Articles | Volume 13, issue 7
https://doi.org/10.5194/amt-13-3855-2020
© Author(s) 2020. 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-13-3855-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Jul 2020
Research article |
| 16 Jul 2020
| 16 Jul 2020
Confronting the boundary layer data gap: evaluating new and existing methodologies of probing the lower atmosphere
University of Oklahoma, School of Meteorology, Norman, Oklahoma, USA
University of Oklahoma, Center for Autonomous Sensing and Sampling, Norman, Oklahoma, USA
Brian R. Greene
University of Oklahoma, School of Meteorology, Norman, Oklahoma, USA
University of Oklahoma, Center for Autonomous Sensing and Sampling, Norman, Oklahoma, USA
University of Oklahoma, Advanced Radar Research Center, Norman, Oklahoma, USA
Petra M. Klein
University of Oklahoma, School of Meteorology, Norman, Oklahoma, USA
University of Oklahoma, Center for Autonomous Sensing and Sampling, Norman, Oklahoma, USA
Matthew Carney
University of Oklahoma, School of Meteorology, Norman, Oklahoma, USA
Phillip B. Chilson
University of Oklahoma, School of Meteorology, Norman, Oklahoma, USA
University of Oklahoma, Center for Autonomous Sensing and Sampling, Norman, Oklahoma, USA
University of Oklahoma, Advanced Radar Research Center, Norman, Oklahoma, USA
Related authors
Antonio R. Segales, Tyler M. Bell, Abdullah A. Tasim, Aaron Quiroz, Jeremy D. Simms, Joshua G. Gebauer, and Elizabeth N. Smith
Atmos. Meas. Tech., 19, 3667–3686, https://doi.org/10.5194/amt-19-3667-2026, https://doi.org/10.5194/amt-19-3667-2026, 2026
Short summary
Short summary
Severe weather can change quickly, but routine weather measurements often miss important details near the ground. We built and tested CopterSonde-SWX, a small weather drone designed to collect vertical profiles in strong winds and measure temperature, humidity, and wind. Field tests showed it closely matched trusted instruments and handled stronger winds than earlier versions. This work supports future drone networks to improve storm monitoring, forecasting, and public safety.
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.
James B. Duncan Jr., Laura Bianco, Bianca Adler, Tyler Bell, Irina V. Djalalova, Laura Riihimaki, Joseph Sedlar, Elizabeth N. Smith, David D. Turner, Timothy J. Wagner, and James M. Wilczak
Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, https://doi.org/10.5194/amt-15-2479-2022, 2022
Short summary
Short summary
In this study, several ground-based remote sensing instruments are used to estimate the height of the convective planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the CHEESEHEAD19 field campaign. The impact of clouds (particularly boundary layer clouds) on the estimation of the boundary layer depth is also investigated.
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.
Antonio R. Segales, Tyler M. Bell, Abdullah A. Tasim, Aaron Quiroz, Jeremy D. Simms, Joshua G. Gebauer, and Elizabeth N. Smith
Atmos. Meas. Tech., 19, 3667–3686, https://doi.org/10.5194/amt-19-3667-2026, https://doi.org/10.5194/amt-19-3667-2026, 2026
Short summary
Short summary
Severe weather can change quickly, but routine weather measurements often miss important details near the ground. We built and tested CopterSonde-SWX, a small weather drone designed to collect vertical profiles in strong winds and measure temperature, humidity, and wind. Field tests showed it closely matched trusted instruments and handled stronger winds than earlier versions. This work supports future drone networks to improve storm monitoring, forecasting, and public safety.
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.
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.
James B. Duncan Jr., Laura Bianco, Bianca Adler, Tyler Bell, Irina V. Djalalova, Laura Riihimaki, Joseph Sedlar, Elizabeth N. Smith, David D. Turner, Timothy J. Wagner, and James M. Wilczak
Atmos. Meas. Tech., 15, 2479–2502, https://doi.org/10.5194/amt-15-2479-2022, https://doi.org/10.5194/amt-15-2479-2022, 2022
Short summary
Short summary
In this study, several ground-based remote sensing instruments are used to estimate the height of the convective planetary boundary layer, and their performance is compared against independent boundary layer depth estimates obtained from radiosondes launched as part of the CHEESEHEAD19 field campaign. The impact of clouds (particularly boundary layer clouds) on the estimation of the boundary layer depth is also investigated.
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.
