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

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Revised manuscript accepted for AMT
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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
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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.