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2503.20743

Topology of the Polar Vortex and Montana Weather

Joshua Dorrington, Sushovan Majhi, Atish Mitra, James Moukheiber, Demi Qin, Jacob Sriraman, Kristian Strommen

incompletemedium confidence
Category
Not specified
Journal tier
Note/Short/Other
Processed
Sep 28, 2025, 12:56 AM

Audit review

The paper and the model align on methodology and phenomena but both leave key gaps. The paper specifies sliding-window delay embeddings with M=7, tau=1 and 30-point windows for 2015–2020 and uses VR persistent homology, reporting a single winter peak per year and the largest signal in early 2016; it also shows H1 snapshots on 2016-02-01 (two prominent features) and 2016-02-03 (one feature) consistent with a merge event (figures described in the text) . However, the paper does not formalize the persistence-norm time series, uniqueness criteria, or margins, and it contains a small inconsistency between the SW formula (vectors in R^{M+1}) and the phrase “vectors of 7 daily observations” when M=7 . The model proposes an executable pipeline that would confirm exactly these claims and dates (including 2016-02-01 vs 2016-02-03) but remains unexecuted due to missing data and also introduces a mis-specified top-2 count that mixes H0 and H1, whereas the paper’s discussion is explicitly H1-centric; the paper’s abstract even frames “vortex splitting” in terms of the number of distinct persistent H1 features .

Referee report (LaTeX)

\textbf{Recommendation:} major revisions

\textbf{Journal Tier:} note/short/other

\textbf{Justification:}

The study is a clear and engaging application of persistent homology to polar vortex dynamics and matches known 2015/2016 meteorological events. However, crucial methodological specifics (the exact persistence-landscape norm and its construction), quantitative validation (uniqueness of peaks, margins, uncertainty), and fully reproducible code/data are missing. A small inconsistency in embedding dimension needs correction, and the loop-feature discussion should remain H1-specific. Addressing these will substantially improve correctness and impact.