Articles | Volume 13, issue 2
https://doi.org/10.5194/gi-13-205-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gi-13-205-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Jul 2024
Research article |
| 08 Jul 2024
| 08 Jul 2024
Comparing triple and single Doppler lidar wind measurements with sonic anemometer data based on a new filter strategy for virtual tower measurements
Kevin Wolz
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
Christopher Holst
Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
Frank Beyrich
Lindenberg Meteorological Observatory, German Meteorological Service (DWD), 15848 Tauche, Germany
Eileen Päschke
Lindenberg Meteorological Observatory, German Meteorological Service (DWD), 15848 Tauche, Germany
Matthias Mauder
Institute of Meteorology and Climate Research – Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology (KIT), 82467 Garmisch-Partenkirchen, Germany
Institute of Hydrology and Meteorology, Technical University of Dresden, 01069 Dresden, Germany
Related authors
Johannes Speidel, Hannes Vogelmann, Andreas Behrendt, Diego Lange, Matthias Mauder, Jens Reichardt, and Kevin Wolz
Atmos. Meas. Tech., 18, 4923–4948, https://doi.org/10.5194/amt-18-4923-2025, https://doi.org/10.5194/amt-18-4923-2025, 2025
Short summary
Short summary
Humidity transport from the Earth's surface into the atmosphere is relevant for many processes. However, knowledge of the actual distribution of humidity concentrations is sparse – mainly due to technological limitations. With the lidar presented herein, it is possible to measure humidity concentrations and their vertical fluxes up to altitudes of > 3 km with high spatiotemporal resolution, opening new possibilities for detailed process understanding and, ultimately, better model representation.
Johannes Speidel, Hannes Vogelmann, Andreas Behrendt, Diego Lange, Matthias Mauder, Jens Reichardt, and Kevin Wolz
Atmos. Meas. Tech., 18, 4923–4948, https://doi.org/10.5194/amt-18-4923-2025, https://doi.org/10.5194/amt-18-4923-2025, 2025
Short summary
Short summary
Humidity transport from the Earth's surface into the atmosphere is relevant for many processes. However, knowledge of the actual distribution of humidity concentrations is sparse – mainly due to technological limitations. With the lidar presented herein, it is possible to measure humidity concentrations and their vertical fluxes up to altitudes of > 3 km with high spatiotemporal resolution, opening new possibilities for detailed process understanding and, ultimately, better model representation.
Rico Kronenberg, Ivan Vorobevskii, Thi Thanh Luong, Uwe Spank, Dongkyun Kim, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2084, https://doi.org/10.5194/egusphere-2025-2084, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
We developed an improved model to better understand how water and energy move through natural landscapes (forest, grasslands, croplands, etc) throughout the day. By using detailed data from study-site in Germany, we tested the model and found its good agreement with micro-meteorological measurements. Unlike many other tools, this model works without needing new adjustments and offers a powerful way to study fast-changing water processes in different environments.
Stefanie Fischer, Ronald Queck, Christian Bernhofer, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2025-2118, https://doi.org/10.5194/egusphere-2025-2118, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Accurate estimates of interception are important to assess the water availability in ecosystems. We analyzed rainfall interception for a forest site from plot to stand scale. During interception, eddy-covariance measurements of evaporation were systematically underestimated accounting for 24% of precipitation, while modelled interception evaporation accounted for 45%. As a consequence, we developed a hybrid correction approach to fit the evaporation data to both the energy and the water balance.
William Morrison, Dana Looschelders, Jonnathan Céspedes, Bernie Claxton, Marc-Antoine Drouin, Jean-Charles Dupont, Aurélien Faucheux, Martial Haeffelin, Christopher C. Holst, Simone Kotthaus, Valéry Masson, James McGregor, Jeremy Price, Matthias Zeeman, Sue Grimmond, and Andreas Christen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-167, https://doi.org/10.5194/essd-2025-167, 2025
Preprint under review for ESSD
Short summary
Short summary
We conducted research using sophisticated wind sensors to better understand wind patterns in Paris. By installing these sensors across the city, we gathered detailed data on wind speeds and directions from 2022 to 2024. This information helps improve weather and climate models, making them more accurate for city environments. Our findings offer valuable insights for scientists studying urban air and weather, improving predictions and understanding of city-scale atmospheric processes.
