Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-2065-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-2065-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Mar 2021
Research article |
| 16 Mar 2021
| 16 Mar 2021
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework
Stefano Letizia
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Lu Zhan
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
Giacomo Valerio Iungo
CORRESPONDING AUTHOR
Wind Fluids and Experiments (WindFluX) Laboratory, Mechanical Engineering Department, The University of Texas at Dallas, 800 W Campbell Road, Richardson, TX 75080, USA
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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
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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.
William 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. Discuss., https://doi.org/10.5194/wes-2024-166, https://doi.org/10.5194/wes-2024-166, 2025
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This study investigates how simple terrain can cause significant variations in wind speed, especially during specific atmospheric conditions like low-level jets. By combining simulations and observations from a real wind farm, we found that downstream turbines generate more power than upstream ones, despite wake effects only impacting the upstream turbines. We highlight the crucial role of the strong vertical wind speed gradient in low-level jets in driving this effect.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-148, https://doi.org/10.5194/wes-2024-148, 2024
Revised manuscript accepted for WES
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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.
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
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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.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
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The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
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Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
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.
William 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. Discuss., https://doi.org/10.5194/wes-2024-166, https://doi.org/10.5194/wes-2024-166, 2025
Preprint under review for WES
Short summary
Short summary
This study investigates how simple terrain can cause significant variations in wind speed, especially during specific atmospheric conditions like low-level jets. By combining simulations and observations from a real wind farm, we found that downstream turbines generate more power than upstream ones, despite wake effects only impacting the upstream turbines. We highlight the crucial role of the strong vertical wind speed gradient in low-level jets in driving this effect.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-148, https://doi.org/10.5194/wes-2024-148, 2024
Revised manuscript accepted for WES
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.
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.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Matteo Puccioni and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 1457–1474, https://doi.org/10.5194/amt-14-1457-2021, https://doi.org/10.5194/amt-14-1457-2021, 2021
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A procedure for correcting the turbulent-energy damping connected with spatial averaging of wind lidars is proposed. This effect of the lidar measuring process is modeled through a low-pass filter, whose order and cut-off frequency are estimated directly from the lidar data. The proposed procedure is first assessed through simultaneous and colocated lidar and sonic-anemometer measurements. Then it is applied to several datasets collected at sites with different terrain roughness.
Lu Zhan, Stefano Letizia, and Giacomo Valerio Iungo
Wind Energ. Sci., 5, 1601–1622, https://doi.org/10.5194/wes-5-1601-2020, https://doi.org/10.5194/wes-5-1601-2020, 2020
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Lidar measurements of wakes generated by isolated wind turbines are leveraged for optimal tuning of parameters of four engineering wake models. The lidar measurements are retrieved as ensemble averages of clustered data with incoming wind speed and turbulence intensity. It is shown that the optimally tuned wake models enable a significantly increased accuracy for predictions of wakes. The optimally tuned models are expected to enable generally enhanced performance for wind farms on flat terrain.
Mithu Debnath, Giacomo Valerio Iungo, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Scott Gunter, Julie K. Lundquist, John L. Schroeder, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, https://doi.org/10.5194/amt-10-1215-2017, 2017
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The XPIA experiment was conducted in 2015 at the Boulder Atmospheric Observatory to estimate capabilities of various remote-sensing techniques for the characterization of complex atmospheric flows. Among different tests, XPIA provided the unique opportunity to perform simultaneous virtual towers with Ka-band radars and scanning Doppler wind lidars. Wind speed and wind direction were assessed against lidar profilers and sonic anemometer data, highlighting a good accuracy of the data retrieved.
Mithu Debnath, G. Valerio Iungo, Ryan Ashton, W. Alan Brewer, Aditya Choukulkar, Ruben Delgado, Julie K. Lundquist, William J. Shaw, James M. Wilczak, and Daniel Wolfe
Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, https://doi.org/10.5194/amt-10-431-2017, 2017
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Triple RHI scans were performed with three simultaneous scanning Doppler wind lidars and assessed with lidar profiler and sonic anemometer data. This test is part of the XPIA experiment. The scan strategy consists in two lidars performing co-planar RHI scans, while a third lidar measures the transversal velocity component. The results show that horizontal velocity and wind direction are measured with good accuracy, while the vertical velocity is typically measured with a significant error.
Katherine McCaffrey, Paul T. Quelet, Aditya Choukulkar, James M. Wilczak, Daniel E. Wolfe, Steven P. Oncley, W. Alan Brewer, Mithu Debnath, Ryan Ashton, G. Valerio Iungo, and Julie K. Lundquist
Atmos. Meas. Tech., 10, 393–407, https://doi.org/10.5194/amt-10-393-2017, https://doi.org/10.5194/amt-10-393-2017, 2017
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During the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) field campaign, the wake and flow distortion from a 300-meter meteorological tower was identified using pairs of sonic anemometers mounted on opposite sides of the tower, as well as profiling and scanning lidars. Wind speed deficits up to 50% and TKE increases of 2 orders of magnitude were observed at wind directions in the wake, along with wind direction differences (flow deflection) outside of the wake.
Aditya Choukulkar, W. Alan Brewer, Scott P. Sandberg, Ann Weickmann, Timothy A. Bonin, R. Michael Hardesty, Julie K. Lundquist, Ruben Delgado, G. Valerio Iungo, Ryan Ashton, Mithu Debnath, Laura Bianco, James M. Wilczak, Steven Oncley, and Daniel Wolfe
Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, https://doi.org/10.5194/amt-10-247-2017, 2017
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This paper discusses trade-offs among various wind measurement strategies using scanning Doppler lidars. It is found that the trade-off exists between being able to make highly precise point measurements versus covering large spatial extents. The highest measurement precision is achieved when multiple lidar systems make wind measurements at one point in space, while highest spatial coverage is achieved through using single lidar scanning measurements and using complex retrieval techniques.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Nadia Smith and Christopher D. Barnet
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CLIMCAPS extends the Aqua AIRS+AMSU record with retrievals from CrIS+ATMS on Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System series (JPSS-1 to JPSS-4). With “continuous”, we mean a data record that is consistent in its characterization of natural variation despite changes in source instrumentation. Here we investigate how sounding continuity can improve across the full CLIMCAPS record (2002 to the present day), spanning multiple instruments and satellites.
Tianwen Wei, Mengya Wang, Kenan Wu, Jinlong Yuan, Haiyun Xia, and Simone Lolli
Atmos. Meas. Tech., 18, 1841–1857, https://doi.org/10.5194/amt-18-1841-2025, https://doi.org/10.5194/amt-18-1841-2025, 2025
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This study analyzes three years of wind lidar measurements to explore the dynamics of the urban planetary boundary layer in Hefei, China. Results reveal that nocturnal low-level jets are most frequent in spring and intensify in summer, significantly enhancing turbulence and shear near the surface, particularly at night. Additionally, cloud cover raises the mixing layer height by approximately 100 m at night due to the greenhouse effect but reduces it by up to 200 m in the afternoon.
Matthieu Vernay, Matthieu Lafaysse, and Clotilde Augros
Atmos. Meas. Tech., 18, 1731–1755, https://doi.org/10.5194/amt-18-1731-2025, https://doi.org/10.5194/amt-18-1731-2025, 2025
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This paper provides a comprehensive evaluation of the quality of radar-based precipitation estimation in mountainous areas and presents a method to mitigate the main shortcomings identified. It then compares three different ensemble analysis methods that combine radar-based precipitation estimates with forecasts from an ensemble numerical weather prediction model.
Sabine Wüst, Lisa Küchelbacher, Franziska Trinkl, and Michael Bittner
Atmos. Meas. Tech., 18, 1591–1607, https://doi.org/10.5194/amt-18-1591-2025, https://doi.org/10.5194/amt-18-1591-2025, 2025
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Information on the energy transported by atmospheric gravity waves (GWs) is crucial for improving atmosphere models. Most space-based studies report the potential energy. We use Aeolus wind data to estimate the kinetic energy (density). However, the data quality is a challenge for such analyses, as the accuracy of the data is in the range of typical GW amplitudes. We find a temporal coincidence between enhanced or breaking planetary waves and enhanced gravity wave kinetic energy density.
