arXiv subjects

Hao Zuo

Publications and source records attributed to Hao Zuo.

7 recordsLinked to original sources

arXiv2026

Spin-chirality-driven nonrelativistic Edelstein effects in two-dimensional antiferromagnets

Charge current-induced magnetic moment accumulation-Edelstein effect has been extensively attracting attention for its promising applications in spintronics. While most prior works focus on the spin-orbit coupling (SOC) induced Edelstein responses that rely on the presence of heavy elements, the nonrelativistic Edelstein effect (in the absence of SOC) that could be applied in a broader material family has been largely unexplored. Here, we perform a combined group-theoretical and ab initio numerical simulation study to show that vector spin chirality could serve as an effective control parameter of nonrelativistic Edelstein responses in antiferromagnetic system. In addition to spin degree of freedom, we also explore the orbital angular momentum contributions to current-induced magnetic moments (dubbed orbital Edelstein effect), which obey distinct symmetry constraints from the spin counterpart. Microscopically, vector spin chirality k gives rise to electronic and Zeeman-like band-geometric quantities, such as the anomalous spin/orbital polarizability and the Berry connection polarizability, which govern the nonrelativistic Edelstein responses. Our work identifies vector spin chirality as a key magnetic order parameter to enable and tune nonrelativistic Edelstein effects, and uncovers a new route toward electrically controlling magnetization without relying on SOC effect.

cond-mat.mtrl-sci↗

arXiv2026

The role of the oceans for subseasonal prediction: insights from eddy-permitting and eddy-rich coupled forecast systems

The oceans play a fundamental role in Earth's climate system, redistributing heat and influencing global and regional climate variability and predictability across weather and climate timescales. The benefits of ocean-atmosphere coupling for initialised predictions depend on the balance between improvements associated with more realistic air-sea interactions and dynamics, and degradations arising from the development of systematic biases at the coupling interface. Here, we draw on recent developments in modelling and data assimilation at ECMWF to revisit the role of ocean-atmosphere coupling in subseasonal predictions. In particular, we evaluate the impact of ocean-atmosphere coupling in 46-day reforecasts produced with the ECMWF Integrated Forecasting System (IFS) and explore the potential for improvements through increased horizontal resolution and a better representation of the ocean mesoscale. We find that ocean-atmosphere coupling significantly enhances ensemble forecast skill in the tropics, with positive effects increasing at longer lead times. In particular, Madden-Julian Oscillation (MJO) forecasts are substantially improved, with forecast skill extended by approximately 5 days compared to the uncoupled configuration. In contrast, ocean-atmosphere coupling has a more limited impact on the extratropical atmosphere at subseasonal timescales, with marginal impacts on the predictability of major tropospheric and stratospheric circulation indices. Finally, we present selected results from an experimental eddy-rich coupled configuration of the IFS, with a horizontal ocean resolution of approximately 8 km. We find that a better-resolved representation of the ocean mesoscale has a limited impact on atmospheric forecasts at subseasonal lead times, which suggests that many of the known deficiencies of the eddy-permitting reference configuration are mitigated by accurate initialisation.

physics.ao-ph↗

arXiv2026

Representing the Surface Ocean in ECMWF's data-driven forecasting system AIFS

Machine-learning (ML) models, such as the AIFS at the ECMWF, have revolutionised weather forecasting in recent years. We present an extension of the AIFS that jointly models the atmosphere and surface ocean, including ocean waves and sea ice. The primary objective of this extension is to enhance machine-learning medium-range forecasting and enable new use cases by expanding the weather state to better capture coupled surface processes. Our approach departs from traditional numerical models by not having two separate models for the atmosphere and marine components. The joint model instead learns correlations across the entire atmosphere-ocean interface in a component-agnostic way, and can exploit the expressive capacity of ML architectures to learn cross-component relationships directly from the data. We leverage tailored and targeted datasets and solve model design challenges such as missing values over land, multi-scale temporal dynamics, and physical realism of forecast fields and demonstrate the utility of loss scaling in guiding the learning process. We evaluate how representing the surface ocean affects medium-range weather forecasts. We also assess the model's ability to predict surface-ocean fields, including wave swell and tropical-cyclone cold wakes. For nearly all evaluated marine variables, we observe an improvement of approximately one day in forecast skill at medium-range lead times compared to physics-based models. Furthermore, we demonstrate that the model is robust to idealised initial conditions outside the training distribution and responds to them in a physically consistent way. Overall, our findings suggest that the joint AIFS modelling approach offers significant potential for combined atmosphere-ocean forecasting. Our work provides a solid foundation for future development of data-driven coupled Earth system models with greater flexibility and physical fidelity.

