Cover Image

Typhoon Tracks Regulated by Feedbacks of Fine-Scale Clouds to Environment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Haoran, Zhang, Shaoqing, Gao, Yang, Wu, Lixin, Cao, Yihan, Cai, Wenju, Wang, Bin, Leung, L. Ruby, Lu, Zebin, Zhong, Zhong, Yu, Xiaolin, Li, Mingkui, Zhu, Chenyu
Format:	Preprint
Published:	2025
Subjects:	Atmospheric and Oceanic Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Organization of Historical Oceanic Overturnings on Cross-Sphere Climate Signals
by: Jiang, Yingjing, et al.
Published: (2025)

AI Models Still Lag Behind Traditional Numerical Models in Predicting Sudden-Turning Typhoons
by: Xu, Daosheng, et al.
Published: (2025)

Assessing the Risks of Typhoon-Induced Multi-Hazards in South Korea
by: Liu, Ziyue, et al.
Published: (2025)

Cracking the Code of Arctic Sea Ice: Why Models Fail to Predict Its Retreat?
by: Gou, Ruijian, et al.
Published: (2025)

ML-Physical Fusion Models Are Accelerating the Paradigm Shift in Operational Typhoon Forecasting
by: Niu, Zeyi
Published: (2025)

Insights from Ex-Typhoon Halong (2025) -- An Arctic Cyclone of Tropical Origin
by: Yang, Mingshi, et al.
Published: (2026)

A Data-Driven Regional Model for Skillful Medium-Range Typhoon Prediction
by: Niu, Zeyi, et al.
Published: (2026)

Intelligent Shanghai Typhoon Model (ISTM): A generative probabilistic emulator for typhoon hybrid modeling
by: Niu, Zeyi, et al.
Published: (2025)

A Simulation Methodology Testbed for Typhoon Sensitivity Analysis: Framework Development and Perturbation-Response Experiments with the Pangu Weather Model
by: Peng, Yuehua, et al.
Published: (2026)

Machine Learning (ML)-Physics Fusion Model Outperforms Both Physics-Only and ML-Only Models in Typhoon Predictions
by: Niu, Zeyi, et al.
Published: (2025)

Terrain-aware Deep Learning for Wind Energy Applications: From Kilometer-scale Forecasts to Fine Wind Fields
by: Lin, Chensen, et al.
Published: (2025)

Improving Typhoon Predictions by Integrating Data-Driven Machine Learning Models with Physics Models Based on the Spectral Nudging and Data Assimilation
by: Niu, Zeyi, et al.
Published: (2024)

CAS-Canglong: A skillful 3D Transformer model for sub-seasonal to seasonal global sea surface temperature prediction
by: Wang, Longhao, et al.
Published: (2024)

Enhancing Near Real Time AI-NWP Hurricane Forecasts: Improving Explainability and Performance Through Physics-Based Models and Land Surface Feedback
by: Sudharsan, Naveen, et al.
Published: (2025)

A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
by: Sorensen, Benedikt Barthel, et al.
Published: (2024)

Recent Weakening of the Global Radiative Feedback
by: Van Loon, Senne, et al.
Published: (2026)

Vapor-Buoyancy Feedback in an Idealized GCM
by: Seidel, Seth, et al.
Published: (2023)

Ecohydrological Controls on Moist Convection and Long-Term Rainfall Feedback
by: Cultra, Elizabeth, et al.
Published: (2026)

Ekman Theory with Damping
by: Wu, Jiacheng, et al.
Published: (2025)

Nonlocal, Pattern-aware Response and Feedback Framework for Regional Climate Change
by: Kooloth, Parvathi, et al.
Published: (2024)

Feedback Processes causing an AMOC Collapse in the Community Earth System Model
by: Vanderborght, Elian, et al.
Published: (2024)

FuXi-Nowcast: Environment-conditioned deep learning for severe convection nowcasting
by: Chen, Lei, et al.
Published: (2025)

Boreal Afforestation's Underestimated Cloud Influence on Earth's Energy Imbalance
by: Ofosu, Enoch, et al.
Published: (2025)

Substantial Diel Changes of Cloud Adjustments to Aerosols in Ship-tracks
by: Yuan, Tianle, et al.
Published: (2025)

Data-Driven Equation Discovery of a Cloud Cover Parameterization
by: Grundner, Arthur, et al.
Published: (2023)

HybridOM: Hybrid Physics-Based and Data-Driven Global Ocean Modeling with Efficient Spatial Downscaling
by: Shu, Ruiqi, et al.
Published: (2026)

On the Stochasticity of Aerosol-Cloud Interactions within a Data-driven Framework
by: Li, Xiang-Yu, et al.
Published: (2024)

The Impact of Cumulus Clouds and CCNs Regeneration on Aerosol Vertical Distribution and Size
by: Arieli, Yael, et al.
Published: (2024)

The Australian Identification, Nowcasting and Tracking Algorithm (A.I.N.T.)
by: Brook, Jordan, et al.
Published: (2025)

Structural Cointegration of the Climate--Carbon Feedback: Evidence from the Last 130,000 Years
by: Nakano, Satoshi, et al.
Published: (2026)

Cloud-aerosol interactions in subtropical marine stratocumulus weaken in a warmer climate
by: Sun, Hongwei, et al.
Published: (2025)

Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network
by: Fernandez, M. A., et al.
Published: (2025)

Enhanced predictions of the Madden-Julian oscillation using the FuXi-S2S machine learning model: Insights into physical mechanisms
by: Cao, Can, et al.
Published: (2025)

Projecting U.S. coastal storm surge risks and impacts with deep learning
by: Rice, Julian R., et al.
Published: (2025)

The early history of Marine Cloud Brightening (MCB); the legacy of John Latham and Stephen Salter
by: Gadian, Alan
Published: (2025)

Cloud droplet size distribution and optical properties only weakly linked to aerosol size
by: Gali, Kadja Flore, et al.
Published: (2025)

Representing Subgrid-Scale Cloud Effects in a Radiation Parameterization using Machine Learning: MLe-radiation v1.0
by: Hafner, Katharina, et al.
Published: (2025)

Interpretable Cross-Sphere Multiscale Deep Learning Predicts ENSO Skilfully Beyond 2 Years
by: Hao, Rixu, et al.
Published: (2025)

Idealized Cumulus Cloud-Scale Motions and the Dynamics of Isolated and Coupled Flows
by: Falcone, Dario P., et al.
Published: (2025)

Cold Pools Reduce the Impacts of Deforestation on Convection Initiation
by: Falk, Nicholas M., et al.
Published: (2025)