Articles | Volume 11, issue 5
https://doi.org/10.5194/wes-11-1653-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1653-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 May 2026
Research article |
| 08 May 2026
| 08 May 2026
Temperature profiling at the American WAKE ExperimeNt (AWAKEN): methodology and uncertainty quantification
National Laboratory of the Rockies, Golden, CO, USA
David D. Turner
National Oceanic and Atmospheric Administration, Global Systems Laboratory, Boulder, CO, USA
Aliza Abraham
National Laboratory of the Rockies, Golden, CO, USA
Luc Rochette
LR Tech, Inc., Lévis, QC, Canada
Patrick J. Moriarty
National Laboratory of the Rockies, Golden, CO, USA
Related authors
Nicola Bodini, Aliza Abraham, Paula Doubrawa, Stefano Letizia, Julie K. Lundquist, Patrick Moriarty, and Ryan Scott
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-33, https://doi.org/10.5194/wes-2026-33, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines create "wakes" of slowed air that reduce power for nearby turbines. To help improve wind energy models, we analyzed data from a large field experiment. We focused on a single day with changing weather patterns. We found that even simple terrain features interacted with the wind to create large variations in power output – up to 80 percent – across a wind farm. This detailed dataset provides a real-world case needed to validate and improve wind energy design tools.
Yelena Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci., 11, 417–442, https://doi.org/10.5194/wes-11-417-2026, https://doi.org/10.5194/wes-11-417-2026, 2026
Short summary
Short summary
The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability in winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Bianca Adler, Laura Bianco, David D. Turner, Joseph B. Olson, Xia Sun, Joshua Gebauer, Nicola Bodini, Stefano Letizia, and James M. Wilczak
EGUsphere, https://doi.org/10.5194/egusphere-2026-97, https://doi.org/10.5194/egusphere-2026-97, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurate operational forecasts of temperature and wind in the coastal marine boundary layer are important for a wide range of applications. Leveraging data that were collected along the U.S. northeast coast during a multi-year period for the Third Wind Forecast Improvement project, we investigated the performance of the operational forecast model and identified systematic errors in wind and temperature forecasts that are now being addressed by the model developers.
William C. Radünz, Bruno Carmo, Julie K. Lundquist, Stefano Letizia, Aliza Abraham, Adam S. Wise, Miguel Sanchez Gomez, Nicholas Hamilton, Raj K. Rai, and Pedro S. Peixoto
Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, https://doi.org/10.5194/wes-10-2365-2025, 2025
Short summary
Short summary
We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
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
Short summary
Short summary
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.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
Nicola Bodini, Emina Maric, Ulrike Egerer, Grant Buster, Luke Lavin, Pavlo Pinchuk, Brandon Benton, and David D. Turner
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-67, https://doi.org/10.5194/wes-2026-67, 2026
Preprint under review for WES
Short summary
Short summary
To plan a reliable United States power grid, engineers need continuous weather data. Because the previous standard record ended in 2013, we created a new, easy-to-use weather database by repackaging complex weather forecasting models onto the same historical grid. We compared our data against real-world observations nationwide, finding it is highly accurate. This provides a seamless, reliable new standard to help plan the future of our national energy system.
Nicola Bodini, Patrick Moriarty, Regis Thedin, Paula Doubrawa, Cristina Archer, Myra Blaylock, Carlo Bottasso, Bruno Carmo, Lawrence Cheung, Camille Dubreuil, Rogier Floors, Thomas Herges, Daniel Houck, Ali Kanjari, Colleen M. Kaul, Christopher Kelley, Ru LI, Julie K. Lundquist, Desirae Major, Anh Kiet Nguyen, Mike Optis, Luan R. C. Parada, Alfredo Peña, Julian Quick, David Ricarte, William C. Radünz, Raj K. Rai, Oscar Garcia Santiago, Jonas Schulte, Knut S. Seim, M. Paul van der Laan, Kisorthman Vimalakanthan, and Adam Wise
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-34, https://doi.org/10.5194/wes-2026-34, 2026
Preprint under review for WES
Short summary
Short summary
Predicting wind farm energy production is challenging because wind patterns are complex. We tested 16 different models against real data from a major field experiment to see which worked best. Surprisingly, the most expensive and detailed models were not always more accurate than simpler ones. We found that feeding models better weather data was the most effective way to improve accuracy. These results help the industry choose the right tools for designing more efficient wind farms.
David D. Turner, Bianca Adler, Laura Bianco, James M. Wilczak, Vincent Michaud-Belleau, and Luc Rochette
Atmos. Meas. Tech., 19, 1573–1586, https://doi.org/10.5194/amt-19-1573-2026, https://doi.org/10.5194/amt-19-1573-2026, 2026
Short summary
Short summary
It is critical that a network of ground-based instruments that measure temperature and humidity profiles be well calibrated, so that differences between any two profiles can be attributed to atmospheric differences and not instrument calibration issues. This study evaluated the relative accuracy of 7 ground-based infrared spectrometers and their ability to measure these profiles, and found that the profile bias was much smaller than the uncertainty in the retrieved profiles themselves.
Nicola Bodini, Aliza Abraham, Paula Doubrawa, Stefano Letizia, Julie K. Lundquist, Patrick Moriarty, and Ryan Scott
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-33, https://doi.org/10.5194/wes-2026-33, 2026
Preprint under review for WES
Short summary
Short summary
Wind turbines create "wakes" of slowed air that reduce power for nearby turbines. To help improve wind energy models, we analyzed data from a large field experiment. We focused on a single day with changing weather patterns. We found that even simple terrain features interacted with the wind to create large variations in power output – up to 80 percent – across a wind farm. This detailed dataset provides a real-world case needed to validate and improve wind energy design tools.
