Articles | Volume 9, issue 12
https://doi.org/10.5194/amt-9-5833-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-5833-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Dec 2016
Research article |
| 06 Dec 2016
Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations
Timothy A. Bonin
CORRESPONDING AUTHOR
School of Meteorology, University of Oklahoma, Norman, OK, USA
now at: Cooperative Institute for Research in the Environmental Sciences and NOAA/Earth
System Research Laboratory/Chemical Sciences Division, Boulder, CO, USA
Jennifer F. Newman
School of Meteorology, University of Oklahoma, Norman, OK, USA
now at: National Wind Technology Center, National Renewable Energy Laboratory, Golden, CO, USA
Petra M. Klein
School of Meteorology, University of Oklahoma, Norman, OK, USA
Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA
Phillip B. Chilson
School of Meteorology, University of Oklahoma, Norman, OK, USA
Advanced Radar Research Center and School of Meteorology, University of Oklahoma, Norman, OK, USA
Sonia Wharton
Lawrence Livermore National Laboratory, Livermore, CA, USA
Related authors
Andrew O. Langford, Christoph J. Senff, Raul J. Alvarez II, Ken C. Aikin, Sunil Baidar, Timothy A. Bonin, W. Alan Brewer, Jerome Brioude, Steven S. Brown, Joel D. Burley, Dani J. Caputi, Stephen A. Conley, Patrick D. Cullis, Zachary C. J. Decker, Stéphanie Evan, Guillaume Kirgis, Meiyun Lin, Mariusz Pagowski, Jeff Peischl, Irina Petropavlovskikh, R. Bradley Pierce, Thomas B. Ryerson, Scott P. Sandberg, Chance W. Sterling, Ann M. Weickmann, and Li Zhang
Atmos. Chem. Phys., 22, 1707–1737, https://doi.org/10.5194/acp-22-1707-2022, https://doi.org/10.5194/acp-22-1707-2022, 2022
Short summary
Short summary
The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) combined lidar, aircraft, and in situ measurements with global models to investigate the contributions of stratospheric intrusions, regional and Asian pollution, and wildfires to background ozone in the southwestern US during May and June 2017 and demonstrated that these processes contributed to background ozone levels that exceeded 70 % of the US National Ambient Air Quality Standard during the 6-week campaign.
Robert Jackson, Paytsar Muradyan, Raghavendra Krishnamurthy, Rob Newsom, Sonia Wharton, Matteo Puccioni, Bhupendra Raut, Seongha Park, Rajesh Sankaran, Scott Collis, Joseph O'Brien, and V. Rao Kotamarthi
EGUsphere, https://doi.org/10.5194/egusphere-2026-2568, https://doi.org/10.5194/egusphere-2026-2568, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The winds in the lowest layer of our atmosphere are commonly sensed using Doppler LiDARs. These LiDARs are commonly set to scan the atmosphere in a single way, which can sometimes miss the most interesting events in our lower atmosphere such as high wind events and mountain valley flow reversals. Here, we present an infrastructure for intelligent sensing, using Argonne National Laboratory's state of the art cloud-based edge computing system Waggle.