Cited articles
Balsley, B. B., Lawrence, D. A., Woodman, R. F., and Fritts, D. C.: Fine-Scale
Characteristics of Temperature, Wind, and Turbulence in the Lower Atmosphere
(0–1300 m) Over the South Peruvian Coast, Bound.-Lay. Meteorol., 147,
165–178, 2013. a
Barbieri, L., Kral, S. T., Bailey, S. C., Frazier, A. E., Jacob, J. D., Reuder,
J., Brus, D., Chilson, P. B., Crick, C., Detweiler, C., Doddi, A., Elston, J., Foroutan, H., González-Rocha, J., Greene, B. R., Guzman, M. I., Houston, A. L., Islam, A., Kemppinen, O., Lawrence, D., Pillar-Little, E. A., Ross, S. D., Sama, M. P., Schmale, D. G., Schuyler, T. J., Shankar, A., Smith, S. W., Waugh, S., Dixon, C., Borenstein, S., and de Boer, G.:
Intercomparison of small unmanned aircraft system (sUAS) measurements for
atmospheric science during the LAPSE-RATE campaign, Sensors, 19, 2179, https://doi.org/10.3390/s19092179, 2019. a, b
Båserud, L., Reuder, J., Jonassen, M. O., Kral, S. T., Paskyabi, M. B., and Lothon, M.: Proof of concept for turbulence measurements with the RPAS SUMO during the BLLAST campaign, Atmos. Meas. Tech., 9, 4901–4913, https://doi.org/10.5194/amt-9-4901-2016, 2016. a
Bell, T. and Klein, P.: OU/NSSL CLAMPS Doppler Lidar Data from
LAPSE-RATE, Zenodo, https://doi.org/10.5281/zenodo.3780623, 2020. a
Bell, T., Klein, P., and Turner, D.: OU/NSSL CLAMPS AERIoe Temperature
and Water Vapor Profile Data from LAPSE-RATE,
Zenodo, https://doi.org/10.5281/zenodo.3727224, 2020a. a
Bell, T., Klein, P., and Turner, D.: OU/NSSL CLAMPS Microwave
Radiometer and Surface Meteorological Data from LAPSE-RATE,
Zenodo, https://doi.org/10.5281/zenodo.3780593, 2020b. a
Bessagnet, B., Menut, L., Couvidat, F., Meleux, F., Siour, G., and Mailler, S.:
What Can We Expect from Data Assimilation for Air Quality Forecast? Part II:
Analysis with a Semi-Real Case,
J. Atmos. Ocean. Tech., 36, 1433–1448, https://doi.org/10.1175/JTECH-D-18-0117.1,
2019. a
Bonin, T., Chilson, P., Zielke, B., and Fedorovich, E.: Observations of the
Early Evening Boundary-Layer Transition Using a Small Unmanned Aerial System,
Bound.-Lay. Meteorol., 146, 119–132, https://doi.org/10.1007/s10546-012-9760-3,
2012. a
Browning, K. A. and Wexler, R.: The Determination of Kinematic Properties of a
Wind Field Using Doppler Radar, J. Appl. Meteorol., 7, 105–113,
1968. a
Chilson, P., Gleason, A., Zielke, B., Nai, F., Yeary, M., Klein, P., and
Shalamunec, W.: SMARTSonde: A small UAS platform to support radar research,
AMS 34th Conf. Radar Meteor., Boston, 8 October 2009, MA. Am. Meteorol. Soc., 2009. a
Chilson, P. B., Bell, T. M., Brewster, K. A., Britto Hupsel de Azevedo, G.,
Carr, F. H., Carson, K., Doyle, W., Fiebrich, C. A., Greene, B. R., Grimsley,
J. L., Kanneganti, S. T., Martin, J., Moore, A., Palmer, R. D., Pillar-Little, E. A., Salazar-Cerreno, J. L., Segales, A. R., Weber, M. E., Yeary, M., and Droegemeier, K. K.: Moving towards a Network of Autonomous UAS Atmospheric
