Showing 1–7 of 7 results for author: Huo, Z

A Data-Driven Regional Model for Skillful Medium-Range Typhoon Prediction

Authors: Zeyi Niu, Wei Huang, Sirong Huang, Zhuo Wang, Mu Mu, Mengqi Yang, Xinhai Han, Haofei Sun, Zhaoyang Huo, Bo Qin

Abstract: Accurate prediction of tropical cyclones remains a major challenge for both numerical weather prediction and emerging artificial intelligence weather prediction systems. While recent global AI models have demonstrated strong skill in large-scale circulation prediction, they often struggle to represent the mesoscale structures critical for tropical cyclone intensity and precipitation. Here we devel… ▽ More Accurate prediction of tropical cyclones remains a major challenge for both numerical weather prediction and emerging artificial intelligence weather prediction systems. While recent global AI models have demonstrated strong skill in large-scale circulation prediction, they often struggle to represent the mesoscale structures critical for tropical cyclone intensity and precipitation. Here we develop the Hybrid Intelligent Typhoon System (HITS), a regional AI forecasting framework for medium-range typhoon prediction over the Asia-Pacific region, trained on a newly constructed 9-km high-resolution typhoon reanalysis dataset. The model combines regional autoregressive prediction with large scale dynamical constraints from the state-of-the-art ECMWF Artificial Intelligence Forecasting System (AIFS), allowing it to remain dynamically consistent with the evolving large-scale circulation while resolving mesoscale structures. HITS is further extended with a structure-aware perceptual training strategy (HITS-LPIPS) that improves the representation of convective and typhoon rainband structures. Experiments show that the hybrid framework substantially improves precipitation structure and typhoon intensity forecasts compared with both purely autoregressive regional AI models and standalone AI downscaling approaches. In particular, HITS-LPIPS reduces intensity errors by up to 47.8% relative to AIFS at a 72 hour lead time and produces a near-unbiased wind-pressure relationship for simulated typhoons. These results demonstrate that dynamically constrained regional AI systems provide a promising pathway for improving medium-range typhoon prediction. △ Less

Submitted 16 March, 2026; originally announced March 2026.

Comments: 17 pages;6 figures

StormDiT: A generative AI model bridges the 2-6 hour 'gray zone' in precipitation nowcasting

Authors: Haofei Sun, Yunfan Yang, Wei Han, Wei Huang, Huaguan Chen, Zhiqiu Gao, Zeting Li, Zhaoyang Huo, Zeyi Niu

Abstract: Accurate short-term warnings for extreme precipitation are critical for global disaster mitigation but are hindered by a persistent predictability barrier at the 2-6 hour horizon -- the "nowcasting gray zone." In this window, traditional observation-based extrapolation fails due to error accumulation, while numerical weather prediction is computationally too slow to resolve storm-scale dynamics. R… ▽ More Accurate short-term warnings for extreme precipitation are critical for global disaster mitigation but are hindered by a persistent predictability barrier at the 2-6 hour horizon -- the "nowcasting gray zone." In this window, traditional observation-based extrapolation fails due to error accumulation, while numerical weather prediction is computationally too slow to resolve storm-scale dynamics. Recent generative AI approaches attempt to bridge this gap by decomposing precipitation into separate deterministic advection and stochastic diffusion components. However, this decomposition can sever fundamental causal links between entangled atmospheric processes, such as the dynamic initiation of convection triggered by boundary advection. Here we present StormDiT, a unified generative model that treats weather evolution as a holistic spatiotemporal problem, learning the coupled physics of the gray zone without human-imposed structural priors. Trained on a massive dataset of 7,720 precipitation events from China, our model achieves a breakthrough in long-horizon stability. On a heavy-rainfall test set, it maintains skillful prediction for strong convection ($\ge$ 35 dBZ) with a Critical Success Index (CSI) near 0.2 across the full 6-hour forecast at 6-minute resolution. Crucially, the model exhibits superior probabilistic calibration, accurately quantifying operational risks. On the public SEVIR benchmark, our unified paradigm more than doubles the state-of-the-art 1-hour performance for heavy rain and establishes the first robust baseline for 3-hour forecasting. Furthermore, interpretability analysis reveals that the model attends to non-local physical precursors, such as outflow boundaries, explicitly validating its emergent understanding of convective organization. △ Less

Submitted 28 January, 2026; originally announced January 2026.