Nicola Bodini, Joseph Olson, Brian Gaudet, Giacomo Valerio Iungo, Mojtaba Shams Solari, Sayahnya Roy, Julie K. Lundquist, Nathan Agarwal, Timothy A. Myers, Bianca Adler, Jeffrey D. Mirocha, Eric James, Laura Bianco, James M. Wilczak, and David D. Turner
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2026-17, https://doi.org/10.5194/wes-2026-17, 2026
Revised manuscript under review for WES
Short summary
Short summary
To improve offshore wind forecasts, the Third Wind Forecast Improvement Project monitored the United States East Coast for eighteen months. We compiled a daily log of weather events using advanced scanners and expert notes. This public dataset identifies important wind patterns, helping scientists test computer models and choose specific cases to study.
Yelena Pichugina, Alan W. Brewer, Sunil Baidar, Robert Banta, Edward Strobach, Brandi McCarty, Brian Carroll, Nicola Bodini, Stefano Letizia, Richard Marchbanks, Michael Zucker, Maxwell Holloway, and Patrick Moriarty
Wind Energ. Sci., 11, 417–442, https://doi.org/10.5194/wes-11-417-2026, https://doi.org/10.5194/wes-11-417-2026, 2026
Short summary
Short summary
The truck-based Doppler lidar system was used during the American Wake Experiment (AWAKEN) to obtain the high-frequency, simultaneous measurements of the horizontal wind speed, direction, and vertical velocity from a moving platform. The paper presents the unique capability of the novel lidar system to characterize the temporal, vertical, and spatial variability in winds at various distances from operating turbines and obtain quantitative estimates of wind speed reduction in the waked flow.
Bianca Adler, Laura Bianco, David D. Turner, Joseph B. Olson, Xia Sun, Joshua Gebauer, Nicola Bodini, Stefano Letizia, and James M. Wilczak
EGUsphere, https://doi.org/10.5194/egusphere-2026-97, https://doi.org/10.5194/egusphere-2026-97, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Accurate operational forecasts of temperature and wind in the coastal marine boundary layer are important for a wide range of applications. Leveraging data that were collected along the U.S. northeast coast during a multi-year period for the Third Wind Forecast Improvement project, we investigated the performance of the operational forecast model and identified systematic errors in wind and temperature forecasts that are now being addressed by the model developers.
Linus von Klitzing, David D. Turner, Diego Lange, and Volker Wulfmeyer
Atmos. Meas. Tech., 19, 359–370, https://doi.org/10.5194/amt-19-359-2026, https://doi.org/10.5194/amt-19-359-2026, 2026
Short summary
Short summary
Many atmospheric science endeavors require temporally resolved profiles of temperature, humidity, and winds. Radiosondes are considered the gold standard for measuring these profiles, but the temporal resolution is frequently too coarse for many applications within the atmospheric boundary layer. This study proposes a new method using a normalized height grid in the temporal interpolation process that yields more accurate profiles in the convective boundary layer.
Anna Voss, Konrad B. Bärfuss, Beatriz Cañadillas, Maik Angermann, Mark Bitter, Matthias Cremer, Thomas Feuerle, Jonas Spoor, Julie K. Lundquist, Patrick Moriarty, and Astrid Lampert
Wind Energ. Sci., 11, 71–88, https://doi.org/10.5194/wes-11-71-2026, https://doi.org/10.5194/wes-11-71-2026, 2026
Short summary
Short summary
This study analyzes onshore wind farm wakes in a semi-complex terrain with data conducted with the research aircraft of TU Braunschweig during the American WAKE experimeNt (AWAKEN). Vertical profiles of temperature, humidity, and wind give insights into the stratification of the atmospheric boundary layer, while horizontal profiles downwind of wind farms reveal an amplification of the reduction in wind speed in a semi-complex terrain, in particular at a distance of 10 km.
Calvin Coulbury, Ivy Tan, David Turner, and Chen Zhou
EGUsphere, https://doi.org/10.5194/egusphere-2025-6553, https://doi.org/10.5194/egusphere-2025-6553, 2026
Short summary
Short summary
Observations of low-level, single-layer clouds above the North Slope of Alaska, within the Arctic circle, are used to assess how cloud properties have changed from 2004 to 2023. Evidence is provided for a cooling influence of cloud property changes on Arctic amplified warming. Local temperature changes result in clouds becoming more opaque, driven by changes in cloud liquid water content. This work represents an avenue for the evaluation and improvement of model representations of Arctic clouds.
William C. Radünz, Bruno Carmo, Julie K. Lundquist, Stefano Letizia, Aliza Abraham, Adam S. Wise, Miguel Sanchez Gomez, Nicholas Hamilton, Raj K. Rai, and Pedro S. Peixoto
Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, https://doi.org/10.5194/wes-10-2365-2025, 2025
Short summary
Short summary
We explore how simple terrain influences spatial variations in wind speed and wind farm performance during a low-level jet. Using simulations, field observations, and turbine production data, we find that downstream turbines produce more power than upstream ones, despite being subjected to wake effects. This counterintuitive result arises because the low-level jet and winds near turbine rotors are highly sensitive to topographic features, leading to stronger winds at the downstream turbines.
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
Atmos. Meas. Tech., 18, 5129–5140, https://doi.org/10.5194/amt-18-5129-2025, https://doi.org/10.5194/amt-18-5129-2025, 2025
Short summary
Short summary
Entrainment is key in understanding temperature and moisture changes within the boundary layer, but it is difficult to observe using ground-based observations. This work used simulations to verify an assumption that simplifies entrainment estimations from ground-based observational data, recognizing that entrainment is the combination of the transfer of heat and moisture from above the boundary layer into it and the change in concentration of heat and moisture as boundary layer depth changes.
Laura Bianco, Reagan Mendeke, Jakob Lindblom, Irina V. Djalalova, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 10, 2117–2136, https://doi.org/10.5194/wes-10-2117-2025, https://doi.org/10.5194/wes-10-2117-2025, 2025
Short summary
Short summary
Adding more renewable energy into the electric grid is a critical part of the strategy to increase energy availability. Reliable numerical weather prediction (NWP) models need to be able to predict the intrinsic nature of weather-dependent resources such as wind ramp events, as wind energy could quickly be available in abundance or temporarily cease to exist. We assess the ability of the operational High Resolution Rapid Refresh NWP model to forecast wind ramp events in the two most recent versions.
