Articles | Volume 11, issue 5
https://doi.org/10.5194/amt-11-2567-2018
https://doi.org/10.5194/amt-11-2567-2018
Research article
 | 
03 May 2018
Research article |  | 03 May 2018

Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons

Shay Gilpin, Therese Rieckh, and Richard Anthes

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Cited articles

Anthes, R., Bernhardt, P., Chen, Y., Cucurull, L., Dymond, K., Ector, D., Healy, S., Ho, S.-P., Hunt, D., Kuo, Y.-H., Liu, H., Manning, K., McCormick, C., Meehan, T., Randel, W., Rocken, C., Schreiner, W., Sokolovskiy, S., Syndergaard, S., Thompson, D. C., Trenberth, K., Wee, T.-K., Yen, N., and Zeng, Z.: The COSMIC/FORMOSAT-3 Mission-Early Results, B. Am. Meteorol. Soc., 89, 313–333, https://doi.org/10.1175/BAMS-89-3-313, 2008. a
Anthes, R. A.: Exploring Earth's atmosphere with radio occultation: contributions to weather, climate and space weather, Atmos. Meas. Tech., 4, 1077–1103, https://doi.org/10.5194/amt-4-1077-2011, 2011. a
Ao, C., Meehan, T., Hajj, G., Mannucci, A., and Beyerle, G.: Lower troposphere refractivity bias in GPS occultation retrievals, J. Geophys. Res., 108, 4577, https://doi.org/10.1029/2002JD003216, 2003. a
Beyerle, G., Schmidt, T., Wickert, J., Heise, S., Rothacher, M., Konig-Langlo, G., and Lauristen, K.: Observations and simulations of receiver-induced refractivity biases in GPS radio occultation, J. Geophys. Res., 111, D12101, https://doi.org/10.1029/2005JD006673, 2006. a
Bodeker, G., Bojinski, S., Cimini, D., Dirksen, R., Haeffelin, M., Hannigan, J., Hurst, D., Leblanc, T., Madonna, F., Maturilli, M., Mikalsen, A., Philipona, R., Reale, T., Seidel, D., Tan, D., Thorne, P., Vömel, H., and Wang, J.: Reference upper-air observations for climate: from concept to realty, B. Am. Meteorol. Soc., 97, 123–135, https://doi.org/10.1175/BAMS-D-14-00072.1, 2016. a
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Short summary
Comparing observational systems when observations are not taken at the exact same time or location can introduce sampling errors that can be come significant during error analysis. In this study, we develop two methods to reduce sampling errors: using ellipse distance constraints rather than circles and subtracting model background. We found that both the ellipses and subtracting model background from the observations reduce sampling errors caused by spatial and temporal differences.