Articles | Volume 15, issue 22 
            
                
                    
            
            
            https://doi.org/10.5194/amt-15-6563-2022
                    © Author(s) 2022. 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-15-6563-2022
                    © Author(s) 2022. This work is distributed under 
the Creative Commons Attribution 4.0 License.
                the Creative Commons Attribution 4.0 License.
The measurement of mean wind, variances, and covariances from an instrumented mobile car in a rural environment
                                            Department of Earth and Space Science and Engineering, York
University, Toronto ON, M3J 1P3, Canada
                                        
                                    Mark Gordon
                                            Department of Earth and Space Science and Engineering, York
University, Toronto ON, M3J 1P3, Canada
                                        
                                    Related authors
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
                                    Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
                                            
                                            
                                        Sepehr Fathi, Mark Gordon, and Jingliang Hao
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-4542, https://doi.org/10.5194/egusphere-2025-4542, 2025
                                    This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT). 
                                    Short summary
                                    Short summary
                                            
                                                Aircraft are often used to measure emissions from industry and other sources by flying downwind of the source and measuring the pollutant winds and concentration. This study uses model simulation to help choose the best flight configuration and parameters for a given source type (e.g. smokestacks, mine faces, or surface emissions). The results provide uncertainty estimates based on downwind flight distances, which can be used to choose the optimal downwind flight distance for their study.
                                            
                                            
                                        Kevin M. Axelrod, Mark Gordon, Mohammad Koushafar, Jingliang Hao, Paul A. Makar, Sepehr Fathi, and Gunho Sohn
                                        EGUsphere, https://doi.org/10.5194/egusphere-2025-4582, https://doi.org/10.5194/egusphere-2025-4582, 2025
                                    This preprint is open for discussion and under review for Geoscientific Model Development (GMD). 
                                    Short summary
                                    Short summary
                                            
                                                The is a study of the plumes that rise from smokestacks. Knowing how these plume behave helps predict downwind pollutant concentrations. We use photos over a 2-year period to investigate how these plumes rise under different conditions and compare this to a commonly used model parameterization. It is found that the equations used to model plume rise in current models do well for some condition, but these equations can over-predict the plume rise, typically during the day when it is hot.
                                            
                                            
                                        Sepehr Fathi, Paul Makar, Wanmin Gong, Junhua Zhang, Katherine Hayden, and Mark Gordon
                                    Atmos. Chem. Phys., 25, 2385–2405, https://doi.org/10.5194/acp-25-2385-2025, https://doi.org/10.5194/acp-25-2385-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                Our study explores the influence of water phase changes in plumes from industrial sources on atmospheric dispersion of emitted pollutants and air quality. Employing PRISM (Plume-Rise-Iterative-Stratified-Moist), a new method, we found that considering these effects significantly improves predictions of pollutant dispersion. This insight enhances our understanding of environmental impacts, enabling more accurate air quality modelling and fostering more effective pollution management strategies.
                                            
                                            
                                        Dane Blanchard, Mark Gordon, Duc Huy Dang, Paul Andrew Makar, and Julian Aherne
                                    Atmos. Chem. Phys., 25, 2423–2442, https://doi.org/10.5194/acp-25-2423-2025, https://doi.org/10.5194/acp-25-2423-2025, 2025
                                    Short summary
                                    Short summary
                                            
                                                This study offers the first known evaluation of water-soluble brown carbon aerosols in the Athabasca oil sands region (AOSR), Canada. Fluorescence spectroscopy analysis of aerosol samples from five regional sites (collected during the summer of 2021) identified oil sands operations as a measurable brown carbon source. Industrial aerosol emissions were unlikely to impact regional radiative forcing. These findings show that fluorescence spectroscopy can be used to monitor brown carbon in the AOSR.
                                            
                                            
                                        Stefan J. Miller, Paul A. Makar, and Colin J. Lee
                                    Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
                                    Short summary
                                    Short summary
                                            
