Version 1
: Received: 25 October 2024 / Approved: 28 October 2024 / Online: 29 October 2024 (05:13:34 CET)
How to cite:
Mehdizadeh, G.; Erfani, E.; McDonough, F.; Hosseinpour, F. Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling. Preprints2024, 2024102154. https://doi.org/10.20944/preprints202410.2154.v1
Mehdizadeh, G.; Erfani, E.; McDonough, F.; Hosseinpour, F. Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling. Preprints 2024, 2024102154. https://doi.org/10.20944/preprints202410.2154.v1
Mehdizadeh, G.; Erfani, E.; McDonough, F.; Hosseinpour, F. Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling. Preprints2024, 2024102154. https://doi.org/10.20944/preprints202410.2154.v1
APA Style
Mehdizadeh, G., Erfani, E., McDonough, F., & Hosseinpour, F. (2024). Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling. Preprints. https://doi.org/10.20944/preprints202410.2154.v1
Chicago/Turabian Style
Mehdizadeh, G., Frank McDonough and Farnaz Hosseinpour. 2024 "Quantifying the Influence of Cloud Seeding on Ice Particle Growth and Snowfall Through Idealized Microphysical Modeling" Preprints. https://doi.org/10.20944/preprints202410.2154.v1
Abstract
Cloud seeding is a weather modification technique for enhancing precipitation in arid and semi-arid regions, including Western U.S. However, designing an optimal cloud seeding operation based on comprehensive evaluation metrics, such as seeding agent dispersion and atmospheric conditions, has yet to be thoroughly explored for this region. This study investigated the impacts of cloud seeding, particularly utilizing silver iodide, on ice particle growth within clouds through numerical modeling. By leveraging the Snow Growth Model for Rimed Snowfall (SGMR), the microphysical processes involved in cloud seeding across five distinct events were simulated. The events were in the Lake Tahoe region, nestled within the Sierra Nevada Mountain ranges in the western U.S. This model was executed based on six primary variables, including cloud top height, cloud base height, cloud top temperature, cloud base temperature, liquid water content, and ice water content. This study incorporated datasets from the Modern-Era Retrospective Analysis for Research and Applications Version 2 and the Clouds and the Earth Radiant Energy System. Findings revealed the importance of ice nucleation, aggregation, diffusion, and riming processes and highlighted the effectiveness of cloud seeding in enhancing ice particle number concentration, ice water content, and snowfall rates. However, event-specific analyses indicated diverse precipitation responses to cloud seeding based on initial atmospheric conditions. The SGMR modeling hints at the importance of improving ice microphysical processes and provides insights for future cloud seeding research using regional and global climate models.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
