Articles | Volume 18, issue 6
https://doi.org/10.5194/amt-18-1339-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-1339-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Mar 2025
Research article |
| 19 Mar 2025
| 19 Mar 2025
Quality assessment of YUNYAO radio occultation data in the neutral atmosphere
Xiaoze Xu
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
State Key Laboratory of Severe Weather Meteorological Science and Technology (LASW), Beijing 100081, China
CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China
Jincheng Wang
CMA Earth System Modeling and Prediction Centre (CEMC), China Meteorological Administration, Beijing 100081, China
Zhiqiu Gao
State Key Laboratory of Atmospheric Environment and Extreme Meteorology, Chinese Academy of Sciences, Beijing, 100029, China
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
Fenghui Li
Tianjin Yunyao Aerospace Technology Company, Ltd., Tianjin 300350, China
Yan Cheng
Tianjin Yunyao Aerospace Technology Company, Ltd., Tianjin 300350, China
Naifeng Fu
Tianjin Yunyao Aerospace Technology Company, Ltd., Tianjin 300350, China
School of Marine Science and Technology, Tianjin University, Tianjin 300350, China
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Cited articles
Anlauf, H., Pingel, D., and Rhodin, A.: Assimilation of GPS radio occultation data at DWD, Atmos. Meas. Tech., 4, 1105–1113, https://doi.org/10.5194/amt-4-1105-2011, 2011.
Anthes, R. and Rieckh, T.: Estimating observation and model error variances using multiple data sets, Atmos. Meas. Tech., 11, 4239–4260, https://doi.org/10.5194/amt-11-4239-2018, 2018.
Anthes, R. A., Bernhardt, P. A., Chen, Y., Cucurull, L., Dymond, K. F., Ector, D., Healy, S. B., Ho, S.-P., Hunt, D. C., Kuo, Y.-H., Liu, H., Manning, K., McCormick, C., Meehan, T. K., Randel W. J., Rocken, C. Schreiner, W. S., Socolovskiy, S. V., Syndergaard, S., Thompson, D. C., Trenberth, K. E., Wee, T.-K., Yen, N. L., and Zeng, Z.: The COSMIC/FORMOSAT-3 mission: Early results, B. Am. Meteor. Soc., 89, 313–334, https://doi.org/10.1175/BAMS-89-3-313, 2008.
Anthes, R. A., Marquardt ,C., Ruston B., and Shao H.: Radio Occultation Modeling Experiment (ROMEX): Determining the impact of radio occultation observations on numerical weather prediction, B. Am. Meteor. Soc., 105, E1552–E1568, 2024.
Ao, C.: Effect of ducting on radio occultation measurements: An assessment based on high-resolution radiosonde soundings, Radio Sci., 42, 1–15, https://doi.org/10.1029/2006RS003485, 2007.
Ao, C. O., Meehan, T., Hajj, G., Mannucci, A., and Beyerle, G.: Lower troposphere refractivity bias in GPS occultation retrievals, J. Geophys. Res.-Atmos., 108, 4577, https://doi.org/10.1029/2002JD003216, 2003.
Aparicio, J. M. and Deblonde, G.: Impact of the assimilation of CHAMP refractivity profiles on Environment Canada global forecasts, Mon. Weather Rev., 136, 257–275, https://doi.org/10.1175/2007MWR1951.1, 2008.
Aparicio, J. M. and Laroche, S.: Estimation of the added value of the absolute calibration of GPS radio occultation data for numerical weather prediction, Mon. Weather Rev., 143, 1259–1274, https://doi.org/10.1175/2007MWR1951.1, 2015.
Bowler, N. E.: An assessment of GNSS radio occultation data produced by Spire, Q. J. Roy. Meteor. Soc., 146, 3772–3788, https://doi.org/10.1002/qj.3872, 2020.
Cardinali, C. and Healy, S.: Impact of GPS radio occultation measurements in the ECMWF system using adjoint-based diagnostics, Q. J. Roy. Meteor. Soc., 140, 2315–2320, https://doi.org/10.1002/qj.2300, 2014.
Corner, B. R., Palmer, R. D., and Larsen, M. F.: A new radiosonde system for profiling the lower troposphere, J. Atmos. Ocean. Tech., 16, 828–836, https://doi.org/10.1175/1520-0426(1999)016<0828:ANRSFP>2.0.CO;2, 1999.