Matteo Puccioni, Sonia Wharton, Stephan F. J. De Wekker, Robert S. Arthur, Tianyi Li, Ye Liu, Sha Feng, Kyle Pressel, Raj K. Rai, Larry K. Berg, and Jerome D. Fast
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-215, https://doi.org/10.5194/wes-2025-215, 2025
Revised manuscript not accepted
Short summary
Short summary
A multi-wind Lidar campaign is conducted in a forest in the United States Southeast, a region featuring low wind resources due to the forest drag. Although the latter reduces the wind for hundreds of meters above ground, we found this effect to be negligible for heights above 8 times the tree height where the wind is dominated by local atmospheric events. This scenario opens to the development of taller turbines harvesting the wind farther away from the ground where the forest drag is minimal.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth N. Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, William Radünz, and Patrick Moriarty
Wind Energ. Sci., 10, 1681–1705, https://doi.org/10.5194/wes-10-1681-2025, https://doi.org/10.5194/wes-10-1681-2025, 2025
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.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
Short summary
Short summary
This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Adam S. Wise, Robert S. Arthur, Aliza Abraham, Sonia Wharton, Raghavendra Krishnamurthy, Rob Newsom, Brian Hirth, John Schroeder, Patrick Moriarty, and Fotini K. Chow
Wind Energ. Sci., 10, 1007–1032, https://doi.org/10.5194/wes-10-1007-2025, https://doi.org/10.5194/wes-10-1007-2025, 2025
Short summary
Short summary
Wind farms can be subject to rapidly changing weather events. In the United States Great Plains, some of these weather events can result in waves in the atmosphere that ultimately affect how much power a wind farm can produce. We modeled a specific event of waves observed in Oklahoma. We determined how to accurately model the event and analyzed how it affected a wind farm’s power production, finding that the waves both decreased power and made it more variable.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, https://doi.org/10.5194/essd-16-2525-2024, 2024
Short summary
Short summary
This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Francesca M. Lappin, Tyler M. Bell, Elizabeth A. Pillar-Little, and Phillip B. Chilson
Atmos. Meas. Tech., 15, 1185–1200, https://doi.org/10.5194/amt-15-1185-2022, https://doi.org/10.5194/amt-15-1185-2022, 2022
Short summary
Short summary
This study evaluates how a classically defined variable, air parcel buoyancy, can be used to interpret transitions in the atmospheric boundary layer (ABL). To capture the high-resolution variations, remotely piloted aircraft systems are used to collect data in two field campaigns. This paper finds that buoyancy has distinct evolutions prior to low-level jet and convective initiation cases. Additionally, buoyancy mixes well to act as an ABL height indicator comparable to other methods.
Andrew O. Langford, Christoph J. Senff, Raul J. Alvarez II, Ken C. Aikin, Sunil Baidar, Timothy A. Bonin, W. Alan Brewer, Jerome Brioude, Steven S. Brown, Joel D. Burley, Dani J. Caputi, Stephen A. Conley, Patrick D. Cullis, Zachary C. J. Decker, Stéphanie Evan, Guillaume Kirgis, Meiyun Lin, Mariusz Pagowski, Jeff Peischl, Irina Petropavlovskikh, R. Bradley Pierce, Thomas B. Ryerson, Scott P. Sandberg, Chance W. Sterling, Ann M. Weickmann, and Li Zhang
Atmos. Chem. Phys., 22, 1707–1737, https://doi.org/10.5194/acp-22-1707-2022, https://doi.org/10.5194/acp-22-1707-2022, 2022
Short summary
Short summary
The Fires, Asian, and Stratospheric Transport–Las Vegas Ozone Study (FAST-LVOS) combined lidar, aircraft, and in situ measurements with global models to investigate the contributions of stratospheric intrusions, regional and Asian pollution, and wildfires to background ozone in the southwestern US during May and June 2017 and demonstrated that these processes contributed to background ozone levels that exceeded 70 % of the US National Ambient Air Quality Standard during the 6-week campaign.
Cited articles
Alvarez II, R. J., Senff, C. J., Langford, A. O., Weickmann, A. M., Law, D. C., Machol, J. L., Merritt, D. A., Marchbanks, R. D., Sandberg, S. P., Brewer, W. A., Hardesty, R. M., and Banta, R. M.: Development and application of a compact, tunable, solid-state airborne Ozone lidar system for boundary layer profiling, J. Atmos. Ocean. Tech., 28, 1258–1272, 2011.
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, 2006.
Barlow, J. F., Dunbar, T. M., Nemitz, E. G., Wood, C. R., Gallagher, M. W., Davies, F., O'Connor, E., and Harrison, R. M.: Boundary layer dynamics over London, UK, as observed using Doppler lidar during REPARTEE-II, Atmos. Chem. Phys., 11, 2111–2125, https://doi.org/10.5194/acp-11-2111-2011, 2011.