Martin Fencl and Marc Schleiss
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A novel disaggregation algorithm for commercial microwave links (CMLs), named CLEAR (CML Segments with Equal Amounts of Rain), is proposed. CLEAR utilizes a multiplicative random cascade generator to control the splitting of link segments. The evaluation performed both on virtual and real CML data shows that CLEAR outperforms a commonly used benchmark algorithm. Moreover, the stochastic nature of CLEAR allows it to represent uncertainty as an ensemble of rain rate distributions along CML paths.
Natalie E. Theeuwes, Janet F. Barlow, Antti Mannisenaho, Denise Hertwig, Ewan O'Connor, and Alan Robins
Atmos. Meas. Tech., 18, 1355–1371, https://doi.org/10.5194/amt-18-1355-2025, https://doi.org/10.5194/amt-18-1355-2025, 2025
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A Doppler lidar was placed in a highly built-up area in London to measure wakes from tall buildings during a period of 1 year. We were able to detect wakes and assess their dependence on wind speed, wind direction, and atmospheric stability.
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EGUsphere, https://doi.org/10.5194/amt-2024-3967, https://doi.org/10.5194/amt-2024-3967, 2025
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We retrieved high-resolution maps of Arctic surface temperature and type using airborne thermal infrared imagery from the HALO-(𝒜𝒞)3 campaign. Our study highlights small-scale surface variability, complementing satellite observations. Surface temperature was retrieved via radiative transfer simulations, while surface type was classified using machine learning. Additionally, we analyzed segment sizes of each surface type, presenting results based on their distance from the sea-ice edge.
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025, https://doi.org/10.5194/amt-18-1269-2025, 2025
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This paper presents a supervised-machine-learning approach for the automatic isolation of nocturnal low-level jets (NLLJs) using observations from a radar wind profiler. This analysis isolated 90 southwesterly NLLJs observed from May to September 2017–2021, highlighting key features in the evolution and morphology of the mid-Atlantic NLLJ.
Linda Megner, Jörg Gumbel, Ole Martin Christensen, Björn Linder, Donal Patrick Murtagh, Nickolay Ivchenko, Lukas Krasauskas, Jonas Hedin, Joachim Dillner, Gabriel Giono, Georgi Olentsenko, Louis Kern, and Jacek Stegman
EGUsphere, https://doi.org/10.5194/egusphere-2025-265, https://doi.org/10.5194/egusphere-2025-265, 2025
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The MATS satellite mission studies atmospheric gravity waves, crucial for momentum transport between atmospheric layers. Launched in November 2022, MATS uses a limb-viewing telescope to capture high-resolution images of Noctilucent clouds and airglow, visualizing wave patterns in the high atmosphere. This paper accompanies the public release of the level 1b data set, i.e. calibrated limb images. Later products will provide global maps of gravity wave properties, airglow and Noctilucent clouds.
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025, https://doi.org/10.5194/amt-18-1185-2025, 2025
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This study explores how the joints in a weather radar's protective cover affect its measurements. We developed a new method to correct these errors, improving the accuracy of the radar's data. Our method was tested during an intense cyclone on Réunion Island, demonstrating significant improvements in data accuracy. This research is crucial for enhancing weather predictions and understanding, particularly in challenging terrains.
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025, https://doi.org/10.5194/amt-18-843-2025, 2025
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Global Navigation Satellite System radio occultation data help monitor climate and weather prediction but are affected by residual ionospheric errors (RIEs). A new excess-phase-gradient method detects and corrects RIEs, showing both positive and negative values, varying by latitude, time, and solar activity. Tests show that RIE impacts polar stratosphere temperatures in models, with differences up to 3–4 K. This highlights the need for RIE correction to improve the accuracy of data assimilation.
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025, https://doi.org/10.5194/amt-18-793-2025, 2025
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Accurate KDP estimates are crucial in radar-based applications. We quantify the uncertainties of several publicly available KDP estimation methods for multiple rainfall intensities. We use C-band weather radar observations and employed a self-consistency KDP, estimated from reflectivity and differential reflectivity, as a framework for the examination. Our study provides guidance for the performance, uncertainties, and optimisation of the methods, focusing mainly on accuracy and robustness.
Marco Coppola, Alessandro Battaglia, Frederic Tridon, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-416, https://doi.org/10.5194/egusphere-2025-416, 2025
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The WIVERN conically scanning Doppler W-band radar, has the potential, for the first time, to map the mesoscale and synoptic variability of cloud dynamics, and precipitation microphysics. This study shows that the oblique angle of incidence will be advantageous compared to standard nadir-looking radars due to substantial clutter suppression over ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
EGUsphere, https://doi.org/10.5194/egusphere-2025-19, https://doi.org/10.5194/egusphere-2025-19, 2025
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Tropospheric gradients provide information on the moisture distribution, whereas ZTDs provide the absolute amount of moisture through integrated water vapor. When TGs are assimilated with ZTDs, it helps the model actuate the moisture fields, correcting its dynamics. In our research, we show evidence that in particular regions with very few GNSS stations, the assimilation of gradients on top of ZTDs can provide the same impact as the assimilation of only ZTDs with dense coverage of GNSS stations.
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 18, 471–485, https://doi.org/10.5194/amt-18-471-2025, https://doi.org/10.5194/amt-18-471-2025, 2025
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This study evaluates and compares a new microwave hyperspectrometer with an infrared hyperspectrometer for clear-sky temperature and water vapor retrievals. The analysis reveals that the information content of the infrared hyperspectrometer exceeds that of the microwave hyperspectrometer and provides higher vertical resolution in ground-based zenith measurements. Leveraging the ground–airborne synergy between the two instruments yielded optimal sounding results.
Shuai Zhang, Haoran Li, and Dmitri Moisseev
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-194, https://doi.org/10.5194/amt-2024-194, 2025
Revised manuscript accepted for AMT
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The data quality of weather radar near coastlines can be affected by echoes from ships, and this interference is exacerbated when pulse compression technology is used. This study developed a hybrid ship clutter identification algorithm based on artificial intelligence and heuristic criteria, effectively mitigating the issue. The successful reproduction of ship tracks in the Gulf of Finland supports this conclusion.
Endrit Shehaj, Stephen Leroy, Kerri Cahoy, Alain Geiger, Laura Crocetti, Gregor Moeller, Benedikt Soja, and Markus Rothacher
Atmos. Meas. Tech., 18, 57–72, https://doi.org/10.5194/amt-18-57-2025, https://doi.org/10.5194/amt-18-57-2025, 2025
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This work investigates whether machine learning (ML) can offer an alternative to existing methods to map radio occultation (RO) products, allowing the extraction of information not visible in direct observations. ML can further improve the results of Bayesian interpolation, a state-of-the-art method to map RO observations. The results display improvements in horizontal and temporal domains, at heights ranging from the planetary boundary layer up to the lower stratosphere, and for all seasons.
Luuk D. van der Valk, Oscar K. Hartogensis, Miriam Coenders-Gerrits, Rolf W. Hut, Bas Walraven, and Remko Uijlenhoet
EGUsphere, https://doi.org/10.5194/egusphere-2024-2974, https://doi.org/10.5194/egusphere-2024-2974, 2025
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Commercial microwave links (CMLs), part of mobile phone networks, transmit comparable signals as instruments specially designed to estimate evaporation. Therefore, we investigate if CMLs could be used to estimate evaporation, even though they have not been designed for this purpose. Our results illustrate the potential of using CMLs to estimate evaporation, especially given their global coverage, but also outline some major drawbacks, often a consequence of unfavourable design choices for CMLs.
Andreas Foth, Moritz Lochmann, Pablo Saavedra Garfias, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 7169–7181, https://doi.org/10.5194/amt-17-7169-2024, https://doi.org/10.5194/amt-17-7169-2024, 2024
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Microwave radiometers are usually not able to provide atmospheric quantities such as temperature profiles during rain. We present a method based on a selection of specific frequencies and elevation angles from microwave radiometer observations. A comparison with a numerical weather prediction model shows the presented method allows low-level temperature profiles during rain to be resolved, with rain rates of up to 2.5 mm h−1,, which was not possible before with state-of-the-art retrievals.
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024, https://doi.org/10.5194/amt-17-7183-2024, 2024
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The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.