Cucurull, L.: Recent impact of COSMIC-2 with improved radio occultation data assimilation algorithms, Weather Forecast., 38, 1829–1847, https://doi.org/10.1175/WAF-D-22-0186.1, 2023.
Cucurull, L. and Derber, J.: Operational implementation of COSMIC observations into NCEP's global data assimilation system, Weather Forecast., 23, 702–711, https://doi.org/10.1175/2008WAF2007070.1, 2008.
Cucurull, L., Derber, J., Treadon, R., and Purser, R.: Assimilation of global positioning system radio occultation observations into NCEP's global data assimilation system, Mon. Weather Rev., 135, 3174–3193, https://doi.org/10.1175/MWR3461.1, 2007.
Eyre, J. R., Bell, W., Cotton, J., English, S. J., Forsythe, M., Healy, S. B., and Pavelin, E. G.: Assimilation of satellite data in numerical weather prediction. Part II: Recent years, Q. J. Roy. Meteor. Soc., 148, 521–556, https://doi.org/10.1002/qj.4228, 2022.
Fu, N. and Li, F.: An Introduction of GNSS Reflectometer Remote Sensing Mission From Yunyao Aerospace Technology Co., Ltd., in: 2021 IEEE Specialist Meeting on Reflectometry using GNSS and other Signals of Opportunity (GNSS+ R), Beijing, China, 14 September 2021, 77–81, https://doi.org/10.1109/GNSSR53802.2021.9617716, 2021.
Gilpin, S., Rieckh, T., and Anthes, R.: Reducing representativeness and sampling errors in radio occultation–radiosonde comparisons, Atmos. Meas. Tech., 11, 2567–2582, https://doi.org/10.5194/amt-11-2567-2018, 2018.
Gorbunov, M. E. and Kirchengast, G.: Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals, Atmos. Meas. Tech., 11, 111–125, https://doi.org/10.5194/amt-11-111-2018, 2018.
Gorbunov, M. E., Vorob'ev, V. V., and Lauritsen, K. B.: Fluctuations of refractivity as a systematic error source in radio occultations, Radio Sci., 50, 656–669, https://doi.org/10.1002/2014RS005639, 2015.
Harnisch, F., Healy, S., Bauer, P., and English, S.: Scaling of GNSS radio occultation impact with observation number using an ensemble of data assimilations, Mon. Weather Rev., 141, 4395–4413, https://doi.org/10.1175/MWR-D-13-00098.1, 2013.
Healy, S. and Thépaut, J.-N.: Assimilation experiments with CHAMP GPS radio occultation measurements, Q. J. Roy. Meteor. Soc., 132, 605–623, https://doi.org/10.1256/qj.04.182, 2006.
Ho, S., Zhou, X., Shao, X., Chen, Y., Jing, X., and Miller, W.: Using the commercial GNSS RO spire data in the neutral atmosphere for climate and weather prediction studies, Remote Sens., 15, 4836, https://doi.org/10.3390/rs15194836, 2023.
Huang, C.-Y., Kuo, Y.-H., Chen, S.-Y., Terng, C.-T., Chien, F.-C., Lin, P.-L., Kueh, M.-T., Chen, S.-H., Yang, M.-J., Wang, C.-J., and Rao, A. S. K. A. V. P.: Impact of GPS radio occultation data assimilation on regional weather predictions, GPS Solut., 14, 35–49, https://doi.org/10.1007/s10291-009-0144-1, 2010.
Kursinski, E., Hajj, G., Schofield, J., Linfield, R., and Hardy, K. R.: Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System, J. Geophys. Res.-Atmos., 102, 23429–23465, https://doi.org/10.1029/97JD01569, 1997.
Lanzante, J. R.: Resistant, robust and non-parametric techniques for the analysis of climate data: Theory and examples, including applications to historical radiosonde station data, Int. J. Climatol., 16, 1197–1226, https://doi.org/10.1002/(SICI)1097-0088(199611)16:11<1197::AID-JOC89>3.0.CO;2-L, 1996.