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
Short summary
Short summary
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.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
Geosci. Model Dev., 18, 4935–4950, https://doi.org/10.5194/gmd-18-4935-2025, https://doi.org/10.5194/gmd-18-4935-2025, 2025
Short summary
Short summary
Satellites have observed Earth's emissions of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about Earth and the atmosphere. We present a tool that runs within Earth system models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Vincent Michaud-Belleau, Michel Gaudreau, Jean Lacoursière, Éric Boisvert, Lalaina Ravelomanantsoa, David D. Turner, and Luc Rochette
Atmos. Meas. Tech., 18, 3585–3609, https://doi.org/10.5194/amt-18-3585-2025, https://doi.org/10.5194/amt-18-3585-2025, 2025
Short summary
Short summary
The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST) is a commercially available ground-based infrared spectroradiometer. It is designed for automated and passive measurement of the thermal radiation emitted by the atmosphere, providing information about the vertical distribution of temperature and humidity, trace gases, clouds, and aerosols in the boundary layer. In this paper, we outline the key characteristics of the ASSIST hardware and signal processing algorithm that yields downwelling radiance spectra in near real-time.
David D. Turner, Maria P. Cadeddu, Julia M. Simonson, and Timothy J. Wagner
Atmos. Meas. Tech., 18, 3533–3546, https://doi.org/10.5194/amt-18-3533-2025, https://doi.org/10.5194/amt-18-3533-2025, 2025
Short summary
Short summary
When deriving a geophysical variable from remote sensors, the uncertainty and information content are critical. The latter quantify specifies what fraction of a real perturbation would be observed in the derived variable. This paper outlines, for the first time, a methodology for propagating the information content from multiple remote sensors into a derived product using horizontal advection as an example.
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
Short summary
Short summary
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.
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
Bianca Adler, David D. Turner, Laura Bianco, Irina V. Djalalova, Timothy Myers, and James M. Wilczak
Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, https://doi.org/10.5194/amt-17-6603-2024, 2024
Short summary
Short summary
Continuous profile observations of temperature and humidity in the lowest part of the atmosphere are essential for the evaluation of numerical weather prediction models and data assimilation for better weather forecasts. Such profiles can be retrieved from passive ground-based remote sensing instruments like infrared spectrometers and microwave radiometers. In this study, we describe three recent modifications to the retrieval framework TROPoe for improved temperature and humidity profiles.
Tessa E. Rosenberger, David D. Turner, Thijs Heus, Girish N. Raghunathan, Timothy J. Wagner, and Julia Simonson
Atmos. Meas. Tech., 17, 6595–6602, https://doi.org/10.5194/amt-17-6595-2024, https://doi.org/10.5194/amt-17-6595-2024, 2024
Short summary
Short summary
This work used model output to show that considering the changes in boundary layer depth over time in the calculations of variables such as fluxes and variance yields more accurate results than cases where calculations were done at a constant height. This work was done to improve future observations of these variables at the top of the boundary layer.
Majid Bastankhah, Marcus Becker, Matthew Churchfield, Caroline Draxl, Jay Prakash Goit, Mehtab Khan, Luis A. Martinez Tossas, Johan Meyers, Patrick Moriarty, Wim Munters, Asim Önder, Sara Porchetta, Eliot Quon, Ishaan Sood, Nicole van Lipzig, Jan-Willem van Wingerden, Paul Veers, and Simon Watson
Wind Energ. Sci., 9, 2171–2174, https://doi.org/10.5194/wes-9-2171-2024, https://doi.org/10.5194/wes-9-2171-2024, 2024
Short summary
Short summary
Dries Allaerts was born on 19 May 1989 and passed away at his home in Wezemaal, Belgium, on 10 October 2024 after battling cancer. Dries started his wind energy career in 2012 and had a profound impact afterward on the community, in terms of both his scientific realizations and his many friendships and collaborations in the field. His scientific acumen, open spirit of collaboration, positive attitude towards life, and playful and often cheeky sense of humor will be deeply missed by many.
Laura Bianco, Bianca Adler, Ludovic Bariteau, Irina V. Djalalova, Timothy Myers, Sergio Pezoa, David D. Turner, and James M. Wilczak
Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, https://doi.org/10.5194/amt-17-3933-2024, 2024
Short summary
Short summary
The Tropospheric Remotely Observed Profiling via Optimal Estimation physical retrieval is used to retrieve temperature and humidity profiles from various combinations of passive and active remote sensing instruments, surface platforms, and numerical weather prediction models. The retrieved profiles are assessed against collocated radiosonde in non-cloudy conditions to assess the sensitivity of the retrievals to different input combinations. Case studies with cloudy conditions are also inspected.
Volker Wulfmeyer, Christoph Senff, Florian Späth, Andreas Behrendt, Diego Lange, Robert M. Banta, W. Alan Brewer, Andreas Wieser, and David D. Turner
Atmos. Meas. Tech., 17, 1175–1196, https://doi.org/10.5194/amt-17-1175-2024, https://doi.org/10.5194/amt-17-1175-2024, 2024
Short summary
Short summary
A simultaneous deployment of Doppler, temperature, and water-vapor lidar systems is used to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of weather forecast and climate models.
Sunil Baidar, Timothy J. Wagner, David D. Turner, and W. Alan Brewer
Atmos. Meas. Tech., 16, 3715–3726, https://doi.org/10.5194/amt-16-3715-2023, https://doi.org/10.5194/amt-16-3715-2023, 2023
Short summary
Short summary
This paper provides a new method to retrieve wind profiles from coherent Doppler lidar (CDL) measurements. It takes advantage of layer-to-layer correlation in wind profiles to provide continuous profiles of up to 3 km by filling in the gaps where the CDL signal is too small to retrieve reliable results by itself. Comparison with the current method and collocated radiosonde wind measurements showed excellent agreement with no degradation in results where the current method gives valid results.
