About

Ruoying He is a Goodnight Innovation Distinguished Professor at NC State University within the Department of Marine, Earth, and Atmospheric Sciences. Her contact information includes a phone number and email address, indicating her active engagement in research and academic activities. The page does not provide specific details about her research focus, background, or key contributions, but her title as a distinguished professor suggests a significant role in her field.

Research topics

• Environmental science
• Computer Science
• Ecology
• Environmental planning
• Engineering
• Business
• Chemistry
• Geography
• Geology
• Environmental engineering
• Statistics
• Mathematics
• Organic chemistry
• Food science
• Oceanography
• Meteorology
• Environmental chemistry
• Environmental health
• Climatology
• Remote sensing
• Economics
• Medicine
• Environmental resource management

Selected publications

• High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

  ArXiv.org · 2026-04-03

  articleOpen access

  The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.

  PublisherOA PDF

• Influence of ocean current on wave systems and energy resources

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations

  arXiv (Cornell University) · 2026-04-03

  preprintOpen access

  The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.

  PublisherDOI

• Advanced Ocean Reanalysis of the Northwestern Atlantic: 1993-2022

  ArXiv.org · 2025-03-10

  preprintOpen access1st authorCorresponding

  A 30-year high-resolution Northwestern Atlantic Ocean Reanalysis (NAOR) is presented. NAOR spans from January 1993 to December 2022 with a 4 km horizontal resolution and 50 vertical layers. It provides enhanced resolution and expands the spatial and temporal coverage of existing ocean reanalysis in the region. NAOR was conducted using the Regional Ocean Modeling System along with Ensemble Optimal Interpolation data assimilation. Open boundary and surface forcing conditions were obtained from GLORYS global ocean reanalysis and ECMWF ERA5 reanalysis. Multiple sources of satellite and in-situ observations were incorporated through the data assimilation. Additionally, major rivers were accounted for to include freshwater riverine discharge. NAOR was extensively evaluated against available independent observations. Spatio-temporal variations of mesoscale circulation, eddies, and boundary currents are well captured. Compared to GLORYS, NAOR offers a more accurate physical and dynamic baseline of the northwestern Atlantic Ocean, which can be utilized for a range of marine and environmental studies as well as climate impact research.

  PublisherOA PDFDOI

• Long-term prediction of the Gulf Stream meander using OceanNet: a principled neural-operator-based digital twin

  Ocean science · 2025-06-20 · 1 citations

  articleOpen accessSenior author

  Abstract. Many meteorological and oceanographic processes throughout the eastern US and western Atlantic Ocean, such as storm tracks and shelf water transport, are influenced by the position and warm sea surface temperature of the Gulf Stream (GS) – the region's western boundary current. Due to highly nonlinear processes associated with the GS, predicting its meanders and frontal position has been a long-standing challenge within the numerical modeling community. Although the weather and climate modeling communities have begun to turn to data-driven machine learning frameworks to overcome analogous challenges, there has been less exploration of such models in oceanography. Using a new dataset from a high-resolution data-assimilative ocean reanalysis (1993–2022) for the northwestern Atlantic Ocean, OceanNet (a neural-operator-based digital twin for regional oceans) was trained to predict the GS's frontal position over subseasonal to seasonal timescales. Here, we present the architecture of OceanNet and the advantages it holds over other machine learning frameworks explored during development. We also demonstrate that predictions of the GS meander are physically reasonable over at least a 60 d period and remain stable for longer. OceanNet can generate a 120 d forecast of the GS meander within seconds, offering significant computational efficiency.

  PublisherOA PDFDOI

• Inundation Processes, Barrier Island Breaching, and Structure Impacts During Hurricane Michael (2018)