Thomas Hocking, Thorsten Mauritsen, and Linda Megner
Atmos. Meas. Tech., 17, 7077–7095, https://doi.org/10.5194/amt-17-7077-2024, https://doi.org/10.5194/amt-17-7077-2024, 2024
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The imbalance between the energy the Earth absorbs from the Sun and the energy the Earth emits back into space gives rise to climate change, but measuring the small imbalance is challenging. We simulate satellites in various orbits to investigate how well they sample the imbalance and find that the best option is to combine at least two satellites that see complementary parts of the Earth and cover the daily and annual cycles. This information is useful when planning future satellite missions.
Almudena Velázquez Blázquez, Carlos Domenech, Edward Baudrez, Nicolas Clerbaux, Carla Salas Molar, and Nils Madenach
Atmos. Meas. Tech., 17, 7007–7026, https://doi.org/10.5194/amt-17-7007-2024, https://doi.org/10.5194/amt-17-7007-2024, 2024
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This paper focuses on the BMA-FLX processor, in which thermal and solar top-of-atmosphere radiative fluxes are obtained from longwave and shortwave radiances measured along track by the EarthCARE Broadband Radiometer (BBR). The BBR measurements, at three fixed viewing angles (fore, nadir, aft), are co-registered either at the surface or at a reference level. A combined flux from the three BRR views is obtained. The algorithm has been successfully validated against test scenes.
Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek
Atmos. Meas. Tech., 17, 6913–6931, https://doi.org/10.5194/amt-17-6913-2024, https://doi.org/10.5194/amt-17-6913-2024, 2024
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Three-dimensional (3D) wind velocity measurements are of major importance for the characterization of atmospheric turbulence. This paper presents a detailed study of the measurement uncertainty of a three-beam wind lidar designed for mounting on airborne platforms. Considering the geometrical constraints, the analysis provides quantitative estimates for the measurement uncertainty of all components of the 3D wind vector. As a result, we propose optimized post-processing for error reduction.
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
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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.
Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-175, https://doi.org/10.5194/amt-2024-175, 2024
Revised manuscript accepted for AMT
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Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the FengYun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
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The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Andreas Walbröl, Hannes J. Griesche, Mario Mech, Susanne Crewell, and Kerstin Ebell
Atmos. Meas. Tech., 17, 6223–6245, https://doi.org/10.5194/amt-17-6223-2024, https://doi.org/10.5194/amt-17-6223-2024, 2024
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We developed retrievals of integrated water vapour (IWV), temperature profiles, and humidity profiles from ground-based passive microwave remote sensing measurements gathered during the MOSAiC expedition. We demonstrate and quantify the benefit of combining low- and high-frequency microwave radiometers to improve humidity profiling and IWV estimates by comparing the retrieved quantities to single-instrument retrievals and reference datasets (radiosondes).
Tessa E. Rosenberger, Thijs Heus, Girish N. Raghunathan, David D. Turner, Timothy J. Wagner, and Julia M. Simonson
EGUsphere, https://doi.org/10.5194/egusphere-2024-2894, https://doi.org/10.5194/egusphere-2024-2894, 2024
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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.
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024, https://doi.org/10.5194/amt-17-6025-2024, 2024
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The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.
Ollie Lewis, Chris Brunt, Malcolm Kitchen, Neill E. Bowler, and Edmund K. Stone
EGUsphere, https://doi.org/10.5194/egusphere-2024-2273, https://doi.org/10.5194/egusphere-2024-2273, 2024
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Humidity observations are crucial for an accurate weather forecast. We propose a new way to measure humidity by measuring how radio signals from commercial aircraft are refracted by the atmosphere. Humidity affects the refractive index of air, allowing its presence to be detected. With thousands of flights in the airspace over the United Kingdom every day, there is the potential for a large increase in the number of humidity measurements for use in weather forecasting models.
Dianrun Zhao, Shanshan Du, Chu Zou, Longfei Tian, Meng Fan, Yulu Du, and Liangyun Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3118, https://doi.org/10.5194/egusphere-2024-3118, 2024
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The TanSat-2 satellite is designed for global carbon monitoring. It provides high-resolution, dual-band observations of solar-induced chlorophyll fluorescence, a key indicator of plant photosynthesis. Through simulations, we optimized the satellite's data processing and found it can retrieve this fluorescence with great accuracy. These findings suggest that TanSat-2 will enhance global monitoring of carbon cycles and vegetation health, offering valuable insights for climate change research.
Sebastian Rhode, Peter Preusse, Jörn Ungermann, Inna Polichtchouk, Kaoru Sato, Shingo Watanabe, Manfred Ern, Karlheinz Nogai, Björn-Martin Sinnhuber, and Martin Riese
Atmos. Meas. Tech., 17, 5785–5819, https://doi.org/10.5194/amt-17-5785-2024, https://doi.org/10.5194/amt-17-5785-2024, 2024
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We investigate the capabilities of a proposed satellite mission, CAIRT, for observing gravity waves throughout the middle atmosphere and present the necessary methodology for in-depth wave analysis. Our findings suggest that such a satellite mission is highly capable of resolving individual wave parameters and could give new insights into the role of gravity waves in general atmospheric circulation and atmospheric processes.
Suyoung Sim, Sungwon Choi, Daeseong Jung, Jongho Woo, Nayeon Kim, Sungwoo Park, Honghee Kim, Ukkyo Jeong, Hyunkee Hong, and Kyung-Soo Han
Atmos. Meas. Tech., 17, 5601–5618, https://doi.org/10.5194/amt-17-5601-2024, https://doi.org/10.5194/amt-17-5601-2024, 2024
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This study evaluates the use of background surface reflectance (BSR) derived from a semi-empirical bidirectional reflectance distribution function (BRDF) model based on GEMS satellite images. Analysis shows that BSR provides improved accuracy and stability compared to Lambertian-equivalent reflectivity (LER). These results indicate that BSR can significantly enhance climate analysis and air quality monitoring, making it a promising tool for accurate environmental satellite applications.
Daniel Durbin, Yadong Wang, and Pao-Liang Chang
Atmos. Meas. Tech., 17, 5397–5411, https://doi.org/10.5194/amt-17-5397-2024, https://doi.org/10.5194/amt-17-5397-2024, 2024
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A method for determining drop size distributions (DSDs) for rain using radar measurements from two frequencies at two polarizations is presented. Following some preprocessing and quality control, radar measurements are incorporated into a model that uses swarm intelligence to seek the most suitable DSD to produce the input measurements.
Witali Krochin, Axel Murk, and Gunter Stober
Atmos. Meas. Tech., 17, 5015–5028, https://doi.org/10.5194/amt-17-5015-2024, https://doi.org/10.5194/amt-17-5015-2024, 2024
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Atmospheric tides are global-scale oscillations with periods of a fraction of a day. Their observation in the middle atmosphere is challenging and rare, as it requires continuous measurements with a high temporal resolution. In this paper, temperature time series of a ground-based microwave radiometer were analyzed with a spectral filter to derive thermal tide amplitudes and phases in an altitude range of 25–50 km at the geographical locations of Payerne and Bern (Switzerland).
Matteo Ottaviani, Gabriel Harris Myers, and Nan Chen
Atmos. Meas. Tech., 17, 4737–4756, https://doi.org/10.5194/amt-17-4737-2024, https://doi.org/10.5194/amt-17-4737-2024, 2024
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We analyze simulated polarization observations over snow to investigate the capabilities of remote sensing to determine surface and atmospheric properties in snow-covered regions. Polarization measurements are demonstrated to aid in the determination of snow grain shape, ice crystal roughness, and the vertical distribution of impurities in the snow–atmosphere system, data that are critical for estimating snow albedo for use in climate models.
Yudong Gao, Lidou Huyan, Zheng Wu, and Bojun Liu
Atmos. Meas. Tech., 17, 4675–4686, https://doi.org/10.5194/amt-17-4675-2024, https://doi.org/10.5194/amt-17-4675-2024, 2024
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A symmetric error model built by symmetric rain rates handles the non-Gaussian error structure of the reflectivity error. The accuracy and linearization of rain rates can further improve the Gaussianity.
José Alex Zenteno-Hernández, Adolfo Comerón, Federico Dios, Alejandro Rodríguez-Gómez, Constantino Muñoz-Porcar, Michaël Sicard, Noemi Franco, Andreas Behrendt, and Paolo Di Girolamo
Atmos. Meas. Tech., 17, 4687–4694, https://doi.org/10.5194/amt-17-4687-2024, https://doi.org/10.5194/amt-17-4687-2024, 2024
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We study how the spectral characteristics of a solid-state laser in an atmospheric temperature profiling lidar using the Raman technique impact the temperature retrieval accuracy. We find that the spectral widening, with respect to a seeded laser, has virtually no impact, while crystal-rod temperature variations in the laser must be kept within a range of 1 K for the uncertainty in the atmospheric temperature below 1 K. The study is carried out through spectroscopy simulations.