Paul Veers, Carlo L. Bottasso, Lance Manuel, Jonathan Naughton, Lucy Pao, Joshua Paquette, Amy Robertson, Michael Robinson, Shreyas Ananthan, Thanasis Barlas, Alessandro Bianchini, Henrik Bredmose, Sergio González Horcas, Jonathan Keller, Helge Aagaard Madsen, James Manwell, Patrick Moriarty, Stephen Nolet, and Jennifer Rinker
Wind Energ. Sci., 8, 1071–1131, https://doi.org/10.5194/wes-8-1071-2023, https://doi.org/10.5194/wes-8-1071-2023, 2023
Short summary
Short summary
Critical unknowns in the design, manufacturing, and operation of future wind turbine and wind plant systems are articulated, and key research activities are recommended.
Maria P. Cadeddu, Virendra P. Ghate, David D. Turner, and Thomas E. Surleta
Atmos. Chem. Phys., 23, 3453–3470, https://doi.org/10.5194/acp-23-3453-2023, https://doi.org/10.5194/acp-23-3453-2023, 2023
Short summary
Short summary
We analyze the variability in marine boundary layer moisture at the Eastern North Atlantic site on a monthly and daily temporal scale and examine its fundamental role in the control of boundary layer cloudiness and precipitation. The study also highlights the complex interaction between large-scale and local processes controlling the boundary layer moisture and the importance of the mesoscale spatial distribution of vapor to support convection and precipitation.
Bianca Adler, James M. Wilczak, Jaymes Kenyon, Laura Bianco, Irina V. Djalalova, Joseph B. Olson, and David D. Turner
Geosci. Model Dev., 16, 597–619, https://doi.org/10.5194/gmd-16-597-2023, https://doi.org/10.5194/gmd-16-597-2023, 2023
Short summary
Short summary
Rapid changes in wind speed make the integration of wind energy produced during persistent orographic cold-air pools difficult to integrate into the electrical grid. By evaluating three versions of NOAA’s High-Resolution Rapid Refresh model, we demonstrate how model developments targeted during the second Wind Forecast Improvement Project improve the forecast of a persistent cold-air pool event.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
Short summary
Short summary
In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, https://doi.org/10.5194/wes-7-2307-2022, 2022
Short summary
Short summary
This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Heather Guy, David D. Turner, Von P. Walden, Ian M. Brooks, and Ryan R. Neely
Atmos. Meas. Tech., 15, 5095–5115, https://doi.org/10.5194/amt-15-5095-2022, https://doi.org/10.5194/amt-15-5095-2022, 2022
Short summary
Short summary
Fog formation is highly sensitive to near-surface temperatures and humidity profiles. Passive remote sensing instruments can provide continuous measurements of the vertical temperature and humidity profiles and liquid water content, which can improve fog forecasts. Here we compare the performance of collocated infrared and microwave remote sensing instruments and demonstrate that the infrared instrument is especially sensitive to the onset of thin radiation fog.
Emmanouil M. Nanos, Carlo L. Bottasso, Filippo Campagnolo, Franz Mühle, Stefano Letizia, G. Valerio Iungo, and Mario A. Rotea
Wind Energ. Sci., 7, 1263–1287, https://doi.org/10.5194/wes-7-1263-2022, https://doi.org/10.5194/wes-7-1263-2022, 2022
Short summary
Short summary
The paper describes the design of a scaled wind turbine in detail, for studying wakes and wake control applications in the known, controllable and repeatable conditions of a wind tunnel. The scaled model is characterized by conducting experiments in two wind tunnels, in different conditions, using different measurement equipment. Results are also compared to predictions obtained with models of various fidelity. The analysis indicates that the model fully satisfies the initial requirements.
James B. Duncan Jr., Laura Bianco, Bianca Adler, Tyler Bell, Irina V. Djalalova, Laura Riihimaki, Joseph Sedlar, Elizabeth N. Smith, David D. Turner, Timothy J. Wagner, and James M. Wilczak
Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, https://doi.org/10.5194/amt-15-2479-2022, 2022
Short summary
Short summary
In this study, several ground-based remote sensing instruments are used to estimate the height of the convective planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the CHEESEHEAD19 field campaign. The impact of clouds (particularly boundary layer clouds) on the estimation of the boundary layer depth is also investigated.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Irina V. Djalalova, David D. Turner, Laura Bianco, James M. Wilczak, James Duncan, Bianca Adler, and Daniel Gottas
Atmos. Meas. Tech., 15, 521–537, https://doi.org/10.5194/amt-15-521-2022, https://doi.org/10.5194/amt-15-521-2022, 2022
Short summary
Short summary
In this paper we investigate the synergy obtained by combining active (radio acoustic sounding system – RASS) and passive (microwave radiometer) remote sensing observations to obtain temperature vertical profiles through a radiative transfer model. Inclusion of the RASS observations leads to more accurate temperature profiles from the surface to 5 km above ground, well above the maximum height of the RASS observations themselves (2000 m), when compared to the microwave radiometer used alone.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
Short summary
Short summary
We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
Short summary
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Cited articles
