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  • Author or Editor: Gregory S. Poulos x
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Gregory S. Poulos
and
James E. Bossert

Abstract

The Atmospheric Studies in Complex Terrain Program conducted a field experiment at the interface of the Rocky Mountains and the Great Plains in the winter of 1991. Extensive meteorological observations were taken in northeastern Colorado near Rocky Flats to characterize overnight conditions in the region. Simultaneously, a tracer dispersion experiment using over 130 samplers to track plume development was conducted by Rocky Flats facility personnel. These two datasets provided an opportunity to investigate the accuracy and applicability of a fully prognostic, primitive equation, mesoscale model to the simulation of complex terrain dispersion.

Meteorological conditions in the Rocky Flats region are forecast for selected case nights using the Regional Atmospheric Modeling System initialized with sounding data taken during the experiment. The forecast winds and temperature are used in a Lagrangian particle dispersion model to predict tracer plume transport. The results of both models are compared to observations taken during the experimental period and qualitatively and quantitatively assessed. It is found that this modeling system is able to reproduce many features of the observed meteorology and dispersion for four overnight cases. Quantitatively, maximum ground concentrations are generally found to be within a factor of 2 of observations and located radially within approximately 50° of azimuth of the observed location. Additional model sensitivity simulations define the role of local terrain features on Rocky Flats area dispersion and indicate the need for improved model initialization techniques when multiple data sources are available. These experiments reveal a promising future for the application of prognostic mesoscale models to emergency response problems in regions of complex terrain.

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C. David Whiteman
,
Sebastian W. Hoch
, and
Gregory S. Poulos

Abstract

At slope and valley floor sites in the Owens Valley of California, the late afternoon near-surface air temperature decline is often followed by a temporary temperature rise before the expected nighttime cooling resumes. The spatial and temporal patterns of this evening warming phenomenon, as seen in the March/April 2006 Terrain-Induced Rotor Experiment, are investigated using a widely distributed network of 51 surface-based temperature dataloggers. Hypotheses on the causes of the temperature rises are tested using heavily instrumented 34-m meteorological towers that were located within the datalogger array. The evening temperature rise follows the development of a shallow temperature deficit layer over the slopes and floor of the valley in which winds blow downslope. Background winds within the valley, freed from frictional deceleration from the earth’s surface by this layer, accelerate. The increased vertical wind shear across the temperature deficit layer eventually creates shear instability and mixes out the layer, creating the observed warming near the ground. As momentum is exchanged during the mixing event, the wind direction near the surface gradually turns from downslope to the background wind direction. After the short period of warming associated with the mixing, ongoing net radiative loss causes a resumption of the cooling.

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Juerg Schmidli
,
Gregory S. Poulos
,
Megan H. Daniels
, and
Fotini K. Chow

Abstract

The dynamics that govern the evolution of nighttime flows in a deep valley, California’s Owens Valley, are analyzed. Measurements from the Terrain-Induced Rotor Experiment (T-REX) reveal a pronounced valley-wind system with often nonclassical flow evolution. Two cases with a weak high pressure ridge over the study area but very different valley flow evolution are presented. The first event is characterized by the appearance of a layer of southerly flow after midnight local time, sandwiched between a thermally driven low-level downvalley (northerly) flow and a synoptic northwesterly flow aloft. The second event is characterized by an unusually strong and deep downvalley jet, exceeding 15 m s−1. The analysis is based on the T-REX measurement data and the output of high-resolution large-eddy simulations using the Advanced Regional Prediction System (ARPS). Using horizontal grid spacings of 1 km and 350 m, ARPS reproduces the observed flow features for these two cases very well. It is found that the low-level along-valley forcing of the valley wind is the result of a superposition of the local thermal forcing and a midlevel (2–2.5 km MSL) along-valley pressure forcing. The analysis shows that the large difference in valley flow evolution derives primarily from differences in the midlevel pressure forcing, and that the Owens Valley is particularly susceptible to these midlevel external influences because of its specific geometry. The results demonstrate the delicate interplay of forces that can combine to determine the valley flow structure on any given night.

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Daran L. Rife
,
Emilie Vanvyve
,
James O. Pinto
,
Andrew J. Monaghan
,
Christopher A. Davis
, and
Gregory S. Poulos

Abstract

This paper describes a new computationally efficient and statistically robust sampling method for generating dynamically downscaled climatologies. It is based on a Monte Carlo method coupled with stratified sampling. A small yet representative set of “case days” is selected with guidance from a large-scale reanalysis. When downscaled, the sample closely approximates the long-term meteorological record at a location, in terms of the probability density function. The method is demonstrated for the creation of wind maps to help determine the suitability of potential sites for wind energy farms. Turbine hub-height measurements at five U.S. and European tall tower sites are used as a proxy for regional climate model (RCM) downscaled winds to validate the technique. The tower-measured winds provide an independent test of the technique, since RCM-based downscaled winds exhibit an inherent dependence upon the large-scale reanalysis fields from which the case days are sampled; these same reanalysis fields would provide the boundary conditions to the RCM. The new sampling method is compared with the current approach widely used within the wind energy industry for creating wind resource maps, which is to randomly select 365 case days for downscaling, with each day in the calendar year being represented. The new method provides a more accurate and repeatable estimate of the long-term record of winds at each tower location. Additionally, the new method can closely approximate the accuracy of the current (365 day) industry approach using only a 180-day sample, which may render climate downscaling more tractable for those with limited computing resources.

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