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John P. Gallagher, Ian G. McKendry, Anne Marie Macdonald, and W. Richard Leaitch

Abstract

A mountain air chemistry observatory has been operational on the summit of Whistler Mountain in British Columbia, Canada, since 2002. A 1-yr dataset of condensation nuclei (CN) concentration from this site has been analyzed along with corresponding meteorological data to assess the frequency and patterns of influence from the planetary boundary layer (PBL). Characterization of air masses sampled from the site as either PBL influenced or representative of the free troposphere (FT) is important to subsequent analysis of the chemistry data. Median CN concentrations and seasonal trends were found to be comparable to other midlatitude mountain sites. Monthly median number concentrations ranged from 120 cm−3 in January to 1601 cm−3 in July. Using well-defined diurnal cycles in CN concentration as an indicator of air arriving from nearby valleys, PBL influence was found to occur on a majority of days during spring and summer and less frequently in late autumn and winter. Days with PBL influence were usually associated with synoptic-scale weather patterns that were conducive to convective mixing processes. Although more common in the warm season, vertical mixing capable of transporting valley air to the mountaintop also occurred in February during a period of high pressure aloft. In contrast, an August case study indicated that the more stable character of marine air masses can at times keep the PBL below the summit on summer days. Considerable variability in the synoptic-scale weather conditions at Whistler means that careful analysis of available datasets must be made to discriminate FT from PBL periods at the observatory.

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John P. Gallagher, Ian G. McKendry, Paul W. Cottle, Anne Marie Macdonald, W. Richard Leaitch, and Kevin Strawbridge

Abstract

A ground-based lidar system that has been deployed in Whistler, British Columbia, Canada, since the spring of 2010 provides a means of evaluating vertical aerosol structure in a mountainous environment. This information is used to help to determine when an air chemistry observatory atop Whistler Mountain (2182 m MSL) is within the free troposphere or is influenced by the valley-based planetary boundary layer (PBL). Three case studies are presented in which 1-day time series images of backscatter data from the lidar are analyzed along with concurrent meteorological and air-chemistry datasets from the mountaintop site. The cases were selected to illustrate different scenarios of diurnal PBL evolution that are expected to be common during their respective seasons. The lidar images corroborate assumptions about PBL influence as derived from analysis of diurnal trends in water vapor, condensation nuclei, and ozone. Use of all of these datasets together bolsters efforts to determine which atmospheric layer the site best represents, which is important when evaluating the provenance of air samples.

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John P. Gallagher, Ian G. McKendry, Kevin Strawbridge, Anne Marie Macdonald, W. Richard Leaitch, and Paul W. Cottle
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Jason C. Knievel, Yubao Liu, Thomas M. Hopson, Justin S. Shaw, Scott F. Halvorson, Henry H. Fisher, Gregory Roux, Rong-Shyang Sheu, Linlin Pan, Wanli Wu, Joshua P. Hacker, Erik Vernon, Frank W. Gallagher III, and John C. Pace

Abstract

Since 2007, meteorologists of the U.S. Army Test and Evaluation Command (ATEC) at Dugway Proving Ground (DPG), Utah, have relied on a mesoscale ensemble prediction system (EPS) known as the Ensemble Four-Dimensional Weather System (E-4DWX). This article describes E-4DWX and the innovative way in which it is calibrated, how it performs, why it was developed, and how meteorologists at DPG use it. E-4DWX has 30 operational members, each configured to produce forecasts of 48 h every 6 h on a 272-processor high performance computer (HPC) at DPG. The ensemble’s members differ from one another in initial-, lateral-, and lower-boundary conditions; in methods of data assimilation; and in physical parameterizations. The predictive core of all members is the Advanced Research core of the Weather Research and Forecasting (WRF) Model. Numerical predictions of the most useful near-surface variables are dynamically calibrated through algorithms that combine logistic regression and quantile regression, generating statistically realistic probabilistic depictions of the atmosphere’s future state at DPG’s observing sites. Army meteorologists view E-4DWX’s output via customized figures posted to a restricted website. Some of these figures summarize collective results—for example, through means, standard deviations, or fractions of the ensemble exceeding thresholds. Other figures show each forecast, individually or grouped—for example, through spaghetti diagrams and time series. This article presents examples of each type of figure.

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