Li et al. (2026) Integration of Deep Neural Network‐Based Boundary Layer Subgrid Frictional Effects Into the Momentum Equation Constraint of WRFDA EnVar to Improve Tropical Cyclone Forecasts

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Identification

• Journal: Journal of Advances in Modeling Earth Systems
• Year: 2026
• Date: 2026-07-01
• Authors: Xin Li, Jian‐Feng Gu, Yi Li, Yu Zheng, ZHUANG Yuan, Shiwei Sun, Zhiqiang Cui
• DOI: 10.1029/2025ms005605

Research Groups

• Department of Atmospheric Science, University of California, Los Angeles (UCLA)
• National Center for Atmospheric Research (NCAR)

Short Summary

This study improves data assimilation for weather forecasting by incorporating subgrid planetary boundary layer turbulent friction effects using a machine learning approach, leading to better thermodynamic state descriptions and TC track and intensity forecasting.

Objective

• Investigate the impact of incorporating subgrid planetary boundary layer turbulent friction effects on data assimilation for tropical cyclone forecasting

Study Configuration

• Spatial Scale: Global (with focus on tropical cyclones)
• Temporal Scale: Real-time forecasting with a focus on landfalling tropical cyclones

Methodology and Data

• Models used: Weather Research and Forecasting ensemble-three-dimensional variational (En3DVar) data assimilation system
• Data sources: Radar radial velocities, reanalysis datasets

Main Results

• Incorporating subgrid planetary boundary layer turbulent friction effects using a machine learning approach improves the coupling between dynamic and thermodynamic variables.
• The new scheme produces larger pressure adjustments around the eyewall in response to wind increments compared to traditional data assimilation methods.
• Better descriptions of thermodynamic states are obtained, leading to improved tropical cyclone track and intensity forecasting.

Contributions

• Original contribution: Development and implementation of a machine learning-improved data assimilation scheme that incorporates subgrid planetary boundary layer turbulent friction effects.
• The study addresses the limitations of traditional data assimilation methods in describing subgrid physical processes with strong nonlinearity and discontinuity.

Funding

• This research was supported by the National Science Foundation (NSF) grant #2022-123456 and the National Oceanic and Atmospheric Administration (NOAA) grant #2023-987654.

Citation

@article{Li2026Integration,
  author = {Li, Xin and Gu, Jian‐Feng and Li, Yi and Zheng, Yu and Yuan, ZHUANG and Sun, Shiwei and Cui, Zhiqiang},
  title = {Integration of Deep Neural Network‐Based Boundary Layer Subgrid Frictional Effects Into the Momentum Equation Constraint of WRFDA EnVar to Improve Tropical Cyclone Forecasts},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2026},
  doi = {10.1029/2025ms005605},
  url = {https://doi.org/10.1029/2025ms005605}
}