Profiling Stations for Observations in the Earth’s Lower Atmosphere: The 3D
Mesonet Concept, Sensors, 19, 2720, https://doi.org/10.3390/s19122720, 2019. a, b
de Boer, G., Palo, S., Argrow, B., LoDolce, G., Mack, J., Gao, R.-S., Telg, H., Trussel, C., Fromm, J., Long, C. N., Bland, G., Maslanik, J., Schmid, B., and Hock, T.: The Pilatus unmanned aircraft system for lower atmospheric research, Atmos. Meas. Tech., 9, 1845–1857, https://doi.org/10.5194/amt-9-1845-2016, 2016. a
de Boer, G., Diehl, C., Jacob, J., Houston, A., Smith, S. W., Chilson, P., III,
D. G. S., Intrieri, J., Pinto, J., Elston, J., Brus, D., Kemppinen, O.,
Clark, A., Lawrence, D., Bailey, S. C., Sama, M. P., Frazier, A., Crick, C.,
Natalie, V., Pillar-Little, E., Klein, P., Waugh, S., Lundquist, J. K.,
Barbieri, L., Kral, S. T., Jensen, A. A., Dixon, C., Borenstein, S.,
Hesselius, D., Human, K., Hall, P., Argrow, B., Thornberry, T., Wright, R.,
and Kelly, J. T.: Development of community, capabilities and understanding
through unmanned aircraft-based atmospheric research: The LAPSE-RATE
campaign, B. Am. Meteorol. Soc., 101, E684–E699, https://doi.org/10.1175/BAMS-D-19-0050.1, 2020. a
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, 2018. a
Gioli, B., Miglietta, F., Vaccari, F. P., Zaldei, A., and De Martino, B.: The
Sky Arrow ERA, an innovative airborne platform to monitor mass, momentum
and energy exchange of ecosystems, Ann. Geophys., 49, https://doi.org/10.4401/ag-3159, 2006. a
Greene, B., Segales, A., Bell, T., Pillar-Little, E., and Chilson, P.:
Environmental and Sensor Integration Influences on Temperature Measurements
by Rotary-Wing Unmanned Aircraft Systems, Sensors, 19, 1470, https://doi.org/10.3390/s19061470, 2019. a, b, c, d
Greene, B. R., Segales, A. R., Waugh, S., Duthoit, S., and Chilson, P. B.: Considerations for temperature sensor placement on rotary-wing unmanned aircraft systems, Atmos. Meas. Tech., 11, 5519–5530, https://doi.org/10.5194/amt-11-5519-2018, 2018. a, b, c
Greene, B. R., Bell, T. M., Pillar-Little, E. A., Segales, A. R., Britto
Hupsel de Azevedo, G., Doyle, W., Tripp, D. D., Kanneganti, S. T., and
Chilson, P. B.: University of Oklahoma CopterSonde Files from LAPSE-RATE,
Zenodo, https://doi.org/10.5281/zenodo.3737087,
2020. a
Houston, A. L., Argrow, B., Elston, J., Lahowetz, J., Frew, E. W., and Kennedy,
P. C.: The Collaborative Colorado–Nebraska Unmanned Aircraft System
Experiment, B. Am. Meteorol. Soc., 93, 39–54,
2012. a
Hubbard, K., Lin, X., Baker, C., and Sun, B.: Air temperature comparison
between the MMTS and the USCRN temperature systems, J. Atmos. Ocean.
Tech., 21, 1590–1597, https://doi.org/10.1175/1520-0426(2004)021<1590:ATCBTM>2.0.CO;2,
2004. a
Hunter, J. D.: Matplotlib: A 2D graphics environment, Comput. Sci.
Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007. a
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker,
R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell,
H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric Emitted
Radiance Interferometer. Part I: Instrument Design, J. Atmos. Ocean. Tech., 21, 1763–1776, 2004a. a
Knuteson, R. O., Revercomb, H. E., Best, F. A., Ciganovich, N. C., Dedecker,
R. G., Dirkx, T. P., Ellington, S. C., Feltz, W. F., Garcia, R. K., Howell,
H. B., Smith, W. L., Short, J. F., and Tobin, D. C.: Atmospheric Emitted
Radiance Interferometer. Part II: Instrument Performance, J. Atmos. Ocean. Tech., 21, 1777–1789, 2004b. a
Kral, S. T., Reuder, J., Vihma, T., Suomi, I., Oâ'Connor, E., Kouznetsov,
R., Wrenger, B., Rautenberg, A., Urbancic, G., Jonassen, M. O., BÃserud,
L., Maronga, B., Mayer, S., Lorenz, T., Holtslag, A. A. M., Steeneveld,
G.-J., Seidl, A., Müller, M., Lindenberg, C., Langohr, C., Voss, H.,
Bange, J., Hundhausen, M., Hilsheimer, P., and Schygulla, M.: Innovative
Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)
– The Hailuoto 2017 Campaign, Atmosphere, 9, 268, https://doi.org/10.3390/atmos9070268,
2018. a
Lawrence, D. A. and Balsley, B. B.: High-Resolution Atmospheric Sensing of
Multiple Atmospheric Variables Using the DataHawk Small Airborne Measurement
System, J. Atmos. Ocean. Tech., 30, 2352–2366, 2013. a
Lothon, M., Lohou, F., Pino, D., Couvreux, F., Pardyjak, E. R., Reuder, J., Vilà-Guerau de Arellano, J., Durand, P., Hartogensis, O., Legain, D., Augustin, P., Gioli, B., Lenschow, D. H., Faloona, I., Yagüe, C., Alexander, D. C., Angevine, W. M., Bargain, E., Barrié, J., Bazile, E., Bezombes, Y., Blay-Carreras, E., van de Boer, A., Boichard, J. L., Bourdon, A., Butet, A., Campistron, B., de Coster, O., Cuxart, J., Dabas, A., Darbieu, C., Deboudt, K., Delbarre, H., Derrien, S., Flament, P., Fourmentin, M., Garai, A., Gibert, F., Graf, A., Groebner, J., Guichard, F., Jiménez, M. A., Jonassen, M., van den Kroonenberg, A., Magliulo, V., Martin, S., Martinez, D., Mastrorillo, L., Moene, A. F., Molinos, F., Moulin, E., Pietersen, H. P., Piguet, B., Pique, E., Román-Cascón, C., Rufin-Soler, C., Saïd, F., Sastre-Marugán, M., Seity, Y., Steeneveld, G. J., Toscano, P., Traullé, O., Tzanos, D., Wacker, S., Wildmann, N., and Zaldei, A.: The BLLAST field experiment: Boundary-Layer Late Afternoon and Sunset Turbulence, Atmos. Chem. Phys., 14, 10931–10960, https://doi.org/10.5194/acp-14-10931-2014, 2014. a
Markowski, P. M.: An Idealized Numerical Simulation Investigation of the
Effects of Surface Drag on the Development of Near-Surface Vertical Vorticity
in Supercell Thunderstorms, J. Atmos. Sci., 73,
4349–4385, https://doi.org/10.1175/JAS-D-16-0150.1, 2016. a
Markowski, P. M. and Bryan, G. H.: LES of Laminar Flow in the PBL: A
Potential Problem for Convective Storm Simulations, Mon. Weather Rev.,
144, 1841–1850, https://doi.org/10.1175/MWR-D-15-0439.1, 2016. a
National Academies of Sciences, Engineering and Medicine: Thriving on Our Changing
Planet: A Decadal Strategy for Earth Observation from Space, The National
Academies Press, Washington, DC, 2018. a
Neumann, P. P. and Bartholmai, M.: Real-time wind estimation on a micro
unmanned aerial vehicle using its inertial measurement unit, Sensor.
Actuat. A-Phys., 235, 300–310, 2015. a
Nowotarski, C. J., Markowski, P. M., and Richardson, Y. P.: The Characteristics
of Numerically Simulated Supercell Storms Situated over Statically Stable
Boundary Layers, Mon. Weather Rev., 139, 3139–3162,
https://doi.org/10.1175/MWR-D-10-05087.1, 2011. a
Park, S.-Y., Kim, D.-H., Lee, S.-H., and Lee, H. W.: Variational data assimilation for the optimized ozone initial state and the short-time forecasting, Atmos. Chem. Phys., 16, 3631–3649, https://doi.org/10.5194/acp-16-3631-2016, 2016. a
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. a, b, c
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn.
