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Two ground-based and two airborne cloud seeding events between 2003 and 2005 from a trace chemistry field experiment conducted by the Desert Research Institute (DRI) in the Payette river basin of Idaho are simulated by the Weather Research and Forecasting (WRF) model in Large Eddy Simulation (LES) mode with a grid spacing of 667-m using the recently developed Wintertime silver iodide ( AgI ) Seeding Parameterization (WASP, Xue et al., 2013a,b ). The WASP in WRF was specifically modified to simulate the emissions of both AgI and Indium oxide ( In 2 O 3 ) particles from ground- and aircraft-based generators and the associated interactions with hydrometeors. Validations of model results against available observations from soundings and SNOwpack TELemetry (SNOTEL) data show that WRF simulations reasonably capture the dynamics, the thermodynamics, and the precipitation patterns. The comparisons of deposited Ag and In concentrations between model results and observations, and the analyses of seeding effects and AgI seeding efficiencies show that: The simulated Ag mass concentrations inside the snow are similar to observations ranging mostly from 0 to 10 −9 kg kg −1 and have high spatial correlations with observations in ground seeding cases (∼ 80 % ) but lower in airborne seeding (∼ 40 % ). The simulated Ag / In ratios range between 60 and 600 indicating that ice nucleation of AgI particle dominates the scavenging of AgI in incorporating Ag into the snow. The overall simulated seeding effect of the four cases is 6 % increase in precipitation over the LES domain, which is similar to what was inferred from the observations for the entire experiment. The magnitude of the AgI seeding efficiency is one order higher in airborne seeding cases (∼ 10 10 kg kg −1 ) compared to ground seeding cases (∼ 10 9 kg kg −1 ).

The impacts of various assumptions of cloud properties represented within a numerical weather prediction model's radiation scheme are demonstrated. In one approach, the model assumed the radiative effective radii of cloud water, cloud ice, and snow were represented by values assigned a priori, whereas a second, “coupled” approach utilized known cloud particle assumptions in the microphysics scheme that evolved during the simulations to diagnose the radii explicitly. This led to differences in simulated infrared (IR) brightness temperatures, radiative fluxes through clouds, and resulting surface temperatures that ultimately affect model-predicted diurnally-driven convection. The combined approach of evaluating simulated versus observed IR brightness temperatures, radiation reaching the ground, and surface temperature forecasts revealed the root model biases better than evaluating any single variable. This study found that the Weather Research and Forecasting (WRF) model predicted less overall clouds than was observed, particularly in the mid‑troposphere, but that properly connecting the assumptions of particle sizes in the microphysics scheme to the radiation scheme resulted in sensible cloud-radiation indirect effects and modest improvements in simulated IR brightness temperature, amount of solar radiation reaching the ground, and surface temperature.