Le Marshall, J., Xiao, Y., Norman, R., Zhang, K., Rea, A., Cucurull, L., Seecamp, R., Steinle, P., Puri, K., and Le, T.: The beneficial impact of radio occultation observations on Australian region forecasts, Aust. Meteorol. Ocean., 60, 121–125, 2010.
Liu, Y. and Xue, J.: Assimilation of GNSS radio occultation observations in GRAPES, Atmos. Meas. Tech., 7, 3935–3946, https://doi.org/10.5194/amt-7-3935-2014, 2014.
Mapes, B. E., Ciesielski, P. E., and Johnson, R. H.: Sampling errors in rawinsonde-array budgets, J. Atmos. Sci., 60, 2697–2714, https://doi.org/10.1175/1520-0469(2003)060<2697:SEIRB>2.0.CO;2, 2003.
Miller, W. J., Chen, Y., Ho, S.-P., and Shao, X.: Evaluating the impacts of COSMIC-2 GNSS RO bending angle assimilation on Atlantic hurricane forecasts using the HWRF model, Mon. Weather Rev., 151, 1821–1847, https://doi.org/10.1175/MWR-D-22-0198.1, 2023.
Miloshevich, L. M., Vömel, H., Paukkunen, A., Heymsfield, A. J., and Oltmans, S. J.: Characterization and correction of relative humidity measurements from Vaisala RS80-A radiosondes at cold temperatures, J. Atmos. Ocean. Tech., 18, 135–156, https://doi.org/10.1175/1520-0426(2001)018<0135:CACORH>2.0.CO;2, 2001.
O'Carroll, A. G., Eyre, J. R., and Saunders, R. W.: Three-way error analysis between AATSR, AMSR-E, and in situ sea surface temperature observations, J. Atmos. Ocean. Tech., 25, 1197–1207, https://doi.org/10.1175/2007JTECHO542.1, 2008.
Poli, P., Healy, S., Rabier, F., and Pailleux, J.: Preliminary assessment of the scalability of GPS radio occultations impact in numerical weather prediction, Geophys. Res. Lett., 35, L23811, https://doi.org/10.1029/2008GL035873, 2008.
Rennie, M.: The impact of GPS radio occultation assimilation at the Met Office, Q. J. Roy. Meteor. Soc., 136, 116–131, https://doi.org/10.1002/qj.521, 2010.
Rocken, C., Anthes, R., Exner, M., Hunt, D., Sokolovskiy, S., Ware, R., Gorbunov, M., Schreiner, W., Feng, D., Herman, B., Kuo, Y.-H. and Zou, X.: Analysis and validation of GPS/MET data in the neutral atmosphere, J. Geophys. Res.-Atmos., 102, 29849–29866, https://doi.org/10.1029/97JD02400, 1997.
Rieckh, T., Sjoberg J. P., and Anthes R. A.: The Three-Cornered Hat Method for Estimating Error Variances of Three or More Atmospheric Datasets. Part II: Evaluating Radio Occultation and Radiosonde Observations, Global Model Forecasts, and Reanalyses, J. Atmos. Ocean. Tech., 102, 1777–1796, https://doi.org/10.1175/JTECH-D-20-0209.1, 2021.
Ruston, B. and Healy, S.: Forecast impact of FORMOSAT-7/COSMIC-2 GNSS radio occultation measurements, Atmos. Sci. Lett., 22, e1019, https://doi.org/10.1002/asl.1019, 2021.
Schreiner, W., Sokolovskiy, S., Hunt, D., Rocken, C., and Kuo, Y.-H.: Analysis of GPS radio occultation data from the FORMOSAT-3/COSMIC and Metop/GRAS missions at CDAAC, Atmos. Meas. Tech., 4, 2255–2272, https://doi.org/10.5194/amt-4-2255-2011, 2011.
Schreiner, W. S., Weiss, J. P., Anthes, R. A., Braun, J., Chu, V., Fong, J., Hunt, D., Kuo, Y.-H., Meehan, T., Serafino, W., Sjoberg, J., Sokolovskit, S., Talaat, E., Wee, T. K., and Zeng, Z.: COSMIC-2 radio occultation constellation: First results, Geophys. Res. Lett., 47, e2019GL086841, https://doi.org/10.1029/2019GL086841, 2020.