Patricia Glocke, Christopher C. Holst, and Susanne A. Benz
EGUsphere, https://doi.org/10.5194/egusphere-2025-1276, https://doi.org/10.5194/egusphere-2025-1276, 2025
Preprint withdrawn
Short summary
Short summary
The study investigates how soil temperatures impact the atmosphere in cities using an urban climate model. Soil temperature has a significant role in modulating surface energy fluxes and atmospheric conditions, with cloud cover playing a critical factor. Incorporating subsurface urban heat islands in climate modeling is essential for understanding the complex interplay between soil and atmosphere, offering valuable guidance for improving urban climate resilience and mitigation strategies.
Patricia Glocke, Christopher C. Holst, Basit Khan, and Susanne A. Benz
Earth Syst. Dynam., 16, 55–74, https://doi.org/10.5194/esd-16-55-2025, https://doi.org/10.5194/esd-16-55-2025, 2025
Short summary
Short summary
Utilizing the urban microclimate model PALM-4U, we show that temperature anomalies of ± 5 K at a depth of 2 m in the soil can impact atmospheric potential air temperatures within idealized domains. The impact depends on the season, time of day, land cover, and lateral boundary conditions of the domain. The magnitude of change depends mostly on seasonality and the time of day, ranging from 0.1 to 0.4 K. Land cover influences the absolute temperature but has a smaller impact on the magnitude.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
Short summary
Short summary
The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Hengheng Zhang, Wei Huang, Xiaoli Shen, Ramakrishna Ramisetty, Junwei Song, Olga Kiseleva, Christopher Claus Holst, Basit Khan, Thomas Leisner, and Harald Saathoff
Atmos. Chem. Phys., 24, 10617–10637, https://doi.org/10.5194/acp-24-10617-2024, https://doi.org/10.5194/acp-24-10617-2024, 2024
Short summary
Short summary
Our study unravels how stagnant winter conditions elevate aerosol levels in Stuttgart. Cloud cover at night plays a pivotal role, impacting morning air quality. Validating a key model, our findings aid accurate air quality predictions, crucial for effective pollution mitigation in urban areas.
Eileen Päschke and Carola Detring
Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024, https://doi.org/10.5194/amt-17-3187-2024, 2024
Short summary
Short summary
Little noise in radial velocity Doppler lidar measurements can contribute to large errors in retrieved turbulence variables. In order to distinguish between plausible and erroneous measurements we developed new filter techniques that work independently of the choice of a specific threshold for the signal-to-noise ratio. The performance of these techniques is discussed both by means of assessing the filter results and by comparing retrieved turbulence variables versus independent measurements.
Changxing Lan, Matthias Mauder, Stavros Stagakis, Benjamin Loubet, Claudio D'Onofrio, Stefan Metzger, David Durden, and Pedro-Henrique Herig-Coimbra
Atmos. Meas. Tech., 17, 2649–2669, https://doi.org/10.5194/amt-17-2649-2024, https://doi.org/10.5194/amt-17-2649-2024, 2024
Short summary
Short summary
Using eddy-covariance systems deployed in three cities, we aimed to elucidate the sources of discrepancies in flux estimations from different software packages. One crucial finding is the impact of low-frequency spectral loss corrections on tall-tower flux estimations. Our findings emphasize the significance of a standardized measurement setup and consistent postprocessing configurations in minimizing the systematic flux uncertainty resulting from the usage of different software packages.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
Short summary
Short summary
Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Sreenath Paleri, Luise Wanner, Matthias Sühring, Ankur Desai, and Matthias Mauder
EGUsphere, https://doi.org/10.5194/egusphere-2023-1721, https://doi.org/10.5194/egusphere-2023-1721, 2023
Preprint archived
Short summary
Short summary
We present a description and evaluation of numerical simulations of field experiment days during the CHEESEHEAD19 field campaign, conducted over a heterogeneous forested domain in Northern Wisconsin, USA. Diurnal simulations, informed and constrained by field measurements for two days during the summer and autumn were performed. The model could simulate near surface time series and profiles of atmospheric state variables and fluxes that matched relatively well with observations.