                                                This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
                                            
                                            
                                        Xuanyi Zhang, Mark Gordon, Paul A. Makar, Timothy Jiang, Jonathan Davies, and David Tarasick
                                    Atmos. Chem. Phys., 23, 13647–13664, https://doi.org/10.5194/acp-23-13647-2023, https://doi.org/10.5194/acp-23-13647-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Measurements of ozone in the atmosphere were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements show that the emissions of other pollutants from oil sands production and processing reduce the amount of ozone in the forest. By using an atmospheric model combined with measurements, we find that the rate at which ozone is absorbed by the forest is lower than typical rates from similar measurements in other forests.
                                            
                                            
                                        Sepehr Fathi, Mark Gordon, and Yongsheng Chen
                                    Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
                                            
                                            
                                        Mark Gordon, Dane Blanchard, Timothy Jiang, Paul A. Makar, Ralf M. Staebler, Julian Aherne, Cris Mihele, and Xuanyi Zhang
                                    Atmos. Chem. Phys., 23, 7241–7255, https://doi.org/10.5194/acp-23-7241-2023, https://doi.org/10.5194/acp-23-7241-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Measurements of the gas sulfur dioxide (SO2) were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements tell us the rate at which SO2 is absorbed by the forest. The measured rate is much higher than what is currently used by air quality models, which is supported by a previous study in this region. This suggests that SO2 may have a much shorter lifetime in the atmosphere at this location than currently predicted by models.
                                            
                                            
                                        Timothy Jiang, Mark Gordon, Paul A. Makar, Ralf M. Staebler, and Michael Wheeler
                                    Atmos. Chem. Phys., 23, 4361–4372, https://doi.org/10.5194/acp-23-4361-2023, https://doi.org/10.5194/acp-23-4361-2023, 2023
                                    Short summary
                                    Short summary
                                            
                                                Measurements of submicron aerosols (particles smaller than 1 / 1000 of a millimeter) were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements tell us how quickly aerosols are absorbed by the forest (known as deposition rate) and how the deposition rate depends on the size of the aerosol. The measurements show good agreement with a parameterization developed from a recent study for deposition of aerosols to a similar pine forest.
                                            
                                            
                                        Sepehr Fathi, Mark Gordon, Paul A. Makar, Ayodeji Akingunola, Andrea Darlington, John Liggio, Katherine Hayden, and Shao-Meng Li
                                    Atmos. Chem. Phys., 21, 15461–15491, https://doi.org/10.5194/acp-21-15461-2021, https://doi.org/10.5194/acp-21-15461-2021, 2021
                                    Short summary
                                    Short summary
                                            
                                                We have investigated the accuracy of aircraft-based mass balance methodologies through computer model simulations of the atmosphere and air quality at a regional high-resolution scale. We have defined new quantitative metrics to reduce emission retrieval uncertainty by evaluating top-down mass balance estimates against the known simulated meteorology and input emissions. We also recommend methodologies and flight strategies for improved retrievals in future aircraft-based studies.
                                            
                                            
                                        Cited articles
                        
                        Achberger, C. and Bärring, L.: Correction of surface air temperature
measurements from a mobile platform, Agr. Forest Meteorol.,
98–99, 227–238, https://doi.org/10.1016/s0168-1923(99)00099-4, 1999. 
                    
                
                        
                        Anderson, A. R., Chapman, M., Drobot, S. D., Tadesse, A., Lambi, B., Wiener,
G., and Pisano, P.: Quality of mobile air temperature and atmospheric
pressure observations from the 2010 Development Test Environment Experiment,
J. Appl. Meteorol. Clim., 51, 691–701, https://doi.org/10.1175/jamc-d-11-0126.1, 2012. 
                    
                
                        
                        Aristodemou, E., Boganegra, L. M., Mottet, L., Pavlidis, D., Constantinou,
A., Pain, C., Robins, A., and ApSimon, H.: How tall buildings affect
turbulent air flows and dispersion of pollution within a neighbourhood,
Environ. Pollut., 233, 782–796,
https://doi.org/10.1016/j.envpol.2017.10.041, 2018. 
                    
                
                        
                        Armi, L. and Mayr, G. J.: Continuously stratified flows across an alpine
crest with a pass: Shallow and Deep Föhn, Q. J. Roy. Meteor. Soc., 133, 459–477, https://doi.org/10.1002/qj.22, 2007. 
                    