Robert Reichert, Natalie Kaifler, and Bernd Kaifler
Atmos. Meas. Tech., 17, 4659–4673, https://doi.org/10.5194/amt-17-4659-2024, https://doi.org/10.5194/amt-17-4659-2024, 2024
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Imagine you want to determine how quickly the pitch of a passing ambulance’s siren changes. If the vehicle is traveling slowly, the pitch changes only slightly, but if it is traveling fast, the pitch also changes rapidly. In a similar way, the wind in the middle atmosphere modulates the wavelength of atmospheric gravity waves. We have investigated the question of how strong the maximum wind may be so that the change in wavelength can still be determined with the help of wavelet transformation.
Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui
Atmos. Meas. Tech., 17, 4613–4627, https://doi.org/10.5194/amt-17-4613-2024, https://doi.org/10.5194/amt-17-4613-2024, 2024
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Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level. Different from the classical method, a new NL correction method for a spaceborne Fourier transform spectrometer is proposed. To overcome the inaccurate linear coefficient from two-point calibration influencing NL correction, an iteration algorithm is established that is suitable for NL correction of both infrared and microwave sensors.
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024, https://doi.org/10.5194/amt-17-4303-2024, 2024
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Crowdsourced smartphone GNSS data were processed with a dedicated data processing pipeline and could produce millimeter-level accurate estimates of zenith total delay (ZTD) – a critical atmospheric variable. This breakthrough not only demonstrates the feasibility of using ubiquitous devices for high-precision atmospheric monitoring but also underscores the potential for a global, cost-effective tropospheric monitoring network.
Almudena Velázquez Blázquez, Edward Baudrez, Nicolas Clerbaux, and Carlos Domenech
Atmos. Meas. Tech., 17, 4245–4256, https://doi.org/10.5194/amt-17-4245-2024, https://doi.org/10.5194/amt-17-4245-2024, 2024
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The Broadband Radiometer measures shortwave and total-wave radiances filtered by the spectral response of the instrument. To obtain unfiltered solar and thermal radiances, the effect of the spectral response needs to be corrected for, done within the BM-RAD processor. Errors in the unfiltering are propagated into fluxes; thus, accurate unfiltering is required for their proper estimation (within BMA-FLX). Unfiltering errors are estimated to be <0.5 % for the shortwave and <0.1 % for the longwave.
Cited articles
Abkar, M. and Porté-Agel, F.: The effect of free-atmosphere stratification on boundary-layer flow and power output from very large wind farms, Energies, 6, 2338–2361, https://doi.org/10.3390/en6052338, 2013. a
Achtemeier, G. L.: The Impact of Data Boundaries upon a Successive Corrections Objective Analysis of Limited-Area Datasets, Mon. Weather Rev., 114, 40–49, https://doi.org/10.1175/1520-0493(1986)114<0040:TIODBU>2.0.CO;2, 1986. a
Achtemeier, G. L.: Modification of a Successive Corrections Objective Analysis for Improved Derivative Calculations, Mon. Weather Rev., 117, 78–86, https://doi.org/10.1175/1520-0493(1989)117<0078:MOASCO>2.0.CO;2, 1989. a
Aitken, M. L. and Lundquist, J. K.: Utility-Scale Wind Turbine Wake Characterization Using Nacelle-Based Long-Range Scanning Lidar, J. Atmos. Ocean. Tech., 31, 1529–1539, https://doi.org/10.1175/JTECH-D-13-00218.1, 2014. a, b, c, d
Arenas, I., García, E., Fu, M. K., Orlandi, P., Hultmark, M., and Leonardi, S.: Comparison between super-hydrophobic, liquid infused and rough surfaces: a direct numerical simulation study, J. Fluid Mech., 869, 500–525, https://doi.org/10.1017/jfm.2019.222, 2019. a
Ashton, R., Iungo, G. V., Viola, F., Gallaire, F., and Camarri, S.: Hub vortex instability within wind turbine wakes : Effects of wind turbulence, loading conditions and blade aerodynamics, Physical Review Fluids, 1, 073 603, https://doi.org/10.1103/PhysRevFluids.1.073603, 2016. a, b
Askelson, M. A., Aubagnac, J. P., and Straka, J. M.: An Adaptation of the Barnes Filter Applied to the Objective Analysis of Radar Data, Mon. Weather Rev., 128, 3050–3082, https://doi.org/10.1175/1520-0493(2000)128<3050:AAOTBF>2.0.CO;2, 2000. a, b
Banakh, V. A., Smalikho, I. N., Köpp, F., and Werner, C.: Measurements of Turbulent Energy Dissipation Rate with a CW Doppler Lidar in the Atmospheric Boundary Layer, J. Atmos. Ocean. Tech., 16, 1044–1061, https://doi.org/10.1175/1520-0426(1999)016<1044:MOTEDR>2.0.CO;2, 1999. a
Banta, R. M., Pichugina, Y. L., and Brewer, W. A.: Turbulent Velocity-Variance Profiles in the Stable Boundary Layer Generated by a Nocturnal Low-Level Jet, J. Atmos. Sci., 63, 2700–2719, https://doi.org/10.1175/JAS3776.1, 2006. a, b
Barnes, S.: Application of Barnes Objective Analysis scheme. Part III: tuning for minimum error., J. Atmos. Ocean. Tech., 11, 1449–1458, https://doi.org/10.1175/1520-0426(1994)011<1459:AOTBOA>2.0.CO;2, 1994c. a
Barnes, S. L.: Mesoscale Objective Map Analysis Using Weighted Time-Series Observations, NOAA Technical Memorandum ERL NSSL-62, National Severe Storms Laboratory Norman, OK, USA, 1973. a
Barnes, S. L.: Applications of the Barnes Objective Analysis Scheme. Part I: Effects of Undersampling, Wave Position and Station Randomness, J. Atmos. Ocean. Tech., 11, 1433–1448, https://doi.org/10.1175/1520-0426(1994)011<1433:AOTBOA>2.0.CO;2, 1994a. a, b, c
Bayley, V. G. and Hammersley, J. M.: The ”Effective” Number of Independent Observations in an Autocorrelated Time Series, Supplement to the Journal of Royal Statistical Society, 8, 184–197, 1946. a
Bell, T. M., Klein, P., Wildmann, N., and Menke, R.: Analysis of flow in complex terrain using multi-Doppler lidar retrievals, Atmos. Meas. Tech., 13, 1357–1371, https://doi.org/10.5194/amt-13-1357-2020, 2020. a
Berg, J., Mann, J., Bechmann, A., Courtney, M. S., and Jørgensen, H. E.: The Bolund Experiment. Part I : Flow Over a Steep, Three-dimensional Hill, Bound.-Lay. Meteorol., 20, 219–243, https://doi.org/10.1007/s10546-011-9636-y, 2011. a
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging measurements of wake dynamics. Part I: one-dimensional scanning, Wind Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010. a
Bodini, N., Zardi, D., and Lundquist, J. K.: Three-dimensional structure of wind turbine wakes as measured by scanning lidar, Atmos. Meas. Tech., 10, 2881–2896, https://doi.org/10.5194/amt-10-2881-2017, 2017. a
Bonin, T. A., Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A. M., Pichugina, Y. L., Banta, R. M., Oncley, S. P., and Wolfe, D. E.: Evaluation of turbulence measurement techniques from a single Doppler lidar, Atmos. Meas. Tech., 10, 3021–3039, https://doi.org/10.5194/amt-10-3021-2017, 2017. a