Julian Steinheuer, Carola Detring, Frank Beyrich, Ulrich Löhnert, Petra Friederichs, and Stephanie Fiedler
Atmos. Meas. Tech., 15, 3243–3260, https://doi.org/10.5194/amt-15-3243-2022, https://doi.org/10.5194/amt-15-3243-2022, 2022
Short summary
Short summary
Doppler wind lidars (DWLs) allow the determination of wind profiles with high vertical resolution and thus provide an alternative to meteorological towers. We address the question of whether wind gusts can be derived since they are short-lived phenomena. Therefore, we compare different DWL configurations and develop a new method applicable to all of them. A fast continuous scanning mode that completes a full observation cycle within 3.4 s is found to be the best-performing configuration.
Charlotte Rahlves, Frank Beyrich, and Siegfried Raasch
Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, https://doi.org/10.5194/amt-15-2839-2022, 2022
Short summary
Short summary
Lidars can measure the wind profile in the lower part of the atmosphere, provided that the wind field is horizontally uniform and does not change during the time of the measurement. These requirements are mostly not fulfilled in reality, and the lidar wind measurement will thus hold a certain error. We investigate different strategies for lidar wind profiling using a lidar simulator implemented in a numerical simulation of the wind field. Our findings can help to improve wind measurements.
Matthias Mauder, Andreas Ibrom, Luise Wanner, Frederik De Roo, Peter Brugger, Ralf Kiese, and Kim Pilegaard
Atmos. Meas. Tech., 14, 7835–7850, https://doi.org/10.5194/amt-14-7835-2021, https://doi.org/10.5194/amt-14-7835-2021, 2021
Short summary
Short summary
Turbulent flux measurements suffer from a general systematic underestimation. One reason for this bias is non-local transport by large-scale circulations. A recently developed model for this additional transport of sensible and latent energy is evaluated for three different test sites. Different options on how to apply this correction are presented, and the results are evaluated against independent measurements.
Stefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian J. Butterworth, Christopher Florian, Matthias Mauder, David M. Plummer, Luise Wanner, Ke Xu, and Ankur R. Desai
Atmos. Meas. Tech., 14, 6929–6954, https://doi.org/10.5194/amt-14-6929-2021, https://doi.org/10.5194/amt-14-6929-2021, 2021
Short summary
Short summary
The key points are the following. (i) Integrative observing system design can multiply the information gain of surface–atmosphere field measurements. (ii) Catalyzing numerical simulations and first-principles machine learning open up observing system simulation experiments to novel applications. (iii) Use cases include natural climate solutions, emission inventory validation, urban air quality, and industry leak detection.
Tamino Wetz, Norman Wildmann, and Frank Beyrich
Atmos. Meas. Tech., 14, 3795–3814, https://doi.org/10.5194/amt-14-3795-2021, https://doi.org/10.5194/amt-14-3795-2021, 2021
Short summary
Short summary
A fleet of quadrotors is presented as a system to measure the spatial distribution of atmospheric boundary layer flow. The big advantage of this approach is that multiple and flexible measurement points in space can be sampled synchronously. The algorithm to calculate the horizontal wind is based on the principle of aerodynamic drag and the related quadrotor dynamics. The validation reveals that an average accuracy of < 0.3 m s−1 for the wind speed and < 8° for the wind direction was achieved.
