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

Related authors

Review Article: Antarctica’s internal architecture: Towards a radiostratigraphically-informed age–depth model of the Antarctic ice sheets
Robert G. Bingham, Julien A. Bodart, Marie G. P. Cavitte, Ailsa Chung, Rebecca J. Sanderson, Johannes C. R. Sutter, Olaf Eisen, Nanna B. Karlsson, Joseph A. MacGregor, Neil Ross, Duncan A. Young, David W. Ashmore, Andreas Born, Winnie Chu, Xiangbin Cui, Reinhard Drews, Steven Franke, Vikram Goel, John W. Goodge, A. Clara J. Henry, Antoine Hermant, Benjamin H. Hills, Nicholas Holschuh, Michelle R. Koutnik, Gwendolyn J.-M. C. Leysinger Vieli, Emma J. Mackie, Elisa Mantelli, Carlos Martín, Felix S. L. Ng, Falk M. Oraschewski, Felipe Napoleoni, Frédéric Parrenin, Sergey V. Popov, Therese Rieckh, Rebecca Schlegel, Dustin M. Schroeder, Martin J. Siegert, Xueyuan Tang, Thomas O. Teisberg, Kate Winter, Shuai Yan, Harry Davis, Christine F. Dow, Tyler J. Fudge, Tom A. Jordan, Bernd Kulessa, Kenichi Matsuoka, Clara J. Nyqvist, Maryam Rahnemoonfar, Matthew R. Siegfried, Shivangini Singh, Verjan Višnjević, Rodrigo Zamora, and Alexandra Zuhr
EGUsphere, https://doi.org/10.5194/egusphere-2024-2593,https://doi.org/10.5194/egusphere-2024-2593, 2024
Short summary
Design and performance of ELSA v2.0: an isochronal model for ice-sheet layer tracing
Therese Rieckh, Andreas Born, Alexander Robinson, Robert Law, and Gerrit Gülle
Geosci. Model Dev., 17, 6987–7000, https://doi.org/10.5194/gmd-17-6987-2024,https://doi.org/10.5194/gmd-17-6987-2024, 2024
Short summary
Evaluating two methods of estimating error variances using simulated data sets with known errors
Therese Rieckh and Richard Anthes
Atmos. Meas. Tech., 11, 4309–4325, https://doi.org/10.5194/amt-11-4309-2018,https://doi.org/10.5194/amt-11-4309-2018, 2018
Short summary
Estimating observation and model error variances using multiple data sets
Richard Anthes and Therese Rieckh
Atmos. Meas. Tech., 11, 4239–4260, https://doi.org/10.5194/amt-11-4239-2018,https://doi.org/10.5194/amt-11-4239-2018, 2018
Short summary
Evaluating tropospheric humidity from GPS radio occultation, radiosonde, and AIRS from high-resolution time series
Therese Rieckh, Richard Anthes, William Randel, Shu-Peng Ho, and Ulrich Foelsche
Atmos. Meas. Tech., 11, 3091–3109, https://doi.org/10.5194/amt-11-3091-2018,https://doi.org/10.5194/amt-11-3091-2018, 2018
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator
Julia Danzer, Magdalena Pieler, and Gottfried Kirchengast
Atmos. Meas. Tech., 17, 4979–4995, https://doi.org/10.5194/amt-17-4979-2024,https://doi.org/10.5194/amt-17-4979-2024, 2024
Short summary
Verification of weather-radar-based hail metrics with crowdsourced observations from Switzerland
Jérôme Kopp, Alessandro Hering, Urs Germann, and Olivia Martius
Atmos. Meas. Tech., 17, 4529–4552, https://doi.org/10.5194/amt-17-4529-2024,https://doi.org/10.5194/amt-17-4529-2024, 2024
Short summary
Atmospheric motion vector (AMV) error characterization and bias correction by leveraging independent lidar data: a simulation using an observing system simulation experiment (OSSE) and optical flow AMVs
Hai Nguyen, Derek Posselt, Igor Yanovsky, Longtao Wu, and Svetla Hristova-Veleva
Atmos. Meas. Tech., 17, 3103–3119, https://doi.org/10.5194/amt-17-3103-2024,https://doi.org/10.5194/amt-17-3103-2024, 2024
Short summary
Description and validation of the Japanese algorithm for radiative flux and heating rate products with all four EarthCARE instruments: Pre-launch test with A-Train
Akira Yamauchi, Kentaroh Suzuki, Eiji Oikawa, Miho Sekiguchi, Takashi Nagao, and Haruma Ishida
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-78,https://doi.org/10.5194/amt-2024-78, 2024
Revised manuscript accepted for AMT
Short summary
Rotary-wing drone-induced flow – comparison of simulations with lidar measurements
Liqin Jin, Mauro Ghirardelli, Jakob Mann, Mikael Sjöholm, Stephan Thomas Kral, and Joachim Reuder
Atmos. Meas. Tech., 17, 2721–2737, https://doi.org/10.5194/amt-17-2721-2024,https://doi.org/10.5194/amt-17-2721-2024, 2024
Short summary

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
Download
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.