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Daniel Breed, Roy Rasmussen, Courtney Weeks, Bruce Boe, and Terry Deshler

Abstract

An overview of the Wyoming Weather Modification Pilot Project (WWMPP) is presented. This project, funded by the State of Wyoming, is designed to evaluate the effectiveness of cloud seeding with silver iodide in the Medicine Bow and Sierra Madre Ranges of south-central Wyoming. The statistical evaluation is based on a randomized crossover design for the two barriers. The description of the experimental design includes the rationale behind the design choice, the criteria for case selection, facilities for operations and evaluation, and the statistical analysis approach. Initial estimates of the number of cases needed for statistical significance used historical Snow Telemetry (SNOTEL) data (1987–2006), prior to the beginning of the randomized seeding experiment. Refined estimates were calculated using high-resolution precipitation data collected during the initial seasons of the project (2007–10). Comparing the sample size estimates from these two data sources, the initial estimates are reduced to 236 (110) for detecting a 10% (15%) change. The sample size estimates are highly dependent on the assumed effect of seeding, on the correlations between the two target barriers and between the target and control sites, and on the variance of the response variable, namely precipitation. In addition to the statistical experiment, a wide range of physical studies and ancillary analyses are being planned and conducted.

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James W. Wilson, Charles A. Knight, Sarah A. Tessendorf, and Courtney Weeks

Abstract

During the Queensland Cloud Seeding Research Program, the “CP2” polarimetric radar parameter differential radar reflectivity Z dr was used to examine the raindrop size evolution in both maritime and continental clouds. The focus of this paper is to examine the natural variability of the drop size distribution. The primary finding is that there are two basic raindrop size evolutions, one associated with continental air masses characterized by relatively high aerosol concentrations and long air trajectories over land and the other associated with maritime air masses with lower aerosol concentrations. The size evolution difference is during the growth stage of the radar echoes. The differential radar reflectivity in the growing continental clouds is dominated by large raindrops, whereas in the maritime clouds differential reflectivity is dominated by small raindrops and drizzle. The drop size evolution in many of the maritime air masses was very similar to those observed in the maritime air of the Caribbean Sea observed with the NCAR S-band polarimetric radar (S-Pol) during the Rain in Cumulus over the Ocean (RICO) experiment. Because the tops of the Queensland continental clouds ascended almost 2 times as fast as the maritime ones in their growth stage, both dynamical and aerosol factors may be important for the systematic difference in drop size evolution. Recommendations are advanced for future field programs to understand better the causes for the observed variability in drop size evolution. Also, considering the natural variability in drop size evolution, comments are provided on conducting and evaluating cloud seeding experiments.

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Roy M. Rasmussen, Sarah A. Tessendorf, Lulin Xue, Courtney Weeks, Kyoko Ikeda, Scott Landolt, Dan Breed, Terry Deshler, and Barry Lawrence

Abstract

The Wyoming Weather Modification Pilot Project randomized cloud seeding experiment was a crossover statistical experiment conducted over two mountain ranges in eastern Wyoming and lasted for 6 years (2008–13). The goal of the experiment was to determine if cloud seeding of orographic barriers could increase snowfall and snowpack. The experimental design included triply redundant snow gauges deployed in a target–control configuration, covariate snow gauges to account for precipitation variability, and ground-based seeding with silver iodide (AgI). The outcomes of this experiment are evaluated with the statistical–physical experiment design and with ensemble modeling. The root regression ratio (RRR) applied to 118 experimental units provided insufficient statistical evidence (p value of 0.28) to reject the null hypothesis that there was no effect from ground-based cloud seeding. Ensemble modeling estimates of the impact of ground-based seeding provide an alternate evaluation of the 6-yr experiment. The results of the model ensemble approach with and without seeding estimated a mean enhancement of precipitation of 5%, with an inner-quartile range of 3%–7%. Estimating the impact on annual precipitation over these mountain ranges requires results from another study that indicated that approximately 30% of the annual precipitation results from clouds identified as seedable within the seeding experiment. Thus the seeding impact is on the order of 1.5% of the annual precipitation, compared to 1% for the statistical–physical experiment, which was not sufficient to reject the null hypothesis. These results provide an estimate of the impact of ground-based cloud seeding in the Sierra Madre and Medicine Bow Mountains in Wyoming that accounts for uncertainties in both initial conditions and model physics.

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Steven D. Miller, Courtney E. Weeks, Randy G. Bullock, John M. Forsythe, Paul A. Kucera, Barbara G. Brown, Cory A. Wolff, Philip T. Partain, Andrew S. Jones, and David B. Johnson

Abstract

Clouds pose many operational hazards to the aviation community in terms of ceilings and visibility, turbulence, and aircraft icing. Realistic descriptions of the three-dimensional (3D) distribution and temporal evolution of clouds in numerical weather prediction models used for flight planning and routing are therefore of central importance. The introduction of satellite-based cloud radar (CloudSat) and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) sensors to the National Aeronautics and Space Administration A-Train is timely in light of these needs but requires a new paradigm of model-evaluation tools that are capable of exploiting the vertical-profile information. Early results from the National Center for Atmospheric Research Model Evaluation Toolkit (MET), augmented to work with the emergent satellite-based active sensor observations, are presented here. Existing horizontal-plane statistical evaluation techniques have been adapted to operate on observations in the vertical plane and have been extended to 3D object evaluations, leveraging blended datasets from the active and passive A-Train sensors. Case studies of organized synoptic-scale and mesoscale distributed cloud systems are presented to illustrate the multiscale utility of the MET tools. Definition of objects on the basis of radar-reflectivity thresholds was found to be strongly dependent on the model’s ability to resolve details of the cloud’s internal hydrometeor distribution. Contoured-frequency-by-altitude diagrams provide a useful mechanism for evaluating the simulated and observed 3D distributions for regional domains. The expanded MET provides a new dimension to model evaluation and positions the community to better exploit active-sensor satellite observing systems that are slated for launch in the near future.

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