Articles | Volume 16, issue 22
https://doi.org/10.5194/amt-16-5495-2023
© Author(s) 2023. 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-16-5495-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Advancing airborne Doppler lidar wind profiling in turbulent boundary layer flow – an LES-based optimization of traditional scanning-beam versus novel fixed-beam measurement systems
Philipp Gasch
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany
James Kasic
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Oliver Maas
Institute of Meteorology and Climatology, Leibniz University Hannover, Hanover, Germany
Zhien Wang
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
Related authors
Christoph Kottmeier, Andreas Wieser, Ulrich Corsmeier, Norbert Kalthoff, Philipp Gasch, Bastian Kirsch, Dörthe Ebert, Zbigniew Ulanowski, Dieter Schell, Harald Franke, Florian Schmidmer, Johannes Frielingsdorf, Thomas Feuerle, and Rudolf Hankers
Atmos. Meas. Tech., 18, 3161–3178, https://doi.org/10.5194/amt-18-3161-2025, https://doi.org/10.5194/amt-18-3161-2025, 2025
Short summary
Short summary
A new aerological dropsonde system for research aircraft has been developed. The system allows up to four sondes to be dropped with one release container, and data from up to 30 sondes can be transmitted simultaneously. The sondes enable high-resolution profiling of temperature, humidity, pressure, and wind. Additional sensors for radioactivity and particles have been integrated and tested. Operations in different campaigns have confirmed the reliability of the system and the quality of data.
Anselm Erdmann and Philipp Gasch
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-222, https://doi.org/10.5194/gmd-2024-222, 2024
Preprint under review for GMD
Short summary
Short summary
A new software for the calculation of quality controlled wind profiles from heterogeneous Doppler lidar measurements is presented. The processing is designed modularly. A provided standard processing chain is validated using radiosondes for three common Doppler lidar types at different locations.
Christoph Kottmeier, Andreas Wieser, Ulrich Corsmeier, Norbert Kalthoff, Philipp Gasch, Bastian Kirsch, Dörthe Ebert, Zbigniew Ulanowski, Dieter Schell, Harald Franke, Florian Schmidmer, Johannes Frielingsdorf, Thomas Feuerle, and Rudolf Hankers
Atmos. Meas. Tech., 18, 3161–3178, https://doi.org/10.5194/amt-18-3161-2025, https://doi.org/10.5194/amt-18-3161-2025, 2025
Short summary
Short summary
A new aerological dropsonde system for research aircraft has been developed. The system allows up to four sondes to be dropped with one release container, and data from up to 30 sondes can be transmitted simultaneously. The sondes enable high-resolution profiling of temperature, humidity, pressure, and wind. Additional sensors for radioactivity and particles have been integrated and tested. Operations in different campaigns have confirmed the reliability of the system and the quality of data.
Anselm Erdmann and Philipp Gasch
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-222, https://doi.org/10.5194/gmd-2024-222, 2024
Preprint under review for GMD
Short summary
Short summary
A new software for the calculation of quality controlled wind profiles from heterogeneous Doppler lidar measurements is presented. The processing is designed modularly. A provided standard processing chain is validated using radiosondes for three common Doppler lidar types at different locations.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Oliver Maas and Siegfried Raasch
Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, https://doi.org/10.5194/wes-7-715-2022, 2022
Short summary
Short summary
In the future there will be very large wind farm clusters in the German Bight. This study investigates how the wind field is affected by these very large wind farms and how much energy can be extracted by the wind turbines. Very large wind farms do not only reduce the wind speed but can also cause a change in wind direction or temperature. The extractable energy per wind turbine is much smaller for large wind farms than for small wind farms due to the reduced wind speed inside the wind farms.
