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Princeton University
Associate Research Scholar

Curriculum vitae
GFDL bibliography (2017 and onwards)
Google scholar

Contact Information

email kun.gao@noaa.gov
phone (609) 452-5882

Focus Areas

  • High resolution atmospheric model development
  • Predictions of hurricanes and other extreme weather events
  • Atmospheric boundary layer and air-sea interactions

Highlights

  • My research was highlighted in Princeton University environmental science newsletter.
  • I have several publications highlighted by GFDL (see 1, 2, 3) and Eos.org.
  • Our hurricane prediction model, T-SHiELD, has demonstrated comparable or even superior performance to the world-leading ECMWF model in 5-Day hurricane track forecasts.

Kun Gao

About me

I am a member of the GFDL FV3 team, mainly focusing on the development of the
Finite-Volume on Cubed-Sphere Dynamical Core (FV3) and
Unified System for Weather-to-Seasonal Prediction SHiELD. I also contribute to
NOAA Research Global-Nest Initiative.

My research at GFDL 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 a global nested version of SHiELD, i.e., T-SHiELD, for North Atlantic Hurricane prediction (leading role).
  • Implementing a 3D-TKE based diffusion scheme into FV3 (mostly collaborating with Dr. Ping Zhu’s team at FIU).
  • Developing a high-resolution regional coupled SHiELD-MOM6-WWIII system (a joint effort among GFDL W, O and Modeling System divisions).

Selected Research Work (leading or co-leading roles)

Area 1: Innovations for the Next-generation 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

Area 2: Improving Hurricane Track, Intensity and Structure Prediction

  • 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
  • Showcased using an 8km nest can dramatically improve the fidelity of hurricane structure representation in a GCM. See Gao et al. 2019 JAMES

Area 3: MJO and Subseasonal Prediction

Area 4: Large Eddies in the Hurricane Boundary Layer