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Rob K. Newsom
,
Larry K. Berg
,
Mikhail Pekour
,
Jerome Fast
,
Qin Xu
,
Pengfei Zhang
,
Qing Yang
,
William J. Shaw
, and
Julia Flaherty

Abstract

The accuracy of winds derived from Next Generation Weather Radar (NEXRAD) level-II data is assessed by comparison with independent observations from 915-MHz radar wind profilers. The evaluation is carried out at two locations with very different terrain characteristics. One site is located in an area of complex terrain within the State Line Wind Energy Center in northeastern Oregon. The other site is located in an area of flat terrain on the east-central Florida coast. The National Severe Storm Laboratory’s two-dimensional variational data assimilation (2DVar) algorithm is used to retrieve wind fields from the KPDT (Pendleton, Oregon) and KMLB (Melbourne, Florida) NEXRAD radars. Wind speed correlations at most observation height levels fell in the range from 0.7 to 0.8, indicating that the retrieved winds followed temporal fluctuations in the profiler-observed winds reasonably well. The retrieved winds, however, consistently exhibited slow biases in the range of 1–2 m s−1. Wind speed difference distributions were broad, with standard deviations in the range from 3 to 4 m s−1. Results from the Florida site showed little change in the wind speed correlations and difference standard deviations with altitude between about 300 and 1400 m AGL. Over this same height range, results from the Oregon site showed a monotonic increase in the wind speed correlation and a monotonic decrease in the wind speed difference standard deviation with increasing altitude. The poorest overall agreement occurred at the lowest observable level (~300 m AGL) at the Oregon site, where the effects of the complex terrain were greatest.

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Qing Yang
,
Larry K. Berg
,
Mikhail Pekour
,
Jerome D. Fast
,
Rob K. Newsom
,
Mark Stoelinga
, and
Catherine Finley

Abstract

One challenge with wind-power forecasts is the accurate prediction of rapid changes in wind speed (ramps). To evaluate the Weather Research and Forecasting (WRF) model's ability to predict such events, model simulations, conducted over an area of complex terrain in May 2011, are used. The sensitivity of the model's performance to the choice among three planetary boundary layer (PBL) schemes [Mellor–Yamada–Janjić (MYJ), University of Washington (UW), and Yonsei University (YSU)] is investigated. The simulated near-hub-height winds (62 m), vertical wind speed profiles, and ramps are evaluated against measurements obtained from tower-mounted anemometers, a Doppler sodar, and a radar wind profiler deployed during the Columbia Basin Wind Energy Study (CBWES). The predicted winds at near–hub height have nonnegligible biases in monthly mean under stable conditions. Under stable conditions, the simulation with the UW scheme better predicts upward ramps and the MYJ scheme is the most successful in simulating downward ramps. Under unstable conditions, simulations using the YSU and UW schemes show good performance in predicting upward ramps and downward ramps, with the YSU scheme being slightly better at predicting ramps with durations longer than 1 h. The largest differences in mean wind speed profiles among simulations using the three PBL schemes occur during upward ramps under stable conditions, which were frequently associated with low-level jets. The UW scheme has the best overall performance in ramp prediction over the CBWES site when evaluated using prediction accuracy and capture-rate statistics, but no single PBL parameterization is clearly superior to the others when all atmospheric conditions are considered.

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Raj K. Rai
,
Larry K. Berg
,
Mikhail Pekour
,
William J. Shaw
,
Branko Kosovic
,
Jeffrey D. Mirocha
, and
Brandon L. Ennis

Abstract

The assumption of subgrid-scale (SGS) horizontal homogeneity within a model grid cell, which forms the basis of SGS turbulence closures used by mesoscale models, becomes increasingly tenuous as grid spacing is reduced to a few kilometers or less, such as in many emerging high-resolution applications. Herein, the turbulence kinetic energy (TKE) budget equation is used to study the spatiotemporal variability in two types of terrain—complex [Columbia Basin Wind Energy Study (CBWES) site, northeastern Oregon] and flat [Scaled Wind Farm Technology (SWiFT) site, west Texas]—using the Weather Research and Forecasting (WRF) Model. In each case, six nested domains [three domains each for mesoscale and large-eddy simulation (LES)] are used to downscale the horizontal grid spacing from ~10 km to ~10 m using the WRF Model framework. The model output was used to calculate the values of the TKE budget terms in vertical and horizontal planes as well as the averages of grid cells contained in the four quadrants of the LES domain. The budget terms calculated along the planes and the mean profile of budget terms show larger spatial variability at the CBWES site than at the SWiFT site. The contribution of the horizontal derivative of the shear production term to the total shear production was found to be ≈45% and ≈15% at the CBWES and SWiFT sites, respectively, indicating that the horizontal derivatives applied in the budget equation should not be ignored in mesoscale model parameterizations, especially for cases with complex terrain with <10-km scale.

