Hong et al. (2025) The Effects of Ice Habit Models on Passive Microwave Snowfall Rate Retrievals

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

Identification

• Journal: Geophysical Research Letters
• Year: 2025
• Date: 2025-10-07
• Authors: Yulan Hong, Huan Meng, Yongzhen Fan, Jun Dong, Tong Ren, Ping Yang
• DOI: 10.1029/2025gl116853

Research Groups

Not explicitly mentioned in the abstract.

Short Summary

This study investigates the significant impact of ice habit models on snowfall rate (SFR) derived from space-borne passive microwave observations, revealing that an optimal ice habit choice is environmentally dependent. It proposes a machine learning model that integrates multiple ice habits, improving SFR retrieval accuracy by approximately 10% overall.

Objective

• To examine the impact of various ice habit models on snowfall rate (SFR) derived from space-borne passive microwave observations.
• To identify environmentally dependent optimal ice habits for SFR retrieval.
• To develop and evaluate a machine learning model that integrates multiple ice habits to improve SFR retrieval accuracy.

Study Configuration

• Spatial Scale: Global (implied by space-borne observations), but no specific resolution or area is mentioned.
• Temporal Scale: Not explicitly mentioned in the abstract.

Methodology and Data

• Models used: Machine learning model (to integrate multiple ice habits).
• Data sources: Space-borne passive microwave observations, Cloud Profiling Radar, ERA5 (reanalysis), National Oceanic and Atmospheric Administration Stage IV (observations).

Main Results

• Snowfall rate (SFR) retrieval is highly sensitive to ice habit assumptions.
• Dense and sphere-like ice particles tend to overestimate SFR, while most non-spherical particles underestimate it.
• SFR biases can differ by more than 200% between extreme ice habit cases.
• The optimal choice of ice habit is environmentally dependent: a hollow bullet rosette performs well in moist and warm conditions, whereas a solid ice sphere excels in cold and dry conditions.
• A machine learning model integrating multiple ice habits improves overall statistical metrics by approximately 10% (and by 40% in deep clouds) compared to single-ice habit methods.

Contributions

• Quantifies the substantial sensitivity and bias (>200%) of space-borne passive microwave SFR retrievals to ice habit assumptions.
• Demonstrates the environmental dependency of optimal ice habits for SFR retrieval.
• Introduces a novel machine learning approach to dynamically integrate multiple ice habits, significantly enhancing the accuracy of SFR retrievals.

Funding

Not explicitly mentioned in the abstract.

Citation

@article{Hong2025Effects,
  author = {Hong, Yulan and Meng, Huan and Fan, Yongzhen and Dong, Jun and Ren, Tong and Yang, Ping},
  title = {The Effects of Ice Habit Models on Passive Microwave Snowfall Rate Retrievals},
  journal = {Geophysical Research Letters},
  year = {2025},
  doi = {10.1029/2025gl116853},
  url = {https://doi.org/10.1029/2025gl116853}
}