Atmospheric and Oceanic Physics

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Showing new listings for Friday, 26 September 2025

New submissions (showing 3 of 3 entries )

[1] arXiv:2509.20365 [cn-pdf, pdf, html, other]

Title: An Analytical and AI-discovered Stable, Accurate, and Generalizable Subgrid-scale Closure for Geophysical Turbulence Show Chinese title

Title: 一种分析和人工智能发现的稳定、准确且可推广的地理物理湍流亚网格闭合方法

Karan Jakhar, Yifei Guan, Pedram Hassanzadeh

Subjects: Atmospheric and Oceanic Physics (physics.ao-ph) ; Machine Learning (cs.LG)

By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics including those of extremes. We also show that the new closure could be derived from a 4th-order truncated Taylor expansion. Prior analytical and AI-based work only found the 2nd-order expansion, which led to unstable LES. The additional terms emerge only when inter-scale energy transfer is considered alongside standard reconstruction criterion in the sparse-equation discovery. Show Chinese abstract

通过结合人工智能和流体物理，我们从少量直接数值模拟（DNS）数据中发现了一个二维湍流的显式闭合表达式。 使用该闭合方法的大涡模拟（LES）准确且稳定，能够再现DNS统计结果，包括极端情况的统计结果。 我们还表明，新的闭合表达式可以从四阶截断的泰勒展开中推导出来。 先前的分析方法和基于人工智能的工作仅找到了二阶展开，这导致了不稳定的LES。 只有在稀疏方程发现中同时考虑尺度间能量传递和标准重构准则时，这些额外项才会出现。

[2] arXiv:2509.20778 [cn-pdf, pdf, other]

Title: Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon Show Chinese title

Title: 印度夏季季风期间高分辨率网格降水数据集与密集雨量计网络的评估

Ajay Bankar, Praveenkumar Venkatesan, Rakesh V, Gaurav Chopra, R I Sujith

Subjects: Atmospheric and Oceanic Physics (physics.ao-ph)

Advancements in remote sensing have led to development of several satellite-derived precipitation products; however, their accuracy must be evaluated before use in scientific and operational studies. This study comprehensively assesses six widely used datasets PERSIANN CCS, CHIRPS, MSWEP, IMERG, AgERA5, and GSMaP ISRO against a dense rain gauge network across Karnataka, a southern Indian state characterized by diverse climatic conditions and complex topography. The analysis focuses on the Indian summer monsoon season for 2011 to 2022. To complement traditional metrics, tools from complex network theory were applied to investigate spatial organization and connectivity patterns of rainfall. A functional climate network approach was used to construct rainfall correlation networks, while event synchronization, a nonlinear measure, quantified the co occurrence of extreme events. Most products reproduced large scale monsoon features, yet their ability to represent intensity categories and extremes varied. GSMaP ISRO showed the highest correlation, lowest bias, and RMSE across subregions, whereas PERSIANN CCS exhibited systematic errors, particularly in Western Ghats, though correlations improved over interior plains. Network-based analysis reaffirmed GSMaP ISROs skill in replicating spatial correlation structures, capturing high coherence in regions dominated by large-scale processes and lower coherence in areas influenced by localized dynamics. The observed rainfall network revealed strong synchronization between the coastal region and central Karnataka, indicating broad spatial co occurrence of extremes, while the Malnad region showed weaker connectivity, suggesting localized events. GSMaP ISRO closely reproduced this degree distribution, reflecting corrections using IMD gridded dataset. Future work should improve sub-daily and localized rainfall estimates, especially in complex terrain. Show Chinese abstract

