Articles | Volume 11, issue 5
Atmos. Meas. Tech., 11, 3021–3029, 2018
https://doi.org/10.5194/amt-11-3021-2018
Atmos. Meas. Tech., 11, 3021–3029, 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 et al.

Related authors

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
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. Discuss., https://doi.org/10.5194/acp-2020-1303,https://doi.org/10.5194/acp-2020-1303, 2021
Preprint under review for ACP
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 Discuss., https://doi.org/10.5194/essd-2020-321,https://doi.org/10.5194/essd-2020-321, 2020
Revised manuscript under review for ESSD
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 Discuss., https://doi.org/10.5194/essd-2020-276,https://doi.org/10.5194/essd-2020-276, 2020
Revised manuscript accepted for ESSD
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 Discuss., https://doi.org/10.5194/essd-2020-218,https://doi.org/10.5194/essd-2020-218, 2020
Preprint under review for ESSD
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: In Situ Measurement | Topic: Instruments and Platforms
The INFRA-EAR: a low-cost mobile multidisciplinary measurement platform for monitoring geophysical parameters
Olivier F. C. den Ouden, Jelle D. Assink, Cornelis D. Oudshoorn, Dominique Filippi, and Läslo G. Evers
Atmos. Meas. Tech., 14, 3301–3317, https://doi.org/10.5194/amt-14-3301-2021,https://doi.org/10.5194/amt-14-3301-2021, 2021
A dedicated robust instrument for water vapor generation at low humidity for use with a laser water isotope analyzer in cold and dry polar regions
Christophe Leroy-Dos Santos, Mathieu Casado, Frédéric Prié, Olivier Jossoud, Erik Kerstel, Morgane Farradèche, Samir Kassi, Elise Fourré, and Amaëlle Landais
Atmos. Meas. Tech., 14, 2907–2918, https://doi.org/10.5194/amt-14-2907-2021,https://doi.org/10.5194/amt-14-2907-2021, 2021
Short summary
Arctic observations and numerical simulations of surface wind effects on Multi-Angle Snowflake Camera measurements
Kyle E. Fitch, Chaoxun Hang, Ahmad Talaei, and Timothy J. Garrett
Atmos. Meas. Tech., 14, 1127–1142, https://doi.org/10.5194/amt-14-1127-2021,https://doi.org/10.5194/amt-14-1127-2021, 2021
Short summary
Distributed wind measurements with multiple quadrotor UAVs in the atmospheric boundary layer
Tamino Wetz, Norman Wildmann, and Frank Beyrich
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-471,https://doi.org/10.5194/amt-2020-471, 2021
Revised manuscript accepted for AMT
The development of the “Storm Tracker” and its applications for atmospheric high-resolution upper-air observations
Wei-Chun Hwang, Po-Hsiung Lin, and Hungjui Yu
Atmos. Meas. Tech., 13, 5395–5406, https://doi.org/10.5194/amt-13-5395-2020,https://doi.org/10.5194/amt-13-5395-2020, 2020
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.