Comparison of pressure, temperature and specific humidity from COSMIC-2 with radiosonde and ERA5

References

• Anthes, R., et al., 2021. COSMIC-2 radio occultation temperature, specific humidity, and precipitable water in Hurricane Dorian (2019). Terrestrial, Atmospheric and Oceanic Sciences, 32, 1–14. doi:10.3319/TAO.2021.06.14.01
   Web of Science ®Google Scholar
• Anthes, R. and Schreiner, W., 2019. Six new satellites watch the atmosphere over Earth’s equator. Eos, 100 doi:10.1029/2019EO131779.
   Google Scholar
• Chan, K.M. and Wood, R., 2013. The seasonal cycle of planetary boundary layer depth determined using COSMIC radio occultation data. Journal of Geophysical Research: Atmospheres, 118 (22), 12422–12434. doi:10.1002/2013JD020147
   Web of Science ®Google Scholar
• Chen, S.-Y., et al., 2021. An analysis study of FORMOSAT-7/COSMIC-2 radio occultation data in the troposphere. Remote Sensing, 13, 717. doi:10.3390/rs13040717
   Web of Science ®Google Scholar
• Fu, E.J., et al., 2009. Assessing COSMIC GPS radio occultation derived atmospheric parameters using Australian radiosonde network data. Procedia Earth and Planetary Science, 1 (1), 1054–1059. doi:10.1016/j.proeps.2009.09.162
   Google Scholar
• Healy, S.B. and Eyre, J.R., 2000. Retrieving temperature, water vapour and surface pressure information from refractive‐index profiles derived by radio occultation: a simulation study. Quarterly Journal of the Royal Meteorological Society, 126 (566), 1661–1683. doi:10.1256/smsqj.56606
   Web of Science ®Google Scholar
• Hersbach, H., et al., 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146 (730), 1999–2049. doi:10.1002/qj.3803
   Web of Science ®Google Scholar
• Ho, S.-P., et al., 2020. Initial assessment of the COSMIC-2/FORMOSAT-7 neutral atmosphere data quality in NESDIS/STAR using In Situ and satellite data. Remote Sensing, 12 (24), 4099. doi:10.3390/rs12244099
   Web of Science ®Google Scholar
• Iacovazzi, R., et al., 2022. COSMIC-2 soundings impacts on a RO-based NOAA microwave satellite data quality monitoring system. Terrestrial, Atmospheric and Oceanic Sciences, 33 (1), 1. doi:10.1007/s44195-022-00008-0
   Web of Science ®Google Scholar
• Jarraud, M., 2008. Guide to meteorological instruments and methods of observation (WMO-No. 8). Geneva, Switzerland: World Meteorological Organisation, 29.
   Google Scholar
• Jiang, C., et al., 2019. A parallel equivalence algorithm based on MPI for GNSS data processing. Journal of Spatial Science, 66 (3), 1–20.
   Web of Science ®Google Scholar
• Jiang, C., et al., 2020. Evaluation of Zenith tropospheric delay derived from ERA5 data over China Using GNSS observations. Remote Sensing, 12 (4), 663. doi:10.3390/rs12040663
   Web of Science ®Google Scholar
• Johnston, B.R., Randel, W.J., and Sjoberg, J.P., 2021. Evaluation of tropospheric moisture characteristics among COSMIC-2, ERA5 and MERRA-2 in the tropics and subtropics. Remote Sensing, 13 (5), 880. doi:10.3390/rs13050880
   Web of Science ®Google Scholar
• Kishore, P., et al., 2009. Global temperature estimates in the troposphere and stratosphere: a validation study of COSMIC/FORMOSAT-3 measurements. Atmospheric Chemistry and Physics, 9 (156), 897–908. doi:10.5194/acp-9-897-2009
   Google Scholar
• Kursinski, E.R., et al., 1997. Observing Earth’s atmosphere with radio occultation measurements using the global positioning system. Journal of Geophysical Research: Atmospheres, 102 (D19), 23429–23465. doi:10.1029/97JD01569
   Web of Science ®Google Scholar
• Kursinski, E.R., Healy, S.B., and Romans, L.J., 2000. Initial results of combining GPS occultations with ECMWF global analyses within a 1DVar framework. Earth, Planets and Space, 52, 885–892. doi:10.1186/BF03352301.