遥感技术的进步导致了多种卫星衍生降水产品的开发;然而，在科学和业务研究中使用之前，必须评估其准确性。 本研究全面评估了六个广泛使用的数据集PERSIANN CCS、CHIRPS、MSWEP、IMERG、AgERA5和GSMaP ISRO，这些数据集与卡纳塔克邦密集的雨量计网络进行对比，卡纳塔克邦是印度南部一个气候条件多样且地形复杂的州。 分析集中在2011年至2022年的印度夏季季风季节。 为了补充传统指标，应用了复杂网络理论的工具来研究降雨的空间组织和连接模式。 采用功能气候网络方法构建降雨相关网络，而事件同步作为一种非线性度量，量化了极端事件的同时发生。 大多数产品再现了大尺度季风特征，但它们在表示强度类别和极端值方面的能力各不相同。 GSMaP ISRO在子区域中表现出最高的相关性、最低的偏差和RMSE，而PERSIANN CCS在西高止山脉地区表现出系统性误差，尽管在内陆平原的相关性有所提高。 基于网络的分析再次确认了GSMaP ISRO在复制空间相关结构方面的技能，在由大尺度过程主导的区域表现出高一致性，在受局部动力学影响的区域则表现出较低的一致性。 观测到的降雨网络显示沿海地区和中央卡纳塔克邦之间有很强的同步性，表明极端事件的大范围同时发生，而马拉德地区显示出较弱的连接性，表明局部事件。 GSMaP ISRO密切再现了这种度分布，反映了使用IMD网格数据集进行的修正。 未来的工作应改进亚日和局部降雨估计，尤其是在复杂地形中。

[3] arXiv:2509.21312 [cn-pdf, pdf, other]

Title: Air Quality and Greenhouse Gas Emissions Assessment of Data Centers in Texas: Quantifying Impacts and Environmental Tradeoffs Show Chinese title

Title: 德克萨斯州数据中心空气质量与温室气体排放评估：量化影响与环境权衡

Ebrahim Eslami

Subjects: Atmospheric and Oceanic Physics (physics.ao-ph)

This study assesses air quality (AQ) and greenhouse gas (GHG) emissions from the rapid expansion of data centers in Texas, a major hub due to infrastructure, electricity markets, and business conditions. AQ impacts were separated from GHG emissions to clarify sources, regulations, and mitigation strategies. Electricity consumption and cooling systems dominate GHG emissions, with a 10 megawatt data center generating about 37,668 metric tons CO2 annually, while construction materials and IT equipment add substantial embodied emissions. Local AQ impacts, often overlooked, arise from diesel backup generators, construction equipment, and commuting. Generator testing alone can emit about 12 metric tons of NOx annually per facility, worsening ozone issues in regions such as Houston and Dallas-Fort Worth. Mitigation strategies include advanced cooling, renewable energy procurement, cleaner backup power (fuel cells, batteries), sustainable construction, and standardized reporting. ERCOT forecasts project 39 to 78 gigawatts of new data center load by 2030, potentially leading to 170 to 205 million metric tons of annual CO2 emissions. Aggressive adoption of renewables and advanced technologies could cut emissions by 50 to 80 percent, avoiding 85 to 165 million metric tons of CO2. The study identifies research and policy gaps, including the need for cumulative air dispersion modeling, AQ-specific regulations, and mandatory efficiency standards. Findings underscore the importance of aligning Texas digital infrastructure growth with environmental and community health protections. Show Chinese abstract

这项研究评估了德克萨斯州数据中心快速扩张带来的空气质量（AQ）和温室气体（GHG）排放情况，德克萨斯州由于基础设施、电力市场和商业条件成为重要枢纽。 将AQ影响与GHG排放分开，以明确来源、法规和缓解策略。 电力消耗和冷却系统是GHG排放的主要来源，一个10兆瓦的数据中心每年可产生约37,668吨二氧化碳，而建筑原材料和IT设备则带来大量隐含排放。 本地AQ影响常常被忽视，来自柴油备用发电机、施工设备和通勤。 仅发电机测试每家设施每年就可能排放约12吨氮氧化物，加剧休斯顿和达拉斯-沃思堡等地区的臭氧问题。 缓解策略包括先进的冷却技术、可再生能源采购、清洁备用电源（燃料电池、电池）、可持续建筑和标准化报告。 ERCOT预测到2030年，新增的数据中心负荷将达到39至78吉瓦，可能导致每年1.7亿至2.05亿吨二氧化碳排放。 积极采用可再生能源和先进技术可将排放量减少50%至80%，避免8500万至1.65亿吨二氧化碳排放。 该研究指出了研究和政策上的空白，包括需要累积空气扩散模型、针对空气质量的法规以及强制性能效标准。 研究结果强调了将德克萨斯州数字基础设施增长与环境保护和社区健康保护相协调的重要性。

Cross submissions (showing 4 of 4 entries )

[4] arXiv:2509.20422 (cross-list from cs.LG) [cn-pdf, pdf, html, other]