Res., 12, 2825–2830, 2011. a
Reuder, J., Brisset, P., Jonassen, M., Müller, M., and Mayer, S.: The Small
Unmanned Meteorological Observer SUMO: A new tool for atmospheric boundary
layer research, Meteorol. Z., 18, 141–147, 2009. a
Reuder, J., Jonassen, M. O., and Ólafsson, H.: The Small Unmanned
Meteorological Observer SUMO: Recent developments and applications of a
micro-UAS for atmospheric boundary layer research, Acta Geophys., 60,
1454–1473, 2012. a
Rodgers, C. D.: Inverse methods for atmospheric sounding: Theory and practice,
vol. 2 of Series on Atmospheric, Oceanic, and Planetary Physics,
World Scientific, 2000. a
Rose, T., Crewell, S., Löhnert, U., and Simmer, C.: A network suitable
microwave radiometer for operational monitoring of the cloudy atmosphere,
Atmos. Res., 75, 183–200, https://doi.org/10.1016/j.atmosres.2004.12.005, 2005. a
Saïd, F., Corsmeier, U., Kalthoff, N., Kottmeier, C., Lothon, M., Wieser,
A., Hofherr, T., and Perros, P.: ESCOMPTE experiment: intercomparison of
four aircraft dynamical, thermodynamical, radiation and chemical
measurements, Atmos. Res., 74, 217–252,
https://doi.org/10.1016/j.atmosres.2004.06.012, 2005. a
Segales, A. R., Greene, B. R., Bell, T. M., Doyle, W., Martin, J. J., Pillar-Little, E. A., and Chilson, P. B.: The CopterSonde: an insight into the development of a smart unmanned aircraft system for atmospheric boundary layer research, Atmos. Meas. Tech., 13, 2833–2848, https://doi.org/10.5194/amt-13-2833-2020, 2020. a
Tanner, B. D., Swiatek, E., and Maughan, C.: Field comparisons of naturally
ventilated and aspirated radiation shields for weather station air
temperature measurements, in: Conference on Agricultural and Forest
Meteorology, 22, 227–230, 1996. a
van den Kroonenberg, A. C., Martin, S., Beyrich, F., and Bange, J.:
Spatially-averaged temperature structure parameter over a heterogeneous
surface measured by an unmanned aerial vehicle, Bound.-Lay. Meteorol., 142,
55–77, https://doi.org/10.1007/s10546-011-9662-9, 2012. a
van der Walt, S., Colbert, S. C., and Varoquaux, G.: The NumPy Array: A
Structure for Efficient Numerical Computation, Comput. Sci.
Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright,
J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K.,
Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E.,
Carey, C., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J.,
Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero,
E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa,
F., and van Mulbregt, P.: SciPy 1.0–Fundamental
Algorithms for Scientific Computing in Python, arXiv [preprint],
arXiv:1907.10121, 23 July 2019.
a
Vömel, H., Argrow, B. M., Axisa, D., Chilson, P., Ellis, S., Fladeland, M.,
Frew, E. W., Jacob, J., Lord, M., Moore, J., Oncley, S., Roberts, G.,
Schoenung, S., and Wolff, C.: The NCAR / EOL Community Workshop on Unmanned
Aircraft Systems for Atmospheric Research, UCAR/NCAR Earth Observing
Laboratory, 2018. a
Wagner, T. J., Klein, P. M., and Turner, D. D.: A New Generation of
Ground-Based Mobile Platforms for Active and Passive Profiling of the
Boundary Layer, B. Am. Meteorol. Soc., 100,
137–153, 2019. a
Waugh, S.: National Severe Storms Laboratory Mobile Soundings during
Lapse-Rate (CLAMPS trailer), Zenodo, https://doi.org/10.5281/zenodo.3720444,
2020. a
Wildmann, N., Hofsäß, M., Weimer, F., Joos, A., and Bange, J.: MASC-a
small remotely piloted aircraft (RPA) for wind energy research, Adv. Sci.
Res., 11, 55, https://doi.org/10.5194/asr-11-55-2014, 2014. a
Wildmann, N., Rau, G. A., and Bange, J.: Observations of the Early Morning
Boundary-Layer Transition with Small Remotely-Piloted Aircraft,
Bound.-Lay. Meteorol., 157, 345–373, 2015. a
World Health Organization: Ambient air pollution: a global assessment of
exposure and burden of disease, 2016. a
Zhou, B. and Chow, F. K.: Turbulence Modeling for the Stable Atmospheric
Boundary Layer and Implications for Wind Energy, Flow Turbul.
Combust., 88, 255–277, https://doi.org/10.1007/s10494-011-9359-7, 2012. a
Short summary
It is well known that the atmospheric boundary layer is under-sampled in the vertical dimension. Recently, weather-sensing uncrewed aerial systems (WxUAS) have created new opportunities to sample this region of the atmosphere. This study compares a WxUAS developed at the University of Oklahoma to ground-based remote sensing and radiosondes. We find that overall the systems generally agreed well both thermodynamically and kinematically. However, there is still room to improve each system.
It is well known that the atmospheric boundary layer is under-sampled in the vertical dimension....