                
                        
                        Belušić, D., Lenschow, D. H., and Tapper, N. J.: Performance of a mobile car platform for mean wind and turbulence measurements, Atmos. Meas. Tech., 7, 1825–1837, https://doi.org/10.5194/amt-7-1825-2014, 2014. 
                    
                
                        
                        Bogren, J. and Gustavsson, T.: Nocturnal Air and road surface temperature
variations in complex terrain, Int. J. Climatol., 11,
443–455, https://doi.org/10.1002/joc.3370110408, 1991. 
                    
                
                        
                        Bonin, T. A., Newman, J. F., Klein, P. M., Chilson, P. B., and Wharton, S.: Improvement of vertical velocity statistics measured by a Doppler lidar through comparison with sonic anemometer observations, Atmos. Meas. Tech., 9, 5833–5852, https://doi.org/10.5194/amt-9-5833-2016, 2016. 
                    
                
                        
                        Britter, R. E., Hunt, J. C., and Richards, K. J.: Air flow over a
two-dimensional hill: Studies of velocity speed-up, roughness effects and
turbulence, Q. J. Roy. Meteor. Soc., 107, 91–110, https://doi.org/10.1002/qj.49710745106, 1981. 
                    
                
                        
                        Conte, M., Contini, D., and Held, A.: Multiresolution decomposition and
wavelet analysis of urban aerosol fluxes in Italy and Austria, Atmos.
Res., 248, 105267, https://doi.org/10.1016/j.atmosres.2020.105267, 2021. 
                    
                
                        
                        Curry, M., Hanesiak, J., Kehler, S., Sills, D. M., and Taylor, N. M.:
Ground-based observations of the thermodynamic and kinematic properties of
Lake-Breeze Fronts in southern Manitoba, Canada, Bound.-Lay. Meteorol.,
163, 143–159, https://doi.org/10.1007/s10546-016-0214-1, 2017. 
                    
                
                        
                        de Boer, G., Waugh, S., Erwin, A., Borenstein, S., Dixon, C., Shanti, W., Houston, A., and Argrow, B.: Measurements from mobile surface vehicles during the Lower Atmospheric Profiling Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) , Earth Syst. Sci. Data, 13, 155–169, https://doi.org/10.5194/essd-13-155-2021, 2021. 
                    
                
                        
                        Finkelstein, P. L. and Sims, P. F.: Sampling error in eddy correlation
flux measurements, J. Geophys. Res.-Atmos., 106,
3503–3509, https://doi.org/10.1029/2000jd900731, 2001. 
                    
                
                        
                        Göckede, M., Kittler, F., and Schaller, C.: Quantifying the impact of emission outbursts and non-stationary flow on eddy-covariance CH4 flux measurements using wavelet techniques, Biogeosciences, 16, 3113–3131, https://doi.org/10.5194/bg-16-3113-2019, 2019. 
                    
                
                        
                        Gong, W. and Ibbetson, A.: A wind tunnel study of turbulent flow over
Model Hills, Bound.-Lay. Meteorol., 49, 113–148,
https://doi.org/10.1007/bf00116408, 1989. 
                    
                
                        
                        Gordon, M.: The measurement of mean wind, variances and covariances from an instrumented mobile car in a rural environment, Borealis, V1 [data set], https://doi.org/10.5683/SP3/IBBDTF, 2022. 
                    
                
                        
                        Gordon, M., Staebler, R. M., Liggio, J., Makar, P., Li, S.-M., Wentzell, J.,
Lu, G., Lee, P., and Brook, J. R.: Measurements of enhanced turbulent
mixing near Highways, J. Appl. Meteorol. Clim., 51,
1618–1632, https://doi.org/10.1175/jamc-d-11-0190.1, 2012. 
                    
                
                        
                        Gromke, C. and Blocken, B.: Influence of avenue-trees on air quality at
the Urban Neighborhood Scale. part I: Quality assurance studies and
turbulent schmidt number analysis for RANS CFD Simulations, Environ. Pollut., 196, 214–223, https://doi.org/10.1016/j.envpol.2014.10.016, 2015. 
                    
                
                        
                        Hanlon, T. and Risk, D.: Using computational fluid dynamics and field experiments to improve vehicle-based wind measurements for environmental monitoring, Atmos. Meas. Tech., 13, 191–203, https://doi.org/10.5194/amt-13-191-2020, 2020. 
                    