Bradley, E. F.: The influence of thermal stability and angle of incidence on the acceleration of wind up a slope, J. Wind Eng. Ind. Aerod., 15, 231–242, 1983. a
Braham, R. R.: Field Experimentation in Weather Modification, J. Am. Stat. Assoc., 74, 57–68, http://www.jstor.org/stable/2286722 (last access: 3 March 2021), 1979. a
Bromm, M., Rott, A., Beck, H., Vollmer, L., Steinfeld, G., and Kühn, M.: Field investigation on the influence of yaw misalignment on the propagation of wind turbine wakes, Wind Energy, 21, 1011–1028, https://doi.org/10.1002/we.2210, 2018. a, b, c, d
Busch, N. E. and Kristensen, L.: Cup Anemometer Overspeeding, J. Appl. Meteorol., 15, 1328–1332, https://doi.org/10.1175/1520-0450(1976)015<1328:cao>2.0.co;2, 1976. a
Buzzi, A., D Gomis, D., A., P. M., and Alonso, S.: A Method to Reduce the Adverse Impact that Inhomogeneous Station Distributions Have on Spatial Interpolation, Mon. Weather Rev., 119, 2465–2491, https://doi.org/10.1175/1520-0493(1991)119<2465:AMTRTA>2.0.CO;2, 1991. a
Caracena, F.: Analytic Approximation of Discrete Field Samples with Weighted Sums and the Gridless Computation of Field Derivatives, J. Atmos. Sci., 44, 3753–3768, https://doi.org/10.1175/1520-0469(1987)044<3753:AAODFS>2.0.CO;2, 1987. a
Caracena, F., Barnes, S. L., and Doswell III, C.: Weighting function parameters for objective interpolation of meteorological data, in: Preprints, 10th Conf. on Weather Forecasting and Analysis, Clearwater Beach, FL, 25–29 June 1984, 109–116, 1984. a
Carbajo Fuertes, F., Markfort, C. D., and Porté-Agel, F.: Wind Turbine Wake Characterization with Nacelle-Mounted Wind Lidars for Analytical Wake Model Validation, Remote Sens.-Basel, 10, 668, https://doi.org/10.3390/rs10050668, 2018. 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. Meteor., 136, 515–533, https://doi.org/10.1007/s10546-010-9512-1, 2010. a
Choukulkar, A., Brewer, W. A., Sandberg, S. P., Weickmann, A., Bonin, T. A., Hardesty, R. M., Lundquist, J. K., Delgado, R., Iungo, G. V., Ashton, R., Debnath, M., Bianco, L., Wilczak, J. M., Oncley, S., and Wolfe, D.: Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign, Atmos. Meas. Tech., 10, 247–264, https://doi.org/10.5194/amt-10-247-2017, 2017. a
Ciri, U., Rotea, M., Santoni, C., and Leonardi, S.: Large-eddy simulations with extremum-seeking control for individual wind turbine power optimization, Wind Energy, 20, 1617–1634, https://doi.org/10.1002/we.2112, 2017. a
Cressman, G. P.: An Operational Objective Analysis System, Mon. Weather Rev., 87, 367–374, https://doi.org/10.1175/1520-0493(1959)087<0367:AOOAS>2.0.CO;2, 1959. a
Cuerva, A. and Sanz-Andrés, A.: On sonic anemometer measurement theory, J. Wind Eng. Ind. Aerod., 88, 25–55, https://doi.org/10.1016/S0167-6105(00)00023-4, 2000. a
Cushman-Roisin, B. and Beckers, J. M.: Introduction, in: Introduction to Geophysical Fluid Dynamics, Academic Press, 7–11, 1990a. a
Cushman-Roisin, B. and Beckers, J. M.: Stratification effects, in: Introduction to Geophysical Fluid Dynamics, p. 319, Academic Press, 1990b. a
Davies, F., Middleton, D. R., and Bozier, K. E.: Urban air pollution modelling and measurements of boundary layer height, Atmos. Environ., 41, 4040–4049, https://doi.org/10.1016/j.atmosenv.2007.01.015, 2007. a
Debnath, M., Iungo, G. V., Ashton, R., Brewer, W. A., Choukulkar, A., Delgado, R., Lundquist, J. K., Shaw, W. J., Wilczak, J. M., and Wolfe, D.: Vertical profiles of the 3-D wind velocity retrieved from multiple wind lidars performing triple range-height-indicator scans, Atmos. Meas. Tech., 10, 431–444, https://doi.org/10.5194/amt-10-431-2017, 2017a. a
Debnath, M., Iungo, G. V., Brewer, W. A., Choukulkar, A., Delgado, R., Gunter, S., Lundquist, J. K., Schroeder, J. L., Wilczak, J. M., and Wolfe, D.: Assessment of virtual towers performed with scanning wind lidars and Ka-band radars during the XPIA experiment, Atmos. Meas. Tech., 10, 1215–1227, https://doi.org/10.5194/amt-10-1215-2017, 2017b. a, b
Debnath, M., Santoni, C., Leonardi, S., and Iungo, G. V.: Towards reduced order modelling for predicting the dynamics of coherent vorticity structures within wind turbine wakes, Philos. T. R. Soc. A., 375, 20160108, https://doi.org/10.1098/rsta.2016.0108, 2017c. a
Doswell, C. A.: Obtaining Meteorologically Significant Surface Divergence Fields Through the Filtering Property of Objective Analysis, Mon. Weather Rev., 105, 885–892, https://doi.org/10.1175/1520-0493(1977)105<0885:OMSSDF>2.0.CO;2, 1977. a
Duncan, Jr., J. B., Hirth, B. D., and Schroeder, J. L.: Enhanced estimation of boundary layer advective properties to improve space-to-time conversion processes for wind energy applications, Wind Energy, 22, 1203–1218, https://doi.org/10.1002/we.2350, https://doi.org/10.1002/we.2350, 2019. a
Eberhard, W. L., Cupp, R. E., and Healy, K. R.: Doppler Lidar Measurement of Profiles of Turbulence and Momentum Flux, J. Atmos. Ocean. Tech., 6, 809–819, https://doi.org/10.1175/1520-0426(1989)006<0809:DLMOPO>2.0.CO;2, 1989. a
El-Asha, S., Zhan, L., and Iungo, G. V.: Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data, Wind Energy, 20, 1823–1839, https://doi.org/10.1002/we.2123, 2017. a, b
Emeis, S.: Surface-Based Remote Sensing of the Atmospheric Boundary Layer, Springer, Berlin/Heidelberg, 2010. a
Emeis, S., Frank, H. R., and Fiedler, F.: Modification of air flow over an escarpment – Results from the Hjardemal experiment, Bound.-Lay. Meteorol., 74, 131–161, 1995. a
Endlich, R. M. and Mancuso, R. L.: Objective analysis of environmental conditions associated with severe thunderstorms and tornadoes, Mon. Weather Rev., 96, 342–350, https://doi.org/10.1126/science.27.693.594, 1968. a
ESDU: Characteristics of atmospheric turbulence near the ground. Part III: Variations in space and time for strong winds (neutral atmosphere), Engineering Sciences Data Unit, Regent Street, London, England, 1975. a
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., Lundquist, J. K., Barthelmie, R. J., Belo-Pereira, M., Brown, W., Chow, F. K., and Gerz, T., e. a.: Peering into microscale details of mountain winds, Bull. Am. Meteorol. Soc., 100, 799–820, https://doi.org/10.1175/BAMS-D-17-0227.1, 2019. a
Foken, T., Göockede, M., Mauder, M., Mahrt, L., B., A., and W., M.: Post-Field Data Quality Control, in: Handbook of Micrometeorology, Atmospheric and Oceanographic Sciences Library, vol. 29, Springer, Dordrecht, 2004. a
Frehlich, R.: Effects of Wind Turbulence on Coherent Doppler Lidar Performance, J. Atmos. Ocean. Tech., 14, 54–75, https://doi.org/10.1175/1520-0426(1997)014<0054:EOWTOC>2.0.CO;2, 1997. a
Frehlich, R. and Cornman, L.: Estimating Spatial Velocity Statistics with Coherent Doppler Lidar, J. Atmos. Ocean. Tech., 19, 355–366, https://doi.org/10.1175/1520-0426-19.3.355, 2002. a