Cited articles
Adler, B., Kalthoff, N., and Kiseleva, O.: Detection of structures in the horizontal wind field over complex terrain using coplanar Doppler lidar scans, Meteorol. Z., 29, 467–481, https://doi.org/10.1127/metz/2020/1031, 2020. a
Augere, B., Valla, M., Durécu, A., Dolfi-Bouteyre, A., Goular, D., Gustave, F., Planchat, C., Fleury, D., Huet, T., and Besson, C.: Three-dimensional wind measurements with the fibered airborne coherent Doppler wind lidar LIVE, Atmosphere, 10, 549–559, https://doi.org/10.3390/atmos10090549, 2019. a
Baidar, S., Tucker, S. C., Beaubien, M., and Hardesty, R. M.: The optical autocovariance wind lidar. Part II: Green OAWL (GrOAWL) airborne performance and validation, J. Atmos. Ocean. Tech., 35, 2099–2116, https://doi.org/10.1175/jtech-d-18-0025.1, 2018. a
Baker, W. E., Atlas, R., Cardinali, C., Clement, A., Emmitt, G. D., Gentry, B. M., Hardesty, R. M., Källén, E., Kavaya, M. J., Langland, R., Ma, Z., Masutani, M., McCarty, W., Pierce, R. B., Pu, Z., Riishojgaard, L. P., Ryan, J., Tucker, S., Weissmann, M., and Yoe, J. G.: Lidar-measured wind profiles: The missing link in the global observing system, B. Am. Meteorol. Soc., 95, 543–564, https://doi.org/10.1175/BAMS-D-12-00164.1, 2014. a
Bucci, L. R., O'Handley, C., Emmitt, G. D., Zhang, J. A., Ryan, K., and Atlas, R.: Validation of an airborne Doppler wind lidar in tropical cyclones, Sensors, 18, 4288, https://doi.org/10.3390/s18124288, 2018. a, b, c, d
Chouza, F., Reitebuch, O., Benedetti, A., and Weinzierl, B.: Saharan dust long-range transport across the Atlantic studied by an airborne Doppler wind lidar and the MACC model, Atmos. Chem. Phys., 16, 11581–11600, https://doi.org/10.5194/acp-16-11581-2016, 2016a. a
Chouza, F., Reitebuch, O., Jähn, M., Rahm, S., and Weinzierl, B.: Vertical wind retrieved by airborne lidar and analysis of island induced gravity waves in combination with numerical models and in situ particle measurements, Atmos. Chem. Phys., 16, 4675–4692, https://doi.org/10.5194/acp-16-4675-2016, 2016b. a, b, c, d
Damiani, R. and Haimov, S.: A high-resolution dual-Doppler technique for fixed multiantenna airborne radar, IEEE T. Geosci. Remote Sens., 44, 3475–3489, https://doi.org/10.1109/TGRS.2006.881745, 2006. a, b
De Wekker, S. F. J., Godwin, K. S., Emmitt, G. D., and Greco, S.: Airborne Doppler lidar measurements of valley flows in complex coastal terrain, J. Appl. Meteorol. Clim., 51, 1558–1574, https://doi.org/10.1175/JAMC-D-10-05034.1, 2012. a, b, c, d
Didlake, A. C., Heymsfield, G. M., Tian, L., and Guimond, S. R.: The coplane analysis technique for three-dimensional wind retrieval using the HIWRAP Airborne Doppler Radar, J. Appl. Meteorol. Clim., 54, 605–623, https://doi.org/10.1175/JAMC-D-14-0203.1, 2015. a
Fernando, H. J. S., Mann, J., Palma, J. M. L. M., Lundquist, J. K., Barthelmie, R. J., Belo-Pereira, M., Brown, W. O. J., Chow, F. K., Gerz, T., Hocut, C. M., Klein, P. M., Leo, L. S., Matos, J. C., Oncley, S. P., Pryor, S. C., Bariteau, L., Bell, T. M., Bodini, N., Carney, M. B., Courtney, M. S., Creegan, E. D., Dimitrova, R., Gomes, S., Hagen, M., Hyde, J. O., Kigle, S., Krishnamurthy, R., Lopes, J. C., Mazzaro, L., Neher, J. M. T., Menke, R., Murphy, P., Oswald, L., Otarola-Bustos, S., Pattantyus, A. K., Rodrigues, C. V., Schady, A., Sirin, N., Spuler, S., Svensson, E., Tomaszewski, J., Turner, D. D., van Veen, L., Vasiljević, N., Vassallo, D., Voss, S., Wildmann, N., and Wang, Y.: The Perdigão: Peering into microscale details of mountain winds, B. Am. Meteorol. Soc., 100, 799–819, https://doi.org/10.1175/bams-d-17-0227.1, 2019. a
Gasch, P.: Advancing airborne Doppler lidar wind measurements for atmospheric boundary layer research, PhD thesis, Karlsruhe Institute of Technology (KIT), Karlsruhe, https://doi.org/10.5445/IR/1000131721, 2021. a, b, c, d