Title: mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations Show Chinese title

Title: mloz：一种用于气候敏感性模拟的高效机器学习臭氧参数化方法

Yiling Ma, Nathan Luke Abraham, Stefan Versick, Roland Ruhnke, Andrea Schneidereit, Ulrike Niemeier, Felix Back, Peter Braesicke, Peer Nowack

Subjects: Machine Learning (cs.LG) ; Atmospheric and Oceanic Physics (physics.ao-ph)

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in standard climate sensitivity simulations, including two-way interactions of ozone with the Quasi-Biennial Oscillation. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With atmospheric temperature profile information as the only input, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4 percent of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON. This highlights the potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, particularly when focusing on climate sensitivity simulations, where ozone trends and variability are known to significantly modulate atmospheric feedback processes. Show Chinese abstract

大气臭氧是太阳辐射的重要吸收体，也是一种重要的温室气体。然而，大多数参与耦合模型比较项目（CMIP）的气候模型仍然缺乏对臭氧的交互表示，这是由于大气化学方案的计算成本较高。在这里，我们引入了一种机器学习参数化方法（mloz），以在标准气候敏感性模拟中交互地模拟对流层和平流层的日臭氧变化和趋势，包括臭氧与准两年振荡的双向相互作用。我们在十年时间尺度上证明了其高保真度，并且在两个不同的气候模型——英国地球系统模型（UKESM）和德国icosahedral非静力（ICON）模型中在线灵活使用。仅以大气温度廓线信息作为输入，mloz的臭氧预测速度比UKESM中的化学方案快约31倍，其贡献的总气候模型运行时间不到4%。特别是，我们还通过将参数化方法从UKESM转移到ICON，证明了其在没有化学方案的不同气候模型中的可转移性。这突显了在缺乏交互化学的CMIP级别气候模型中广泛采用的潜力，特别是在关注气候敏感性模拟时，臭氧趋势和变化已知会显著调节大气反馈过程。

[5] arXiv:2509.20561 (cross-list from eess.SY) [cn-pdf, pdf, html, other]

Title: Adaptive Altitude Control of a Tethered Multirotor Autogyro under Varying Wind Speeds using Differential Rotor Braking Show Chinese title

Title: 基于差动旋翼制动的变风速下系留多旋翼自转旋翼机自适应高度控制

Tasnia Noboni, Tuhin Das

Subjects: Systems and Control (eess.SY) ; Atmospheric and Oceanic Physics (physics.ao-ph)

A tethered multirotor autogyro can function as an unmanned aerial vehicle for energy-efficient and prolonged deployment, as it uses the available wind energy to sustain flight. This article presents an adaptive altitude control strategy for such a device. At a constant wind speed, the equilibrium altitude can be approximated by a quadratic function of the pitch angle. The proposed adaptive control estimates the coefficients of this quadratic function. The estimates are used for altitude control and to attain the maximum altitude (and minimum horizontal drift) for a given wind speed. A feedback controller based on regenerative differential rotor braking is used as the actuation to modulate the autogyro's pitch angle. Implementation of the controller using a control-oriented, higher-order dynamic model demonstrates the controller's capability to regulate the altitude and maintain stable flights under varying wind speeds. Based on the system's maximum altitude tracking performance, the adaptive control is adjusted to improve performance under substantial changes in wind speeds. Show Chinese abstract

系留多旋翼自转旋翼机可以作为无人飞行器用于节能和长时间部署，因为它利用可用的风能来维持飞行。 本文提出了一种针对此类装置的自适应高度控制策略。 在恒定风速下，平衡高度可以近似为俯仰角的二次函数。 所提出的自适应控制估计该二次函数的系数。 这些估计用于高度控制，并在给定风速下实现最大高度（和最小水平漂移）。 基于再生差分旋翼制动的反馈控制器用作执行机构，以调节自转旋翼机的俯仰角。 使用面向控制的高阶动态模型实现控制器，展示了控制器在不同风速下调节高度和保持稳定飞行的能力。 基于系统的最大高度跟踪性能，自适应控制被调整以在风速大幅变化时提高性能。

[6] arXiv:2509.20966 (cross-list from astro-ph.GA) [cn-pdf, pdf, html, other]