Efficient Quantum Simulation of Non-Adiabatic Molecular Dynamics with Precise Electronic Structure

Authors: Tianyi Li, Yumeng Zeng, Qiming Ding, Zixuan Huo, Xiaosi Xu, Jiajun Ren, Diandong Tang, Xiaoxia Cai, Xiao Yuan

Abstract: In the study of non-adiabatic chemical processes such as photocatalysis and photosynthesis, non-adiabatic molecular dynamics (NAMD) is an indispensable theoretical tool, which requires precise potential energy surfaces (PESs) of ground and excited states. Quantum computing offers promising potential for calculating PESs that are intractable for classical computers. However, its realistic applicati… ▽ More In the study of non-adiabatic chemical processes such as photocatalysis and photosynthesis, non-adiabatic molecular dynamics (NAMD) is an indispensable theoretical tool, which requires precise potential energy surfaces (PESs) of ground and excited states. Quantum computing offers promising potential for calculating PESs that are intractable for classical computers. However, its realistic application poses significant challenges to the development of quantum algorithms that are sufficiently general to enable efficient and precise PES calculations across chemical systems with diverse properties, as well as to seamlessly adapt existing NAMD theories to quantum computing. In this work, we introduce a quantum-adapted extension to the Landau-Zener-Surface-Hopping (LZSH) NAMD. This extension incorporates curvature-driven hopping corrections that protect the population evolution while maintaining the efficiency gained from avoiding the computation of non-adiabatic couplings (NACs), as well as preserving the trajectory independence that enables parallelization. Furthermore, to ensure the high-precision PESs required for surface hopping dynamics, we develop a sub-microhartree-accurate PES calculation protocol. This protocol supports active space selection, enables parallel acceleration either on quantum or classical clusters, and demonstrates adaptability to diverse chemical systems - including the charged H3+ ion and the C2H4 molecule, a prototypical multi-reference benchmark. This work paves the way for practical application of quantum computing in NAMD, showcasing the potential of parallel simulation on quantum-classical heterogeneous clusters for ab-initio computational chemistry. △ Less

Submitted 10 December, 2025; v1 submitted 1 December, 2025; originally announced December 2025.

Intelligent Shanghai Typhoon Model (ISTM): A generative probabilistic emulator for typhoon hybrid modeling

Authors: Zeyi Niu, Wei Huang, Sirong Huang, Bo Qin, Mengqi Yang, Haofei Sun, Zhaoyang Huo, Haixia Xiao

Abstract: To address the systematic underestimation of typhoon intensity in artificial intelligence weather prediction (AIWP) models, we propose the Intelligent Shanghai Typhoon Model (ISTM): a unified regional-to-typhoon generative probabilistic forecasting system based on a two-stage UNet-Diffusion framework. ISTM learns a downscaling mapping from 4 years of 25 km ERA5 reanalysis to a 9 km high resolution… ▽ More To address the systematic underestimation of typhoon intensity in artificial intelligence weather prediction (AIWP) models, we propose the Intelligent Shanghai Typhoon Model (ISTM): a unified regional-to-typhoon generative probabilistic forecasting system based on a two-stage UNet-Diffusion framework. ISTM learns a downscaling mapping from 4 years of 25 km ERA5 reanalysis to a 9 km high resolution typhoon reanalysis dataset, enabling the generation of kilometer-scale near-surface variables and maximum radar reflectivity from coarse resolution fields. The evaluation results show that the two-stage UNet-Diffusion model significantly outperforms both ERA5 and the baseline UNet regression in capturing the structure and intensity of surface winds and precipitation. After fine-tuning, ISTM can effectively map AIFS forecasts, an advanced AIWP model, to high-resolution forecasts from AI-physics hybrid Shanghai Typhoon Model, substantially enhancing typhoon intensity predictions while preserving track accuracy. This positions ISTM as an efficient AI emulator of hybrid modeling system, achieving fast and physically consistent downscaling. The proposed framework establishes a unified pathway for the co-evolution of AIWP and physics-based numerical models, advancing next-generation typhoon forecasting capabilities. △ Less

Submitted 22 August, 2025; originally announced August 2025.

Transforming Acidic Corrosion and Embrittlement into a Hydrogen-Trapping Cage

Authors: Ankang Chen, Jiewen Liu, Zihao Huo, Chuang Liu, Yongming Sui, Xuan Liu, Qingkun Yuan, Yan Li, Guangtong Wang, Bao Yuan, Defang Duan, Gang Liu, Bo Zou

