Kun Gao

About me

I am a core member of the GFDL FV3 team, focusing on the development of the
Finite-Volume on Cubed-Sphere Dynamical Core (FV3) and
Unified System for Weather-to-Seasonal Prediction SHiELD.

My current research at GFDL mainly covers three areas:

• Developing advanced high-resolution numerical models to improve the prediction of intensity, track, and rainfall of hurricanes on the medium-range timescale.
• Understanding how storm-scale processes (e.g., convection, large eddies) affect hurricane structure and intensity changes.
• Exploring the prediction of hurricanes and other extreme weather events on subseasonal-to-seasonal timescales.

Current Modeling Projects

• Developing the km-scale global-nested T-SHiELD for North Atlantic Hurricane prediction (leading role).
• Expanding the use of FV3 and SHiELD for turbulent gray-zone modeling (leading role).
• Developing the high-resolution coupled SHiELD-MOM6-SIS2-LM4 system.

Selected Research Work (leading or co-leading roles)

Area 1: Innovations for the Next-generation 100m-scale Hurricane Forecasting Model

• We used a multi-level nested strategy to archive 100m scale resolution, which captured the fine-scale finger-like features at the hurricane eye/eyewall interface. See Gao et al. 2024 GRL
• We implemented an inline three-dimensional turbulent kinetic energy (TKE) based diffusion scheme in FV3 dynamical core to provide a unified, physically grounded treatment of subgrid diffusion for the turbulent gray zone and large-eddy simulation scales. See Gao et al. 2026 JAMES
• We demonstrate that a prototype 500-m-resolution dynamical hurricane model can predict the timing and spatial distribution of large-eddy features along the inner edge of the hurricane eyewall more than 50 hours in advance and capture localized extreme wind gusts of 90–100 m/s, comparable to the strongest winds observed in major hurricanes. See Gao et al. 2026, under review

Area 2: Improving Hurricane Track, Intensity and Structure Prediction

• Leveraged AI Numerical Weather Prediction guidance to improve high-resolution dynamical hurricane forecasts in T-SHiELD. See Gao et al. 2026, under review
• Investigated how the model-resolved convection in convection-permitting models significantly affects hurricane track prediction. See Gao et al. 2023 GRL
• Built a novel framework for evaluating the wind structure of landfalling hurricanes based on sparse in-situ observations. See Chen et al. 2023 GRL
• Examined how the horizontal tracer advection algorithm alone can play a surprising role in hurricane intensity forecasts. See Gao et al. 2021 JAS

Area 3: MJO and Subseasonal Prediction

• Improved MJO prediction and propagation across the Maritime Continent with embedding a high-resolution nest in a GCM. See Zavadoff et al. 2023 GRL
• Explored the prediction of monthly hurricane activity over the North Atlantic. See Gao et al. 2019 GRL
• Showcased the representation of hurricane activity in the Gulf region, where most GCMs struggle, in the GFDL non-hydrostatic 25km HiRAM. See Gao et al. 2017 JGR-Atmosphere.

Area 4: Large Eddies in the Hurricane Boundary Layer

• Conducted a series of studies examining the nature and impact of roll vortices in the hurricane boundary layer during my PhD at the University of Rhode Island, advised by Dr. Isaac Ginis, in collaboration with NRL.

  See Gao and Ginis 2014, Gao and Ginis 2016, Gao et al. 2017, Gao and Ginis 2018.