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Professional Specialist

Cooperative Institute for Modeling the Earth System – Princeton University

Weather and Climate Dynamics Division

Curriculum vitae

Bibliography

Google scholar

Contact Information:

email linjiong.zhou@noaa.gov

phone (609) 452-6555

Research Interests:

  • Weather and Climate Modeling
  • Dynamics and Physics Coupling
  • Cloud Microphysics Parameterization
  • Medium-range Weather Prediction
  • Convective-Scale Extreme Weather Prediction

Links:

Linjiong Zhou

I am a research scientist at Princeton University, working at the Geophysical Fluid Dynamics Laboratory (GFDL). I lead the SHiELD (System for High-resolution prediction on Earth-to-Local Domains) global prediction system development as part of the NOAA Research Global-Nest Initiative. My research focuses on developing a novel integrated dynamics-physics coupling framework and advanced yet efficient cloud microphysics parameterizations to enhance global weather prediction across scales—from large-scale systems to mesoscale phenomena. To achieve convective-scale prediction, we have developed a variable-resolution SHiELD with 3-km grid spacing covering the Contiguous United States (CONUS) and a global 6.5-km SHiELD. I am actively contributing to SHiELD developments, including those supporting the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) and Different Models-Same Initial Conditions (DIMOSIC) projects.

Integrated Dynamics-Physics Coupling Framework

Zhou and Lucas (2022) propose an integrated dynamics-physics coupling framework for weather and climate models. In this framework, each physical parameterization is advanced on its natural timescale, thermodynamic processes are revised to include moist effects, and the results are integrated into the dynamical core. This study showcases the framework using a cloud microphysics scheme integrated within the dynamical core of the GFDL SHiELD model, termed “in-line microphysics.” One year of weather forecasts demonstrates significantly improved prediction skill with in-line microphysics compared to traditional coupling methods. Additionally, in-line microphysics captures larger-amplitude and higher-frequency variations in cloud structures within tropical cyclones, offering a marked improvement over conventional approaches.

GFDL Cloud Microphysics Parameterization

Zhou et al. (2022) introduce the third generation of the GFDL cloud microphysics scheme (GFDL MP v3), implemented in SHiELD. Compared to its predecessor, GFDL MP v2 (Harris et al. 2020), version 3 has been reorganized, optimized, and modularized for improved functionality. This update redefines the particle size distributions (PSDs) of all hydrometeor categories to better align with observations and calculates cloud droplet number concentrations (CDNC) using MERRA2 aerosol data. These advancements, combined with consistent PSD use across processes, enhance prediction accuracy. One year of global 13-km, 10-day forecasts showed significant improvements in geopotential height, air temperature, and specific humidity, as well as cloud fraction and liquid water path predictions.

Variable-resolution SHiELD — 3-km Prediction in CONUS

Zhou et al. (2019) explore the use of variable-resolution grid-stretching in the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to enable convective-scale predictions in SHiELD. This approach embeds a high-resolution 3-km grid over CONUS within a global framework, eliminating side boundary limitations typical of limited-area models. The integration of an enhanced five-category cloud microphysics scheme, later named GFDL Microphysics, improves SHiELD’s ability to capture extreme precipitation and diurnal cycles. Analyzing two years of simulations, this configuration achieves forecast skill comparable to operational GFS while excelling in high-resolution regions. Case studies, including squall lines and tropical cyclones, highlight SHiELD’s capability to unify global and regional forecasting within a single model.

Global 6.5-km Prediction System

Zhou et al. (2024) describe the 6.5-km SHiELD, a global model designed to bridge medium-range weather prediction and global storm-resolving simulation while remaining practical for real-time use. This system represents a leap forward from the 13-km SHiELD, featuring a scale-aware suite of parameterizations optimized for resolutions below 10 km. Comparative analyses over a three-year hindcast period reveal substantial improvements in global, regional, and storm-scale predictions, particularly for tropical cyclones and mesoscale convection. These findings establish the 6.5-km SHiELD as a robust tool for addressing synoptic weather systems and specific storm-scale phenomena within a unified framework.