大气与海洋物理

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[1] arXiv:2510.01209 (交叉列表自 physics.geo-ph) [中文pdf, pdf, html, 其他]

标题： 一种用于量化涡旋识别准则中非线性不确定性传播的数学框架 显示英文标题

标题： A Mathematical Framework for Quantifying Nonlinear Uncertainty Propagation in Eddy Identification Criteria

Charlotte Moser, Nan Chen, Stephen Wiggins

主题： 地球物理 (physics.geo-ph) ; 大气与海洋物理 (physics.ao-ph) ; 数据分析、统计与概率 (physics.data-an)

海洋涡旋是旋转的中尺度特征，在海洋运输和混合中起着基础性作用。 涡旋识别依赖于本质上是非线性函数的诊断标准。 然而，由于其湍流特性以及使用稀疏和噪声观测数据，估计海洋流场存在不确定性。 这种不确定性与非线性诊断相互作用，使其量化复杂化，并限制了涡旋识别的准确性。 在本文中，开发了一个数学和计算框架，用于研究涡旋识别，该框架具有解析可处理性。 它旨在解决不确定性如何与涡旋诊断中的非线性相互作用，以及当引入额外的观测信息时，涡旋诊断中的不确定性如何减少的问题。 该框架采用了一个简单的随机模型来模拟流场，以模仿湍流动力学，从而能够对涡旋统计中的不确定性进行闭式求解。 它还利用了一个非线性但解析可处理的数据同化方案来纳入观测数据，促进了对涡旋识别中不确定性减少的研究，这种不确定性通过信息论被严格量化。 应用于广泛使用的涡旋诊断标准Okubo-Weiss（OW）参数，该框架得出了三个关键结果。 首先，闭式公式揭示了尽管流场不确定性是均匀的，OW不确定性却表现出非均匀的空间模式。 其次，它显示了OW期望值的局部极小值（涡旋中心）与其不确定性局部极大值之间的紧密联系。 第三，它揭示了一个实际的信息屏障：诊断中的不确定性减少最终趋于饱和，限制了额外观测的好处。 显示英文摘要

Ocean eddies are swirling mesoscale features that play a fundamental role in oceanic transport and mixing. Eddy identification relies on diagnostic criteria that are inherently nonlinear functions of the flow variables. However, estimating the ocean flow field is subject to uncertainty due to its turbulent nature and the use of sparse and noisy observations. This uncertainty interacts with nonlinear diagnostics, complicating its quantification and limiting the accuracy of eddy identification. In this paper, an analytically tractable mathematical and computational framework for studying eddy identification is developed. It aims to address how uncertainty interacts with the nonlinearity in the eddy diagnostics and how the uncertainty in the eddy diagnostics is reduced when additional information from observations is incorporated. The framework employs a simple stochastic model for the flow field that mimics turbulent dynamics, allowing closed-form solutions for assessing uncertainty in eddy statistics. It also leverages a nonlinear, yet analytically tractable, data assimilation scheme to incorporate observations, facilitating the study of uncertainty reduction in eddy identification, which is quantified rigorously using information theory. Applied to the Okubo-Weiss (OW) parameter, a widely used eddy diagnostic criterion, the framework leads to three key results. First, closed formulae reveal inhomogeneous spatial patterns in the OW uncertainty despite homogeneous flow field uncertainty. Second, it shows a close link between local minima of the OW expectation (eddy centers) and local maxima of its uncertainty. Third, it reveals a practical information barrier: the reduction in uncertainty in diagnostics asymptotically saturates, limiting the benefit of additional observations.

[2] arXiv:2510.02260 (交叉列表自 astro-ph.EP) [中文pdf, pdf, html, 其他]

标题： 用谐波特征映射孤立系外行星类比的云驱动大气动力学与化学 显示英文标题

标题： Mapping the Cloud-Driven Atmospheric Dynamics & Chemistry of an Isolated Exoplanet Analog with Harmonic Signatures

Michael K. Plummer, Francis P. Cocchini, Peter A. Kearns, Allison McCarthy, Étienne Artigau, Nicolas B. Cowan, Roman Akhmetshyn, Johanna Vos, Evert Nasedkin, Channon Visscher, Björn Benneke, René Doyon, Stanimir A. Metchev, Jason F. Rowe, Genaro Suárez

评论： 16页，6图，已提交

主题： 地球与行星天体物理学 (astro-ph.EP) ; 太阳与恒星天体物理学 (astro-ph.SR) ; 大气与海洋物理 (physics.ao-ph) ; 地球物理 (physics.geo-ph)

