the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Aug 2026
Global Sampling Characteristics and Coverage Complementarity of 12 GNSS Radio Occultation Constellations
Abstract. This study uses observations from COSMIC-2, FY-3, GRACE-C/D, KOMPSAT-5, MetOp-A/C, PAZ, PlanetiQ, Sentinel-6A, Spire, TerraSAR-X/TanDEM-X, TM-1, and YY-1 during 1–7 January 2026 to compare their spatial distributions, grid coverage, local-time sampling, and retrieval accuracy; it also assesses the benefits of multi-mission integration and the potential effects of COSMIC-2 retirement on the integrated observing capability. The results show that orbital configuration determines latitudinal and local-time sampling, whereas event abundance and constellation size primarily control grid coverage. Large constellations such as YY-1 and TM-1 provide the strongest global continuity, while missions with different orbital inclinations offer complementary sampling across latitude bands. Combining all missions substantially reduces spatial gaps and provides near-global coverage at 1.0° and 2.0° resolutions. Within 6 h windows, joint coverage reaches 70.98 % at 2.0° resolution. The combined observations also broaden local-time sampling, whereas COSMIC-2 remains particularly important for equatorial coverage. Therefore, the retirement of COSMIC-2 would reduce equatorial sampling capability and should be considered in the development of future multi-mission GNSS-RO observing systems. Comparisons with ERA5 indicate broadly consistent retrieval accuracy among most missions between 10 and 35 km, with larger differences in the lower troposphere. These findings demonstrate the value of integrating missions with diverse orbital configurations and highlight the need to sustain complementary sampling after the retirement of key missions.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-4663', Anonymous Referee #1, 24 Aug 2026
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RC2: 'Comment on egusphere-2026-4663', Anonymous Referee #2, 31 Aug 2026
This paper is a useful summary of the orbits and spatiotemporal coverage of almost all of the radio occultation (RO) missions, starting with GPS-MET in 1995 through the commercial missions in 2026. The exception is the absence of GeoOptics (launch in 2017, Chang et al. Earth, Planets and Space (2022) https://doi.org/10.1186/s40623-022-01667-6 ). Although the paper has the potential to be a good contribution to the scientific literature, it can be improved in several ways.
The Abstract states that the paper “assesses the benefits of multi-mission integration and the potential effects of COSMIC- 2 retirement on the integrated observing capability.” However, it does not discuss the impact (benefit) of RO observations in the three application areas of RO-space weather, climate, and weather prediction and why complete coverage of Earth every day and all local times is important in these applications. Therefore, it is unclear why differences in space and local time are important, what the benefits are of multi-missions, or what the impacts of the loss of COSMIC-2 will be. For example, do the gaps over the Russia-Ukraine region make any difference in the accuracy of NWP forecasts or climate data records? Are the extremely small differences between the combined 11 missions excluding COSMIC-2 (black lines) and combined 12 missions including COSMIC-2 (red lines) in Fig. 10 important? Why is coverage of all local times important?
The brief section on the comparative performance of all the RO missions is weak and misleading. The details of the ROPP method for calculating bending angles from ERA5, including equations 8-10 unnecessary, because ROPP is well documented. A much shorter summary of the ROPP method with references would be sufficient.
The RMS differences between RO and ERA5 include the bias differences, which is also shown separately. Therefore, it would be preferable to show the standard deviation of differences instead of RMS differences.
I assume the ERA5 data are interpolated to the positions and times of the RO data, but this is not stated. The most important issue, however, is the large sampling differences among the different RO missions. It is well known that the biases and standard deviation of errors in RO vary strongly with latitude, as well as with longitude. Both biases and uncertainties are much larger in low latitudes compared to high latitudes; therefore, comparing COSMIC-2 with the other missions globally as is done here is meaningless. I suggest eliminating Section 5 entirely.
Minor comments:
- Adding spacing between paragraphs and references would make the paper easier to read.
- A reference to the Radio Occultation Modeling Experiment (ROMEX https://irowg.org/ro-modeling-experiment-romex/ ) would be useful. A special issue of AMT that describes the benefits of increasing numbers of RO observations is available at https://amt.copernicus.org/articles/special_issue1367.html
- Some of references are not ideal or the best available, e.g. Jaeggi et al., 2021 for COSMIC-2 line 84. More detailed peer-reviewed references are available, for example Schreiner et al. 2020 https://doi.org/10.1029/2019GL086841.
- Some of the figures need more interpretation. For example, what are the different colors in Fig. 9? Why are the very small differences between the four parts of Fig. 9 important?
- Section 4.3—impact of C2 retirement. What impact would this have on weather forecasts, climate, and space weather?
Citation: https://doi.org/10.5194/egusphere-2026-4663-RC2
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General comments:
This paper compares the spatial and temporal coverage of the globe by a dozen different radio occultation platforms. This work is likely to be of interest to the community as COSMIC-2 decommissioning evaluations are currently ongoing. The paper also has information on some commercial RO platforms that are not widely available, and the characteristics of these platforms may be of interest to the community.
The paper is clearly written, and the figures are pertinent to the text and easy to interpret. I have only one major comment and a handful of minor comments.
Specific comments:
Major comment:
1. Are lat/lon bin boxes the best choice for this analysis when the areal size of the boxes has extreme variation on a global scale? Consider whether it might be better to use boxes of equivalent areal coverage for some of the calculations. If this is not possible, please consider if there are any options for presentation/display or calculation that can help compensate for the different spatial coverage of the binning boxes with latitude.
Minor comments:
1. Can you make any statement of whether the 1-week period used is indicative of longer periods?
2. ERA reanalysis was used to evaluate observation errors, but the reanalysis may have ingested these obs - what if some were used and others were not? Please check if any of these data types were used in the ERA reanalysis and note that in the text.
3. Figure 9: Consider if this type of diagram is the best way to present this data? the wedges appear as areas, not as lengths, so 50% appears much more than twice as much as 25%.
4. Consider for map plots using an equal-area projection such as Gall-Peters. This could help in part also with the major comment.
5. Regarding the gap in observations over Russia/Ukraine, there is a paper on this: D.L. Wu, "GNSS signal jamming as observed from radio occultation", IEEE J of Selected Topics in Applied Earth Observations and Remote Sensing, 2024.
Technical corrections:
L 180: n_i(r) should be in math style
Equation 10 ; is this r or r_t?