Behrendt, A., Wulfmeyer, V., Hammann, E., Muppa, S. K., and Pal, S.: Profiles of second- to fourth-order moments of turbulent temperature fluctuations in the convective boundary layer: first measurements with rotational Raman lidar, Atmos. Chem. Phys., 15, 5485–5500, https://doi.org/10.5194/acp-15-5485-2015, 2015.
Bonin, T., Blumberg, W., Klein, P., and Chilson, P.: Thermodynamic and Turbulence Characteristics of the Southern Great Plains Nocturnal Boundary Layer Under Differing Turbulent Regimes, Bound.-Lay. Meteorol., 157, 401–420, https://doi.org/10.1007/s10546-015-0072-2, 2015.
Brinkmann, W. A. R.: Strong downslope winds at Boulder, Colorado, Mon. Weather Rev., 102, 592–602, 1974.
Davies, F., Collier, C. G., Pearson, G. N., and Bozier, K. E.: Doppler lidar measurements of turbulent structure function over an urban area, J. Atmos. Ocean. Tech., 21, 753–761, 2004.
Davis, J. C., Collier, C. G., Davies, F., and Bozier, K. E.: Spatial variations of sensible heat flux over and urban area measured using Doppler lidar, Meteorol. Appl., 15, 367–380, 2008.
Dunbar, T. M., Barlow, J. F., and Belcher, S. E.: An optimal inverse method using Doppler lidar measurements to estimate the surface sensible heat flux, Bound.-Lay. Meteorol., 150, 49–67, 2014.
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.
Frehlich, R. and Cornman, L.: Estimating spatial velocity statistics with coherent Doppler lidar, J. Atmos. Ocean. Techn., 19, 355–366, 2002.
Frehlich, R. G. and Yadlowsky, M. J.: Performance of mean-frequency estimators for Doppler radar and lidar, J. Atmos. Ocean. Tech., 11, 1217–1230, 1994.
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, 2014.
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., 97, 18409–18423, 1992.
Grund, C. J., Banta, R. M., George, J. L., Howell, J. N., Post, M. J., Richter, R. A., and Weickmann, A. M.: High-resolution Doppler lidar for boundary layer and cloud research, J. Atmos. Ocean. Tech., 18, 376–393, 2001.
Higgins, C. W., Froidevaux, M., Simeonov, V., Vercauteren, N., Barry, C., and Parlange, M. B.: The effect of scale on the applicability of Taylor's frozen turbulent hypothesis in the atmospheric boundray layer, Bound.-Lay. Meteorol., 143, 379–391, 2013.
Hogan, R. J., Grant, A. L. M., Illingworth, A. J., Pearson, G. N., and O'Connor, E. J.: Vertical velocity variance and skewness in clear and cloud-topped boundary layers as revealed by Doppler lidar, Q. J. Roy. Meteor. Soc., 135, 635–643, 2009.
Hollinger, D. Y. and Richardson, A. D.: Uncertainty in eddy covariance measurements and its application to physiological models, Tree Physiol., 25, 873–885, 2005.
Kaimal, J. C. and Gaynor, J. E.: The Boulder Atmospheric Observatory, J. Clim. Appl. Meteorol., 22, 863–880, 1983.
Kirchner, J. W.: Aliasing in 1/fα noise spectra: origins, consequences, and remedies, Phys. Rev. E, 71, 1–16, https://doi.org/10.1103/PhysRevE.71.066110, 2005.
Kolmogorov, A.: The local structure of turbulence in incompressible viscous fluid for very large Reynolds' numbers, Dokl. Akad. Nauk. SSSR, 30, 301–305, 1941.
Krishnamurthy, R., Calhoun, R., Billings, B., and Doyle, J.: Wind turbulence estimates in a valley by coherent Doppler lidar, Meteorol. Appl., 18, 361–371, 2011.
Lenschow, D. H. and Stankov, B. B.: Length scales in the convective boundary layer, J. Atmos. Sci., 43, 1198–1209, 1986.
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, 1994.