年轻行星质量天体和棕矮星在L/T光谱过渡附近表现出比场棕矮星更强的光谱测光变异性。 多云区域、极光过程、平流层热点和复杂的碳化学反应都被提出作为这种变异性可能的来源。 使用JWST/NIRISS和NIRSpec仪器收集的时间分辨、低至中等分辨率光谱，我们对SIMP J0136进行谐波分析，SIMP J0136是一个高度可变的、年轻的、孤立的行星质量天体。 在压力水平（> 1 bar）处的奇次谐波（k=3），对应于铁和橄榄石云的形成，表明在多云的、很可能在赤道的区域存在南北半球不对称性。 我们利用推断出的谐波以及一维次恒星大气模型，按大气压力水平映射流量变异性。 这些垂直图显示了深层对流天气层与上覆分层和辐射大气之间强大的相互作用。 我们在近红外中识别出明显的时间变化结构，我们将其解释为行星尺度波（例如罗斯比波或开尔文波）相关的云调制。 我们检测到水（S/N = 14.0）、一氧化碳（S/N = 13.0）和甲烷（S/N = 14.9）分子特征的变异性。 橄榄石云调制与上覆的一氧化碳和水丰度呈负相关，并与深层甲烷吸收相关，这表明云形成、大气化学和温度结构之间的复杂相互作用。 此外，我们识别出甲烷和一氧化碳吸收带之间的显著谐波行为，提供了时间分辨非平衡碳化学的证据。 在最低压力下（< 100 mbar），我们发现映射的甲烷线从吸收转变为发射，支持通过电子沉降的高空极光加热的证据。 显示英文摘要

Young planetary-mass objects and brown dwarfs near the L/T spectral transition exhibit enhanced spectrophotometric variability over field brown dwarfs. Patchy clouds, auroral processes, stratospheric hot spots, and complex carbon chemistry have all been proposed as potential sources of this variability. Using time-resolved, low-to-mid-resolution spectroscopy collected with the JWST/NIRISS and NIRSpec instruments, we apply harmonic analysis to SIMP J0136, a highly variable, young, isolated planetary-mass object. Odd harmonics (k=3) at pressure levels (> 1 bar) corresponding to iron and forsterite cloud formation suggest North/South hemispheric asymmetry in the cloudy, and likely equatorial, regions. We use the inferred harmonics, along with 1-D substellar atmospheric models, to map the flux variability by atmospheric pressure level. These vertical maps demonstrate robust interaction between deep convective weather layers and the overlying stratified and radiative atmosphere. We identify distinct time-varying structures in the near-infrared that we interpret as planetary-scale wave (e.g., Rossby or Kelvin)-associated cloud modulation. We detect variability in water (S/N = 14.0), carbon monoxide (S/N = 13.0), and methane (S/N = 14.9) molecular signatures. Forsterite cloud modulation is anti-correlated with overlying carbon monoxide and water abundances and correlated with deep methane absorption, suggesting complex interaction between cloud formation, atmospheric chemistry, and temperature structure. Furthermore, we identify distinct harmonic behavior between methane and carbon monoxide absorption bands, providing evidence for time-resolved disequilibrium carbon chemistry. At the lowest pressures (< 100 mbar), we find that the mapped methane lines transition from absorption to emission, supporting evidence of high-altitude auroral heating via electron precipitation.

替换提交 (展示 2 之 2 条目 )

[3] arXiv:2507.04802 (替换) [中文pdf, pdf, 其他]

标题： 可解释机器学习在城市热缓解中的应用：多尺度驱动因素的归因与加权 显示英文标题

标题： Interpretable Machine Learning for Urban Heat Mitigation: Attribution and Weighting of Multi-Scale Drivers

David Tschan, Zhi Wang, Jan Carmeliet, Yongling Zhao

主题： 大气与海洋物理 (physics.ao-ph) ; 机器学习 (cs.LG)

城市热岛（UHIs）在热浪（HWs）期间通常会被加剧，并对公共健康构成风险。 缓解城市热岛需要城市规划者首先估算不同土地利用类型（LUTs）和驱动因素如何在不同尺度上影响城市热量——从天气尺度的气候背景过程到小尺度的城市和尺度桥梁特征。 本研究提出将这些驱动因素分别分类为驱动（D）、城市（U）和局部（L）特征。 为了提高可解释性并增强计算效率，提出了一种区分LUT的机器学习方法，作为气象研究与预报模型（WRF）与Noah地表模型（LSM）耦合的快速模拟器，用于预测地表温度（TSK）和2米空气温度（T2）。 使用随机森林回归（RFR）和极端梯度提升（XGB），在2017年和2019年瑞士苏黎世的热浪（HW）期间基于WRF输出进行训练，本研究提出了基于LUT的（LB）模型，这些模型根据尺度和实际可控性对特征进行分类，允许选择性地进行类别加权。 这种方法使得针对特定类别的特征排名以及对T2和TSK对最重要小尺度驱动因素的敏感性估计成为可能——最显著的是表面发射率、反照率和叶面积指数（LAI）。 采用LB框架的模型在统计学上比不采用该框架的模型更准确，当在训练中包含更多HW数据时表现更好。 由于RFR-XGB在单位权重下表现稳健，该方法显著提高了可解释性。 尽管需要减少不确定性并在其他城市测试该方法，但所提出的方法为城市规划者提供了一个以可行性为中心的UHI缓解评估的直接框架。 显示英文摘要