Abkar, M. and Porté-Agel, F.: Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study, Phys. Fluids, 27, https://doi.org/10.1063/1.4913695, 2015. a
Abraham, A., Puccioni, M., Jordan, A., Maric, E., Bodini, N., Hamilton, N., Letizia, S., Klein, P. M., Smith, E. N., Wharton, S., Gero, J., Jacob, J. D., Krishnamurthy, R., Newsom, R. K., Pekour, M., Radünz, W., and Moriarty, P.: Operational wind plants increase planetary boundary layer height: an observational study, Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, 2025. a
Adler, B., Turner, D. D., Bianco, L., Djalalova, I. V., Myers, T., and Wilczak, J. M.: Improving solution availability and temporal consistency of an optimal-estimation physical retrieval for ground-based thermodynamic boundary layer profiling, Atmos. Meas. Tech., 17, 6603–6624, https://doi.org/10.5194/amt-17-6603-2024, 2024. a
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.: Quantifying Wind Turbine Wake Characteristics from Scanning Remote Sensor Data, J. Atmos. Ocean. Tech., 31, 765–787, https://doi.org/10.1175/JTECH-D-13-00104.1, 2014. a
Baidya Roy, S. and Traiteur, J. J.: Impacts of wind farms on surface air temperatures, P. Natl. Acad. Sci. USA, 107, 17899–17904, https://doi.org/10.1073/pnas.1000493107, 2010. a
Bianco, L., Adler, B., Bariteau, L., Djalalova, I. V., Myers, T., Pezoa, S., Turner, D. D., and Wilczak, J. M.: Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models, Atmos. Meas. Tech., 17, 3933–3948, https://doi.org/10.5194/amt-17-3933-2024, 2024. a
Blumberg, W. G., Turner, D. D., Löhnert, U., and Castleberry, S.: Ground-based temperature and humidity profiling using spectral infrared and microwave observations. Part II: Actual retrieval performance in clear-sky and cloudy conditions, J. Appl. Meteorol. Clim., 54, 2305–2319, https://doi.org/10.1175/JAMC-D-15-0005.1, 2015. a, b, c
Blumberg, W. G., Wagner, T. J., Turner, D. D., and Correia, J.: Quantifying the accuracy and uncertainty of diurnal thermodynamic profiles and convection indices derived from the atmospheric emitted radiance interferometer, J. Appl. Meteorol. Clim., 56, 2747–2766, https://doi.org/10.1175/JAMC-D-17-0036.1, 2017. a, b
Bodini, N., Optis, M., Liu, Y., Gaudet, B., Krishnamurthy, R., Kumler, A., Rosencrans, D., Rybchuk, A., Tai, S.-L., Berg, L., Musial, W., Lundquist, J. K., Purkayastha, A., Young, E., and Draxl, C.: Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf, Tech. Rep. February 2024, National Renewable Energy Laboratory, Golden, CO, https://docs.nrel.gov/docs/fy24osti/88215.pdf (last access: 26 April 2026), 2024. a
Campbell Scientific: Model HMP45C Temperature and Relative Humidity Probe Instruction Manual, Campbell Scientific, https://s.campbellsci.com/documents/af/manuals/hmp45c.pdf (last access: 26 April 2026), 2007. a
Chamorro, L. P. and Porté-Agel, F.: Effects of Thermal Stability and Incoming Boundary-Layer Flow Characteristics on Wind-Turbine Wakes: A Wind-Tunnel Study, Bound.-Lay. Meteorol., 136, 515–533, https://doi.org/10.1007/s10546-010-9512-1, 2010. a
Clifton, A.: 135-m Meteorological Masts at the National Wind Technology Center, Tech. Rep. July, National Renewable Energy Laboratory, Golden, CO, https://wind.nlr.gov/MetData/Publications/NWTC_135m_MetMasts.pdf (last access: 26 April 2026), 2014. a
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: A summary of the AER codes, J. Quant. Spectrosc. Ra., 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005. a
de Silva, C. M., Marusic, I., Woodcock, J. D., and Meneveau, C.: Scaling of second- and higher-order structure functions in turbulent boundary layers, J. Fluid Mech., 769, 654–686, 2015. a
Doubrawa, P., Quon, E. W., Martinez-Tossas, L. A., Shaler, K., Debnath, M., Hamilton, N., Herges, T. G., Maniaci, D., Kelley, C. L., Hsieh, A. S., Blaylock, M. L., van der Laan, P., Andersen, S. J., Krueger, S., Cathelain, M., Schlez, W., Jonkman, J., Branlard, E., Steinfeld, G., Schmidt, S., Blondel, F., Lukassen, L. J., and Moriarty, P.: Multimodel validation of single wakes in neutral and stratified atmospheric conditions, Wind Energy, 23, https://doi.org/10.1002/we.2543, 2020. a
Frost, C. and Thompson, S. G.: Correcting for regression dilution bias: comparison of methods for a single predictor variable, J. R. Stat. Soc. A Stat., 163, 173–189, https://doi.org/10.1111/1467-985X.00164, 2000. a
Goldberg, L.: Site A1 – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959687, 2023a. a
Goldberg, L.: Site A2 – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959689, 2023b. a
Goldberg, L.: Site A5 – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959693, 2023c. a
Goldberg, L.: Site A7 – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959695, 2023d. a
Goldberg, L.: Site B – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/2570093, 2023e. a, b
Goldberg, L.: Site C1a – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959700, 2023f. a, b
Goldberg, L.: Site G – Surface Meteorological Station/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/1959708, 2023g. a, b
Haghi, K. R., Geerts, B., Chipilski, H. G., Johnson, A., Degelia, S., Imy, D., Parsons, D. B., Adams-Selin, R. D., Turner, D. D., and Wang, X.: Bore-ing into Nocturnal Convection, B. Am. Meteorol. Soc., 100, 1103–1121, https://doi.org/10.1175/BAMS-D-17-0250.1, 2019. a
Hamilton, N.: NWTC Ceilometer (1) Pre-campaign/Reviewed Data, https://wdh.energy.gov/ds/awaken/nwtc.ceil.z01.b0 (last access: 26 August 2025), 2022. a