Lenschow, D. H., Wulfmeyer, V., and Senff, C.: Measuring Second- through fourth-order moments in noisy data, J. Atmos. Ocean. Tech., 17, 1330–1347, 2000.
Lenschow, D. H., Lothon, M., Major, S. D., Sullivan, P. P., and Canut, G.: A comparison of higher-order vertical velocity moments in the convective boudnary layer from lidar with in situ measurements and large-eddy simulation, Bound.-Lay. Meteorol., 143, 107–123, 2012.
Lindseth, B., Brown, W. O. J., Jordan, J., Law, D., Hock, T., Cohn, S. A., and Popović, Z.: A new portable 449-MHz spaced antenna wind profiler radar, IEEE T. Geosci. Remote S., 50, 3544–3553, 2012.
Lothon, M., Lenschow, D. H., and Major, S. D.: Coherence and scale of vertical velocity in the convective boundary layer from a Doppler lidar, Bound.-Lay. Meteorol., 121, 521–536, 2006.
Lothon, M., Lenschow, D. H., and Major, S. D.: Doppler lidar measurements of vertical velocity spectra in the convective boundary layer, Bound.-Lay. Meteorol., 132, 205–226, 2009.
Machol, J. L., Ayers, T., Schwenz, K. T., Koenig, K. W., Hardesty, R. M., Senff, C. J., Krainak, M. A., Abshire, J. B., Bravo, H. E., and Sandberg, S. P.: Preliminary measurements with an automated compact differential absorption lidar for profiling of water vapor, Appl. Optics, 43, 3110–3121, 2004.
Machol, J. L., Marchbanks, R. D., Senff, C. J., McCarty, B. J., Eberhard, W. L., Brewer, W. A., Richter, R. A., II, R. J. A., Law, D. C., Weickmann, A. M., and Sandberg, S. P.: Scanning tropospheric ozone and aerosol lidar with double-gated photomultipliers, Appl. Optics, 48, 512–524, 2009.
Mahrt, L.: Flux Sampling Errors for Aircraft and Towers, J. Atmos. Ocean. Tech., 15, 416–429, 1998.
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, 2013.
McCaffrey, K., Quelet, P., Choukulkar, A., Wilczak, J. M., Wolfe, D. E., Oncley, S., Brewer, A., Debnath, M., Ashton, R., Iungo, G. V., and Lundquist, J. K.: Identification of Tower Wake Distortions Using Sonic Anemometer and Lidar Measurements, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-179, in review, 2016.
McNicholas, C. and Turner, D. D.: Characterizing the convective boundary layer turbulence with a High Spectral Resolution Lidar, J. Geophys. Res.-Atmos., 119, 12910–12927, 2014.
Monin, A. S. and Yaglom, A. M.: Statistical Fluid Mechanics, MIT Press, Cambridge, Mass., USA, 874 pp., 1979.
Muppa, S. K., Behrendt, A., Späth, F., Wulfmeyer, V., Metzendorf, S., and Riede, A.: Turbulent Humidity Fluctuations in the Convective Boundary Layer : Case Studies Using Water Vapour Differential Absorption Lidar Measurements, Bound.-Lay. Meteorol., 158, 43–66, https://doi.org/10.1007/s10546-015-0078-9, 2016.
Newman, J. F., Klein, P. M., Wharton, S., Sathe, A., Bonin, T. A., Chilson, P. B., and Muschinski, A.: Evaluation of three lidar scanning strategies for turbulence measurements, Atmos. Meas. Tech., 9, 1993–2013, https://doi.org/10.5194/amt-9-1993-2016, 2016.
NOAA: ESRL PSD, BAO Tower Data, available at http://www.esrl.noaa.gov/psd/technology/bao/, 2014.
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 turbulence 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, 2010.
Pal, S., Haeffelin, M., and Batchvarova, E.: Exploring a geophysical process-based attribution technique for the determination of the atmospheric boundary layer depth using aerosol lidar and near-surface meteorological measurements, J. Geophys. Res.-Atmos., 118, 9277–9295, https://doi.org/10.1002/jgrd.50710, 2013.
