Articles | Volume 9, issue 5
https://doi.org/10.5194/amt-9-1993-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-1993-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Evaluation of three lidar scanning strategies for turbulence measurements
Jennifer F. Newman
CORRESPONDING AUTHOR
School of Meteorology, University of Oklahoma, Norman, OK,
USA
now at: National Wind Technology Center, National Renewable
Energy Laboratory, Golden, CO, USA
Petra M. Klein
School of Meteorology, University of Oklahoma, Norman, OK,
USA
Sonia Wharton
Atmospheric, Earth and Energy Division, Lawrence Livermore
National Laboratory, Livermore, CA, USA
Ameya Sathe
DTU Wind Energy, Risø Campus, Roskilde, Denmark
now at: DONG Energy, Copenhagen, Denmark
Timothy A. Bonin
School of Meteorology, University of Oklahoma, Norman, OK,
USA
now at: Cooperative Institute for Research in the
Environmental Sciences, University of Colorado, and National Oceanic
and Atmospheric Administration/Earth System Research Laboratory,
Boulder, CO, USA
Phillip B. Chilson
School of Meteorology, University of Oklahoma, Norman, OK,
USA
Advanced Radar Research Center, University of Oklahoma,
Norman, OK, USA
Andreas Muschinski
NorthWest Research Associates, Boulder, CO, USA
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The winds in the lowest layer of our atmosphere are commonly sensed using Doppler LiDARs. These LiDARs are commonly set to scan the atmosphere in a single way, which can sometimes miss the most interesting events in our lower atmosphere such as high wind events and mountain valley flow reversals. Here, we present an infrastructure for intelligent sensing, using Argonne National Laboratory's state of the art cloud-based edge computing system Waggle.
Matteo Puccioni, Sonia Wharton, Stephan F. J. De Wekker, Robert S. Arthur, Tianyi Li, Ye Liu, Sha Feng, Kyle Pressel, Raj K. Rai, Larry K. Berg, and Jerome D. Fast
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Revised manuscript not accepted
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A multi-wind Lidar campaign is conducted in a forest in the United States Southeast, a region featuring low wind resources due to the forest drag. Although the latter reduces the wind for hundreds of meters above ground, we found this effect to be negligible for heights above 8 times the tree height where the wind is dominated by local atmospheric events. This scenario opens to the development of taller turbines harvesting the wind farther away from the ground where the forest drag is minimal.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
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This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
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This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
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Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
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This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Francesca M. Lappin, Tyler M. Bell, Elizabeth A. Pillar-Little, and Phillip B. Chilson
Atmos. Meas. Tech., 15, 1185–1200, https://doi.org/10.5194/amt-15-1185-2022, https://doi.org/10.5194/amt-15-1185-2022, 2022
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Andrew O. Langford, Christoph J. Senff, Raul J. Alvarez II, Ken C. Aikin, Sunil Baidar, Timothy A. Bonin, W. Alan Brewer, Jerome Brioude, Steven S. Brown, Joel D. Burley, Dani J. Caputi, Stephen A. Conley, Patrick D. Cullis, Zachary C. J. Decker, Stéphanie Evan, Guillaume Kirgis, Meiyun Lin, Mariusz Pagowski, Jeff Peischl, Irina Petropavlovskikh, R. Bradley Pierce, Thomas B. Ryerson, Scott P. Sandberg, Chance W. Sterling, Ann M. Weickmann, and Li Zhang
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The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) combined lidar, aircraft, and in situ measurements with global models to investigate the contributions of stratospheric intrusions, regional and Asian pollution, and wildfires to background ozone in the southwestern US during May and June 2017 and demonstrated that these processes contributed to background ozone levels that exceeded 70 % of the US National Ambient Air Quality Standard during the 6-week campaign.
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Short summary
Remote sensing devices known as lidars are often used to take measurements at potential wind farm sites. These instruments are however not optimized for measuring turbulence, small-scale changes in wind speed. In this manuscript, the impact of lidar configurations and atmospheric conditions on turbulence accuracy is explored. A new method was developed to correct lidar turbulence measurements and is described in detail such that other lidar users can apply it to their own instruments.
Remote sensing devices known as lidars are often used to take measurements at potential wind...