Hamilton, N.: AWAKEN Site A1 – NREL Ceilometer (Vaisala CL51)/Reviewed Data, Wind Data Hub [data set], https://doi.org/10.21947/2221790, 2023. a
Han, G. and Zhang, X.: Applicability of Taylor's frozen hypothesis and elliptic model in the atmospheric surface layer, Bound.-Lay. Meteorol., 184, 423–440, https://doi.org/10.1007/s10546-022-00711-y, 2022. a
Holdridge, D.: Balloon-Borne Sounding System (SONDE) Instrument Handbook, Tech. rep., Atmospheric Radiation Measurement, https://www.arm.gov/publications/tech_reports/handbooks/sonde_handbook.pdf (last access: 26 April 2026), 2020. a
Huwald, H., Higgins, C. W., Boldi, M.-O., Bou-Zeid, E. R., Lehning, M., and Parlange, M. B.: Albedo effect on radiative errors in air temperature measurements, Water Resour. Res., 45, W08431, https://doi.org/10.1029/2008WR007600, 2009. a
International Electrotechnical Commission: IEC 61400-12-1:2022 – Wind energy generation systems – Part 12-1: Power performance measurements of electricity producing wind turbines, IEC Standard, https://webstore.iec.ch/en/publication/68499 (last access: 26 April 2026), 2022. a
International Energy Agency: Electricity 2025: Analysis and forecast to 2027, Tech. rep., International Energy Agency, https://iea.blob.core.windows.net/assets/0f028d5f-26b1-47ca-ad2a-5ca3103d070a/Electricity2025.pdf (last access: 26 April 2026), 2025. a
Keeler, E., Burk, K., and Kyrouac, J.: Balloon-Borne Sounding System (SONDEWNPN), 2023-04-10 to 2023-08-22, Southern Great Plains (SGP), Billings, OK (Site H for AWAKEN) (S6), ARM [data set], https://doi.org/10.5439/1595321, 2023. a
Keith, D. W., DeCarolis, J. F., Denkenberger, D. C., Lenschow, D. H., Malyshev, S. L., Pacala, S., and Rasch, P. J.: The Influence of Large-Scale Wind Power on Global Climate, P. Natl. Acad. Sci. USA, 101, 16115–16120, https://doi.org/10.1073/pnas.0406930101, 2004. a
Klein, P., Bonin, T. A., Newman, J. F., Turner, D. D., Chilson, P. B., Wainwright, C. E., Blumberg, W. G., Mishra, S., Carney, M., Jacacobsen, E. P., Wharton, S., and Newsom, R. K.: LABLE: A multi-institutional, student-led, atmospheric boundary layer experiment, B. Am. Meteorol. Soc., 96, 1743–1764, https://doi.org/10.1175/BAMS-D-13-00267.1, 2015. a, b, c, d
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker, R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell, H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric Emitted Radiance Interferometer. Part I: Instrument design, J. Atmos. Ocean. Tech., 21, 1763–1776, https://doi.org/10.1175/JTECH-1662.1, 2004a. a, b
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker, R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell, H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric Emitted Radiance Interferometer. Part II: Instrument performance, J. Atmos. Ocean. Tech., 21, 1777–1789, https://doi.org/10.1175/JTECH-1663.1, 2004b. a
Krishnamurthy, R., Newsom, R. K., Chand, D., and Shaw, W. J.: Boundary Layer Climatology at ARM Southern Great Plains, Tech. Rep. PNNL-30832, Pacific Northwest National Laboratory (PNNL), https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-30832.pdf, (last access: 20 August 2025), 2021. a
Krishnamurthy, R., Newsom, R. K., Kaul, C. M., Letizia, S., Pekour, M., Hamilton, N., Chand, D., Flynn, D., Bodini, N., and Moriarty, P.: Observations of wind farm wake recovery at an operating wind farm, Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, 2025. a
Lee, J. C. Y. and Fields, M. J.: An overview of wind-energy-production prediction bias, losses, and uncertainties, Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, 2021. a
Letizia, S.: NWTC Thermodynamic Profiler (Assist II-10) Pre-campaign/Summary Data, https://wdh.energy.gov/ds/awaken/nwtc.assist.z01.00 (last access: 26 August 2025), 2022a. a
Letizia, S.: NWTC Thermodynamic Profiler (Assist II-11) Pre-campaign/Summary Data, https://wdh.energy.gov/ds/awaken/nwtc.assist.z02.00 (last access: 26 August 2025), 2022b. a
Letizia, S.: NWTC Thermodynamic Profiler (Assist II-12) Pre-campaign/Summary Data, https://wdh.energy.gov/ds/awaken/nwtc.assist.z03.00 (last access: 26 August 2025), 2022c. a
Letizia, S.: StefanoWind/ASSIST_analysis: wes-2025-198, Zenodo [code], https://doi.org/10.5281/zenodo.19801301, 2026a. a
Letizia, S.: StefanoWind/TROPoe_processor: wes-2025-198, Zenodo [code], https://doi.org/10.5281/zenodo.19801303, 2026b. a
Letizia, S., Robey, R., Bodini, N., Sanchez Gomez, M., Lundquist, J. K., Krishnamurthy, R., and Moriarty, P. J.: Tilted lidar profiling: Development and testing of a novel scanning strategy for inhomogeneous flows, J. Renew. Sustain. Ener., 16, 043310, https://doi.org/10.1063/5.0209729, 2024. a, b
Li, Y., Kalnay, E., Motesharrei, S., Rivas, J., Kucharski, F., Kirk-Davidoff, D., Bach, E., and Zeng, N.: Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation, Science, 361, 1019–1022, https://doi.org/10.1126/science.aar5629, 2018. a
Liu, N., Zhao, X., Zhang, X., Zhao, J., Wang, H., and Wu, D.: Remotely Sensed Evidence of the Divergent Climate Impacts of Wind Farms on Croplands and Grasslands, Sci. Total Environ., 905, 167203, https://doi.org/10.1016/j.scitotenv.2023.167203, 2023. a
Maahn, M., Turner, D. D., Löhnert, U., Posselt, D. J., Ebell, K., Mace, G. G., and Comstock, J. M.: Optimal estimation retrievals and their uncertainties, B. Am. Meteorol. Soc., 101, E1512–E1523, https://doi.org/10.1175/BAMS-D-19-0027.1, 2020. a, b, c