Gasch, P.: Simulation results for Gasch et al., 2023, AMT: Advancing airborne Doppler lidar wind profiling in turbulent boundary layer flow – an LES-based optimization of traditional scanning-beam versus novel fixed-beam measurement systems, Karlsruhe Institute of Technology (KIT) [data set], https://doi.org/10.35097/1810, 2023. a
Gasch, P., Wieser, A., Lundquist, J. K., and Kalthoff, N.: An LES-based airborne Doppler lidar simulator and its application to wind profiling in inhomogeneous flow conditions, Atmos. Meas. Tech., 13, 1609–1631, https://doi.org/10.5194/amt-13-1609-2020, 2020. a, b, c, d
Geerts, B., Raymond, D. J., Grubišić, V., Davis, C. A., Barth, M. C., Detwiler, A., Klein, P. M., Lee, W.-C., Markowski, P. M., Mullendore, G. L., and Moore, J. A.: Recommendations for in situ and remote sensing capabilities in atmospheric convection and turbulence, B. Am. Meteorol. Soc., 99, 2463–2470, https://doi.org/10.1175/BAMS-D-17-0310.1, 2018. a, b
Greco, S., Emmitt, G. D., Garstang, M., and Kavaya, M.: Doppler Aerosol WiNd (DAWN) lidar during CPEX 2017: Instrument performance and data utility, Remote Sens., 12, 2951, https://doi.org/10.3390/rs12182951, 2020. a
Guimond, S. R., Tian, L., Heymsfield, G. M., and Frasier, S. J.: Wind retrieval algorithms for the IWRAP and HIWRAP airborne Doppler radars with applications to hurricanes, J. Atmos. Ocean. Tech., 31, 1189–1215, https://doi.org/10.1175/JTECH-D-13-00140.1, 2014. a
Kavaya, M. J., Beyon, J. Y., Koch, G. J., Petros, M., Petzar, P. J., Singh, U. N., Trieu, B. C., and Yu, J.: The Doppler aerosol wind (DAWN) airborne, wind-profiling coherent-detection lidar system: Overview and preliminary flight results, J. Atmos. Ocean. Tech., 31, 826–842, https://doi.org/10.1175/JTECH-D-12-00274.1, 2014. a
Kiemle, C., Wirth, M., Fix, A., Rahm, S., Corsmeier, U., and Di Girolamo, P.: Latent heat flux measurements over complex terrain by airborne water vapour and wind lidars, Q. J. Roy. Meteor. Soc., 137, 190–203, https://doi.org/10.1002/qj.757, 2011. a
Kunz, M., Abbas, S. S., Bauckholt, M., Böhmländer, A., Feuerle, T., Gasch, P., Glaser, C., Groß, J., Hajnsek, I., Handwerker, J., Hase, F., Khordakova, D., Knippertz, P., Kohler, M., Lange, D., Latt, M., Laube, J., Martin, L., Mauder, M., Möhler, O., Mohr, S., Reitter, R., Rettenmeier, A., Rolf, C., Saathoff, H., Schrön, M., Schütze, C., Spahr, S., Späth, F., Vogel, F., Völksch, I., Weber, U., Wieser, A., Wilhelm, J., Zhang, H., and Dietrich, P.: Swabian MOSES 2021: An interdisciplinary field campaign for investigating convective storms and their event chains, Front. Earth Sci., 10, 1886, https://doi.org/10.3389/feart.2022.999593, 2022. a
Lenschow, D. H. and Stankov, B. B.: Length scales in the convective boundary layer, J. Atmos. Sci., 43, 1198–1209, https://doi.org/10.1175/1520-0469(1986)043<1198:LSITCB>2.0.CO;2, 1986. a
Lenschow, D. H., Mann, J., and Kristensen, L.: How long is long enough when measuring fluxes and other turbulence statistics?, J. Atmos. Ocean. Tech., 11, 661–673, https://doi.org/10.1175/1520-0426(1994)011<0661:HLILEW>2.0.CO;2, 1994. a
Leon, D. and Vali, G.: Retrieval of three-dimensional particle velocity from airborne Doppler radar data, J. Atmos. Ocean. Tech., 15, 860–870, https://doi.org/10.1175/1520-0426(1998)015<0860:ROTDPV>2.0.CO;2, 1998. a
Leon, D., Vali, G., and Lothon, M.: Dual-Doppler analysis in a single plane from an airborne platform, J. Atmos. Ocean. Tech., 23, 3–22, https://doi.org/10.1175/JTECH1820.1, 2006. a, b
Lorsolo, S., Gamache, J., and Aksoy, A.: Evaluation of the hurricane research division Doppler radar analysis software using synthetic data, J. Atmos. Ocean. Tech., 30, 1055–1071, https://doi.org/10.1175/JTECH-D-12-00161.1, 2013. a
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
Petty, G. W.: Sampling error in aircraft flux measurements based on a high-resolution large eddy simulation of the marine boundary layer, Atmos. Meas. Tech., 14, 1959–1976, https://doi.org/10.5194/amt-14-1959-2021, 2021. a, b, c
