as fixed-radius spherical caps with a positive temperature anomaly at uniform-random longitude and a chosen latitude band. The sphere-aware pipeline aggregates each sample onto a HEALPix-NESTED grid and applies a sphere-harmonic band-pass matched filter — a high-pass that suppresses the cosine-of-latitude SST baseline followed by a Gaussian beam matched to the cap diameter — then reads out the maximum, mean, and standard deviation of the inverse-spherical-harmonic-transform response field. The lat-lon-flat baseline cross-correlates the same SST anomaly with an equator-shape cap template and reads out the same three statistics. Both feed identical logistic-regression classifier heads. The classifiers are trained on samples with events at low latitudes only and evaluated at four progressively higher test latitude bands. The full pipeline is reproducible via the repository's environment.yml and Snakefile.
https://w3id.org/sciencelive/np/RAYkg28oVS9Ns_4iDocjaKYGJXQ3VifTt96CyAy56Qrko/spherical-ml-latitude-invariance-study-2026
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We test the latitude-invariance aspect of the claim. Scope: the within-discipline regime — same task (binary marine-heatwave detection), same data distribution (synthetic global SST), same training set (events confined to low latitudes only). The variable being swept is the test latitude band: we evaluate the trained classifier at progressively higher latitudes to see whether the substrate-aware pipeline preserves detection accuracy where the lat-lon-flat baseline loses it.
https://w3id.org/sciencelive/np/RAYkg28oVS9Ns_4iDocjaKYGJXQ3VifTt96CyAy56Qrko/spherical-ml-latitude-invariance-study-2026
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Anne Fouilloux
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2026-05-06T20:23:13.819Z
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NP created using Declaring a replication study design according to FORRT
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