Articles | Volume 11, issue 5
https://doi.org/10.5194/amt-11-3021-2018
https://doi.org/10.5194/amt-11-3021-2018
Research article
 | 
24 May 2018
Research article |  | 24 May 2018

Is it feasible to estimate radiosonde biases from interlaced measurements?

Stefanie Kremser, Jordis S. Tradowsky, Henning W. Rust, and Greg E. Bodeker

Related authors

The MAPM (Mapping Air Pollution eMissions) method for inferring particulate matter emissions maps at city scale from in situ concentration measurements: description and demonstration of capability
Brian Nathan, Stefanie Kremser, Sara Mikaloff-Fletcher, Greg Bodeker, Leroy Bird, Ethan Dale, Dongqi Lin, Gustavo Olivares, and Elizabeth Somervell
Atmos. Chem. Phys., 21, 14089–14108, https://doi.org/10.5194/acp-21-14089-2021,https://doi.org/10.5194/acp-21-14089-2021, 2021
Short summary
A global total column ozone climate data record
Greg E. Bodeker, Jan Nitzbon, Jordis S. Tradowsky, Stefanie Kremser, Alexander Schwertheim, and Jared Lewis
Earth Syst. Sci. Data, 13, 3885–3906, https://doi.org/10.5194/essd-13-3885-2021,https://doi.org/10.5194/essd-13-3885-2021, 2021
Short summary
Southern Ocean cloud and aerosol data: a compilation of measurements from the 2018 Southern Ocean Ross Sea Marine Ecosystems and Environment voyage
Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021,https://doi.org/10.5194/essd-13-3115-2021, 2021
Short summary
The winter 2019 air pollution (PM2.5) measurement campaign in Christchurch, New Zealand
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021,https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
Indicators of Antarctic ozone depletion: 1979 to 2019
Greg E. Bodeker and Stefanie Kremser
Atmos. Chem. Phys., 21, 5289–5300, https://doi.org/10.5194/acp-21-5289-2021,https://doi.org/10.5194/acp-21-5289-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Instruments and Platforms
High-resolution wind speed measurements with quadcopter uncrewed aerial systems: calibration and verification in a wind tunnel with an active grid
Johannes Kistner, Lars Neuhaus, and Norman Wildmann
Atmos. Meas. Tech., 17, 4941–4955, https://doi.org/10.5194/amt-17-4941-2024,https://doi.org/10.5194/amt-17-4941-2024, 2024
Short summary
High-altitude balloon-launched uncrewed aircraft system measurements of atmospheric turbulence and qualitative comparison with infrasound microphone response
Anisa N. Haghighi, Ryan D. Nolin, Gary D. Pundsack, Nick Craine, Aliaksei Stratsilatau, and Sean C. C. Bailey
Atmos. Meas. Tech., 17, 4863–4889, https://doi.org/10.5194/amt-17-4863-2024,https://doi.org/10.5194/amt-17-4863-2024, 2024
Short summary
Evaluation of the hyperspectral radiometer (HSR1) at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site
Kelly A. Balmes, Laura D. Riihimaki, John Wood, Connor Flynn, Adam Theisen, Michael Ritsche, Lynn Ma, Gary B. Hodges, and Christian Herrera
Atmos. Meas. Tech., 17, 3783–3807, https://doi.org/10.5194/amt-17-3783-2024,https://doi.org/10.5194/amt-17-3783-2024, 2024
Short summary
Cost-effective off-grid automatic precipitation samplers for pollutant and biogeochemical atmospheric deposition
Alessia A. Colussi, Daniel Persaud, Melodie Lao, Bryan K. Place, Rachel F. Hems, Susan E. Ziegler, Kate A. Edwards, Cora J. Young, and Trevor C. VandenBoer
Atmos. Meas. Tech., 17, 3697–3718, https://doi.org/10.5194/amt-17-3697-2024,https://doi.org/10.5194/amt-17-3697-2024, 2024
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

Cited articles

Box, G. E. P. and Jenkins, G. M.: Time Series Analysis: forecasting and control, Prentice Hall, New Jersey, USA, 1976. a
Chambers, J. M. and Hastie, T. H. (Eds.): Statistical Models in S, Wadsworth & Brooks/Cole, Pacific Grove, California, USA, 1992. a
GCOS-171, W. T. R. N.: The GCOS Reference Upper-Air Network (GRUAN) GUIDE, WMO, Geneva, Switzerland, 2013. a, b
Haimberger, L., Tavolato, C., and Sperka, S.: Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations, J. Climate, 25, 8108–3131, https://doi.org/10.1175/JCLI-D-11-00668.1, 2012. a
Jeannet, P., Bower, C., and Calpini, B.: Global criteria for tracing the improvements of radiosondes over the last decades, WMO/TD No. 1433, IOM Report No. 95, World Meteorological Organization, Geneva, Switzerland, 32 pp., 2008. a
Download
Short summary
We investigate the feasibility of quantifying the difference in biases of two instrument types (i.e. radiosondes) by flying the old and new instruments on alternating days, so-called interlacing, to statistically derive the systematic biases between the instruments. While it is in principle possible to estimate the difference between two instrument biases from interlaced measurements, the number of required interlaced flights is very large for reasonable autocorrelation coefficient values.