Xu et al. (2025) A One‐Dimensional Variational Precipitation Retrieval Algorithm Considering Cloud Types for Western North Pacific Tropical Cyclones Using FengYun‐3E Microwave Sounders

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2025
• Date: 2025-10-01
• Authors: Jintao Xu, Ziqiang Ma, Hao Hu, Xiaoqing Li, Xiang Fang
• DOI: 10.1029/2025jd044523

Research Groups

Not explicitly mentioned in the abstract.

Short Summary

This study develops an enhanced one-dimensional variational (1DVAR) precipitation retrieval algorithm for tropical cyclones using Chinese FengYun-3E (FY-3E) passive microwave observations. The algorithm significantly improves retrieval accuracy and reduces systematic bias by incorporating precipitation-type differentiation and utilizing the Advanced Radiative Transfer Modeling System (ARMS) as the forward operator.

Objective

• To develop an enhanced one-dimensional variational (1DVAR) precipitation retrieval algorithm tailored for monitoring tropical cyclones (TCs) using Chinese FengYun-3E (FY-3E) passive microwave observations.
• To investigate the impact of introducing a precipitation-type differentiation module into the 1DVAR framework (1DVARDPT) on retrieval performance.
• To evaluate the superiority of the Advanced Radiative Transfer Modeling System (ARMS) over the Community Radiative Transfer Model (CRTM) as the forward operator for precipitation-sensitive radiative transfer modeling in this context.

Study Configuration

• Spatial Scale: Northwestern Pacific (tropical cyclone environments).
• Temporal Scale: Analysis across 19 tropical cyclones.

Methodology and Data

• Models used: Global Scene-Dependent Atmospheric Retrieval Testbed (GSDART), one-dimensional variational (1DVAR) algorithm (specifically 1DVARDPT), Advanced Radiative Transfer Modeling System (ARMS), Community Radiative Transfer Model (CRTM).
• Data sources: Chinese FengYun-3E (FY-3E) passive microwave observations, specifically from the microwave humidity and temperature sounders (MWTHS).

Main Results

• Introducing precipitation-type differentiation (1DVARDPT) significantly improved retrieval performance, reducing relative bias from -9.89% to 2.02% and mean absolute error (MAE) from 0.38 mm/hr to 0.32 mm/hr.
• The 1DVARDPT algorithm enhanced the detection of both light/moderate and heavy precipitation.
• Using ARMS instead of CRTM as the forward operator markedly reduced systematic underestimation, improving bias from -23.50% to -9.89%.
• ARMS demonstrated superiority in precipitation-sensitive radiative transfer modeling compared to CRTM.

Contributions

• Development of an enhanced 1DVAR precipitation retrieval algorithm (1DVARDPT) specifically for tropical cyclones using FY-3E passive microwave observations.
• Demonstration of the significant positive impact of incorporating precipitation-type variability into 1DVAR retrieval frameworks for improved accuracy and bias reduction.
• Highlighting the superior performance of ARMS over CRTM as a forward operator for precipitation-sensitive radiative transfer modeling, leading to reduced systematic underestimation.
• Underscoring the strong potential of FY-3E observations for advancing microwave-based precipitation estimation.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Xu2025OneDimensional,
  author = {Xu, Jintao and Ma, Ziqiang and Hu, Hao and Li, Xiaoqing and Fang, Xiang},
  title = {A One‐Dimensional Variational Precipitation Retrieval Algorithm Considering Cloud Types for Western North Pacific Tropical Cyclones Using FengYun‐3E Microwave Sounders},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2025},
  doi = {10.1029/2025jd044523},
  url = {https://doi.org/10.1029/2025jd044523}
}