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Janek Uin
,
Allison C. Aiken
,
Manvendra K. Dubey
,
Chongai Kuang
,
Mikhail Pekour
,
Cynthia Salwen
,
Arthur J. Sedlacek
,
Gunnar Senum
,
Scott Smith
,
Jian Wang
,
Thomas B. Watson
, and
Stephen R. Springston

Abstract

Aerosols alter Earth’s radiative budget both directly and indirectly through interaction with clouds. Continuous observations are required to reduce the uncertainties in climate models associated with atmospheric processing and the interactions between aerosols and clouds. Field observations of aerosols are a central component of the Atmospheric Radiation Measurement (ARM) Facility’s global measurements. The ARM mission goal is to “provide the climate research community with strategically located in situ and remote sensing observatories designed to improve the understanding and representation, in climate and earth system models, of clouds and aerosols as well as their interactions and coupling with the Earth’s surface.” Since 1996, ARM has met this goal by operating Aerosol Observing Systems (AOS) for in situ measurement of aerosols. Currently the five ARM AOSs are the most comprehensive field deployable aerosol systems in the United States. The AOS suite includes seven measurement classes: number concentration, size distribution, chemical composition, radiative and optical properties, hygroscopicity, trace gases, and supporting meteorological conditions. AOSs are designed as standardized measurement platforms to enable intercomparison across the ARM Facility for regional process studies within a global context. The instrumentation and measurement capabilities of the ARM AOSs, along with a history of their design and field deployments are presented here.

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Jerome D. Fast
,
Larry K. Berg
,
Lizbeth Alexander
,
David Bell
,
Emma D’Ambro
,
John Hubbe
,
Chongai Kuang
,
Jiumeng Liu
,
Chuck Long
,
Alyssa Matthews
,
Fan Mei
,
Rob Newsom
,
Mikhail Pekour
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Tamara Pinterich
,
Beat Schmid
,
Siegfried Schobesberger
,
John Shilling
,
James N. Smith
,
Stephen Springston
,
Kaitlyn Suski
,
Joel A. Thornton
,
Jason Tomlinson
,
Jian Wang
,
Heng Xiao
, and
Alla Zelenyuk

Abstract

Shallow convective clouds are common, occurring over many areas of the world, and are an important component in the atmospheric radiation budget. In addition to synoptic and mesoscale meteorological conditions, land–atmosphere interactions and aerosol–radiation–cloud interactions can influence the formation of shallow clouds and their properties. These processes exhibit large spatial and temporal variability and occur at the subgrid scale for all current climate, operational forecast, and cloud-system-resolving models; therefore, they must be represented by parameterizations. Uncertainties in shallow cloud parameterization predictions arise from many sources, including insufficient coincident data needed to adequately represent the coupling of cloud macrophysical and microphysical properties with inhomogeneity in the surface-layer, boundary layer, and aerosol properties. Predictions of the transition of shallow to deep convection and the onset of precipitation are also affected by errors in simulated shallow clouds. Coincident data are a key factor needed to achieve a more complete understanding of the life cycle of shallow convective clouds and to develop improved model parameterizations. To address these issues, the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) campaign was conducted near the Atmospheric Radiation Measurement (ARM) Southern Great Plains site in north-central Oklahoma during the spring and summer of 2016. We describe the scientific objectives of HI-SCALE as well as the experimental approach, overall weather conditions during the campaign, and preliminary findings from the measurements. Finally, we discuss scientific gaps in our understanding of shallow clouds that can be addressed by analysis and modeling studies that use HI-SCALE data.

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Adam C. Varble
,
Stephen W. Nesbitt
,
Paola Salio
,
Joseph C. Hardin
,
Nitin Bharadwaj
,
Paloma Borque
,
Paul J. DeMott
,
Zhe Feng
,
Thomas C. J. Hill
,
James N. Marquis
,
Alyssa Matthews
,
Fan Mei
,
Rusen Öktem
,
Vagner Castro
,
Lexie Goldberger
,
Alexis Hunzinger
,
Kevin R. Barry
,
Sonia M. Kreidenweis
,
Greg M. McFarquhar
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Lynn A. McMurdie
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Mikhail Pekour
,
Heath Powers
,
David M. Romps
,
Celeste Saulo
,
Beat Schmid
,
Jason M. Tomlinson
,
Susan C. van den Heever
,
Alla Zelenyuk
,
Zhixiao Zhang
, and
Edward J. Zipser

Abstract

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft. A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.

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Jian Wang
,
Rob Wood
,
Michael P. Jensen
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J. Christine Chiu
,
Yangang Liu
,
Katia Lamer
,
Neel Desai
,
Scott E. Giangrande
,
Daniel A. Knopf
,
Pavlos Kollias
,
Alexander Laskin
,
Xiaohong Liu
,
Chunsong Lu
,
David Mechem
,
Fan Mei
,
Mariusz Starzec
,
Jason Tomlinson
,
Yang Wang
,
Seong Soo Yum
,
Guangjie Zheng
,
Allison C. Aiken
,
Eduardo B. Azevedo
,
Yann Blanchard
,
Swarup China
,
Xiquan Dong
,
Francesca Gallo
,
Sinan Gao
,
Virendra P. Ghate
,
Susanne Glienke
,
Lexie Goldberger
,
Joseph C. Hardin
,
Chongai Kuang
,
Edward P. Luke
,
Alyssa A. Matthews
,
Mark A. Miller
,
Ryan Moffet
,
Mikhail Pekour
,
Beat Schmid
,
Arthur J. Sedlacek
,
Raymond A. Shaw
,
John E. Shilling
,
Amy Sullivan
,
Kaitlyn Suski
,
Daniel P. Veghte
,
Rodney Weber
,
Matt Wyant
,
Jaemin Yeom
,
Maria Zawadowicz
, and
Zhibo Zhang

Abstract

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from 21 June to 20 July 2017 and from 15 January to 18 February 2018 in the Azores. The flights were designed to maximize the synergy between in situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

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