the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2024
The PAZ Polarimetric Radio Occultation Research Dataset for Scientific Applications
Abstract. Polarimetric Radio Occultations (PRO) represent an augmentation of the standard Radio Occultation (RO) technique that provides precipitation and clouds vertical information along with the standard thermodynamic products. A combined dataset that contains both the PRO observable and the RO standard retrievals, the resPrf, has been developed with the aim to foster the use of these unique observations and to fully exploit the scientific implication of having information about vertical cloud structures with intrinsically collocated thermodynamic state of the atmosphere. This manuscript describes such dataset and provides detailed information on the processing of the observations. The procedure followed at UCAR to combine both H and V observations to generate the equivalent profiles as in standard RO missions is described in detail, and the obtained refractivity is shown to be of equivalent quality as that from TerraSAR-X. The steps of the processing of the PRO observations are detailed, derived products such as the top-of-the-signal are described, and validation is provided.
Furthermore, the dataset contains the simulated ray-trajectories for the PRO observation, and co-located information with global satellite-based precipitation products, such as merged rain rate retrievals or passive microwave observations. These co-locations are used for further validation of the PRO observations and they are also provided within the resPrf profiles for additional use. It is also shown how accounting for external co-located information can improve significantly the effective PRO horizontal resolution, tackling one of the challenges of the technique.
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RC1: 'Comment on essd-2024-150', Anonymous Referee #1, 07 Jul 2024
Review comments for “the PAZ polarimetric radio occultation research dataset for scientific applications” by Padulles et al.
This paper summarizes the PAZ satellite core Level 2 product dataset and its usability for scientific applications. The original scientific goal for the PAZ mission was to demonstrate polarimetric RO can be used to retrieve heavy precipitation. Research over the years went beyond the original goal to discover the signal association to clouds, and the dataset described in this manuscript is carefully crafted to be particularly suitable for studying cloud-precipitation-thermodynamic interactions. The dataset contains three main groups: (1) RO and phase difference profiles; (2) rays for limb sounding that accounts for earth’s rotation as well; (3) collocated cloud and precipitation products from other satellites, including the IMERG precipitation rate, 10.8 um infrared band brightness temperature (TB) and passive microwave (PMW) TBs. The second group is particularly important for fair study the cloud-precipitation-thermodynamic interactions by synergizing limb and nadir soundings together. Several examples are provided in the manuscript to showcase how this dataset could be useful and providing high-quality measurements.
This is a high quality dataset, not only the data quality, but more importantly the scientific quality. Being able to sensing thermodynamic structures within clouds and precipitation, this dataset contains high potential to help tackling some of the long-standing challenges that the majority of current spaceborne passive remote sensing products having in cloudy and precipitating scenes. Even better, the producers of this dataset (i.e., key authors on this paper), took careful consideration of caveats matching limb measurements with nadir measurements by providing the ray paths and matched TBs along the rays. Hence I strongly support the publication of this dataset to the public.
There are some minor issues that I think should be addressed before the acceptance though, mainly to clear out some parts that can cause confusions. Also, as a standard procedure to publish a dataset, the independent validation part feels a bit too weak in the current version. It would be really helpful to have some additional water vapor and temperature sounding comparison results to show in the revised version against other available RO products (global mean as well as regional error distributions) to assess the overall quality of PAZ’s Prf products.
Minor comments:
L124: what’s a Doppler model and why you need it for forward modeling (?) or retrieval purpose?
General procedures from Step 1 to Step 12 around Line 130: there’s no discussion regarding the U and V components when you do the decomposition. Are they negligible or your receiver can always perfectly align with the I & Q component? This is never clear to me. Also, it is never mentioned in this manuscript how you tease out possible orientation rotation due to magnetic field in Earth’s upper atmosphere when the RO rays pathed through?
Fig. 2: It’s not clear if resPrf depends on input from UCAR wetPrf2? Does resPrf contain temperature and water vapor retrievals or just the bending angle (or refractivity)?
Line 195: how do you tell if a suddent delta_Phi increase is not due to intersecting heavy precip, but rather untrustworthy measurements?
Section 4.1, paragraph 1: this paragraph is really confusing. Up till reading this paragraph that I started to realize that your resPrf is different from the standard atmPrf and wetPrf products that UCAR produce for the PAZ mission. Could you provide a percentage rate how many PAZ RO measurements pass the QC and can successfully generate atmPrf and wetPrf at UCAR?
Line 240: this is another major part that confuses me: so does the “coll” group contain the imagery from collocated nadir sensor measurements (PMW TB, IR TB, IMERG) or the sensor TBs are instead projected to the ray paths as shown in Fig. 4?
Line 260: one caveat for this assumption is that there’s no temperature inversion layer and no large perturbations (e.g., caused by gravity wave near the convective clouds) that cause artifacts in your decision process, correct? Please clarify here.
Line 271: does your “coll” product also include the view-angle from cross-track scanning PMW measurements? TB needs to be corrected for the slantwise view-angle or otherwise the climatology can be nontrivially biased.
