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Montie M. Orgill
,
John D. Kincheloe
, and
Robert A. Sutherland

Abstract

The mesoalpha-scale upper-level sounding network data collected during the 1984 ASCOT meteorological and tracer experiments provided a unique opportunity to analyze the nocturnal drainage wind in four different valleys in western Colorado, and to examine the effects of the synoptic-and mesoscale ambient conditions on these valley drainage winds. The six experimental periods, although biased because of a “fair weather” selection process, provided an additional opportunity to examine “good” and “poor” drainage scenarios. The results show that drainage winds fill all four valleys up to 80%–l00% of their valley depths under favorable nocturnal radiative longwave cooling (1.0°–1.5°C h−1) and light (<5 m s−1) ambient winds. Valley drainage, once established, is rather resistant to erosion from above because of the large source regions of these valleys, their large volume fluxes and inertia, and the persistent stable conditions inside these valleys. Wind erosion was observed on three nights when the drainage depth was reduced to less than half of the valley depth. The principal contributing factors to wind erosion processes were above-valley along-valley wind component opposing the drainage, valley stability, height of the 5 m s−1 isotach above the valley, and total above-valley wind acceleration. Generally, wind erosion processes appear to be especially active when above-valley wind speeds exceed 5 m s−1 and accelerations exceed 0.00040 m s−1. Other contributing factors that cause variable or terminated drainage depths are precipitation-evaporation effects causing nonradiative drainage events, wind shear above the valley, cloudiness, frontal passages, and synoptic winds directed in the down-valley direction.

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Montie M. Orgill
,
John D. Kincheloe
, and
Robert A. Sutherland

Abstract

An experimental weather classification, analysis, and nowcasting system, based upon a combination of artificial intelligence techniques and conventional numerical modeling, and designed for use as a real-time range/field forecasting aid, is described. In particular, a computer-based prototype coupled knowledge-based system, called PROCANS (Prototype Coupled Analysis and Nowcasting System), tailored for applications at the U.S. Army field testing range at Fort Hunter Liggett, California, is used as an example to demonstrate and evaluate the overall concept. The components of the system are: 1) a rule-based meteorological scenario evaluator for analysis and classification of weather scenarios, 2) a nowcaster that uses four analogical and rule-based expert subsystems for nowcasting radiation fog, wind gustiness, thunderstorms, and precipitation, 3) a numerical transport and diffusion module based upon either Gaussian or Monte Carlo particle-trajectory models to simulate airflow and diffusion patterns, and 4) a master database for storing information for possible retrieval, comparing current weather scenarios with past scenarios for possible matching and for analogical and conceptual reasoning to aid future predictions. A preliminary evaluation of PROCANS shows that coupled knowledge-based systems have potential as an integrated local analysis and prediction tool or forecaster's aid for field operations such as smoke screening.

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