Basit Khan, Sabine Banzhaf, Edward C. Chan, Renate Forkel, Farah Kanani-Sühring, Klaus Ketelsen, Mona Kurppa, Björn Maronga, Matthias Mauder, Siegfried Raasch, Emmanuele Russo, Martijn Schaap, and Matthias Sühring
Geosci. Model Dev., 14, 1171–1193, https://doi.org/10.5194/gmd-14-1171-2021, https://doi.org/10.5194/gmd-14-1171-2021, 2021
Short summary
Short summary
An atmospheric chemistry model has been implemented in the microscale PALM model system 6.0. This article provides a detailed description of the model, its structure, input requirements, various features and limitations. Several pre-compiled ready-to-use chemical mechanisms are included in the chemistry model code; however, users can also easily implement other mechanisms. A case study is presented to demonstrate the application of the new chemistry model in the urban environment.
Cited articles
Ansmann, A. and Müller, D.: Lidar and Atmospheric Aerosol Particles, in: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, edited by: Weitkamp, C., Scholars Portal, New York, 105–141, https://doi.org/10.1007/0-387-25101-4_4, 2005.
Beck, H. and Kühn, M.: Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements, Remote Sens., 9, 561, https://doi.org/10.3390/rs9060561, 2017.
Beyrich, F., Leps, J.-P., Mauder, M., Bange, J., Foken, T., Huneke, S., Lohse, H., Lüdi, A., Meijninger, W. M. L., Mironov, D., Weisensee, U., and Zittel, P.: Area-Averaged Surface Fluxes Over the Litfass Region Based on Eddy-Covariance Measurements, Bound.-Lay. Meteorol, 121, 33–65, https://doi.org/10.1007/s10546-006-9052-x, 2006.
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.
Browning, K. A. and Wexler, R.: The Determination of Kinematic Properties of a Wind Field Using Doppler Radar, J. Appl. Meteor., 7, 105–113, https://doi.org/10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2, 1968.
Calhoun, R., Heap, R., Princebac, M., Sommer, J., Fernando, H., and Ligon, D.: Measurement of winds flowing toward an urban area using coherent doppler lidar, in: Fifth Conference on the Urban Environment, CD-ROM, 3.8, American Meteorological Society, Vancouver, BC, Canada, 2004.
Chanin, M. L., Garnier, A., Hauchecorne, A., and Porteneuve, J.: A Doppler lidar for measuring winds in the middle atmosphere, Geophys. Res. Lett., 16, 1273–1276, https://doi.org/10.1029/GL016i011p01273, 1989.
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.
Emeis, S., Harris, M., and Banta, R. M.: Boundary-layer anemometry by optical remote sensing for wind energy applications, Meteorol. Z., 16, 337–347, https://doi.org/10.1127/0941-2948/2007/0225, 2007.
Fischler, M. A. and Bolles, R. C.: Random sample consensus, Commun. ACM, 24, 381–395, https://doi.org/10.1145/358669.358692, 1981.
Frehlich, R. and Kelley, N.: Measurements of Wind and Turbulence Profiles With Scanning Doppler Lidar for Wind Energy Applications, IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing, 1, 42–47, https://doi.org/10.1109/JSTARS.2008.2001758, 2008.
Fuertes, F. C., Iungo, G. V., and Porté-Agel, F.: 3D Turbulence Measurements Using Three Synchronous Wind Lidars: Validation against Sonic Anemometry, J. Atmos. Ocean. Tech., 31, 1549–1556, https://doi.org/10.1175/JTECH-D-13-00206.1, 2014.
Hartigan, J. A. and Hartigan, P. M.: The dip test of unimodality, Ann. Stat., 13, 70–84, 1985.
Hoaglin, D. C., Mosteller, F., and Tukey, J. W.: Wiley Series in Probability and Mathematical Statistics, Understanding Robust and Exploratory Data Analysis, 447, Wiley, ISBN 978-0-471-38491-5, 1983.
Huffaker, R. M. and Hardesty, R. M.: Remote sensing of atmospheric wind velocities using solid-state and CO/sub 2/ coherent laser systems, Proc. IEEE, 84, 181–204, https://doi.org/10.1109/5.482228, 1996.