Mahrt, L., Belušić, D., and Acevedo, O.: Small-Scale Spatial Variation of the Nocturnal Wind Field, Bound.-Lay. Meteorol., 180, 225–245, https://doi.org/10.1007/s10546-021-00627-z, 2021. a
Michaud-Belleau, V., Gaudreau, M., Lacoursière, J., Boisvert, É., Ravelomanantsoa, L., Turner, D. D., and Rochette, L.: The Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST): instrument design and signal processing, Atmos. Meas. Tech., 18, 3585–3609, https://doi.org/10.5194/amt-18-3585-2025, 2025. a, b, c
Miller, L. M. and Keith, D. W.: Climatic Impacts of Wind Power, Joule, 2, 2618–2632, https://doi.org/10.1016/j.joule.2018.09.009, 2018. a
Mlawer, E. J. and Turner, D. D.: Spectral Radiation Measurements and Analysis in the ARM Program, Meteor. Mon., 57, 14.1–14.17, https://doi.org/10.1175/amsmonographs-d-15-0027.1, 2016. a, b
Moravec, D., Barták, V., and Puš, V.: Wind turbine impact on near-ground air temperature, Renewable Energy, 123, 627–633, https://doi.org/10.1016/j.renene.2018.02.049, 2018. a
Moriarty, P., Bodini, N., Letizia, S., et al.: Overview of preparation for the American Wake Experiment (AWAKEN), J. Renew. Sustain. Ener., 16, https://doi.org/10.1063/5.0141683, 2024. a, b
Nakamura, R. and Mahrt, L.: Air Temperature Measurement Errors in Naturally Ventilated Radiation Shields, J. Atmos. Ocean. Tech., 22, 1046–1058, https://doi.org/10.1175/JTECH1762.1, 2005. a, b, c, d
Nallapareddy, A., Shapiro, A., and Gourley, J. J.: A Climatology of Nocturnal Warming Events Associated with Cold-Frontal Passages in Oklahoma, J. Appl. Meteorol. Clim., 50, 2042–2061, https://doi.org/10.1175/JAMC-D-11-020.1, 2011. a
Newsom, R., Shi, Y., and Krishnamurthy, R.: Doppler Lidar (DLFPT), 2010-10-22 to 2025-08-04, Southern Great Plains (SGP), Central Facility, Lamont, OK (C1), ARM [data set], https://doi.org/10.5439/1025185, 2023. a
Pekour, M.: Site A2 – PNNL Surface Flux Station/Daily Fluxes, Wind Data Hub [data set], https://doi.org/10.21947/1899850, 2022a. a
Pekour, M.: Site A5 – PNNL Surface Flux Station/Daily Fluxes, Wind Data Hub [data set], https://doi.org/10.21947/1899848, 2022b. a
Radünz, W. C., Carmo, B., Lundquist, J. K., Letizia, S., Abraham, A., Wise, A. S., Sanchez Gomez, M., Hamilton, N., Rai, R. K., and Peixoto, P. S.: Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign, Wind Energ. Sci., 10, 2365–2393, https://doi.org/10.5194/wes-10-2365-2025, 2025. a
Rajewski, D. A., Takle, E. S., Lundquist, J. K., Oncley, S., Prueger, J. H., Horst, T. W., Rhodes, M. E., Pfeiffer, R., Hatfield, J. L., Spoth, K. K., and Doorenbos, R. K.: Crop Wind Energy Experiment (CWEX): Observations of Surface-Layer, Boundary Layer, and Mesoscale Interactions with a Wind Farm, B. Am. Meteorol. Soc., 94, 655–672, https://doi.org/10.1175/BAMS-D-11-00240.1, 2013. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, vol. 2, World scientific, https://doi.org/10.1142/9789812813718, 2000. a, b
Shaw, W. J., Berg, L. K., Debnath, M., Deskos, G., Draxl, C., Ghate, V. P., Hasager, C. B., Kotamarthi, R., Mirocha, J. D., Muradyan, P., Pringle, W. J., Turner, D. D., and Wilczak, J. M.: Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer, Wind Energ. Sci., 7, 2307–2334, https://doi.org/10.5194/wes-7-2307-2022, 2022. a
Shippert, T., Newsom, R., Riihimaki, L., and Zhang, D.: Doppler Lidar Vertical Velocity Statistics (DLPROFWSTATS4NEWS), 2016-05-03 to 2023-11-01, Southern Great Plains (SGP), Waukomis, OK (Extended) (E37), ARM [data set], https://doi.org/10.5439/1178583, 2023. a
Siegel, R.: Thermal Radiation Heat Transfer: Vol. 3: Radiation Transfer with Absorbing, Emitting, and Scattering Media, vol. 164, Scientific and Technical Information Division, National Aeronautics and Space Administration, https://ntrs.nasa.gov/citations/19710021465 (last access: 26 April 2026), 1971. a
Smith, C. M., Barthelmie, R. J., and Pryor, S. C.: In situ observations of the influence of a large onshore wind farm on near-surface temperature, turbulence intensity and wind speed profiles, Environ. Res. Lett., 8, 034006, https://doi.org/10.1088/1748-9326/8/3/034006, 2013. a
St. Martin, C. M., Lundquist, J. K., Clifton, A., Poulos, G. S., and Schreck, S. J.: Wind turbine power production and annual energy production depend on atmospheric stability and turbulence, Wind Energ. Sci., 1, 221–236, https://doi.org/10.5194/wes-1-221-2016, 2016. a
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Springer, Dordrecht, ISBN 978-90-277-2768-8, https://doi.org/10.1007/978-94-009-3027-8, 1988. a
Sun, J., Mahrt, L., Nappo, C., and Lenschow, D. H.: Wind and Temperature Oscillations Generated by Wave–Turbulence Interactions in the Stably Stratified Boundary Layer, J. Atmos. Sci., 72, 1484–1503, https://doi.org/10.1175/JAS-D-14-0129.1, 2015. a
Taylor, G. I.: The Spectrum of Turbulence, P. Roy. Soc. A-Math. Phy., 164, 476–490, https://doi.org/10.1098/rspa.1938.0032, 1938. a
Turner, D. D.: TROPoe v0.12, Docker [code], https://hub.docker.com/layers/davidturner53/tropoe, last access: 26 April 2026. a, b
Turner, D. D. and Blumberg, W. G.: Improvements to the AERIoe thermodynamic profile retrieval algorithm, IEEE J. Sel. Top. Appl., 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968, 2019. a, b, c, d
Turner, D. D. and Löhnert, U.: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based Atmospheric Emitted Radiance Interferometer (AERI), J. Appl. Meteorol. Clim., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1, 2014. a, b, c, d, e