Title: Vacuum Ultraviolet Photoabsorption Spectra of Icy Isoprene and its Oligomers Show Chinese title

Title: 真空紫外光吸收光谱研究冰态异戊二烯及其低聚物

R Ramachandran, S Pavithraa, J K Meka, K K Rahul, J -I Lo, S -L Chou, B -M Cheng, B N Rajasekhar, Anil Bhardwaj, N J Mason, B Sivaraman

Comments: 4 pages, 4 figures

Journal-ref: Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 268 (2022), 120586

Subjects: Astrophysics of Galaxies (astro-ph.GA) ; Atmospheric and Oceanic Physics (physics.ao-ph) ; Chemical Physics (physics.chem-ph)

Isoprene and its oligomers, terpenes, are expected to be present, along with other complex organic molecules, in the diverse environments of the ISM and in our solar system. Due to insufficient spectral information of these molecules at low temperature, the detection and understanding of the importance of these molecules has been rather incomplete. For this purpose, we have carried out the vacuum ultraviolet (VUV) photoabsorption measurements on pure molecular ices of isoprene and a few simple terpenes: limonene, $\alpha$-pinene and $\beta$-pinene by forming icy mantles on cold dust analogs. From these experiments, we report the first low-temperature (10 K) VUV spectra of isoprene and its oligomers, limonene, $\alpha$-pinene, and $\beta$-pinene. VUV photoabsorption spectra of all the molecules reported here reveal similarities in the ice and gas phase, as expected, except for isoprene, where a prominent red shift is observed in the ice phase absorption. This unique property of isoprene, along with distinctive absorption at longer wavelengths, supports its candidature for detection on icy bodies. Show Chinese abstract

异戊二烯及其低聚物、萜烯预计会与其他复杂有机分子一起存在于星际介质和我们太阳系的多样化环境中。由于这些分子在低温下的光谱信息不足，对这些分子的检测和重要性的理解一直相当不完整。为此，我们对纯分子冰状的异戊二烯和几种简单的萜烯：柠檬烯、$\alpha$-蒎烯和$\beta$-蒎烯进行了真空紫外（VUV）光吸收测量，方法是在冷尘埃模拟物上形成冰层。通过这些实验，我们报告了异戊二烯及其低聚物、柠檬烯、$\alpha$-蒎烯和$\beta$-蒎烯的首次低温（10 K）VUV光谱。这里报道的所有分子的VUV光吸收光谱显示出与气相相似的特性，正如预期的那样，除了异戊二烯，在冰相吸收中观察到了明显的红移。异戊二烯的这一独特性质以及在较长波长处的特征吸收支持了其在冰体上的检测候选资格。

[7] arXiv:2509.21005 (cross-list from physics.flu-dyn) [cn-pdf, pdf, html, other]

Title: Data-driven modeling of wind farm wake flow based on multi-scale feature recognition Show Chinese title

Title: 基于多尺度特征识别的风电场尾流建模数据驱动方法

Dong Xu, Zhaobin Li, Xiaolei Yang, Peng Hou, Bruno Carmo, Xuerui Mao

Subjects: Fluid Dynamics (physics.flu-dyn) ; Atmospheric and Oceanic Physics (physics.ao-ph) ; Data Analysis, Statistics and Probability (physics.data-an)

Accurate, efficient prediction of wind flow with wake effects is crucial for wind-farm layout and power forecasting. Existing approaches-physical measurements, numerical simulations, physics-based models, and data-driven models-face trade-offs: the first two are time- and resource-intensive; physics-based models can lack accuracy due to limited physics; data-driven methods leverage abundant, high-quality data and are increasingly popular. We propose a rapid, data-driven wake-flow model inspired by video-frame interpolation and the principle of similarity. Field data are transformed into images; multi-scale feature recognition then identifies, matches, and interpolates wake structures using Scale-Invariant Feature Transform (SIFT) and Dynamic Time Warping (DTW) to generate intermediate flow fields. Six representative mini wind-farm cases validate the approach, spanning variations in turbine spacing, turbine size, combined spacing-size variations, different turbine counts, and wind-direction misalignment. Across cases, the method achieves a mean absolute percentage error (MAPE) of 0.68-2.28%. Because it flexibly computes both 2D and 3D wake fields, the method offers substantial computational-efficiency gains over large-eddy simulation (LES) and Meteodyn WT when 2D accuracy suffices for industrial needs. Accordingly, it provides a practical alternative to measurements, high-fidelity simulations, and simplified physics-based models, enabling efficient expansion of wake-flow databases for wind-farm design and power prediction while balancing speed and accuracy. Show Chinese abstract