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. a, b
Reitebuch, O., Werner, C., Leike, I., Delville, P., P. H. Flamant, A. C., and Engelbart, D.: Experimental validation of wind profiling performed by the airborne 10 µm–heterodyne Doppler lidar WIND, J. Atmos. Ocean. Tech., 18, 1331–1344, https://doi.org/10.1175/1520-0426(2001)018<1331:evowpp>2.0.co;2, 2001. a
Reitebuch, O., Lemmerz, C., Lux, O., Marksteiner, U., Witschas, B., and Neely, R. R.: WindVal-final report FR-Joint DLR-ESA-NASA wind validation for Aeolus, Tech. rep., DLR. OP-PA, ESA, https://doi.org/10.5270/esa-uc463ur, 2017. a
Salesky, S. T., Chamecki, M., and Bou-Zeid, E.: On the nature of the transition between roll and cellular organization in the convective boundary layer, Bound.-Lay. Meteorol., 163, 41–68, 2017. a
Schröter, M., Bange, J., and Raasch, S.: Simulated airborne flux measurements in a LES generated convective boundary layer, Bound.-Lay. Meteorol., 95, 437–456, https://doi.org/10.1023/a:1002649322001, 2000. a, b
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
Strauss, L., Serafin, S., Haimov, S., and Grubišić, V.: Turbulence in breaking mountain waves and atmospheric rotors estimated from airborne in situ and Doppler radar measurements, Q. J. Roy. Meteor. Soc., 141, 3207–3225, https://doi.org/10.1002/qj.2604, 2015. a
Sühring, M. and Raasch, S.: Heterogeneity-induced heat-flux patterns in the convective boundary layer: Can they be detected from observations and is there a blending height? – A large-eddy simulation study for the LITFASS-2003 experiment, Bound.-Lay. Meteorol., 148, 309–331, https://doi.org/10.1007/s10546-013-9822-1, 2013. a, b
Sühring, M., Metzger, S., Xu, K., Durden, D., and Desai, A.: Trade-offs in flux disaggregation: A large-eddy simulation study, Bound.-Lay. Meteorol., 170, 69–93, https://doi.org/10.1007/s10546-018-0387-x, 2019. a
Tucker, S. C., Weimer, C. S., Baidar, S., and Hardesty, R. M.: The Optical Autocovariance Wind Lidar (OAWL), Part I: Instrument Development and Demonstration, J. Atmos. Ocean. Tech., 35, 2079–2097, https://doi.org/10.1175/JTECH-D-18-0024.1, 2018 a
Turk, F. J., Hristova-Veleva, S., Durden, S. L., Tanelli, S., Sy, O., Emmitt, G. D., Greco, S., and Zhang, S. Q.: Joint analysis of convective structure from the APR-2 precipitation radar and the DAWN Doppler wind lidar during the 2017 Convective Processes Experiment (CPEX), Atmos. Meas. Tech., 13, 4521–4537, https://doi.org/10.5194/amt-13-4521-2020, 2020. a
Weissmann, M., Braun, F., Gantner, L., Mayr, G., Rahm, S., and Reitebuch, O.: The Alpine Mountain – Plain Circulation: Airborne Doppler Lidar Measurements and, Mon. Weather Rev., 133, 3095–3109, https://doi.org/10.1175/MWR3012.1, 2005a. a
Weissmann, M., Busen, R., Dörnbrack, A., Rahm, S., and Reitebuch, O.: Targeted observations with an airborne wind lidar, J. Atmos. Ocean. Tech., 22, 1706–1719, https://doi.org/10.1175/JTECH1801.1, 2005b. a, b, c
Weitkamp, C., ed.: Lidar – range-resolved optical remote sensing of the atmosphere, Springer Science & Business Media, New York, NY, ISBN 0-387-40075-3, 2005. a
Witschas, B., Rahm, S., Dörnbrack, A., Wagner, J., and Rapp, M.: Airborne wind lidar measurements of vertical and horizontal winds for the investigation of orographically induced gravity waves, J. Atmos. Ocean. Tech., 34, 1371–1386, https://doi.org/10.1175/JTECH-D-17-0021.1, 2017. a, b
Short summary
This paper rethinks airborne wind measurements and investigates a new design for airborne Doppler lidar systems. Recent advances in lidar technology allow the use of multiple lidar systems with fixed viewing directions instead of a single lidar attached to a scanner. Our simulation results show that the proposed new design offers great potential for both higher accuracy and higher-resolution airborne wind measurements.
This paper rethinks airborne wind measurements and investigates a new design for airborne...