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Arlin B. Super and Bruce A. Boe

Abstract

During March 1986, several airborne and ground-based silver iodide (AgI) seeding experiments were conducted over the Grand Mesa, Colorado, during a three-day period of northerly flow and shallow orographic cloud. While little natural snowfall was observed during these experiments, supercooled liquid water formed over the windward slopes and evaporated to the lee of the mesa of many hours. Seeding-induced microphysical changes coincident with the AgI plumes were found in all eight experiments, (including two that employed ground-based seeding) by aircraft sampling about 500 m above the mesa top. Precipitation rates estimated from ice particle images at light levels suggested increases within the seeded volumes in all but one experiment. Surface precipitation increases were observed in three aircraft seeding experiments and one ground-based seeding experiment that coincided with the passage of AgI plumes aloft. Surface observations were not possible during the other ground-based seeding experiment, but some increase in snowfall is thought probable. Three aircraft seeding experiments failed to show surface snowfall increases, and reasons for this are explored.

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S. A. Tessendorf, B. Boe, B. Geerts, M. J. Manton, S. Parkinson, and R. Rasmussen
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Arlin B. Super, Bruce A. Boe, Edmond W. Holroyd III, and James A. Heimbach Jr.

Abstract

A series of winter orographic cloud seeding experiments is described in which the seeding agent and associated changes in cloud microphysics are monitored to within 300 m of the target areas (Montana and Colorado), and at the surface (Colorado only). This, the first paper in a three-part series, discusses the underlying physical hypothesis and experimental approach, and describes in detail the instrumentation used. The results of the physical evaluations, presented in Parts II and III, show that marked microphysical changes were caused by both ground-based and aircraft seeding with silver iodide.

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Oli G. B. Sveinsson, Jose D. Salas, Duane C. Boes, and Roger A. Pielke Sr.

Abstract

The stochastic analysis, modeling, and simulation of climatic and hydrologic processes such as precipitation, streamflow, and sea surface temperature have usually been based on assumed stationarity or randomness of the process under consideration. However, empirical evidence of many hydroclimatic data shows temporal variability involving trends, oscillatory behavior, and sudden shifts. While many studies have been made for detecting and testing the statistical significance of these special characteristics, the probabilistic framework for modeling the temporal dynamics of such processes appears to be lacking. In this paper a family of stochastic models that can be used to capture the dynamics of abrupt shifts in hydroclimatic time series is proposed. The applicability of such “shifting mean models” are illustrated by using time series data of annual Pacific decadal oscillation (PDO) indices and annual streamflows of the Niger River.

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