准确且高效地预测有尾流效应的风场流动对于风电场布局和功率预测至关重要。现有的方法——物理测量、数值模拟、基于物理的模型和数据驱动模型——都面临权衡：前两种方法耗时且资源密集；基于物理的模型由于物理知识的局限性可能缺乏准确性；数据驱动方法利用大量高质量的数据，正变得越来越受欢迎。我们提出了一种快速的数据驱动尾流模型，灵感来自视频帧插值和相似性原理。现场数据被转换为图像；多尺度特征识别然后使用尺度不变特征变换（SIFT）和动态时间规整（DTW）来识别、匹配和插值尾流结构，以生成中间流动场。六个具有代表性的小型风电场案例验证了该方法，涵盖了涡轮机间距、涡轮机尺寸、间距-尺寸组合变化、不同涡轮机数量以及风向不对齐的变化。在所有案例中，该方法实现了0.68%-2.28%的平均绝对百分比误差（MAPE）。由于它能够灵活计算二维和三维尾流场，当二维精度足以满足工业需求时，该方法在计算效率上相比大涡模拟（LES）和Meteodyn WT有显著提升。因此，它为测量、高保真模拟和简化的基于物理的模型提供了一个实用的替代方案，在提高风电场设计和功率预测的尾流数据库效率的同时，平衡了速度与准确性。

Replacement submissions (showing 1 of 1 entries )

[8] arXiv:2509.19648 (replaced) [cn-pdf, pdf, html, other]

Title: S$^2$Transformer: Scalable Structured Transformers for Global Station Weather Forecasting Show Chinese title

Title: S$^2$变压器：用于全球气象预报的可扩展结构变压器

Hongyi Chen, Xiucheng Li, Xinyang Chen, Yun Cheng, Jing Li, Kehai Chen, Liqiang Nie

Comments: arXiv admin note: substantial text overlap with arXiv:2509.18115

Subjects: Machine Learning (cs.LG) ; Atmospheric and Oceanic Physics (physics.ao-ph)

Global Station Weather Forecasting (GSWF) is a key meteorological research area, critical to energy, aviation, and agriculture. Existing time series forecasting methods often ignore or unidirectionally model spatial correlation when conducting large-scale global station forecasting. This contradicts the intrinsic nature underlying observations of the global weather system, limiting forecast performance. To address this, we propose a novel Spatial Structured Attention Block in this paper. It partitions the spatial graph into a set of subgraphs and instantiates Intra-subgraph Attention to learn local spatial correlation within each subgraph, and aggregates nodes into subgraph representations for message passing among the subgraphs via Inter-subgraph Attention -- considering both spatial proximity and global correlation. Building on this block, we develop a multiscale spatiotemporal forecasting model S$^2$Transformer by progressively expanding subgraph scales. The resulting model is both scalable and able to produce structured spatial correlation, and meanwhile, it is easy to implement. The experimental results show that it can achieve performance improvements up to 16.8% over time series forecasting baselines at low running costs. Show Chinese abstract

全球站点天气预报（GSWF）是一个关键的气象研究领域，对能源、航空和农业至关重要。 现有的时间序列预测方法在进行大规模全球站点预测时，往往忽略或单向建模空间相关性。 这与全球天气系统的观测内在本质相矛盾，限制了预测性能。 为了解决这个问题，本文提出了一种新颖的空间结构注意力模块。 它将空间图划分为一组子图，并实例化子图内注意力以学习每个子图内的局部空间相关性，同时通过子图间注意力将节点聚合为子图表示，以实现子图之间的信息传递——同时考虑空间邻近性和全局相关性。 基于该模块，我们开发了一个多尺度时空预测模型 S$^2$Transformer，通过逐步扩展子图规模来构建。 该模型既具有可扩展性，又能生成结构化的空间相关性，同时易于实现。 实验结果表明，它可以在低运行成本下比时间序列预测基线方法提升高达16.8%的性能。