Illingworth, A. J., Cimini, D., Haefele, A., Haeffelin, M., Hervo, M., Kotthaus, S., Löhnert, U., Martinet, P., Mattis, I., O'Connor, E. J., and Potthast, R.: How Can Existing Ground-Based Profiling Instruments Improve European Weather Forecasts?, B. Am. Meteorol. Soc., 100, 605–619, https://doi.org/10.1175/BAMS-D-17-0231.1, 2019.
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.
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.
Mariani, Z., Crawford, R., Casati, B., and Lemay, F.: A Multi-Year Evaluation of Doppler Lidar Wind-Profile Observations in the Arctic, Remote Sens., 12, 323, https://doi.org/10.3390/rs12020323, 2020.
Mauder, M. and Zeeman, M. J.: Field intercomparison of prevailing sonic anemometers, Atmos. Meas. Tech., 11, 249–263, https://doi.org/10.5194/amt-11-249-2018, 2018.
Mauder, M., Oncley, S. P., Vogt, R., Weidinger, T., Ribeiro, L., Bernhofer, C., Foken, T., Kohsiek, W., Bruin, H. A. R. de, and Liu, H.: The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods, Bound.-Lay. Meteorol., 123, 29–54, https://doi.org/10.1007/s10546-006-9139-4, 2007.
Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H. P., Schmidt, M., and Steinbrecher, R.: A strategy for quality and uncertainty assessment of long-term eddy-covariance measurements, Agr. Forest Meteorol., 169, 122–135, https://doi.org/10.1016/j.agrformet.2012.09.006, 2013.
Mauder, M., Eggert, M., Gutsmuths, C., Oertel, S., Wilhelm, P., Voelksch, I., Wanner, L., Tambke, J., and Bogoev, I.: Comparison of turbulence measurements by a CSAT3B sonic anemometer and a high-resolution bistatic Doppler lidar, Atmos. Meas. Tech., 13, 969–983, https://doi.org/10.5194/amt-13-969-2020, 2020.
Newman, J. F., Bonin, T. A., Klein, P. M., Wharton, S., and Newsom, R. K.: Testing and validation of multi-lidar scanning strategies for wind energy applications, Wind Energ., 19, 2239–2254, https://doi.org/10.1002/we.1978, 2016.
Park, S., Kim, S.-W., Park, M.-S., and Song, C.-K.: Measurement of Planetary Boundary Layer Winds with Scanning Doppler Lidar, Remote Sens., 10, 1261, https://doi.org/10.3390/rs10081261, 2018.
Päschke, E., Leinweber, R., and Lehmann, V.: An assessment of the performance of a 1.5 µm Doppler lidar for operational vertical wind profiling based on a 1-year trial, Atmos. Meas. Tech., 8, 2251–2266, https://doi.org/10.5194/amt-8-2251-2015, 2015.
Päschke, E. and Detring, C.: Noise filtering options for conically scanning Doppler lidar measurements with low pulse accumulation, Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024, 2024.
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., 8, 782, https://doi.org/10.3390/rs8090782, 2016.
Pichugina, Y., Banta, R., Brewer, A., Choukulkar, A., Marquis, M., Olson, J., and Hardesty, M.: Doppler Lidar in the Wind Forecast Improvement Projects, EPJ Web Conf., 119, 10001, https://doi.org/10.1051/epjconf/201611910001, 2016.
Rahlves, C., Beyrich, F., and Raasch, S.: Scan strategies for wind profiling with Doppler lidar – an large-eddy simulation (LES)-based evaluation, Atmos. Meas. Tech., 15, 2839–2856, https://doi.org/10.5194/amt-15-2839-2022, 2022.
Robey, R. and Lundquist, J. K.: Behavior and mechanisms of Doppler wind lidar error in varying stability regimes, Atmos. Meas. Tech., 15, 4585–4622, https://doi.org/10.5194/amt-15-4585-2022, 2022.
Rott, A., Schneemann, J., Theuer, F., Trujillo Quintero, J. J., and Kühn, M.: Alignment of scanning lidars in offshore wind farms, Wind Energ. Sci., 7, 283–297, https://doi.org/10.5194/wes-7-283-2022, 2022.
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.
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.