   Web of Science ®Google Scholar
• Lasota, E., 2021. Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data. Earth, Planets and Space, 73 (1), 1–18. doi:10.1186/s40623-021-01548-4
   Web of Science ®Google Scholar
• Li, S., et al., 2021. Regional Zenith tropospheric delay modeling based on least squares support vector machine using GNSS and ERA5 data. Remote Sensing, 13 (5), 1004. doi:10.3390/rs13051004
   Web of Science ®Google Scholar
• Lilja, A., et al., 2018. Review of the history and future of automatic upper air soundings. TECO-2018, Amsterdam, the Netherlands, 8–11.
   Google Scholar
• Mateus, P., Mendes Virgílio, B., and Plecha, S.M., 2021. HGPT2: an ERA5-based global model to estimate relative humidity. Remote Sensing, 13 (11), 2179. doi:10.3390/rs13112179
   Web of Science ®Google Scholar
• Neiman, P.J., et al., 2008. Diagnosis of an intense atmospheric river impacting the Pacific Northwest: storm summary and offshore vertical structure observed with COSMIC satellite retrievals. Monthly Weather Review, 136 (11), 4398–4412. doi:10.1175/2008MWR2550.1
   Web of Science ®Google Scholar
• Pedatella, N., 2022. Ionospheric variability during the 2020-2021 SSW: COSMIC-2 observations and WACCM-X simulations. Atmosphere, 13 (3), 3. doi:10.3390/atmos13030368
   Web of Science ®Google Scholar
• Pedatella, N.M. and Anderson, J.L., 2022. The impact of assimilating COSMIC-2 observations of electron density in WACCMX. Journal of Geophysical Research: Space Physics, 127, 1.
   Web of Science ®Google Scholar
• Poli, P., Joiner, J., and Kursinski, E.R., 2002. 1DVAR analysis of temperature and humidity using GPS radio occultation refractivity data. Journal of Geophysical Research: Atmospheres, 107 (D20), ACL 14-1-ACL 14–20. doi:10.1029/2001JD000935
   Web of Science ®Google Scholar
• Rieckh, T., Sjoberg, J.P., and Anthes, R.A., 2021. 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. Journal of Atmospheric and Oceanic Technology, 38 (10), 1777–1796.
   Web of Science ®Google Scholar
• Rocken, C., et al., 2000. COSMIC system description. Terrestrial, Atmospheric and Oceanic Sciences, 11 (1), 21–52. doi:10.3319/TAO.2000.11.1.21(COSMIC)
   Web of Science ®Google Scholar
• Schreiner, W., et al., 2006, Estimates of the precision of GPS radio occultations in the neutral atmosphere from the COSMIC/FORMOSAT-3 mission. AGU Fall Meeting Abstracts.
   Google Scholar
• Schreiner, W.S., et al., 2020. COSMIC‐2 radio occultation constellation: first results. Geophysical Research Letters, 47 (4), e2019GL086841.