Pearson, G., Davies, F., and Collier, C.: An analysis of the performance of the UFAM pulsed Doppler lidar for observing the boundary layer, J. Atmos. Ocean. Tech., 26, 240–250, 2009.
Pearson, G., Davies, F., and Collier, C.: Remote sensing of the tropical rain forest boundary layer using pulsed Doppler lidar, Atmos. Chem. Phys., 10, 5891–5901, https://doi.org/10.5194/acp-10-5891-2010, 2010.
Peltola, O., Hensen, A., Helfter, C., Belelli Marchesini, L., Bosveld, F. C., van den Bulk, W. C. M., Elbers, J. A., Haapanala, S., Holst, J., Laurila, T., Lindroth, A., Nemitz, E., Röckmann, T., Vermeulen, A. T., and Mammarella, I.: Evaluating the performance of commonly used gas analysers for methane eddy covariance flux measurements: the InGOS inter-comparison field experiment, Biogeosciences, 11, 3163–3186, https://doi.org/10.5194/bg-11-3163-2014, 2014.
Pichugina, Y. L., Banta, R. M., Kelley, N. D., Jonkman, B. J., Tucker, S. C., Newsom, R. K., and Brewer, W. A.: Horizontal-velocity and variance measurements in the stable boundary layer using Doppler lidar: Sensitivity to averaging procedures, J. Atmos. Ocean. Tech., 25, 1307–1327, 2008.
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.
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.
Shukla, K. K., Phanikumar, D. V., Newsom, R. K., Kumar, K. N., Ratman, M. V., Naja, M., and Singh, N.: Estimation of the mixing layer height over a high altitude site in Central Himalayan region by using Doppler lidar, J. Atmos. Sol.-Terr. Phy., 48-53, 48–53, 2014.
Smith, D. A., Harris, M., Coffey, A. S., Mikkelsen, T., Jørgensen, H. E., Mann, J., and Danielian, R.: Wind Lidar evluation at the Danish wind test site in Høvsøre, Wind Energy, 9, 87–93, 2006.
Taylor, G. I.: The spectrum of turbulence, Proc. Roy. Soc., A164, 476–490, 1938.
Tucker, S. C., Brewer, W. A., Banta, R. M., Senff, C. J., Sandberg, S. P., Law, D. C., Weickmann, A. M., and Hardesty, R. M.: Doppler lidar estimation of mixing height using turbulence, shear, and aerosol profiles, J. Atmos. Ocean. Tech., 26, 673–688, 2009.
Turner, D. D., Ferrare, R. A., Wulfmeyer, V., and Scarino, A. J.: Aircraft evaluation of ground-based Raman lidar water vapor turbulence profiles in convective mixed layers, J. Atmos. Ocean. Tech., 31, 1078–1088, 2014a.
Turner, D. D., Wulfmeyer, V., Berg, L. K., and Schween, J. H.: Water vapor turbulence profiles in stationary continental convective mixed layers, J. Geophys. Res.-Atmos., 119, 11151–11165, 2014b.
Van Weverberg, K., Boutle, I. A., Morcrette, C. J., and Newsom, R. K.: Towards retrieving critical relative humidity from ground-based remote-sensing observations, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.2874, 2016.
Wulfmeyer, V., Pal, S., Turner, D. D., and Wagner, E.: Can water vapour Raman lidar resolve profiles of turbulent variables in the convective boundary layer?, Bound.-Lay. Meteorol., 136, 253–284, 2010.
Short summary
Turbulence measurements are important to boundary layer meteorology and related fields. Doppler lidars are capable of providing continuous profiles of turbulence statistics. Herein, the most direct turbulence measurement, vertical velocity variance, is validated with those from sonic anemometers. Spectra are also compared. A method of calculating velocity variance using the autocovariance is shown to improve the accuracy of the measurement by mitigating effects of noise and averaging.
Turbulence measurements are important to boundary layer meteorology and related fields. Doppler...