                
                        
                        Hertwig, D., Gough, H. L., Grimmond, S., Barlow, J. F., Kent, C. W., Lin, W.
E., Robins, A. G., and Hayden, P.: Wake characteristics of tall buildings
in a realistic urban canopy, Bound.-Lay. Meteorol., 172, 239–270,
https://doi.org/10.1007/s10546-019-00450-7, 2019. 
                    
                
                        
                        Hunt, J. C. R., Poulton, E. C., and Mumford, J. C.: The effects of wind on
people; new criteria based on wind tunnel experiments, Build.
Environ., 11, 15–28, https://doi.org/10.1016/0360-1323(76)90015-9, 1976. 
                    
                
                        
                        Kim, Y., Huang, L., Gong, S., and Jia, Q. C.: A new approach to quantifying vehicle induced turbulence for complex traffic scenarios, Chinese J. Chem. Eng., 1, 71–78, https://doi.org/10.1016/j.cjche.2015.11.025, 2016. 
                    
                
                        
                        Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd-8-3695-2015, 2015. 
                    
                
                        
                        Krayenhoff, E. S., Jiang, T., Christen, A., Martilli, A., Oke, T. R.,
Bailey, B. N., Nazarian, N., Voogt, J. A., Giometto, M. G., Stastny, A.,
and Crawford, B. R.: A multi-layer urban canopy meteorological model with
trees (BEP-tree): Street tree impacts on pedestrian-level climate, Urban
Climate, 32, 100590, https://doi.org/10.1016/j.uclim.2020.100590, 2020. 
                    
                
                        
                        Langford, B., Acton, W., Ammann, C., Valach, A., and Nemitz, E.: Eddy-covariance data with low signal-to-noise ratio: time-lag determination, uncertainties and limit of detection, Atmos. Meas. Tech., 8, 4197–4213, https://doi.org/10.5194/amt-8-4197-2015, 2015. 
                    
                
                        
                        Lee, J. P. and Lee, S. J.: PIV analysis on the shelter effect of a bank of
real fir trees, J. Wind Eng. Ind. Aerod., 110, 40–49, https://doi.org/10.1016/j.jweia.2012.07.003, 2012. 
                    
                
                        
                        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. 
                    
                
                        
                        Lenschow, D. H., Wulfmeyer, V., and Senff, C.: Measuring second- through
fourth-order moments in Noisy Data, J. Atmos. Ocean. Tech., 17, 1330–1347,
https://doi.org/10.1175/1520-0426(2000)017<1330:MSTFOM>2.0.CO;2, 2000. 
                    
                
                        
                        Lyu, J., Wang, C. M., and Mason, M. S.: Review of models for predicting
wind characteristics behind windbreaks, J. Wind Eng. Ind. Aerod., 199, 104117, https://doi.org/10.1016/j.jweia.2020.104117, 2020. 
                    
                
                        
                        Mahrt, L., Richardson, S., Seaman, N., and Stauffer, D.: Turbulence in the
nocturnal boundary layer with light and variable winds, Q. J. Roy. Meteor. Soc., 138, 1430–1439, https://doi.org/10.1002/qj.1884, 2012. 
                    
                
                        
                        Mann, J. and Lenschow, D. H.: Errors in airborne flux measurements,
J. Geophys. Res., 99, 14519, https://doi.org/10.1029/94jd00737, 1994. 
                    
                
                        
                        Markowski, P. M., Lis, N. T., Turner, D. D., Lee, T. R., and Buban, M. S.:
Observations of near-surface vertical wind profiles and vertical momentum
fluxes from vortex-SE 2017: Comparisons to Monin-Obukhov similarity theory,
Mon. Weather Rev., 147, 3811–3824, https://doi.org/10.1175/mwr-d-19-0091.1, 2019. 
                    
                
                        
                        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. 
                    
                
                        
                        Mayr, G. J. and Armi, L.: Föhn as a response to changing upstream and
downstream air masses, Q. J. Roy. Meteor. Soc., 134, 1357–1369, https://doi.org/10.1002/qj.295, 2008. 
                    
