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Timothy A. Bonin
,
Brian J. Carroll
,
R. Michael Hardesty
,
W. Alan Brewer
,
Kristian Hajny
,
Olivia E. Salmon
, and
Paul B. Shepson

Abstract

A Halo Photonics Stream Line XR Doppler lidar has been deployed for the Indianapolis Flux Experiment (INFLUX) to measure profiles of the mean horizontal wind and the mixing layer height for quantification of greenhouse gas emissions from the urban area. To measure the mixing layer height continuously and autonomously, a novel composite fuzzy logic approach has been developed that combines information from various scan types, including conical and vertical-slice scans and zenith stares, to determine a unified measurement of the mixing height and its uncertainty. The composite approach uses the strengths of each measurement strategy to overcome the limitations of others so that a complete representation of turbulent mixing is made in the lowest km, depending on clouds and aerosol distribution. Additionally, submeso nonturbulent motions are identified from zenith stares and removed from the analysis, as these motions can lead to an overestimate of the mixing height. The mixing height is compared with in situ profile measurements from a research aircraft for validation. To demonstrate the utility of the measurements, statistics of the mixing height and its diurnal and annual variability for 2016 are also presented. The annual cycle is clearly captured, with the largest and smallest afternoon mixing heights observed at the summer and winter solstices, respectively. The diurnal cycle of the mixing layer is affected by the mean wind, growing slower in the morning and decaying more rapidly in the evening with lighter winds.

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Austin P. Hope
,
Israel Lopez-Coto
,
Kris Hajny
,
Jay M. Tomlin
,
Robert Kaeser
,
Brian Stirm
,
Anna Karion
, and
Paul B. Shepson

Abstract

We investigated the ability of three planetary boundary layer (PBL) schemes in the Weather Research and Forecasting (WRF) Model to simulate boundary layer turbulence in the “gray zone” (i.e., resolutions from 100 m to 1 km). The three schemes chosen are the well-established MYNN PBL scheme and the two newest PBL schemes added to WRF: the three-dimensional scale-adaptive turbulent kinetic energy scheme (SMS-3DTKE) and the E–ε parameterization scheme (EEPS). The SMS-3DTKE scheme is designed to be scale aware and avoid the double counting of TKE in simulations within the gray zone. We evaluated their performance using aircraft measurements obtained during three research flights immediately downwind of Manhattan, New York City, New York. The MYNN PBL scheme simulates TKE best, despite not being scale aware and slightly underestimating TKE from observations, whereas the SMS-3DTKE scheme appears to be overly scale aware for the three flights examined, in particular, when combined with the MM5 surface layer scheme. The EEPS scheme significantly underestimates TKE, mostly in the elevated layers of the boundary layer. In addition, we examined the impact of flow over tall buildings on observed TKE and found that only the windiest day showed a significant increase in TKE directly downwind of Manhattan. This impact was not reproduced by any of the model configurations, regardless of the land-use data selected, although the better resolved National Land Cover Database (NLCD) land use led to a slight improvement of the spatial distribution of TKE, implying that more explicit representation of the impact of tall buildings may be needed to fully capture their impact on boundary layer turbulence.

Significance Statement

Because the majority of the world’s population lives in cities, it is important to accurately simulate the atmosphere above and around these cities including the turbulence caused by tall buildings. This turbulence can significantly impact the mixing and dilution of air pollutants and other toxins in highly populated urban environments. The scale of cities often falls into what is known as the “gray zone” for turbulence modeling, which has been analyzed theoretically before but rarely in varied real-world conditions. Our analysis around New York City, New York, suggests that model turbulence schemes can match observations relatively well even at gray zone scales, although newer schemes require refinement, and all schemes tend to underestimate turbulence downwind of tall buildings.

Open access
Annmarie G. Carlton
,
Joost de Gouw
,
Jose L. Jimenez
,
Jesse L. Ambrose
,
Alexis R. Attwood
,
Steven Brown
,
Kirk R. Baker
,
Charles Brock
,
Ronald C. Cohen
,
Sylvia Edgerton
,
Caroline M. Farkas
,
Delphine Farmer
,
Allen H. Goldstein
,
Lynne Gratz
,
Alex Guenther
,
Sherri Hunt
,
Lyatt Jaeglé
,
Daniel A. Jaffe
,
John Mak
,
Crystal McClure
,
Athanasios Nenes
,
Thien Khoi Nguyen
,
Jeffrey R. Pierce
,
Suzane de Sa
,
Noelle E. Selin
,
Viral Shah
,
Stephanie Shaw
,
Paul B. Shepson
,
Shaojie Song
,
Jochen Stutz
,
Jason D. Surratt
,
Barbara J. Turpin
,
Carsten Warneke
,
Rebecca A. Washenfelder
,
Paul O. Wennberg
, and
Xianling Zhou

Abstract

The Southeast Atmosphere Studies (SAS), which included the Southern Oxidant and Aerosol Study (SOAS); the Southeast Nexus (SENEX) study; and the Nitrogen, Oxidants, Mercury and Aerosols: Distributions, Sources and Sinks (NOMADSS) study, was deployed in the field from 1 June to 15 July 2013 in the central and eastern United States, and it overlapped with and was complemented by the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. SAS investigated atmospheric chemistry and the associated air quality and climate-relevant particle properties. Coordinated measurements from six ground sites, four aircraft, tall towers, balloon-borne sondes, existing surface networks, and satellites provide in situ and remotely sensed data on trace-gas composition, aerosol physicochemical properties, and local and synoptic meteorology. Selected SAS findings indicate 1) dramatically reduced NOx concentrations have altered ozone production regimes; 2) indicators of “biogenic” secondary organic aerosol (SOA), once considered part of the natural background, were positively correlated with one or more indicators of anthropogenic pollution; and 3) liquid water dramatically impacted particle scattering while biogenic SOA did not. SAS findings suggest that atmosphere–biosphere interactions modulate ambient pollutant concentrations through complex mechanisms and feedbacks not yet adequately captured in atmospheric models. The SAS dataset, now publicly available, is a powerful constraint to develop predictive capability that enhances model representation of the response and subsequent impacts of changes in atmospheric composition to changes in emissions, chemistry, and meteorology.

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