Frisch, A. S.: On the measurement of second moments of turbulent wind velocity with a single Doppler radar over non-homogeneous terrain, Bound.-Lay. Meteorol., 54, 29–39, 1991. a
Fuertes Carbajo, F. and Porté-Agel, F.: Using a Virtual Lidar Approach to Assess the Accuracy of the Volumetric Reconstruction of a Wind Turbine Wake, Remote Sens.-Basel, 10, 721, https://doi.org/10.3390/rs10050721, 2018. a, b, c
Gal-Chen, T., Xu, M., and Eberhard, W. L.: Estimations of atmospheric boundary layer fluxes and other turbulence parameters from Doppler lidar data, J. Geophys. Res.-Atmos., 97, 18409–18423, https://doi.org/10.1029/91JD03174, 1992. a, b
Garcia, E. T., Aubrun, S., Boquet, M., Royer, P., Coupiac, O., and Girard, N.: Wake meandering and its relationship with the incoming wind characteristics: a statistical approach applied to long-term on-field observations, J. Phys. Conf. Ser., 854, 012045, https://doi.org/10.1088/1742-6596/854/1/012045, 2017. a, b, c
George, W. K., Beuther, P. D., and Lumley, J. L.: Processing of random signals, Proceedings of the Dynamic Flow Conference 1978 on Dynamic Measurements in Unsteady Flows, Springer, Dordrecht, 1978. a
Gomis, D. and Alonso, S.: Diagnosis of a Cyclogenetic Event in the Western Mediterranean Using an Objective Technique for Scale Separation, Mon. Weather Rev., 118, 723–736, https://doi.org/10.1175/1520-0493(1990)118<0723:DOACEI>2.0.CO;2, 1990. a
Greenberg, M.: Advanced Engineering Mathematics, 2nd edn., Prentice Hall, Upper Saddle River, NJ, 1998. a
Halios, C. H. and Barlow, J. F.: Observations of the Morning Development of the Urban Boundary Layer Over London, UK, Taken During the ACTUAL Project, Bound.-Lay. Meteorol., 166, 395–422, https://doi.org/10.1007/s10546-017-0300-z, 2018. a
Haugen, D. A., Kaimal, J. C., and Bradley, E. F.: An experimental study of Reynolds stress and heat flux in the atmospheric surface layer, Q. J. Roy. Meteorol. Soc., 97, 168–180, https://doi.org/10.1002/qj.49709741204, 1971. a
Högström, U., Hunt, J. C. R., and Smedman, A. S.: Theory and measurements for turbulence spectra and variances in the atmospheric neutral surface layer, Bound.-Lay. Meteor., 103, 101–124, https://doi.org/10.1023/A:1014579828712, 2002. a
Holzäpfel, F., Stephan, A., Heel, T., and Körner, S.: Enhanced wake vortex decay in ground proximity triggered by plate lines, Aircr. Eng. Aerosp. Technol., 88, 206–214, https://doi.org/10.1108/AEAT-02-2015-0045, 2016. a
Huang, M., Gao, Z., Miao, S., Chen, F., LeMone, M. A., Li, J., Hu, F., and Wang, L.: Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015, Bound.-Lay. Meteorol., 162, 503–522, https://doi.org/10.1007/s10546-016-0205-2, 2017. a
Iungo, G. V. and Porté-Agel, F.: Volumetric Lidar Scanning of Wind Turbine Wakes under Convective and Neutral Atmospheric Stability Regimes, J. Atmos. Ocean. Tech., 31, 2035–2048, https://doi.org/10.1175/JTECH-D-13-00252.1, 2014. a, b, c
Iungo, G. V., Viola, F., Camarri, S., Porté-Agel, F., and Gallaire, F.: Linear stability analysis of wind turbine wakes performed on wind tunnel measurements, J. Fluid Mech., 737, 499–526, https://doi.org/10.1017/jfm.2013.569, 2013a. a, b
Iungo, G. V., Wu, Y., and Porté-Agel, F.: Field Measurements of Wind Turbine Wakes with Lidars, J. Atmos. Ocean. Tech., 30, 274–287, https://doi.org/10.1175/JTECH-D-12-00051.1, 2013b. a, b
Iungo, G. V., Letizia, S., and Zhan, L.: Quantification of the axial induction exerted by utility-scale wind turbines by coupling LiDAR measurements and RANS simulations, J. Phys. Conf. Ser., 1037, https://doi.org/10.1088/1742-6596/1037/7/072023, 2018. a, b
Ivanell, S., Mikkelsen, R., Sørensen, J. N., and Henningson, D.: Stability analysis of the tip vortices of a wind turbine, Wind Energy, 13, 705–715, https://doi.org/10.1002/we.391, 2010. a
Jonkman, J., Butterfield, S., Musial, W., and Scott, G.: Definition of a 5-MW Reference Wind Turbine for Offshore System Development, Technical report NREL/TP-500-38060, NREL, 1617 Cole Boulevard, Golden, CO, USA, 2009. a
Käsler, Y., S., R., Simmet, R., and Kühn, M.: Wake Measurements of a Multi-MW Wind Turbine with Coherent Long-Range Pulsed Doppler Wind Lidar, J. Atmos. Ocean. Tech., 27, 1529–1532, https://doi.org/10.1175/2010JTECHA1483.1, 2010. a
Kim, D., Kim, T., Oh, G., Huh, J., and Ko, K.: A comparison of ground-based LiDAR and met mast wind measurements for wind resource assessment over various terrain conditions, J. Wind Eng. Ind. Aerod., 158, 109–121, https://doi.org/10.1016/j.jweia.2016.09.011, 2016. a
Koch, S. E., Des Jardins, M., and Kocin, P. J.: An Interactive Barnes objective Map Analysis Scheme for Use with Satellite and Conventional Data, J. Clim. Appl. Meteorol., 22, 1487–1503, https://doi.org/10.1175/1520-0450(1983)022<1487:AIBOMA>2.0.CO;2, 1983. a, b, c
Kongara, S., Calhoun, R., Choukulkar, A., and Boldi, M. O.: Velocity retrieval for coherent Doppler lidar, Int. J. Remote Sens., 33, 3596–3613, https://doi.org/10.1080/01431161.2011.631948, 2012. a, b
Köpp, F., Rahm, S., Smalikho, I., Dolfi, A., Cariou, J. P., Harris, M., and Young, R. I.: Comparison of wake-vortex parameters measured by pulsed and continuous-wave lidars, J. Aircr., 42, 916–923, https://doi.org/10.2514/1.8177, 2005. a
Krishnamurthy, R., Choukulkar, A., Calhoun, R., Fine, J., Oliver, A., and Barr, K. S.: Coherent Doppler lidar for wind farm characterization, Wind Energy, 16, 189–206, https://doi.org/10.1002/we.539, 2013. a
Kropfli, R. A.: Single Doppler Radar Measurements of Turbulence Profiles in the Convective Boundary Layer, J. Atmos. Ocean. Tech., 3, 305–314, https://doi.org/10.1175/1520-0426(1986)003<0305:SDRMOT>2.0.CO;2, 1986. a
Kumer, V. M., Reudera, J., Svardalc, B., Sætrec, C., and Eecen, P.: Characterisation of Single Wind Turbine Wakes with Static and Scanning WINTWEX-W LiDAR Data, Energy Procedia, 80, 245–254, https://doi.org/10.1016/j.egypro.2015.11.428, 12th Deep Sea Offshore Wind R and D Conference, EERA DeepWind 2015, Trondheim, Norway, 2–5 February 2015. a, b
Kunkel, G. J. and Marusic, I.: Study of the near-wall-turbulent region of the high-Reynolds-number boundary layer using an atmospheric flow, J. Fluid Mech., 548, 375–402, https://doi.org/10.1017/S0022112005007780, 2006. a, b, c
Letizia, S. and Iungo, G. V.: LiSBOA, GitHub, available at:
https://github.com/UTD-WindFluX/LiSBOA, last access: 4 March 2021. a
Letizia, S., Zhan, L., and Iungo, G. V.: LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes, Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, 2021. a
Lhermitte, R. M.: Note on the Observation of Small-Scale Atmospheric Turbulence by Doppler Radar Techniques, Radio Sci., 4, 1241–1246, https://doi.org/10.1029/RS004i012p01241, 1969. a
Liu, H. Y., , Bo, T. L., and Liang, Y. R.: The variation of large-scale structure inclination angles in high Reynolds number atmospheric surface layers, Phys. Fluids, 29, 035104, https://doi.org/10.1063/1.4978803, 2017. a