   Web of Science ®Google Scholar
• Shao, X., et al., 2021a. Comparison of COSMIC-2 radio occultation retrievals with RS41 and RS92 radiosonde humidity and temperature measurements. Terrestrial, Atmospheric & Oceanic Sciences, 32. doi:10.3319/TAO.2021.12.30.02
   Web of Science ®Google Scholar
• Shao, X., et al., 2021b. Consistency and stability of SNPP ATMS microwave observations and COSMIC-2 radio occultation over oceans. Remote Sensing, 13 (18), 3754. doi:10.3390/rs13183754
   Web of Science ®Google Scholar
• Shen, Z., et al., 2021. Assessment of the homogeneity of long-term multi-mission RO-based temperature climatologies. Remote Sensing, 13 (12), 2278. doi:10.3390/rs13122278
   Web of Science ®Google Scholar
• Sokolovskiy, S., 2003. Effect of superrefraction on inversions of radio occultation signals in the lower troposphere. Radio Science, 38 (3), 3. doi:10.1029/2002RS002728
   Web of Science ®Google Scholar
• Sokolovskiy, S., et al., 2010. On the uncertainty of radio occultation inversions in the lower troposphere. Journal of Geophysical Research: Atmospheres, 115 (D22), D22. doi:10.1029/2010JD014058
   Google Scholar
• Sun, B., et al., 2010. Comparing radiosonde and COSMIC atmospheric profile data to quantify differences among radiosonde types and the effects of imperfect collocation on comparison statistics. Journal of Geophysical Research, 115, D23. doi:10.1029/2010JD014457
   Web of Science ®Google Scholar
• Veenus, V., et al., 2022. A comparison of temperature and relative humidity measurements derived from COSMIC-2 radio occultations with radiosonde observations made over the Asian summer monsoon region. Remote Sensing Letters, 13 (4), 394–405. doi:10.1080/2150704X.2022.2033345
   Web of Science ®Google Scholar
• Vergados, P., et al., 2015. On the comparisons of tropical relative humidity in the lower and middle troposphere among COSMIC radio occultations and MERRA and ECMWF data sets. Atmospheric Measurement Techniques, 8 (4), 1789–1797. doi:10.5194/amt-8-1789-2015
   Web of Science ®Google Scholar
• Wang, B., 2002a. The time–space structure of the Asian–Pacific summer monsoon: a fast annual cycle view. Journal of Climate, 15 (15), 2001–2019.
   Web of Science ®Google Scholar
• Wang, B., 2002b. Rainy season of the Asian–Pacific summer monsoon. Journal of Climate, 15 (4), 386–398. doi:10.1175/1520-0442(2002)015<0386:RSOTAP>2.0.CO;2
   Web of Science ®Google Scholar
• Wang, J., et al., 2007. A near‐global, 2‐hourly data set of atmospheric precipitable water from ground‐based GPS measurements. Journal of Geophysical Research: Atmospheres, 112, D11.
   Web of Science ®Google Scholar
• Wang, S., et al., 2020. Establishment of atmospheric weighted mean temperature model in the polar regions. Advances in Space Research, 65 (1), 518–528. doi:10.1016/j.asr.2019.10.001
   Web of Science ®Google Scholar
• Wang, S., et al., 2022. Intercomparison of total precipitable water derived from COSMIC-2 and three different microwave radiometers over the ocean. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–10.
   Web of Science ®Google Scholar
• Wang, B.R., Liu, X.Y., and Wang, J.K., 2013. Assessment of COSMIC radio occultation retrieval product using global radiosonde data. Atmospheric Measurement Techniques, 6 (4), 1073–1083. doi:10.5194/amt-6-1073-2013
   Web of Science ®Google Scholar
• Wee, T.-K., et al., 2022. Atmospheric GNSS RO 1D-Var in use at UCAR: description and validation. Remote Sensing, 14 (21), 5614. doi:10.3390/rs14215614
   Web of Science ®Google Scholar
• Wilson, R., Dalaudier, F., and Luce, H., 2011. Can one detect small-scale turbulence from standard meteorological radiosondes? Atmospheric Measurement Techniques, 4 (18), 795–804. doi:10.5194/amt-4-795-2011
   Google Scholar
• Yamazaki, Y., et al., 2022. Examining the wind shear theory of Sporadic E with ICON/MIGHTI winds and COSMIC-2 Radio 2 occultation data. Geophysical Research Letters, 49 (1), 1. doi:10.1029/2021GL096202
   PubMed Web of Science ®Google Scholar
• Yao, Y.B., et al., 2013. A new global zenith tropospheric delay model GZTD. Chinese Journal of Geophysics, 56 (7), 2218–2227.
   Web of Science ®Google Scholar
• Yao, Y.B., Zhu, S., and Yue, S.Q., 2012. A globally applicable, season-specific model for estimating the weighted mean temperature of the atmosphere. Journal of Geodesy, 86 (12), 1125–1135. doi:10.1007/s00190-012-0568-1
   Web of Science ®Google Scholar