Smith, E. K. and Weintraub, S.: The constants in the equation for atmospheric refractive index at radio frequencies, P. IRE, 41, 1035–1037, https://doi.org/10.1109/JRPROC.1953.274297, 1953.
Sokolovskiy, S.: Effect of superrefraction on inversions of radio occultation signals in the lower troposphere, Radio Sci., 38, 1058, https://doi.org/10.1029/2002RS002728, 2003.
Sokolovskiy, S., Rocken, C., Schreiner, W., and Hunt, D.: On the uncertainty of radio occultation inversions in the lower troposphere, J. Geophys. Res.-Atmos., 115, D22111, https://doi.org/10.1029/2010JD014058, 2010.
Sokolovskiy, S., Schreiner, W., Zeng, Z., Hunt, D., Lin, Y.-C., and Kuo, Y.-H.: Observation, analysis, and modeling of deep radio occultation signals: Effects of tropospheric ducts and interfering signals, Radio Sci., 49, 954–970, https://doi.org/10.1002/2014RS005436, 2014.
Sun, Y., Bai, W., Liu, C., Liu, Y., Du, Q., Wang, X., Yang, G., Liao, M., Yang, Z., Zhang, X., Meng, X., Zhao, D., Xia, J., Cai, Y., and Kirchengast, G.: The FengYun-3C radio occultation sounder GNOS: a review of the mission and its early results and science applications, Atmos. Meas. Tech., 11, 5797–5811, https://doi.org/10.5194/amt-11-5797-2018, 2018.
UCAR COSMIC Program: COSMIC-2 Data Products, UCAR/NCAR – COSMIC [data set], https://doi.org/10.5065/T353-C093, 2019.
Ware, R., Exner, M., Feng, D., Gorbunov, M., Hardy, K., Herman, B., Kuo, Y., Meehan, T., Melbourne, W., Rocken, C., Schreiner, S., Solheim, F., Zou, X., Anthes, R., Businger, S., and Trenberth, K.: GPS sounding of the atmosphere from low Earth orbit: Preliminary results, B. Am. Meteor. Soc., 77, 19–40, https://doi.org/10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2, 1996.
Wickert, J., Beyerle, G., König, R., Heise, S., Grunwaldt, L., Michalak, G., Reigber, Ch., and Schmidt, T.: GPS radio occultation with CHAMP and GRACE: A first look at a new and promising satellite configuration for global atmospheric sounding, Ann. Geophys., 23, 653–658, https://doi.org/10.5194/angeo-23-653-2005, 2005.
Xie, F., Syndergaard, S., Kursinski, E. R., and Herman, B. M.: An approach for retrieving marine boundary layer refractivity from GPS occultation data in the presence of superrefraction, J. Atmos. Ocean. Tech., 23, 1629–1644, https://doi.org/10.1175/JTECH1996.1, 2006.
Xie, F., Wu, D. L., Ao, C. O., Kursinski, E. R., Mannucci, A. J., and Syndergaard, S.: Super-refraction effects on GPS radio occultation refractivity in marine boundary layers, Geophys. Res. Lett., 37, 2010GL043299, https://doi.org/10.1029/2010GL043299, 2010.
Xu, X.: Quality Assessment of YUNYAO GNSS-RO Refractivity Data in the Neutral Atmosphere, Zenodo [data set], https://doi.org/10.5281/zenodo.13374107, 2024.
Xu, X. and Zou, X.: Comparison of MetOp-A/-B GRAS radio occultation data processed by CDAAC and ROM, GPS Solut., 24, 34, https://doi.org/10.1007/s10291-019-0949-5, 2020.
Zou, X. and Zeng, Z.: A quality control procedure for GPS radio occultation data, J. Geophys. Res., 111, D02112, https://doi.org/10.1029/2005JD005846, 2006.
Short summary
Commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellites are generally cheap and can generate a large volume of globally distributed observations in a short period of time. To evaluate the practical application value of these data, we must assess their quality. We evaluate the quality of YUNYAO RO data. By using the “three-cornered hat” method and comparing with data from Metop-C and COSMIC-2, it was found that the YUNYAO GNSS-RO data are of high quality.
Commercial Global Navigation Satellite System (GNSS) radio occultation (RO) satellites are...