                
                        
                        Miller, S. J., Gordon, M., Staebler, R. M., and Taylor, P. A.: A study of
the spatial variation of vehicle-induced turbulence on highways using
measurements from a mobile platform, Bound.-Lay. Meteorol., 171,
1–29, https://doi.org/10.1007/s10546-018-0416-9, 2019. 
                    
                
                        
                        Mochida, A., Tabata, Y., Iwata, T., and Yoshino, H.: Examining tree canopy
models for CFD prediction of wind environment at pedestrian level, J. Wind Eng. Ind. Aerod., 96, 1667–1677, https://doi.org/10.1016/j.jweia.2008.02.055, 2008. 
                    
                
                        
                        Paterna, E., Crivelli, P., and Lehning, M.: Decoupling of mass flux and
turbulent wind fluctuations in drifting snow, Geophys. Res. Lett.,
43, 4441–4447, https://doi.org/10.1002/2016gl068171, 2016. 
                    
                
                        
                        Rannik, Ü., Mammarella, I., Aalto, P., Keronen, P., Vesala, T., and
Kulmala, M.: Long-term aerosol particle flux observations part I:
Uncertainties and time-average statistics, Atmos. Environ., 43,
3431–3439, https://doi.org/10.1016/j.atmosenv.2009.02.049, 2009. 
                    
                
                        
                        Rannik, Ü., Peltola, O., and Mammarella, I.: Random uncertainties of flux measurements by the eddy covariance technique, Atmos. Meas. Tech., 9, 5163–5181, https://doi.org/10.5194/amt-9-5163-2016, 2016. 
                    
                
                        
                        Salmond, J. A.: Wavelet analysis of intermittent turbulence in a very stable
nocturnal boundary layer: Implications for the vertical mixing of ozone,
Bound.-Lay. Meteorol., 114, 463–488, https://doi.org/10.1007/s10546-004-2422-3, 2005. 
                    
                
                        
                        Salmond, J. A. and McKendry, I. G.: A review of turbulence in the very
stable nocturnal boundary layer and its implications for Air Quality,
Prog. Phys. Geog., 29, 171–188, https://doi.org/10.1191/0309133305pp442ra, 2005. 
                    
                
                        
                        Schaller, C., Göckede, M., and Foken, T.: Flux calculation of short turbulent events – comparison of three methods, Atmos. Meas. Tech., 10, 869–880, https://doi.org/10.5194/amt-10-869-2017, 2017. 
                    
                
                        
                        Schiehlen, W.: White noise excitation of road vehicle structures, Sadhana,
31, 487–503, https://doi.org/10.1007/bf02716788, 2006. 
                    
                
                        
                        Smith, S. A., Brown, A. R., Vosper, S. B., Murkin, P. A., and Veal, A. T.:
Observations and simulations of cold air pooling in Valleys, Bound.-Lay.
Meteorol., 134, 85–108, https://doi.org/10.1007/s10546-009-9436-9, 2010. 
                    
                
                        
                        Starkenburg, D., Metzger, S., Fochesatto, G. J., Alfieri, J. G., Gens, R.,
Prakash, A., and Cristóbal, J.: Assessment of despiking methods for
turbulence data in Micrometeorology, J. Atmos. Ocean. Tech., 33, 2001–2013, https://doi.org/10.1175/jtech-d-15-0154.1, 2016. 
                    
                
                        
                        Straka, J. M., Rasmussen, E. N., and Fredrickson, S. E.: A mobile mesonet
for Finescale Meteorological Observations, J. Atmos. Ocean. Tech., 13, 921–936, https://doi.org/10.1175/1520-0426(1996)013<0921:AMMFFM>2.0.CO;2, 1996. 
                    
                
                        
                        Strunin, M. A. and Hiyama, T.: Applying wavelet transforms to analyse
aircraft-measured turbulence and turbulent fluxes in the atmospheric
boundary layer over Eastern Siberia, Hydrol. Process., 18,
3081–3098, https://doi.org/10.1002/hyp.5750, 2004. 
                    
                
                        
                        Su, J., Wang, L., Gu, Z., Song, M., and Cao, Z.: Effects of real trees and
their structure on pollutant dispersion and flow field in an idealized
Street Canyon, Atmos. Pollut. Res., 10, 1699–1710,
https://doi.org/10.1016/j.apr.2019.07.001, 2019. 
                    