Turner, D. D. and Löhnert, U.: Ground-based temperature and humidity profiling: combining active and passive remote sensors, Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021, 2021. a, b
Turner, D. D., Tobin, D. C., Clough, S. A., Brown, P. D., Ellingson, R. G., Mlawer, E. J., Knuteson, R. O., Revercomb, H. E., Shippert, T. R., Smith, W. L., and Shephard, M. W.: The QME AERI LBLRTM: A closure experiment for downwelling high spectral resolution infrared radiance, J. Atmos. Sci., 61, 2657–2675, https://doi.org/10.1175/JAS3300.1, 2004. a
Turner, D. D., Knuteson, R. O., Revercomb, H. E., Lo, C., and Dedecker, R. G.: Noise Reduction of Atmospheric Emitted Radiance Interferometer (AERI) Observations Using Principal Component Analysis, J. Atmos. Ocean. Tech., 23, 1223–1238, https://doi.org/10.1175/JTECH1906.1, 2006. a
Turner, D. D., Adler, B., Bianco, L., Wilczak, J. M., Michaud-Belleau, V., and Rochette, L.: Intercomparison of seven collocated ground-based infrared spectrometer radiance observations and retrieved thermodynamic profiles, Atmos. Meas. Tech., 19, 1573–1586, https://doi.org/10.5194/amt-19-1573-2026, 2026. a, b
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O., Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V., Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P., Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J., Robertson, A., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A., and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Walsh-Thomas, J. M., Cervone, G., Agouris, P., and Manca, G.: Further evidence of impacts of large-scale wind farms on land surface temperature, Renew. Sust. Energ. Rev., 16, 6432–6437, https://doi.org/10.1016/j.rser.2012.07.004, 2012. a
Wang, C. and Prinn, R. G.: Potential climatic impacts and reliability of very large-scale wind farms, Atmos. Chem. Phys., 10, 2053–2061, https://doi.org/10.5194/acp-10-2053-2010, 2010. a
Wang, H., Barthelmie, R. J., Clifton, A., and Pryor, S. C.: Wind measurements from arc scans with Doppler wind lidar, J. Atmos. Ocean. Tech., 32, 2024–2040, https://doi.org/10.1175/JTECH-D-14-00059.1, 2015. a
Wind Data Hub: AWAKEN dataset, Wind Data Hub [data set], https://wdh.energy.gov/project/awaken, last access: 3 May 2026. a
Wu, S. and Archer, C. L.: Near-Ground Effects of Wind Turbines: Observations and Physical Mechanisms, Mon. Weather Rev., 149, 879–898, https://doi.org/10.1175/MWR-D-20-0186.1, 2021. a
Wu, S., Archer, C. L., and Mirocha, J. D.: New Insights on Wind Turbine Wakes from Large-Eddy Simulation: Wake Contraction, Dual Nature, and Temperature Effects, Wind Energy, 27, https://doi.org/10.1002/we.2827, 2023. a, b
Wyngaard, J.: On Surface Layer Turbulence, American Meteorological Society, Boston, 101–149, 1973. a
Wyngaard, J., Izumi, Y., and Collins, S.: Behavior of the Refractive-Index-Structure Parameter near the Ground, J. Opt. Soc. Am., 61, 1646–1650, https://doi.org/10.1364/JOSA.61.001646, 1971. a
Xia, G., Zhou, L., Freedman, J. M., Baidya Roy, S., Harris, R. A., and Cervarich, M. C.: A case study of effects of atmospheric boundary layer turbulence, wind speed, and stability on wind farm induced temperature changes using observations from a field campaign, Clim. Dynam., 46, 2179–2196, https://doi.org/10.1007/s00382-015-2696-9, 2016. a
Xia, G., Zhou, L., Minder, J. R., Fovell, R. G., and Jiménez, P. A.: Simulating Impacts of Real-World Wind Farms on Land Surface Temperature Using the WRF Model: Physical Mechanisms, Clim. Dynam., 53, 1723–1739, https://doi.org/10.1007/s00382-019-04725-0, 2019. a
Zhan, L., Letizia, S., and Iungo, G. V.: LiDAR measurements for an onshore wind farm : Wake variability for different incoming wind speeds and atmospheric stability regimes, Wind Energy, 23, https://doi.org/10.1002/we.2430, 2019. a
Zhang, D., Ermold, B., and Morris, V.: Ceilometer (CEIL), 1997-09-14 to 2025-08-25, Southern Great Plains (SGP), Central Facility, Lamont, OK (C1), ARM [data set], https://doi.org/10.5439/1181954, 2023a. a
Zhang, D., Ermold, B., and Morris, V.: Ceilometer (CEIL), 2022-11-29 to 2023-09-07, Southern Great Plains (SGP), Billings, OK (Site H for AWAKEN) (S6), ARM [data set], https://doi.org/10.5439/1181954, 2023b. a
Zhou, L., Tian, Y., Baidya Roy, S., Thorncroft, C., Bosart, L. F., and Hu, Y.: Impacts of wind farms on land surface temperature, Nat. Clim. Change, 2, 539–543, https://doi.org/10.1038/nclimate1505, 2012. a
Zhou, L., Tian, Y., Baidya Roy, S., Dai, Y., and Chen, H.: Diurnal and seasonal variations of wind farm impacts on land surface temperature over western Texas, Clim. Dynam., 41, 307–326, https://doi.org/10.1007/s00382-012-1485-y, 2013. a
Short summary
Characterizing the wind resource is much more than just measuring wind speeds. In fact, the physics of the atmosphere is governed by a complex interplay of different quantities, temperature being one of the most important. We used a new technology to remotely sense temperature profiles around wind farms at AWAKEN (American WAKE ExperimeNt). Here, we discuss the methodology and guide readers through a comprehensive, step-by-step validation effort to quantify the accuracy of temperature profiling for wind energy.
Characterizing the wind resource is much more than just measuring wind speeds. In fact, the...
Altmetrics
Final-revised paper
Preprint