  Earth and Space Science · 2025-11-01 · 1 citations

  articleOpen access

  Abstract We demonstrate the increased ability to forecast hurricane impacts with a coupled numerical modeling system by simulating ocean waves, water levels, currents, sediment transport, and structural damage to predict inundation, coastal morphological change, and residential building impacts. The Coupled‐Ocean‐Atmosphere‐Waves‐Sediment‐Transport (COAWST) modeling system is applied to simulate Hurricane Michael (category 5, 2018) that made landfall near Tyndall Air Force Base, FL, in the northern Gulf of America, causing severe devastation and flooding. Atmospheric forcings from the Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS‐TC) are used to drive the ocean and wave models on a series of nested grids. Results identify that coastal inundation at Mexico Beach is due to surge from winds and waves, supplemented by pulses of infragravity wave motions that propagate landward into the inundation region. Seed lines observed on interior building walls also demonstrate variable changes in water level. In addition, a machine learning model was applied to hindcast structure damages, caused mostly by waves and winds, with a 72% accuracy estimate of substantial damage in proximity of landfall. The storm also created a breach across Cape San Blas, the adjacent barrier spit, due to large surge and low dune elevations. Dune locations with vegetated land cover are shown to reduce wave‐energy dissipation and reduce erosion, whereas locations without land cover had increased breaching potential. The breach occurred during the maximum ocean‐side water level, and the delayed high water on the bay side allowed a pressure gradient to drive flow seaward and promote breach development.

  PublisherOA PDFDOI

• Investigation of GIS post-arc current test device and post-arc recovery behavior

  Journal of Physics Conference Series · 2025-01-01

  articleOpen access

  Abstract GIS is crucial in high-voltage power systems, necessitating high standards of reliability and ease of maintenance. The circuit breaker, as the system’s core component, is vital for the safe operation of both GIS and the broader power grid. During the interruption of high current, plasma exists in the circuit breaker’s contact gap, and its movement, influenced by transient recovery voltage (TRV), leads to post-arc current. Accurately measuring this post-arc current is essential for assessing the circuit breaker’s performance. This paper presents a self-developed testing device that evaluates post-arc current under harmonic influences, exploring how varying short-circuit current frequencies and zero-front current slopes affect the characteristics of the post-arc current in a 126 kV pressurized gas circuit breaker.

  PublisherOA PDFDOI

• Gulf Stream Near Cape Hatteras Modulates Sea Level Variability Along the Southeastern Coast of North America

  Geophysical Research Letters · 2025-04-01 · 4 citations

  articleOpen accessSenior authorCorresponding

  Abstract Studies suggest a strong link between low‐frequency sea level variability in the South Atlantic Bight (SAB) and open ocean dynamics. However, the mechanisms driving this connection remain unclear. By analyzing a high‐resolution, three‐dimensional baroclinic ocean reanalysis, we identify a pathway that links open ocean dynamics to SAB coastal sea level variability through the shelf edge near Cape Hatteras. Gulf Stream meanders in this region induce sea level fluctuations that propagate along the entire SAB shelf. Using an idealized barotropic model, we further demonstrate that topographic waves mediate the propagation of the Gulf Stream signal onto the shelf. Moreover, the Gulf Stream variability is driven by zonal wind stress in the Northwest Atlantic, which is likely modulated by the North Atlantic Oscillation. These findings offer new insights into regional sea level prediction and contribute to broader climate research efforts.

  PublisherOA PDFDOI

• OceanAI: A Conversational Platform for Accurate, Transparent, Near-Real-Time Oceanographic Insights

  ArXiv.org · 2025-11-02

  preprintOpen accessSenior author

  Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.

  PublisherOA PDFDOI

• Author response for "Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity"

  2025-09-16

  peer-review
  PublisherDOI

Recent grants

• Collaborative Research: Frontal Interaction and Atmospheric Forcing North of Cape Hatteras, an Analysis and Modeling Study

  NSF · $233k · 2009–2012

• Collaborative Research: An Observational and Modeling Study of the Physical Processes Driving Exchanges between the Shelf and the Deep Ocean At Cape Hatteras

  NSF · $685k · 2016–2022

• RAPID: 3-D Model Forecast of the Vertical and Horizontal Distributions of the Oil Plumes Arising From the DeepWater Horizon Spill

  NSF · $81k · 2010–2012

Frequent coauthors

• Robert H. Weisberg

  University of South Florida St. Petersburg

  63 shared

• Joseph B. Zambon

  North Carolina State University

  37 shared

• Mike Muglia

  Center for Coastal Studies

  35 shared

• Harvey Seim

  University of North Carolina at Chapel Hill

  32 shared

• Shun Mao

  North Carolina State University

  28 shared

• Magdalena Andres

  Woods Hole Oceanographic Institution

  26 shared

• Lei Huang

  Chinese Academy of Agricultural Sciences

  26 shared

• John M. Bane

  University of North Carolina at Chapel Hill

  26 shared

Labs

• Marine, Earth and Atmospheric SciencesPI

Awards & honors

• Goodnight Innovation Distinguished Professor