                
                        
                        Taylor, N. M., Sills, D. M., Hanesiak, J. M., Milbrandt, J. A., Smith, C.
D., Strong, G. S., Skone, S. H., McCarthy, P. J., and Brimelow, J. C.: The
understanding severe thunderstorms and Alberta Boundary Layers Experiment
(unstable) 2008, B. Am. Meteorol. Soc., 92, 739–763, https://doi.org/10.1175/2011bams2994.1, 2011. 
                    
                
                        
                        Taylor, P. A. and Salmon, J. R.: A model for the correction of surface
wind data for sheltering by upwind obstacles, J. Appl. Meteorol., 32, 1683–1694, https://doi.org/10.1175/1520-0450(1993)032<1683:AMFTCO>2.0.CO;2, 1993. 
                    
                
                        
                        Torrence, C. and Compo, G. P.: A practical guide to wavelet analysis,
B. Am. Meteorol. Soc., 79, 61–78,
https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2, 1998a. 
                    
                
                        
                        Torrence, C. and Compo, G. P.: A practical guide to wavelet analysis, GitHub [code], https://github.com/chris-torrence/wavelets (last access: 1 December 2021), 1998b. 
                    
                
                        
                        Turner, D. B.: Comparison of three methods for calculating the standard
deviation of the wind direction, J. Clim. Appl. Meteorol., 25, 703–707, https://doi.org/10.1175/1520-0450(1986)025<0703:COTMFC>2.0.CO;2, 1986. 
                    
                
                        
                        Van de Wiel, B. J., Moene, A. F., Jonker, H. J., Baas, P., Basu, S., Donda,
J. M., Sun, J., and Holtslag, A. A.: The minimum wind speed for sustainable
turbulence in the nocturnal boundary layer, J. Atmos.
Sci., 69, 3116–3127, https://doi.org/10.1175/jas-d-12-0107.1, 2012. 
                    
                
                        
                        von der Heyden, L., Deventer, M. J., Graus, M., Karl, T., Lamprecht, C.,
and Held, A.: Aerosol particles during the Innsbruck Air Quality Study
(INNAQS): The impact of transient fluxes on total aerosol number exchange,
Atmos. Environ., 190, 389–400, https://doi.org/10.1016/j.atmosenv.2018.07.041, 2018. 
                    
                
                        
                        White, L. D.: Mobile observations of a quasi-frontal transient moisture
boundary in the Deep South, Weather Forecast., 29, 1356–1373,
https://doi.org/10.1175/waf-d-14-00009.1, 2014. 
                    
                
                        
                        Wilczak, J. M., Oncley, S. P., and Stage, S. A.: Sonic anemometer tilt
correction algorithms, Bound.-Lay. Meteorol., 99, 127–150,
https://doi.org/10.1023/a:1018966204465, 2001. 
                    
                
                        
                        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,
https://doi.org/10.1007/s10546-010-9494-z, 2010. 
                    
                
                        
                        Wyngaard, J. C.: The effects of probe-induced flow distortion on atmospheric
turbulence measurements: Extension to scalars, J. Atmos. Sci., 45, 3400–3412, https://doi.org/10.1175/1520-0469(1988)045<3400:TEOPIF>2.0.CO;2, 1988. 
                    
                
                        
                        Yu, Y., Liu, J., Chauhan, K., de Dear, R., and Niu, J.: Experimental study
on convective heat transfer coefficients for the human body exposed to
turbulent wind conditions, Build. Environ., 169, 106533,
https://doi.org/10.1016/j.buildenv.2019.106533, 2020. 
                    
                Short summary
            This research investigates the measurement of atmospheric turbulence using a low-cost instrumented car that travels at near-highway speeds and is impacted by upwind obstructions and other on-road traffic. We show that our car design can successfully measure the mean flow and atmospheric turbulence near the surface. We outline a technique to isolate and remove the effects of sporadic passing traffic from car-measured velocity variances and discuss potential measurement uncertainties.
            This research investigates the measurement of atmospheric turbulence using a low-cost...
            
         
 
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
                        
                                         
             
             
            