Liu, Z., Barlow, J. F., Chan, P. W., Fung, J. C. H., Li, Y., Ren, C., Mak, H. W. L., and Ng, E.: A review of progress and applications of pulsed Doppler Wind LiDARs, Remote Sens.-Basel, 11, 1–47, https://doi.org/10.3390/rs11212522, 2019. a, b
Lundquist, J. K., Churchfield, M. J., Lee, S., and Clifton, A.: Quantifying error of lidar and sodar Doppler beam swinging measurements of wind turbine wakes using computational fluid dynamics, Atmos. Meas. Tech., 8, 907–920, https://doi.org/10.5194/amt-8-907-2015, 2015. a
Lundquist, J. K., Wilczak, J. M., Ashton, R., Bianco, L., Brewer, W. A., Choukulkar, A., Clifton, A., Debnath, M., Delgado, R., Friedrich, K., Gunter, S., Hamidi, A., Iungo, G. V., Kaushik, A., Kosovic, B., Langan, P., Lass, A., Lavin, E., Lee, J. C. Y., McCaffrey, K. L., Newsom, R. K., Noone, D. C., Oncley, S. P., Quelet, P. T., Sandberg, S. P., Schroeder, J. L., Shaw, W. J., Sparling, L., Martin, C. S., Pe, A. S., Strobach, E., Tay, K., Vanderwende, B. J., Weickmann, A., Wolfe, D., and Worsnop, R.: Assessing state-of-The-Art capabilities for probing the atmospheric boundary layer the XPIA field campaign, Bull. Am. Meteorol. Soc., 98, 289–314, https://doi.org/10.1175/BAMS-D-15-00151.1, 2017. a
Machefaux, E., Larsen, G. C., Troldborg, N., Hansen, K. S., Angelou, N., Mikkelsen, T., and Mann, J.: Investigation of wake interaction using full-scale lidar measurements and large eddy simulation, Wind Energy, 19, 1535–1551, https://doi.org/10.1002/we.1936, 2015. a, b, c
Machefaux, E., Larsen, G. C., Koblitz, T., Troldborg, N., Kelly, M. C., Chougule, A., Hansen, K. S., and Rodrigo, J. S.: An experimental and numerical study of the atmospheric stability impact on wind turbine wakes, Wind Energy, 19, 1785–1805, https://doi.org/10.1002/we.1950, 2016. a, b, c, d
Maddox, R. A.: An Objective Technique for Separating Macroscale and Mesoscale Features in Meteorological Data, Mon. Weather Rev., 108, 1108–1121, https://doi.org/10.1175/1520-0493(1980)108<1108:AOTFSM>2.0.CO;2, 1980. a
Mann, J., Cariou, J. P., Courtney, M., Parmentier, R., Mikkelsen, T., Wagner, R., Lindelöw, P., Sjöholm, M., and Enevoldsen, K.: Comparison of 3D turbulence measurements using three staring wind lidars and a sonic anemometer, Meteorol. Z., https://doi.org/10.1127/0941-2948/2009/0370, 2009. a
Mann, J., Peña, A., Bingöl, F., Wagner, R., and Courtney, M. S.: Lidar Scanning of Momentum Flux in and above the Atmospheric Surface Layer, J. Atmos. Ocean. Tech., 27, 959–976, https://doi.org/10.1175/2010JTECHA1389.1, 2010. a, b, c, d
Mann, J., Menke, R., Vasiljević, N., Berg, J., and Troldborg, N.: Challenges in using scanning lidars to estimate wind resources in complex terrain, J. Phys. Conf. Ser., 1037, 0–8, https://doi.org/10.1088/1742-6596/1037/7/072017, 2018. a
Manninen, A. J., O'Connor, E. J., Vakkari, V., and Petäjä, T.: A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland, Atmos. Meas. Tech., 9, 817–827, https://doi.org/10.5194/amt-9-817-2016, 2016. a
Mayor, S. D., Lenschow, D. H., Schwiesow, R. L., Mann, J., Frush, C. L., and Simon, M. K.: Validation of NCAR 10.6-µm CO2 Doppler Lidar Radial Velocity Measurements and Comparison with a 915-MHz Profiler, J. Atmos. Ocean. Tech., 14, 1110–1126, https://doi.org/10.1175/1520-0426(1997)014<1110:VONMCD>2.0.CO;2, 1997. a
Metzger, M., McKeon, B. J., and Holmes, H.: The near-neutral atmospheric surface layer: turbulence and non-stationarity, Phil. Trans. R. Soc. A, 365, 859–876, https://doi.org/10.1098/rsta.2006.1946, 2007. a
Mirocha, J. D., Rajewski, D. A., Marjanovic, N., Lundquist, J. K., Kosović, B., Draxl, C., and Churchfield, M. J.: Investigating wind turbine impacts on near-wake flow using profiling lidar data and large-eddy simulations with an actuator disk model, J. Renew. Sustain. Ener., 7, 1–21, https://doi.org/10.1063/1.4928873, 2015. a
Mohr, C. and Vaughan, R.: An Economical Procedure for Cartesian Interpolation and Display of Reflectivity Factor Data in Three-Dimensional Space, J. Appl. Meteorol., 18, 661–670, 1979. a
Muñoz-Esparza, D., Cañadillas, B., Neumann, T., and Van Beeck, J.: Turbulent fluxes, stability and shear in the offshore environment: Mesoscale modelling and field observations at FINO1, J. Renew. Sustain. Ener., 4, 063136, https://doi.org/10.1063/1.4769201, 2012. a
Newsom, R., Berg, L., Shaw, W. J., and Fischer, M. L.: Turbine-scale wind field measurements using dual-Doppler lidar, Wind Energy, 18, 219–235, https://doi.org/10.1002/we.1691, 2014. a
Newsom, R. K. and Banta, R. M.: Assimilating Coherent Doppler Lidar Measurements into a Model of the Atmospheric Boundary Layer. Part I: Algorithm Development and Sensitivity to Measurement Error, J. Atmos. Ocean. Tech., 21, 1328–1345, https://doi.org/10.1175/1520-0426(2004)021<1328:ACDLMI>2.0.CO;2, 2004. a
Newsom, R. K., Calhoun, R., Ligon, D., and Allwine, J.: Linearly Organized Turbulence Structures Observed Over a Suburban Area by Dual-Doppler Lidar, Bound.-Lay. Meteorol., 127, 111–130, https://doi.org/10.1007/s10546-007-9243-0, 2008. a, b, c, d
Newsom, R. K., Brewer, W. A., Wilczak, J. M., Wolfe, D. E., Oncley, S. P., and Lundquist, J. K.: Validating precision estimates in horizontal wind measurements from a Doppler lidar, Atmos. Meas. Tech., 10, 1229–1240, https://doi.org/10.5194/amt-10-1229-2017, 2017. a
O'Connor, E. J., Illingworth, A. J., Brooks, I. M., Westbrook, C. D., Hogan, R. J., Davies, F., and Brooks, B. J.: A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar and Independent Evaluation from Balloon-Borne In Situ Measurements, J. Atmos. Ocean. Tech., 27, 1652–1664, https://doi.org/10.1175/2010JTECHA1455.1, 2010. a, b, c, d, e
Orlanski, I.: A simple boundary condition for unbounded hyperbolic flows, J. Comput. Phys., 21, 251–269, https://doi.org/10.1016/0021-9991(76)90023-1, 1976. a
Pardyjak, E. R. and Stoll, R.: Improving measurement technology for the design of sustainable cities, Meas. Sci. Technol., 28, 092001, https://doi.org/10.1088/1361-6501/aa7c77, 2017. a
Pahlow, M., Parlange, M. B., and Porté-Agel, F.: On Monin–Obukhov similarity in the stable atmospheric boundary layer, Bound.-Lay. Meteorol., 99, 225–248, https://doi.org/10.1023/A, 2001. a
Pauscher, L., Vasiljevic, N., Callies, D., Lea, G., Mann, J., Klaas, T., Hieronimus, J., Gottschall, J., Schwesig, A., Kühn, M., and Courtney, M.: An inter-comparison study of multi- and DBS lidar measurements in complex terrain, Remote Sens.-Basel, 8, 782, https://doi.org/10.3390/rs8090782, 2016. a
Petersen, D. P. and Middleton, D.: Sampling and reconstruction of wave-number-limited functions in N-dimensional Euclidean spaces, Inf. Control., 5, 279–323, https://doi.org/10.1016/S0019-9958(62)90633-2, 1962. a
Puccioni, M. and Iungo, G. V.: Spectral correction of turbulent energy damping on wind LiDAR measurements due to range-gate averaging, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2020-27, in review, 2020. a, b, c, d
