| 25 Nov 2025
Processing Multiple GNSS RO Data Using FSI and ROPP: Results from the ROMEX
Abstract. Global Navigation Satellite System (GNSS) Radio Occultation (RO) is a vital technique in atmospheric remote sensing, providing all-weather, high-resolution vertical observations that support numerical weather prediction (NWP) and atmospheric research. To enhance understanding of GNSS RO processing uncertainties and inter-algorithm consistency, NOAA/STAR developed an independent RO inversion algorithm based on the Full Spectrum Inversion (FSI) technique to derive bending angle and refractivity profiles from excess phase data. As part of the international Radio Occultation Modeling Experiment (ROMEX), endorsed by the International Radio Occultation Working Group (IROWG), STAR’s FSI results were systematically compared with outputs from the community standard Radio Occultation Processing Package (ROPP) and EUMETSAT datasets. Leveraging multi-GNSS RO observations from both commercial and government-funded missions, the study evaluates consistency across processing approaches using the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) as the reference and structural differences against the three-dataset mean for the ROMEX period. Results reveal high overall agreement, while identifying variations linked to the signal-to-noise ratio (SNR) and mission characteristics, providing critical insights for interpreting ROMEX forecast impact studies and improving GNSS RO data assimilation systems.
The paper is interesting as a source of homogenized structural uncertainty studies, where the ROMEX database has been used to provide a coherent sampling of different source data. It is relevant to be aware of difficult to reduce errors, sometimes systematic, and that some atmospheric circumstances lead to retrievals of better quality, while others are quite dependent on apparently minor details of the retrieval. The issue of the retrieval uncertainty is still insufficiently addressed.
The subject is thus interesting and a good application of the ROMEX database. There however are a number of corrections and clarifications that I believe are important, as detailed below.
L150 and Tab1: The volume of Tianmu is indeed small within the ROMEX ensemble. However the average I have, admittedly on a superficial count, is of the order of 300 profiles/day, rather than 100/day. Please verify.
L213, etc but also elsewhere: The authors mention interpolation of certain quantities onto a common sampling grid. Since certain quantities such as position are non-trivial to interpolate to the required accuracy, please indicate summarily some details about the sufficiency of the interpolation. A quadratic is mentioned. Is a quadratic interpolation sufficient at 1Hz?
L234: “intersection of the line-of-sight with the WGS84 ellipsoid”. The authors seem to be defining the SLTA=0 line and tangent point. This straight line between satellites is not a “line-of-sight”, as the propagation trajectory is bent. Also, this line at this moment is not intersecting the ellipsoid, but is tangent to it. Please adjust the wording.
L235: Presumably also the center of curvature has to be computed, and the coordinate system adjusted to be centered at that point.
L257: Please provide some references to some of the existing studies around this truncation. This is a significantly complex issue, and the referencing should indicate its non-trivial character.
L272-296: The MSIS-90 is ok for the temperature and to establish the approximate range in moist refractivity. However, one of the major issues is the presence of large vertical gradients of relative humidity, which may lead to large bending angles, superrefraction, etc. This model phase does not account for this possibility, nor seems to be considering the range of moistures. Is this model only identifying bit slips? Or will it introduce (false positive) bit corrections when there are RH gradients?
L300: There are many parameters here. Although I agree that filtering is to some extent desirable, are these parameters optimal? Have other filtering parameters been tested, with the optimal results chosen? Under which criteria are the mentioned parameters optimal?
L324: With MSP there may be a number of subcomponents that are stationary. There may be from none under anomalous propagation to several under multipath, although there is often just one dominant component. Should not L327 (eq 9) be a sum of perhaps several stationary components? Or has it been assumed that we focus only in the dominant? Does the text apply only to a case where there is one dominant component? Or does eq 9 express only one of the stationary components, in case there were several? Please clarify.
L363: Does this projection modify only the satellite orbits? Or, since the radius and center of curvature vary during the occultation, are these somehow reprojected also?
L386: Does this criterion reject the low troposphere?
L399, eq 17: Aren’t you using alpha in a different sense wrt before? Before it was the bending angle, while here it would suggest that alpha is the instantaneous bending angle minus the average bending angle (thus something like alpha-). Please make sure that the variable naming is consistent.
L417: Please reference Eq 18, as it is not the definition of the neutral bending angle, but a good practical approximation to obtain it from alpha L1 and alpha L2 (Vorobev & Krasilnikova, 1994).
L441: Could this climatological data be specified?
L456…: Is this procedure valid only in hindcast? Or can it be applied in Near-Real-Time? (Availability of ERA5). It is also unclear in L457 if sigma_Year means the yearly variability of O-B, or the variability of B. In L466, thus later, it appears that it is O-B. Besides, is this statistic global? It could have been regional, by latitude band… Please clarify.
L470: Is this intended to analyze only height between 8-40km (or 10-40). The most interesting part is below 5 km. Is the method inappropriate here?
L512: Comment here if the ionosphere was particularly active during the study period, thus if these are average circumstances, worse-case, etc.
L514…: Comment here on the typical SNR of the different missions.
L581: Isn’t there a GO-to-FSI switch at a given altitude? 25km? It is mentioned again below, but would it not be appropriate to remind it somewhere in this section?
L654…: The average and std between methods are shown, but not compared to the O-retrieval. Would it not be appropriate to indicate the fraction of the O-B differences that are explained by structural uncertainties of the retrieval? That is, are the O-B dominated by the retrieval method? Or is the exact retrieval a secondary source of uncertainty? This fraction is likely a function of altitude.
L840: Does the FSI being presented restrict the vertical range where it is practical to apply? Some issues mentioned above would suggest that the procedure may be inappropriate for the low troposphere. Is it the case? Or the method does provide an alternative estimation at all practical altitudes?