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
Related authors
No articles found.
Yongzhu Liu, Xiaoye Zhang, Wei Han, Chao Wang, Wenxing Jia, Deying Wang, Zhaorong Zhuang, and Xueshun Shen
Geosci. Model Dev., 18, 4855–4876, https://doi.org/10.5194/gmd-18-4855-2025, https://doi.org/10.5194/gmd-18-4855-2025, 2025
Short summary
Short summary
In order to investigate the feedbacks of chemical data assimilation on meteorological forecasts, we developed a strongly coupled aerosol–meteorology four-dimensional variational (4D-Var) assimilation system, CMA-GFS-AERO 4D-Var, based on the framework of the incremental analysis scheme of the China Meteorological Administration Global Forecasting System (CMA-GFS) operational global numerical weather model. The results show that assimilating BC (black carbon) observations can generate analysis increments not only for BC but also for atmospheric variables.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Short summary
Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Xiaozhong Cao, Qiyun Guo, Haowen Luo, Rongkang Yang, Peng Zhang, Jianping Guo, Jincheng Wang, Die Xiao, Jianping Du, Zhongliang Sun, Shijun Liu, Sijie Chen, and Anfan Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2012, https://doi.org/10.5194/egusphere-2025-2012, 2025
Short summary
Short summary
This study aims to introduce in-situ profiling techniques and cost-effective technology for upper-air observation—the Round-trip Drifting Sounding System (RDSS)—which reduces costs relative to intensive sounding and achieves three sounding phases: Ascent-Drift-Descent (ADD). The RDSS not only provides additional data for weather analysis and numerical prediction models but also makes substantial contributions to targeted observations.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
Short summary
Short summary
Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
Minghua Liu, Wei Han, Yunfan Yang, Haofei Sun, and Ruoying Yin
EGUsphere, https://doi.org/10.5194/egusphere-2025-680, https://doi.org/10.5194/egusphere-2025-680, 2025
Short summary
Short summary
This research develops a machine learning approach to estimate atmospheric temperature and humidity profiles using satellite and weather data. The results showed that our method could accurately retrieve profiles with a high degree of precision. However, we found some limitations in very humid conditions, suggesting that further improvements to the model are needed. Our findings could help enhance the reliability of atmospheric measurements and contribute to better weather predictions.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
Short summary
Short summary
The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Yunfan Yang, Wei Han, Haofei Sun, Jun Li, Jiapeng Yan, and Zhiqiu Gao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-175, https://doi.org/10.5194/amt-2024-175, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Our research improves satellite-based precipitation monitoring by using deep learning to reconstruct radar observations from passive microwave radiances. Active radar is costly, so we focus on a more accessible approach. Using data from the FengYun-3G satellite, we successfully tracked severe weather like Typhoon Khanun and heavy rainfall in Beijing in 2023. This method enhances global precipitation data and helps better understand extreme weather.
Hejun Xie, Lei Bi, and Wei Han
Geosci. Model Dev., 17, 5657–5688, https://doi.org/10.5194/gmd-17-5657-2024, https://doi.org/10.5194/gmd-17-5657-2024, 2024
Short summary
Short summary
A radar operator plays a crucial role in utilizing radar observations to enhance numerical weather forecasts. However, developing an advanced radar operator is challenging due to various complexities associated with the wave scattering by non-spherical hydrometeors, radar beam propagation, and multiple platforms. In this study, we introduce a novel radar operator named the Accurate and Efficient Radar Operator developed by ZheJiang University (ZJU-AERO) which boasts several unique features.
Jianbin Zhang, Zhiqiu Gao, Yubin Li, and Yuncong Jiang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-187, https://doi.org/10.5194/gmd-2023-187, 2023
Preprint withdrawn
Short summary
Short summary
This study developed a deep learning model called CNN-BiLSTM-AM for convective weather forecasting. The results showed that the CNN-BiLSTM-AM model outperformed traditional machine learning algorithms in predicting convective weather, with higher accuracy as the forecast lead time increased. When compared to subjective forecasts by forecasters, the objective approach of the CNN-BiLSTM-AM model also demonstrated advantages in various metrics.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Liang Wang, Bingcheng Wan, Shaohui Zhou, Haofei Sun, and Zhiqiu Gao
Geosci. Model Dev., 16, 2167–2179, https://doi.org/10.5194/gmd-16-2167-2023, https://doi.org/10.5194/gmd-16-2167-2023, 2023
Short summary
Short summary
The past 24 h TC trajectories and meteorological field data were used to forecast TC tracks in the northwestern Pacific from hours 6–72 based on GRU_CNN, which we proposed in this paper and which has better prediction results than traditional single deep-learning methods. The historical steering flow of cyclones has a significant effect on improving the accuracy of short-term forecasting, while, in long-term forecasting, the SST and geopotential height will have a particular impact.
Zexia Duan, Zhiqiu Gao, Qing Xu, Shaohui Zhou, Kai Qin, and Yuanjian Yang
Earth Syst. Sci. Data, 14, 4153–4169, https://doi.org/10.5194/essd-14-4153-2022, https://doi.org/10.5194/essd-14-4153-2022, 2022
Short summary
Short summary
Land–atmosphere interactions over the Yangtze River Delta (YRD) in China are becoming more varied and complex, as the area is experiencing rapid land use changes. In this paper, we describe a dataset of microclimate and eddy covariance variables at four sites in the YRD. This dataset has potential use cases in multiple research fields, such as boundary layer parametrization schemes, evaluation of remote sensing algorithms, and development of climate models in typical East Asian monsoon regions.
Jianbing Jin, Mijie Pang, Arjo Segers, Wei Han, Li Fang, Baojie Li, Haochuan Feng, Hai Xiang Lin, and Hong Liao
Atmos. Chem. Phys., 22, 6393–6410, https://doi.org/10.5194/acp-22-6393-2022, https://doi.org/10.5194/acp-22-6393-2022, 2022
Short summary
Short summary
Super dust storms reappeared in East Asia last spring after being absent for one and a half decades. Accurate simulation of such super sandstorms is valuable, but challenging due to imperfect emissions. In this study, the emissions of these dust storms are estimated by assimilating multiple observations. The results reveal that emissions originated from both China and Mongolia. However, for northern China, long-distance transport from Mongolia contributes much more dust than Chinese deserts.
Hamidreza Omidvar, Ting Sun, Sue Grimmond, Dave Bilesbach, Andrew Black, Jiquan Chen, Zexia Duan, Zhiqiu Gao, Hiroki Iwata, and Joseph P. McFadden
Geosci. Model Dev., 15, 3041–3078, https://doi.org/10.5194/gmd-15-3041-2022, https://doi.org/10.5194/gmd-15-3041-2022, 2022
Short summary
Short summary
This paper extends the applicability of the SUEWS to extensive pervious areas outside cities. We derived various parameters such as leaf area index, albedo, roughness parameters and surface conductance for non-urban areas. The relation between LAI and albedo is also explored. The methods and parameters discussed can be used for both online and offline simulations. Using appropriate parameters related to non-urban areas is essential for assessing urban–rural differences.
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...
