Zhang et al. (2025) Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-09-10
• Authors: Qi Zhang, Bin Deng, Shudong Wang, Fei Dong, Min Shao
• DOI: 10.3390/rs17183133

Research Groups

Not specified in the provided text.

Short Summary

This study introduces the novel EnKF1D-Var data assimilation framework, integrating multi-source ground-based remote sensing observations to significantly reduce biases in temperature and water vapor profiles within the low troposphere, particularly during daytime.

Objective

• To develop and evaluate a novel data assimilation framework (EnKF1D-Var) for integrating multi-source vertical observations of water vapor and temperature from ground-based remote sensing instruments to improve the accuracy of atmospheric profiles.

Study Configuration

• Spatial Scale: Low troposphere at Anqing Station, subtropical China. Vertical resolution of 15 m.
• Temporal Scale: Three-month-long study (May to July 2024). Hourly temporal resolution.

Methodology and Data

• Models used: Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, driven by Global Forecast System (GFS) forecasts.
• Data sources: Ground-based Microwave Radiometer (GMWR), Mie–Raman Aerosol Lidar (MRL), and Global Navigation Satellite System Meteorology sensor (GNSS/MET) observations.

Main Results

• The EnKF1D-Var framework effectively reduces biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating observational error covariance matrices.
• Maximum humidity corrections reached up to 0.075 g/kg.
• Temperature bias reductions exceeded 3%.
• Incremental analysis revealed distinct contributions from instruments: GNSS/MET played a dominant role in temperature adjustment, with GMWR providing supplementary support, while MRL observations were primarily responsible for improvements in water vapor retrieval.

Contributions

• Presents a novel data assimilation framework (EnKF1D-Var) for integrating diverse ground-based remote sensing observations.
• Provides a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer, demonstrating reasonable application of multiple ground-based remote sensing observations.

Funding

Not specified in the provided text.

Citation

@article{Zhang2025MultiSource,
  author = {Zhang, Qi and Deng, Bin and Wang, Shudong and Dong, Fei and Shao, Min},
  title = {Multi-Source Retrieval of Thermodynamic Profiles from an Integrated Ground-Based Remote Sensing System Using an EnKF1D-Var Framework},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17183133},
  url = {https://doi.org/10.3390/rs17183133}
}