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利用随机孪生体解析天气状态维度中的密度和几何结构。

Disentangling density and geometry in weather regime dimensions using stochastic twins.

作者信息

Platzer Paul, Chapron Bertrand, Messori Gabriele

机构信息

Laboratoire d'Océanographie Physique et Spatiale, Univ. Brest/Ifremer/CNRS/IRD, Plouzané, F-29280 France.

Odyssey, Inria/IMT/CNRS, Plouzané, F-29280 France.

出版信息

NPJ Clim Atmos Sci. 2025;8(1):203. doi: 10.1038/s41612-025-01086-w. Epub 2025 May 28.

DOI:10.1038/s41612-025-01086-w
PMID:40452898
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12119342/
Abstract

Large-scale atmospheric variability can be summarized by recurring patterns called weather regimes. Their properties, including predictability, have been studied using the local dimension, a geometrical estimate of degrees of freedom from multifractal theory. Local dimension estimates vary across regimes, decrease when a single regime dominates, and increase during transitions, supporting their dynamical significance. However, these variations stem not only from geometry but also from sampling density. We develop a null-hypothesis test using stochastic twins-Gaussian mixture-based surrogates matching atmospheric sampling density but with constant geometry-applied to ERA5 500 hPa fields. Density effects alone explain over 25% of local dimension variance and reproduce the dimension drop near regime peaks, indicating this behavior is density-driven, not geometric. The remaining variability is plausibly geometry-driven. This approach, applicable to any observed system with known sampling distribution, offers a new framework for interpreting local dimension estimates in atmospheric and oceanic data.

摘要

大规模大气变率可以通过称为天气型的反复出现的模式来概括。其属性，包括可预测性，已使用局部维数进行研究，局部维数是多重分形理论中自由度的几何估计。局部维数估计在不同天气型之间变化，当单一天气型占主导时减小，在转变期间增加，这支持了它们的动力学意义。然而，这些变化不仅源于几何结构，还源于采样密度。我们使用随机孪生——基于高斯混合的替代物开发了一种零假设检验，该替代物匹配大气采样密度但具有恒定的几何结构，并应用于ERA5 500百帕场。仅密度效应就解释了超过25%的局部维数方差，并重现了天气型峰值附近的维数下降，表明这种行为是由密度驱动的，而非几何结构驱动的。其余的变率可能是由几何结构驱动的。这种方法适用于任何具有已知采样分布的观测系统，为解释大气和海洋数据中的局部维数估计提供了一个新框架。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/dafbfddf5a53/41612_2025_1086_Fig8_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/6712399009af/41612_2025_1086_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/e423f64aa1ff/41612_2025_1086_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/0dc62ff3a179/41612_2025_1086_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/7ad66c5747a9/41612_2025_1086_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/dafbfddf5a53/41612_2025_1086_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/f540dd1c37c8/41612_2025_1086_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/e4f1b41d83cf/41612_2025_1086_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/16bf238110be/41612_2025_1086_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/6712399009af/41612_2025_1086_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/e423f64aa1ff/41612_2025_1086_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/0dc62ff3a179/41612_2025_1086_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/7ad66c5747a9/41612_2025_1086_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3ef2/12119342/dafbfddf5a53/41612_2025_1086_Fig8_HTML.jpg

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Chaos. 2025 Apr 1;35(4). doi: 10.1063/5.0250492.
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J Stat Phys. 2025;192(2):34. doi: 10.1007/s10955-025-03416-x. Epub 2025 Feb 15.
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Statistical performance of local attractor dimension estimators in non-Axiom A dynamical systems.非公理A动力系统中局部吸引子维数估计器的统计性能。
Chaos. 2023 Jul 1;33(7). doi: 10.1063/5.0152370.
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Geophys Res Lett. 2019 Jul 16;46(13):7810-7818. doi: 10.1029/2019GL082843. Epub 2019 Jul 9.
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