Articles | Volume 9, issue 5
https://doi.org/10.5194/amt-9-2253-2016
https://doi.org/10.5194/amt-9-2253-2016
Research article
 | Highlight paper
 | 
23 May 2016
Research article | Highlight paper |  | 23 May 2016

Retrieving atmospheric turbulence information from regular commercial aircraft using Mode-S and ADS-B

Jacek M. Kopeć, Kamil Kwiatkowski, Siebren de Haan, and Szymon P. Malinowski

Related authors

EMADDC: high quality, quickly available and high volume wind and temperature observations from aircraft using the Mode-S EHS infrastructure
Siebren de Haan, Paul de Jong, Michal Koutek, and Jan Sondij
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-110,https://doi.org/10.5194/amt-2024-110, 2024
Revised manuscript under review for AMT
Short summary
The ratio of transverse to longitudinal turbulent velocity statistics for aircraft measurements
Jakub L. Nowak, Marie Lothon, Donald H. Lenschow, and Szymon P. Malinowski
EGUsphere, https://doi.org/10.5194/egusphere-2024-1366,https://doi.org/10.5194/egusphere-2024-1366, 2024
Short summary
Applicability of the low-cost OPC-N3 optical particle counter for microphysical measurements of fog
Katarzyna Nurowska, Moein Mohammadi, Szymon Malinowski, and Krzysztof Markowicz
Atmos. Meas. Tech., 16, 2415–2430, https://doi.org/10.5194/amt-16-2415-2023,https://doi.org/10.5194/amt-16-2415-2023, 2023
Short summary
Contactless optical hygrometry in LACIS-T
Jakub L. Nowak, Robert Grosz, Wiebke Frey, Dennis Niedermeier, Jędrzej Mijas, Szymon P. Malinowski, Linda Ort, Silvio Schmalfuß, Frank Stratmann, Jens Voigtländer, and Tadeusz Stacewicz
Atmos. Meas. Tech., 15, 4075–4089, https://doi.org/10.5194/amt-15-4075-2022,https://doi.org/10.5194/amt-15-4075-2022, 2022
Short summary
Cloud microphysical measurements at a mountain observatory: comparison between shadowgraph imaging and phase Doppler interferometry
Moein Mohammadi, Jakub L. Nowak, Guus Bertens, Jan Moláček, Wojciech Kumala, and Szymon P. Malinowski
Atmos. Meas. Tech., 15, 965–985, https://doi.org/10.5194/amt-15-965-2022,https://doi.org/10.5194/amt-15-965-2022, 2022
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
The role of time averaging of eddy covariance fluxes on water use efficiency dynamics of maize
Arun Rao Karimindla, Shweta Kumari, Saipriya S R, Syam Chintala, and BVN P. Kambhammettu​​​​​​​
Atmos. Meas. Tech., 17, 5477–5490, https://doi.org/10.5194/amt-17-5477-2024,https://doi.org/10.5194/amt-17-5477-2024, 2024
Short summary
Number- and size-controlled rainfall regimes in the Netherlands: physical reality or statistical mirage?
Marc Schleiss
Atmos. Meas. Tech., 17, 4789–4802, https://doi.org/10.5194/amt-17-4789-2024,https://doi.org/10.5194/amt-17-4789-2024, 2024
Short summary
The Far-INfrarEd Spectrometer for Surface Emissivity (FINESSE) – Part 2: First measurements of the emissivity of water in the far-infrared
Laura Warwick, Jonathan E. Murray, and Helen Brindley
Atmos. Meas. Tech., 17, 4777–4787, https://doi.org/10.5194/amt-17-4777-2024,https://doi.org/10.5194/amt-17-4777-2024, 2024
Short summary
Hailstorm events in the Central Andes of Peru: insights from historical data and radar microphysics
Jairo M. Valdivia, José Luis Flores-Rojas, Josep J. Prado, David Guizado, Elver Villalobos-Puma, Stephany Callañaupa, and Yamina Silva-Vidal
Atmos. Meas. Tech., 17, 2295–2316, https://doi.org/10.5194/amt-17-2295-2024,https://doi.org/10.5194/amt-17-2295-2024, 2024
Short summary
Hybrid instrument network optimization for air quality monitoring
Nishant Ajnoti, Hemant Gehlot, and Sachchida Nand Tripathi
Atmos. Meas. Tech., 17, 1651–1664, https://doi.org/10.5194/amt-17-1651-2024,https://doi.org/10.5194/amt-17-1651-2024, 2024
Short summary

Cited articles

Cho, J. Y. N., Newell, R. E., Anderson, B. E., Barrick, J. D. W., and Thornhill, K. L.: Characterizations of tropospheric turbulence and stability layers from aircraft observations, J. Geophys. Res., 108, 8784, https://doi.org/10.1029/2002JD002820, 2003.
Cornman, L. B., Morse, C. S., and Cunning, G.: Real-time estimation of atmospheric turbulence severity from in-situ aircraft measurements, J. Aircraft, 32, 171–177, https://doi.org/10.2514/3.46697, 1995.
Drüe, C., Deimel, S., and Hoff, A.: A low-cost approach to derive upper-air wind measurements from ADS-B, 13th EMS Annual Meeting, Reading, UK, 9–13 September 2013, available at: http://presentations.copernicus.org/EMS2013-702_presentation.pdf (last access: 21 August 2015), 2013.
Frehlich, R. and Sharman, R.: Climatology of velocity and tempera-ture turbulence statistics determined from rawinsonde and ACARS/AMDAR data, J. Appl. Meteorol. Clim., 49, 1149–1169, https://doi.org/10.1175/2010JAMC2196.1, 2010.
Gill, P. G. and Buchanan, P.: An ensemble based turbulence forecasting system, Meteorol. Appl., 21, 12–19, https://doi.org/10.1002/met.1373, 2014.
Download
Short summary
This paper is presenting a feasibility study focused on methods of estimating the turbulence intensity based on a class of navigational messages routinely broadcast by the commercial aircraft (known as ADS-B and Mode-S). Using this kind of information could have potentially significant impact on aviation safety. Three methods have been investigated.