Sathe, A., Mann, J., Vasiljevic, N., and Lea, G.: A six-beam method to measure turbulence statistics using ground-based wind lidars, Atmos. Meas. Tech., 8, 729–740, https://doi.org/10.5194/amt-8-729-2015, 2015.
Smalikho, I. N. and Banakh, V. A.: Measurements of wind turbulence parameters by a conically scanning coherent Doppler lidar in the atmospheric boundary layer, Atmos. Meas. Tech., 10, 4191–4208, https://doi.org/10.5194/amt-10-4191-2017, 2017.
Stawiarski, C., Träumner, 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.
Steinheuer, J., Detring, C., Beyrich, F., Löhnert, U., Friederichs, P., and Fiedler, S.: A new scanning scheme and flexible retrieval for mean winds and gusts from Doppler lidar measurements, Atmos. Meas. Tech., 15, 3243–3260, https://doi.org/10.5194/amt-15-3243-2022, 2022.
Strauch, R. G., Merritt, D. A., Moran, K. P., Earnshaw, K. B., and van Kamp, D. de: The Colorado Wind-Profiling Network, J. Atmos. Ocean. Tech., 1, 37–49, https://doi.org/10.1175/1520-0426(1984)001<0037:TCWPN>2.0.CO;2, 1984.
Weitkamp, C. (Ed.): Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, in: Springer Series in Optical Sciences, Scholars Portal, 1 online resource, Springer, New York, https://doi.org/10.1007/b106786, 2005.
Werner, C.: Doppler Wind Lidar, in: Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere, edited by: Weitkamp, C., Scholars Portal, New York, 325–354, https://doi.org/10.1007/0-387-25101-4_12, 2005.
Wolz, K. and Mauder, M.: Doppler Lidar virtual tower horizontal wind speed, wind direction, TKE, and gust data measured between 1 June and 14 September 2020 in Falkenberg (Tauche), Germany, Zenodo [data set], https://doi.org/10.5281/zenodo.10461733, 2024.
Wulfmeyer, V., Hardesty, R. M., Turner, D. D., Behrendt, A., Cadeddu, M. P., Di Girolamo, P., Schlüssel, P., van Baelen, J., and Zus, F.: A review of the remote sensing of lower tropospheric thermodynamic profiles and its indispensable role for the understanding and the simulation of water and energy cycles, Rev. Geophys., 53, 819–895, https://doi.org/10.1002/2014RG000476, 2015.
Wulfmeyer, V., Turner, D. D., Baker, B., Banta, R., Behrendt, A., Bonin, T., Brewer, W. A., Buban, M., Choukulkar, A., Dumas, E., Hardesty, R. M., Heus, T., Ingwersen, J., Lange, D., Lee, T. R., Metzendorf, S., Muppa, S. K., Meyers, T., Newsom, R., Osman, M., Raasch, S., Santanello, J., Senff, C., Späth, F., Wagner, T., and Weckwerth, T.: A New Research Approach for Observing and Characterizing Land–Atmosphere Feedback, B. Am. Meteorol. Soc., 99, 1639–1667, https://doi.org/10.1175/BAMS-D-17-0009.1, 2018.
Zeeman, M., Holst, C. C., Kossmann, M., Leukauf, D., Münkel, C., Philipp, A., Rinke, R., and Emeis, S.: Urban Atmospheric Boundary-Layer Structure in Complex Topography: An Empirical 3D Case Study for Stuttgart, Germany, Front. Earth Sci., 10, 840112, https://doi.org/10.3389/feart.2022.840112, 2022.
Short summary
We compared wind measurements using different lidar setups at various heights. The triple Doppler lidar, sonic anemometer, and two single Doppler lidars were tested. Overall, the lidar methods showed good agreement with the sonic anemometer. The triple Doppler lidar performed better than single Doppler lidars, especially at higher altitudes. We also developed a new filtering approach for virtual tower scanning strategies. Single Doppler lidars provide reliable wind data over flat terrain.
We compared wind measurements using different lidar setups at various heights. The triple...
