Project Leads

This is an interdisciplinary and collaborative project among scientists at UC Davis, NCAR, UC Berkeley, and SEI and funded by the California Energy Commission.

PI: Adele Igel
co-PI: Sarah Tessendorf
Project Manager: Jamie Wolff
co-I: Lulin Xue, Sisi Chen, Andrew Schwartz, Aubrey Dugger, Charles Young, David Yates

Project Summary

To support California’s goal of utilizing hydropower as part of a portfolio of clean energy strategies, improved understanding and quantification of how cloud seeding can be used as a water resource management strategy is needed. This project will assess and identify the optimal conditions for cloud seeding in California wintertime orographic clouds using a combination of modeling and observational analysis. The assessment will include a comprehensive analysis of the lifetime of the snow augmentation process from initial snow formation to melt and runoff in the spring. The primary modeling tools to be used in this study are state-of-the-art technologies, including WRF-WxModⓇ, WRF-HydroⓇ, and iSnobal. WRF-WxMod is a cloud-seeding simulation model that includes a cloud-seeding parameterization to explicitly simulate the microphysical processes related to the release, dispersion, and nucleation of silver iodide (AgI) and its subsequent growth into snow. It produces spatial information on where and how much precipitation occurred due to cloud seeding. It has been demonstrated as a tool to quantify and optimize the impacts of cloud seeding for programs in several western U.S. states and abroad. WRF-Hydro and iSnobal are the WRF hydrological model and snowmelt model, respectively. Both of these models are optimized for use in the Sierra Nevada and have been incorporated into California water resource management practices at the California Department of Water Resources. The project will also include new measurements of background aerosol concentrations in the Sierras and analysis of radar data from previous cloud-seeding efforts.

Collectively, these efforts will modernize and better quantify the understanding of cloud-seeding impacts in the state of California in accord with recent advances in cloud-seeding science that have occurred in other western states, such as Idaho (e.g., the SNOWIE project). These results will be utilized by key partners and stakeholders to determine how cloud seeding can be part of their water management and hydropower planning strategies, as well as to revise existing, or initiate new, cloud-seeding programs. To date, cloud-seeding precipitation impact assessments for programs in the state of California rely primarily on statistical methods with large sources of uncertainty that are only valid at snow gauge sites, therefore lacking spatial impact information. Moreover, the impact of cloud seeding on streamflow is also often unquantified, or contains large sources of uncertainty due to the aforementioned limitations in statistical precipitation impact assessment. By using WRF-WxMod coupled with WRF-Hydro, more detailed and spatially-distributed impact estimates on both precipitation and streamflow can be quantified, which is important when the goal is to improve water supply reliability in specific basins with hydropower reservoirs.