Urban heat islands (UHIs) are often accentuated during heat waves (HWs) and pose a public health risk. Mitigating UHIs requires urban planners to first estimate how urban heat is influenced by different land use types (LUTs) and drivers across scales - from synoptic-scale climatic background processes to small-scale urban- and scale-bridging features. This study proposes to classify these drivers into driving (D), urban (U), and local (L) features, respectively. To increase interpretability and enhance computation efficiency, a LUT-distinguishing machine learning approach is proposed as a fast emulator for Weather Research and Forecasting model (WRF) coupled to the Noah land surface model (LSM) to predict ground- (TSK) and 2-meter air temperature (T2). Using random forest regression (RFR) with extreme gradient boosting (XGB) trained on WRF output over Zurich, Switzerland, during heatwave (HW) periods in 2017 and 2019, this study proposes LUT-based (LB) models that categorize features by scales and practical controllability, allowing optional categorical weighting. This approach enables category-specific feature ranking and sensitivity estimation of T2 and TSK to most important small-scale drivers - most notably surface emissivity, albedo, and leaf area index (LAI). Models employing the LB framework are statistically significantly more accurate than models that do not, with higher performance when more HW data is included in training. With RFR-XGB robustly performing optimal with unit weights, the method substantially increase interpretability. Despite the needs to reduce uncertainties and test the method on other cities, the proposed approach offers urban planners a direct framework for feasibility-centered UHI mitigation assessment.

[4] arXiv:2509.00631 (替换) [中文pdf, pdf, html, 其他]

标题： 利用时间融合变换器从稀疏GNSS数据中预测电离层 显示英文标题

标题： Forecasting the Ionosphere from Sparse GNSS Data with Temporal-Fusion Transformers

Giacomo Acciarini, Simone Mestici, Halil Kelebek, Linnea Wolniewicz, Michael Vergalla, Madhulika Guhathakurta, Umaa Rebbapragada, Bala Poduval, Atılım Güneş Baydin, Frank Soboczenski

主题： 机器学习 (cs.LG) ; 人工智能 (cs.AI) ; 大气与海洋物理 (physics.ao-ph)

电离层对全球导航卫星系统(GNSS)、卫星通信和低地球轨道(LEO)操作具有关键影响，但由于太阳、地磁和热层驱动之间的非线性耦合，其变化的准确预测仍然具有挑战性。总电子含量(TEC)是一个关键的电离层参数，它来自GNSS观测，但其可靠预测受到全球测量稀疏性和经验模型准确性有限的限制，特别是在强烈的空间天气条件下。在本工作中，我们提出了一种用于电离层TEC预测的机器学习框架，该框架利用时间融合变换器(TFT)来预测稀疏的电离层数据。我们的方法适应异构输入源，包括太阳辐射、地磁指数和GNSS导出的垂直TEC，并应用了预处理和时间对齐策略。2010-2025年的实验表明，该模型能够实现最多提前24小时的稳健预测，均方根误差低至3.33 TECU。结果表明，太阳极紫外辐射提供了最强的预测信号。除了预测准确性外，该框架通过基于注意力的分析提供了可解释性，支持实际应用和科学发现。为了鼓励可重复性和社区驱动的发展，我们发布了完整的实现作为开源工具包\texttt{离子学}。 显示英文摘要

The ionosphere critically influences Global Navigation Satellite Systems (GNSS), satellite communications, and Low Earth Orbit (LEO) operations, yet accurate prediction of its variability remains challenging due to nonlinear couplings between solar, geomagnetic, and thermospheric drivers. Total Electron Content (TEC), a key ionospheric parameter, is derived from GNSS observations, but its reliable forecasting is limited by the sparse nature of global measurements and the limited accuracy of empirical models, especially during strong space weather conditions. In this work, we present a machine learning framework for ionospheric TEC forecasting that leverages Temporal Fusion Transformers (TFT) to predict sparse ionosphere data. Our approach accommodates heterogeneous input sources, including solar irradiance, geomagnetic indices, and GNSS-derived vertical TEC, and applies preprocessing and temporal alignment strategies. Experiments spanning 2010-2025 demonstrate that the model achieves robust predictions up to 24 hours ahead, with root mean square errors as low as 3.33 TECU. Results highlight that solar EUV irradiance provides the strongest predictive signals. Beyond forecasting accuracy, the framework offers interpretability through attention-based analysis, supporting both operational applications and scientific discovery. To encourage reproducibility and community-driven development, we release the full implementation as the open-source toolkit \texttt{ionopy}.