Mehdizadeh et al. (2025) Assessing Orographic Cloud Seeding Impacts Through Integration of Remote Sensing from Multispectral Satellite, Radar Data, and In Situ Observations in the Western United States

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Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-09-12
• Authors: Ghazal Mehdizadeh, Frank McDonough, Farnaz Hosseinpour
• DOI: 10.3390/rs17183161

Research Groups

This study evaluates the impacts of wintertime cloud seeding in the western United States using an integrated remote sensing approach, revealing significant regional variability in seeding signatures linked to local atmospheric conditions. It demonstrates that favorable conditions lead to strong microphysical changes and increased radar reflectivity, while less favorable conditions result in weaker responses.

Objective

• To evaluate the impacts of wintertime cloud seeding events in the western United States using an integrated remote sensing approach.

Study Configuration

• Spatial Scale: Western United States, focusing on the Lake Tahoe area, the Santa Rosa Range, and the Ruby Mountains.
• Temporal Scale: Wintertime cloud seeding events.

Methodology and Data

• Models used: No specific numerical models were mentioned; the study utilized an integrated remote sensing approach.
• Data sources: Multispectral observations from the Advanced Baseline Imager (ABI) aboard the GOES-R series satellites and regional radar reflectivity mosaics derived from NEXRAD data.

Main Results

• Significant regional variability in cloud seeding impacts was observed across the study areas.
• Tahoe events consistently showed strong seeding signatures, including droplet-to-ice phase transitions, cloud top cooling, thickened cloud structures, and increased radar reflectivity.
• Favorable atmospheric conditions, such as colder temperatures, elevated mid-to-upper tropospheric moisture, and sufficient supercooled liquid water, were linked to strong seeding responses.
• Events in the Santa Rosa Range generally exhibited weaker responses due to warmer, drier conditions and limited cloud development.
• The Ruby Mountains presented mixed outcomes regarding seeding effectiveness.
• The study improved the detection of seeding impacts by characterizing the progression from initial cloud phase transitions to hydrometeor development.

Contributions

• Improves the detection and characterization of cloud seeding impacts by detailing microphysical changes and precipitation development.
• Highlights the critical importance of aligning cloud seeding strategies with specific local atmospheric conditions for effectiveness.
• Demonstrates the practical value and utility of satellite-based remote sensing tools for evaluating cloud seeding effectiveness, especially in regions with limited ground-based data.
• Advances both the scientific understanding and operational practices of weather modification through the application of remote sensing.

Funding

Citation

@article{Mehdizadeh2025Assessing,
  author = {Mehdizadeh, Ghazal and McDonough, Frank and Hosseinpour, Farnaz},
  title = {Assessing Orographic Cloud Seeding Impacts Through Integration of Remote Sensing from Multispectral Satellite, Radar Data, and In Situ Observations in the Western United States},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs17183161},
  url = {https://doi.org/10.3390/rs17183161}
}