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, Bull. Am. Meteorol. Soc., 94, 655–672, https://doi.org/10.1175/BAMS-D-11-00240.1, 2013. a
Rye, B. and Hardesty, M.: Discrete Spectral Peak Estimation in Incoherent Backscatter Heterodyne Lidar. I: Spectral Accumulation and the Cramer-Rao Lower Bound, IEEE T. Geosci. Remote, 31, 16–27, https://doi.org/10.1109/36.210440, 1993. a, b
Santoni, C., Ciri, U., Rotea, M., and Leonardi, S.: Development of a high fidelity CFD code for wind farm control, Proceedings of the American Control Conference, 1–3 July 2015, 1715–1720, https://doi.org/10.1109/ACC.2015.7170980, 2015. a
Santoni, C., Carrasquillo, K., Arenas-Navarro, I., and Leonardi, S.: Effect of tower and nacelle on the flow past a wind turbine, Wind Energy, 20, 1927–1939, https://doi.org/10.1002/we.2130, 2017. a
Sasaki, Y.: A theoretical interpretation of anisotropically weighted smoothing on the basis of numerical variational analysis, Mon. Weather Rev., 99, 698–707, 1971. a
Sathe, A. and Mann, J.: A review of turbulence measurements using ground-based wind lidars, Atmos. Meas. Tech., 6, 3147–3167, https://doi.org/10.5194/amt-6-3147-2013, 2013. a
Sathe, A., Mann, J., Gottschall, J., and Courtney, M. S.: Can Wind Lidars Measure Turbulence?, J. Atmos. Ocean. Tech., 28, 853–868, https://doi.org/10.1175/JTECH-D-10-05004.1, 2011. a, b, c, d
Schween, J. H., Hirsikko, A., Löhnert, U., and Crewell, S.: Mixing-layer height retrieval with ceilometer and Doppler lidar: from case studies to long-term assessment, Atmos. Meas. Tech., 7, 3685–3704, https://doi.org/10.5194/amt-7-3685-2014, 2014. a
Seaman, R. S.: Tuning the Barnes Objective Analysis Parameters by Statistical Interpolation Theory, J. Atmos. Ocean. Tech., 6, 993–1000, https://doi.org/10.1175/1520-0426(1989)006<0993:TTBOAP>2.0.CO;2, 1989. a
Sekar, A. P. K., van Dooren, M. F., Mikkelsen, T., Sjöholm, M., Astrup, P., and Kühn, M.: Evaluation of the LINCOM wind field reconstruction method with simulations and full-scale measurements, J. Phys. Conf. Ser., 1037, 052 008, https://doi.org/10.1088/1742-6596/1037/5/052008, 2018. a
Shannon, C. E.: Communication in the Presence of Noise, Proceedings of the IEEE, 72, 1192–1201, https://doi.org/10.1109/PROC.1984.12998, 1984. a
Smith, D. R. and Leslie, F. W.: Error Determination of a Successive Correction Type Objective Analysis Scheme, J. Atmos. Ocean. Tech., 1, 120–130, https://doi.org/10.1175/1520-0426(1984)001<0120:EDOASC>2.0.CO;2, 1984. a, b
Smith, D. R., Pumphry, M. E., and Snow, J. T.: A Comparison of Errors in objectively Analyzed Fields for Uniform and Nonuniform Station Distributions, J. Atmos. Ocean. Tech., 3, 84–97, https://doi.org/10.1175/1520-0426(1986)003<0084:ACOEIO>2.0.CO;2, 1986. a
Stawiarski, C., Traumner, K., Knigge, C., and Calhoun, R.: Scopes and challenges of dual-Doppler lidar wind measurements-an error analysis, J. Atmos. Ocean. Tech., 30, 2044–2062, https://doi.org/10.1175/JTECH-D-12-00244.1, 2013. a, b
Stawiarski, C., Träumner, K., Kottmeier, C., Knigge, C., and Raasch, S.: Assessment of Surface-Layer Coherent Structure Detection in Dual-Doppler Lidar Data Based on Virtual Measurements, Bound.-Lay. Meteorol., 156, 371–393, https://doi.org/10.1007/s10546-015-0039-3, 2015. a, b
Stull, R. B.: Preface, in: An introduction to Boundary Layer Meteorology, p. xi, Kluwer Academic Publisher, P.O. 80x 17. 3300 AA Dordredt, the Netherlands, 1988. a
Tang, W., Chan, P. W., and Haller, G.: Lagrangian coherent structure analysis of terminal winds detected by lidar. Part I: Turbulence structures, J. Appl. Meteorol. Clim., 50, 325–338, https://doi.org/10.1175/2010JAMC2508.1, 2011. a
Taylor, P. A. and Teunissen, H. W.: The Askervin hill project: overview and background data, Bound.-Lay. Meteorol., 39, 15–39, 1987. a
Thobois, L., Cariou, J. P., and Gultepe, I.: Review of Lidar-Based Applications for Aviation Weather, Pure Appl. Geophys., 176, 1959–1976, https://doi.org/10.1007/s00024-018-2058-8, 2019. a
Trapp, R. J. and Doswell, C. A.: Radar data objective analysis, J. Atmos. Ocean. Tech., 17, 105–120, https://doi.org/10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2, 2000. a, b, c
Trujillo, J. J., F., B., Larsen, G. C., Mann, J., and Kühn, M.: Light detection and ranging measurements of wake dynamics. Part II: two-dimensional scanning, Wind Energy, 14, 61–75, https://doi.org/10.1002/we.402, 2011. a, b, c
Trujillo, J. J., Seifert, J. K., Würth, I., Schlipf, D., and Kühn, M.: Full-field assessment of wind turbine near-wake deviation in relation to yaw misalignment, Wind Energ. Sci., 1, 41–53, https://doi.org/10.5194/wes-1-41-2016, 2016. a, b, c
Vakkari, V., O'Connor, E. J., Nisantzi, A., Mamouri, R. E., and Hadjimitsis, D. G.: Low-level mixing height detection in coastal locations with a scanning Doppler lidar, Atmos. Meas. Tech., 8, 1875–1885, https://doi.org/10.5194/amt-8-1875-2015, 2015. a
Vakkari, V., Manninen, A. J., O'Connor, E. J., Schween, J. H., van Zyl, P. G., and Marinou, E.: A novel post-processing algorithm for Halo Doppler lidars, Atmos. Meas. Tech., 12, 839–852, https://doi.org/10.5194/amt-12-839-2019, 2019. a
Van Dooren, M. F., Trabucchi, D., and Kühn, M.: A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements, Remote Sens.-Basel, 8, 809, https://doi.org/10.3390/rs8100809, 2016.
a, b
Viola, F., Iungo, G. V., Camarri, S., Porté-Agel, F., and Gallaire, F.: Prediction of the hub vortex instability in a wind turbine wake: Stability analysis with eddy-viscosity models calibrated on wind tunnel data, J. Fluid Mech., 750, R1, https://doi.org/10.1017/jfm.2014.263, 2014. a, b
Wang, H. and Barthelmie, R. J.: Wind turbine wake detection with a single Doppler wind lidar, J. Phys. Conf. Ser., 625, 012017, https://doi.org/10.1088/1742-6596/625/1/012017, 2015. a, b
Wheeler, A. J. and Ganji, A. R.: Measuring Fluid Flow Rate, Fluid Velocity, Fluid Level and Combustion Pollutants, in: Introduction to engineering experimentation, Pearson Higher Education, 1 Lake St., Upper Saddle River, New Jersey, 07458., 2010a. a
Wilson, D. A.: Doppler Radar Studies of Boundary Layer, Wind Profiles and Turbulence in Snow Conditions, Proc. 14th Conference on Radar Meteorology, Tucson, USA, p. 191–196, 1970. a
Xia, Q., Lin, C. L., Calhoun, R., and Newsom, R. K.: Retrieval of Urban Boundary Layer Structures from Doppler Lidar Data. Part I: Accuracy Assessment, J. Atmos. Sci., 65, 3–20, https://doi.org/10.1175/2007JAS2328.1, 2008. a, b
Xu, Q. and Gong, J.: Background error covariance functions for Doppler radial-wind analysis, Q. J. Roy. Meteorol. Soc., 129, 1703–1720, https://doi.org/10.1256/qj.02.129, 2002. a
Zieba, A. and Ramza, P.: Standard deviation of the mean of autocorrelated observations estimated with the use of the autocorrelation function estimated from the data, Metrol. Meas. Syst., XVIII, 529–542, https://doi.org/10.1017/cbo9780511921247.018, 2011. a
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and...