Fig. 5: caption misses to describe what is N and what is Nmodel. Reading the context around Fig. 5 making me wonder about the temperature and water vapor retrieval qualities and whether that’s part of this dataset that needs to be validated or assessed at least.
Fig. 6: caption misses to explain the grey dashed lines.
Section 6.2.1: you misses a very important reason to explain the underestimation of CTH from your method: for cloud/snow ice, delta_phi is only large when particles are dominantly horizontally oriented, which not happens in the convective core but in anvils (which is below the convective core) or stratiform precipitating region. Please add in this explanation as well if seeing appropriate.
Fig. 7: change “geometry” to “after geometry correction”.
Line 396: how do you deal with the cloud fraction (CF) in a grid box?
Line 414: I think it would better to have some discussions regarding data assimilation of the Delta_phi to plant a seed for future applications, especially considering that your group had done some preliminary work regarding this aspect to make PAZ measurements more useful for NWP.
Citation: https://doi.org/10.5194/essd-2024-150-RC1 - AC1: 'Reply on RC1', Ramon Padullés, 14 Oct 2024
-
RC2: 'Comment on essd-2024-150', Anonymous Referee #2, 22 Aug 2024
Review of the manuscript essd-2024-150 titled “the PAZ polarimetric radio occultation research dataset for scientific applications” by Padulles et al.
The manuscript is a description paper of a new data set of polarimetric radio occultation (PRO) observations produced by PAZ satellite.
PRO observations, like the regular RO bending angle observations, contain information on the atmosphere along the ray path that connects the transmitter GNSS satellite and the receiver LEO satellite. However, PRO observations differ significantly from the regular RO observations in that, unlike the regular RO measurements like bending angles, local spherical symmetry approximation is not justifiable for hydrometeor distributions that are sensed by PRO measurements. To analyze PRO data it is thus primordial to have accurate 3D information of the ray trajectories, but such information is only available after performing ray tracing, which is difficult and hindered the use of PRO observations by wider community.
The new data set presented in this paper is ground breaking in resolving this situation by providing pre-computed ray trajectories along with the PRO measurements. This is an important contribution that is expected to foster and facilitate the use of PRO data from wider users. The paper is also very well organized and well written. I have only minor suggestions that the authors can choose to incorporate or not at their discretion.Minor comments:
- line 156 " only that in the PAZ case...": maybe you meant to write "the only difference bing that ..."- line 158 and elsewhere: The use of the word "differentitian" to mean the subtraction of V-pol excess phase from H-pol excess phase is confusing. Please consider using different wording, like "differencing" for example.
- Paragraph starting at line 165: It would be useful to include some short explanation about how geometric and wave optics are different here.
- The first paragraph of section 4.1: I assume, from the description in this paragraph, that the 1D (vertical) refractivity profile from the UCAR retrieval is used to compute the ray tracing assuming that the profile is horizontally uniform. If so, making this point explicit in the text and, if possible, discussing any limitation from this approach would be useful for the data users.
- line 219 "...one each 0.1km": Put "apart" after 0.1km.
- line 244 "Likewise": Replace with "Like" or "As with".
- line 254 "sense": Replace with "sensed"
- line 284, 341 and elsewhere, "specially": Replace with "especially" (or rephrase).
- line 307 "equal to" : consider replacing this with "close to" because, even when there is no precipitation at the ground, there can be hydrometeors aloft.
- line 328 "arount" : replace with "around"
- Figure 6: Please give precise definition of the "Height". I assume it is the tangent height which is also the lowest height of the ray. If so, please explicitly state so.
- Paragraph starting at line 343: In this paragraph the mean and standard deviation of delta Phi is used, but the statistics is taken over which samples are not clearly stated. Please clarify.
- line 406 "maximum WC": not clear it is maximum with respect to what. I guess it is the largest values of WC over the vertical direction within each colmn?
- Section 7: An important caveat in interpreting the results shown is section is that, in ERA5, hydrometeor water content associated with cumulus parametrization is not provided. As a result, the KDP equivalent computed solely from large-scale clouds will inevitably underestimate the observed KDP (and hence delta Phi). This will justify the model's underestimation shown in Figure 8d.
Citation: https://doi.org/10.5194/essd-2024-150-RC2 - AC2: 'Reply on RC2', Ramon Padullés, 14 Oct 2024
Status: closed
-
RC1: 'Comment on essd-2024-150', Anonymous Referee #1, 07 Jul 2024
Review comments for “the PAZ polarimetric radio occultation research dataset for scientific applications” by Padulles et al.
This paper summarizes the PAZ satellite core Level 2 product dataset and its usability for scientific applications. The original scientific goal for the PAZ mission was to demonstrate polarimetric RO can be used to retrieve heavy precipitation. Research over the years went beyond the original goal to discover the signal association to clouds, and the dataset described in this manuscript is carefully crafted to be particularly suitable for studying cloud-precipitation-thermodynamic interactions. The dataset contains three main groups: (1) RO and phase difference profiles; (2) rays for limb sounding that accounts for earth’s rotation as well; (3) collocated cloud and precipitation products from other satellites, including the IMERG precipitation rate, 10.8 um infrared band brightness temperature (TB) and passive microwave (PMW) TBs. The second group is particularly important for fair study the cloud-precipitation-thermodynamic interactions by synergizing limb and nadir soundings together. Several examples are provided in the manuscript to showcase how this dataset could be useful and providing high-quality measurements.
