the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Forward operator for polarimetric radio occultation measurements
Katrin Lonitz
Sean Healy
Abstract. Global Navigation Satellite System (GNSS) Polarimetric Radio-Occultation (PRO) observations sense the presence of hydrometeor particles along the ray path by measuring the difference of excess phases in horizontally and vertically polarised carrier waves. As a first step towards using these observations in data assimilation and model diagnostics, a forward operator for GNSS-PRO observable ΦDP (polarimetric differential phase shift) has been implemented by extending the existing two-dimensional forward operator for radio-occultation bending angle observations. Evaluation on heavy precipitation cases showed that the implemented forward operator can simulate very accurately the observed ΦDP in synoptic-scale atmospheric river (AR) cases. For tropical cyclone cases it is more challenging to produce reasonable ΦDP simulations, due to the highly sensitive of ΦDP with respect to displacement of the position of the tropical cyclones. It was also found that snow is the dominant contributor to the simulated ΦDP, and that the ability to compute the ray paths in two dimensions is essential to accurately simulate ΦDP.
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Daisuke Hotta et al.
Status: open (until 22 Oct 2023)
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CC1: 'Comment on amt-2023-132', J. S. Haase, 11 Sep 2023
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This is a very interesting paper contrasting the properties of RO in ARs and Hurricanes. A valuable addition to your review would be the simulations by Murphy et al., 2019, in atmospheric rivers:
Murphy Jr, M. J., Haase, J. S., Padullés, R., Chen, S. H., & Morris, M. A. (2019). The potential for discriminating microphysical processes in numerical weather forecasts using airborne polarimetric radio occultations. Remote Sensing, 11(19), 2268.
Your paper raises an interesting question about the relative contributions of ice and snow. In the case in Murphy et al., 2019, snow was shown to have a significant contribution however the impact of snow and ice was higher in the profile, and decreased below the freezing level. The case illustrated in Murphy et al, is similar to your 2021-01-14 AR case. Why does the dPhi for snow continue to increase approaching the surface in many of your simulations? It would be useful to indicate freezing level on your profiles.
It would be useful to check with Ramon Padulles and Estel Cardellach about the working group that put together the list of AR cases, to which Michael Murphy made a significant contribution and add him along with the rest of the group in the acknowledgements.
Citation: https://doi.org/10.5194/amt-2023-132-CC1 -
AC1: 'Reply on CC1', Daisuke Hotta, 21 Sep 2023
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Dear Prof. Haase,
Thank you very much for your interest in our work and for the valuable comments.
First of all, we would like to apologize for not being aware of Murphy et al. (2019). This study is indeed very relevant to ours, and we will make sure to add discussion on comparison between our results and theirs in the next revision.
We also sincerely apologize for not being aware of contributions from you and Dr. Murphy regarding the selection of AR and TC cases. We will surely add thanks to him in the acknowledgement in the next revision.Thank you for your comment on snow contribution to dPhi extending to lower levels in many of the AR cases. This is indeed a very interesting point that is worth investigated in more detail.
As per suggestion, I replotted all the panels of Figure 1 with the freezing levels included. The revised Figure 1 is attached to this reply. The freezing levels here are taken from the PAZ data in netCDF format, which are in turn reproduced from UCAR's AtmPrf post-processed retrieval data.
Consistent with Murphy et al. (2019), in particular their Figures 11a and 11b, dPhi-contributions from snow tend to peak at or slightly above the freezing levels at the tangent points in many of the cases.
A particularly interesting case is "2020-12-20 AR" (the first, top-left panel), in which the freezing level is well below 1km due to its relatively high latitude of 51.36N (Table 1), which explains why contribution from snow dominates even at the lowest observed level.
We note that the geometry of the ray paths also play a role here because, even when the tangent point height is below the freezing level, the ray paths on both sides, one extending from the tangent point towards the emitting GPS satellite and the other towards the receiver satellite (PAZ), go through higher altitudes where temperature is below freezing. This point is clear, for example, in the plots we showed in Figure 5 of our manuscript which show presence of snow along the ray paths away from the tangent points. In the manuscript, we very briefly mentioned this point in Section 5.1 (the fourth paragraph from page top on page 15). This paragraph only contained a single sentence and we intend to expand this paragraph a little more in our next revision.Once again, we appreciate your comment.
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AC1: 'Reply on CC1', Daisuke Hotta, 21 Sep 2023
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Daisuke Hotta et al.
Daisuke Hotta et al.
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