physics.ao-ph↗

arXiv2024

Normal surface singularities of small degrees

The notion of the Yau sequence was introduced by Tomaru, as an attempt to extend Yau's elliptic sequence for (weakly) elliptic singularities to normal surface singularities of higher fundamental genera. In this paper, we obtain the canonical cycle using the Yau cycle for certain surface singularities of degree two. Furthermore, we obtain a formula of arithmetic genera and an upper bound of geometric genera for these singularities. We also give some properties about the classification of weighted dual graphs of certain surface singularities of degree two.

math.AG↗

arXiv2024

Impact of an Ensemble of Ocean Data Assimilations in ECMWF's next generation ocean reanalysis system

An Ensemble of Data Assimilations (EDA) can provide valuable information on the analysis and short-range forecast uncertainties. The present ECMWF operational ocean analysis and reanalysis system, called ORAS5, produces an ensemble but does not exploit it for the specification of the background-error covariance matrix $\mathbf{B}$, a key component of the data assimilation system. In this article, we describe EDA developments for the ocean, which take advantage of the short-range forecast ensemble for specifying, in two distinct ways, parameters of a covariance model representation of $\mathbf{B}$. First, we generate a climatological ensemble over an extended period to produce seasonally varying climatological estimates of background-error variances and horizontal correlation length-scales. Second, on each assimilation cycle, we diagnose flow-dependent variances from the ensemble and blend them with the climatological estimates to form hybrid variances. We also use the ensemble to diagnose flow-dependent vertical correlation length-scales. We demonstrate for the Argo-rich period that this new, hybrid formulation of $\mathbf{B}$ results in a significant reduction of background errors compared to the parameterized formulation of $\mathbf{B}$ used in ORAS5. The new ocean EDA system will be employed in ORAS6, ECMWF's next generation ocean reanalysis system.

physics.ao-ph↗

arXiv2024

Multi-Modal UAV Detection, Classification and Tracking Algorithm -- Technical Report for CVPR 2024 UG2 Challenge

This technical report presents the 1st winning model for UG2+, a task in CVPR 2024 UAV Tracking and Pose-Estimation Challenge. This challenge faces difficulties in drone detection, UAV-type classification and 2D/3D trajectory estimation in extreme weather conditions with multi-modal sensor information, including stereo vision, various Lidars, Radars, and audio arrays. Leveraging this information, we propose a multi-modal UAV detection, classification, and 3D tracking method for accurate UAV classification and tracking. A novel classification pipeline which incorporates sequence fusion, region of interest (ROI) cropping, and keyframe selection is proposed. Our system integrates cutting-edge classification techniques and sophisticated post-processing steps to boost accuracy and robustness. The designed pose estimation pipeline incorporates three modules: dynamic points analysis, a multi-object tracker, and trajectory completion techniques. Extensive experiments have validated the effectiveness and precision of our approach. In addition, we also propose a novel dataset pre-processing method and conduct a comprehensive ablation study for our design. We finally achieved the best performance in the classification and tracking of the MMUAD dataset. The code and configuration of our method are available at https://github.com/dtc111111/Multi-Modal-UAV.

cs.RO↗

arXiv2024

OCEAN: An Openspace Collision-free Trajectory Planner for Autonomous Parking Based on ADMM

In this paper, we propose an Openspace Collision-freE trAjectory plaNner (OCEAN) for autonomous parking. OCEAN is an optimization-based trajectory planner accelerated by Alternating Direction Method of Multiplier (ADMM) with enhanced computational efficiency and robustness, and is suitable for all scenes with few dynamic obstacles. Starting from a hierarchical optimization-based collision avoidance framework, the trajectory planning problem is first warm-started by a collision-free Hybrid A* trajectory, then the collision avoidance trajectory planning problem is reformulated as a smooth and convex dual form, and solved by ADMM in parallel. The optimization variables are carefully split into several groups so that ADMM sub-problems are formulated as Quadratic Programming (QP), Sequential Quadratic Programming (SQP),and Second Order Cone Programming (SOCP) problems that can be efficiently and robustly solved. We validate our method both in hundreds of simulation scenarios and hundreds of hours of public parking areas. The results show that the proposed method has better system performance compared with other benchmarks.

cs.RO↗