Abstract: The vision of a hydrogen economy demands efficient platforms to close the gap between sustainable proton sources and solid-state hydrogen carriers. Metal hydrides serve as key carriers, yet their synthesis remains constrained by the energy-intensive use of high-pressure H2, which fragments the hydrogen chain. Here, we overturn this paradigm by transforming two classic degradation mechanisms, acidi… ▽ More The vision of a hydrogen economy demands efficient platforms to close the gap between sustainable proton sources and solid-state hydrogen carriers. Metal hydrides serve as key carriers, yet their synthesis remains constrained by the energy-intensive use of high-pressure H2, which fragments the hydrogen chain. Here, we overturn this paradigm by transforming two classic degradation mechanisms, acidic corrosion and hydrogen embrittlement, into a constructive materials-design strategy. We demonstrate that synergistic control of these processes in acid enables the in-situ engineering of a "hydrogen-trapping cage" (HTC) microstructure within metals. Composed of a dense defect network, this cage directly captures and stabilizes protons as hydrides under mild conditions, guided by the universal criterion |DeltaPeq| > DeltaPph. Using this platform, we synthesize over 20 hydrides, including challenging targets such as LiH and NaH, and showcase its functional power with a cage-rich titanium hydride electrocatalyst. This catalyst achieves an exceptional current density of 1.07 A cm-2 for nitrate-to-ammonia conversion, attributed to rapid H- transport within the engineered cage. This work establishes a transformative "failure-to-function" paradigm, delivering an integrated platform that unifies hydrogen capture, stabilization, and conversion. △ Less

Submitted 5 January, 2026; v1 submitted 5 June, 2025; originally announced June 2025.

Thermal superscatterer: amplification of thermal scattering signatures for arbitrarily shaped thermal materials

Authors: Yichao Liu, Yawen Qi, Fei Sun, Jinyuan Shan, Hanchuan Chen, Yuying Hao, Hongmin Fei, Binzhao Cao, Xin Liu, Zhuanzhuan Huo

Abstract: The concept of superscattering is extended to the thermal field through the design of a thermal superscatterer based on transformation thermodynamics. A small thermal scatterer of arbitrary shape and conductivity is encapsulated with an engineered negative-conductivity shell, creating a composite that mimics the scattering signature of a significantly larger scatterer. The amplified signature can… ▽ More The concept of superscattering is extended to the thermal field through the design of a thermal superscatterer based on transformation thermodynamics. A small thermal scatterer of arbitrary shape and conductivity is encapsulated with an engineered negative-conductivity shell, creating a composite that mimics the scattering signature of a significantly larger scatterer. The amplified signature can match either a conformal larger scatterer (preserving conductivity) or a geometry-transformed one (modified conductivity). The implementation employs a positive-conductivity shell integrated with active thermal metasurfaces, demonstrated through three representative examples: super-insulating thermal scattering, super-conducting thermal scattering, and equivalent thermally transparent effects. Experimental validation shows the fabricated superscatterer amplifies the thermal scattering signature of a small insulated circular region by nine times, effectively mimicking the scattering signature of a circular region with ninefold radius. This approach enables thermal signature manipulation beyond physical size constraints, with potential applications in thermal superabsorbers/supersources, thermal camouflage, and energy management. △ Less

Submitted 18 May, 2025; originally announced June 2025.

Comments: 19 pages,6 figures

Enhanced DNA sequencing performance through edge-hydrogenation of graphene electrodes

Authors: Yuhui He, Ralph H. Scheicher, Anton Grigoriev, Rajeev Ahuja, Shibing Long, ZongLiang Huo, Ming Liu

Abstract: We propose using graphene electrodes with hydrogenated edges for solid-state nanopore-based DNA sequencing, and perform molecular dynamics simulations in conjunction with electronic transport calculations to explore the potential merits of this idea. The results of our investigation show that, compared to the unhydrogenated system, edge-hydrogenated graphene electrodes facilitate the temporary for… ▽ More We propose using graphene electrodes with hydrogenated edges for solid-state nanopore-based DNA sequencing, and perform molecular dynamics simulations in conjunction with electronic transport calculations to explore the potential merits of this idea. The results of our investigation show that, compared to the unhydrogenated system, edge-hydrogenated graphene electrodes facilitate the temporary formation of H-bonds with suitable atomic sites in the translocating DNA molecule. As a consequence, the average conductivity is drastically raised by about 3 orders of magnitude while exhibiting significantly reduced statistical variance. We have furthermore investigated how these results are affected when the distance between opposing electrodes is varied and have identified two regimes: for narrow electrode separation, the mere hindrance due to the presence of protruding hydrogen atoms in the nanopore is deemed more important, while for wider electrode separation, the formation of H-bonds becomes the dominant effect. Based on these findings, we conclude that hydrogenation of graphene electrode edges represents a promising approach to reduce the translocation speed of DNA through the nanopore and substantially improve the accuracy of the measurement process for whole-genome sequencing. △ Less

Submitted 30 November, 2010; originally announced December 2010.

Comments: 15 pages, 4 figures

Journal ref: Adv. Funct. Mater. 21, 2674 (2011)