Idaho Power conducts a winter cloud seeding program to augment snowfall along the Snake River Basin and its tributaries for hydro-generation purposes. The program is presently focused in two areas; the Payette River watershed and the upper Snake River system in eastern Idaho.
Idaho Power has invested over 15 years of research and development in the subject of cloud seeding to enhance winter snowpack. Snowpack enhancement activities rely heavily upon operational forecast models to provide guidance on when conditions are generally suitable for cloud seeding. High resolution, tailored forecast models can also provide more accurate guidance for cloud seeding operations and other activities of interest to Idaho Power, such as stream flow forecasting.
A “Phase One” cloud seeding feasibility study using the Weather Research and Forecasting (WRF) mesoscale model was undertaken by the National Center for Atmospheric Research (NCAR) Research Applications Laboratory (RAL) in order to provide model-based guidance on the effectiveness of cloud seeding using the existing ground generators, aircraft tracks, and planned new ground generator sites in the Payette, Eastern Idaho, and Western Wyoming regions operated by Idaho Power. The WRF model was run coupled with a silver iodide (AgI) cloud seeding parameterization (Xue et al. 2013a,b) to simulate potential cloud seeding impacts from 10 actual cloud seeding events across the southern Idaho region.
A “Phase Two” study was conducted by NCAR in order to investigate the sensitivities of the seeding effect in the WRF cloud seeding model and compare the WRF model output with available observations. The sensitivity tests showed that the simulated seeding effect is most impacted by the seeding rate and whether the seeding occurred via airborne seeding or ground generators. Airborne seeding was shown to be more effective than ground in all cases due to the improved targeting of the AgI to the optimal cloud regions for snow crystal growth and fallout. The results also showed that small changes in the background meteorological conditions (such as wind speed and relative humidity) could induce precipitation changes similar in magnitude to the seeding effect.
The optimal configuration for cloud seeding simulations needs to effectively simulate precipitation, cloud water, and the dispersion of AgI. The model configuration tests indicated that the initialization data and the choice of domain and resolution were highly influential factors impacting the simulated precipitation, while the boundary layer scheme had less of an impact (on the simulated precipitation). However, Phase Two analysis with ice nuclei measurements (from Wyoming) concluded that the boundary layer schemes do not adequately simulate the vertical diffusion of AgI from ground seeding. Rather, a Large Eddy Simulation (LES) framework coupled with high horizontal and vertical model resolution seemed to capture the transport of AgI much more adequately.
NCAR is currently conducting a two-year “Phase Three” effort, building upon the cloud seeding modeling research conducted in Phases One and Two, to develop and implement a real-time cloud seeding forecast guidance system using WRF and the cloud seeding parameterization (Xue et al. 2013a,b). As part of this effort, NCAR is working with the University of Arizona to run a research version of WRF to obtain tailored precipitation and cloud seeding forecasting relevant to the Idaho Power cloud seeding operations.