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Evaluating the Precipitation Impacts of Cloud Seeding in Southern Wyoming and Northern Colorado Using WRF-WxMod within an Ensemble Modeling Framework

  • National Center for Atmospheric Research
  • University of Wyoming
  • Inc.

Research output: Contribution to journalArticlepeer-review

Abstract

Wintertime precipitation in the western United States is essential for water supply, affecting agriculture, ecosystems, local economies, and beyond. Due to increasing concerns over water resources, the Wyoming Water Development Commission (WWDC) has invested in research to assess the potential of glaciogenic cloud seeding to enhance winter precipitation. This study uses the Weather Research and Forecasting Model Weather Modification (WRF-WxMod), within an ensemble modeling framework, to simulate a winter season (2019–20) of aerial cloud seeding over the Medicine Bow and Sierra Madre Ranges in Wyoming and the Never Summer Range in Colorado. Twenty-seven cases of operational cloud seeding were analyzed to quantify precipitation impacts. Ensemble-mean results for the 2019–20 season showed an increase of over 10 mm of liquid-equivalent precipitation at sites in the highest elevations of the target area, with an overall mean of 9478 acre-feet (AF) (ensemble range of 6058–14 030 AF) of additional precipitation across the North Platte and Little Snake River basins combined. In addition to investigating the results from the 2019–20 season, a case study from 9 to 10 December 2021 was evaluated in depth, as a “zigzag” signature resulting from a back-and-forth crosswind flight pattern of a seeding aircraft was observed on the Cheyenne NEXRAD, similar to what was observed in the Seeded and Natural Orographic Wintertime Clouds: The Idaho Experiment (SNOWIE) field campaign. This unambiguous seeding signature makes this an ideal case to compare WRF-WxMod to observations.

Original languageEnglish
Pages (from-to)607-632
Number of pages26
JournalJournal of Applied Meteorology and Climatology
Volume65
Issue number4
DOIs
StatePublished - Apr 2026

Keywords

  • Cloud seeding
  • Ensembles
  • Mesoscale models

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