This is a high quality dataset, not only the data quality, but more importantly the scientific quality. Being able to sensing thermodynamic structures within clouds and precipitation, this dataset contains high potential to help tackling some of the long-standing challenges that the majority of current spaceborne passive remote sensing products having in cloudy and precipitating scenes. Even better, the producers of this dataset (i.e., key authors on this paper), took careful consideration of caveats matching limb measurements with nadir measurements by providing the ray paths and matched TBs along the rays. Hence I strongly support the publication of this dataset to the public.
There are some minor issues that I think should be addressed before the acceptance though, mainly to clear out some parts that can cause confusions. Also, as a standard procedure to publish a dataset, the independent validation part feels a bit too weak in the current version. It would be really helpful to have some additional water vapor and temperature sounding comparison results to show in the revised version against other available RO products (global mean as well as regional error distributions) to assess the overall quality of PAZ’s Prf products.
Minor comments:
L124: what’s a Doppler model and why you need it for forward modeling (?) or retrieval purpose?
General procedures from Step 1 to Step 12 around Line 130: there’s no discussion regarding the U and V components when you do the decomposition. Are they negligible or your receiver can always perfectly align with the I & Q component? This is never clear to me. Also, it is never mentioned in this manuscript how you tease out possible orientation rotation due to magnetic field in Earth’s upper atmosphere when the RO rays pathed through?
Fig. 2: It’s not clear if resPrf depends on input from UCAR wetPrf2? Does resPrf contain temperature and water vapor retrievals or just the bending angle (or refractivity)?
Line 195: how do you tell if a suddent delta_Phi increase is not due to intersecting heavy precip, but rather untrustworthy measurements?
Section 4.1, paragraph 1: this paragraph is really confusing. Up till reading this paragraph that I started to realize that your resPrf is different from the standard atmPrf and wetPrf products that UCAR produce for the PAZ mission. Could you provide a percentage rate how many PAZ RO measurements pass the QC and can successfully generate atmPrf and wetPrf at UCAR?
Line 240: this is another major part that confuses me: so does the “coll” group contain the imagery from collocated nadir sensor measurements (PMW TB, IR TB, IMERG) or the sensor TBs are instead projected to the ray paths as shown in Fig. 4?
Line 260: one caveat for this assumption is that there’s no temperature inversion layer and no large perturbations (e.g., caused by gravity wave near the convective clouds) that cause artifacts in your decision process, correct? Please clarify here.
Line 271: does your “coll” product also include the view-angle from cross-track scanning PMW measurements? TB needs to be corrected for the slantwise view-angle or otherwise the climatology can be nontrivially biased.
Fig. 5: caption misses to describe what is N and what is Nmodel. Reading the context around Fig. 5 making me wonder about the temperature and water vapor retrieval qualities and whether that’s part of this dataset that needs to be validated or assessed at least.
Fig. 6: caption misses to explain the grey dashed lines.
Section 6.2.1: you misses a very important reason to explain the underestimation of CTH from your method: for cloud/snow ice, delta_phi is only large when particles are dominantly horizontally oriented, which not happens in the convective core but in anvils (which is below the convective core) or stratiform precipitating region. Please add in this explanation as well if seeing appropriate.
Fig. 7: change “geometry” to “after geometry correction”.
Line 396: how do you deal with the cloud fraction (CF) in a grid box?
Line 414: I think it would better to have some discussions regarding data assimilation of the Delta_phi to plant a seed for future applications, especially considering that your group had done some preliminary work regarding this aspect to make PAZ measurements more useful for NWP.
Citation: https://doi.org/10.5194/essd-2024-150-RC1 - AC1: 'Reply on RC1', Ramon Padullés, 14 Oct 2024
-
RC2: 'Comment on essd-2024-150', Anonymous Referee #2, 22 Aug 2024
Review of the manuscript essd-2024-150 titled “the PAZ polarimetric radio occultation research dataset for scientific applications” by Padulles et al.
The manuscript is a description paper of a new data set of polarimetric radio occultation (PRO) observations produced by PAZ satellite.
PRO observations, like the regular RO bending angle observations, contain information on the atmosphere along the ray path that connects the transmitter GNSS satellite and the receiver LEO satellite. However, PRO observations differ significantly from the regular RO observations in that, unlike the regular RO measurements like bending angles, local spherical symmetry approximation is not justifiable for hydrometeor distributions that are sensed by PRO measurements. To analyze PRO data it is thus primordial to have accurate 3D information of the ray trajectories, but such information is only available after performing ray tracing, which is difficult and hindered the use of PRO observations by wider community.
The new data set presented in this paper is ground breaking in resolving this situation by providing pre-computed ray trajectories along with the PRO measurements. This is an important contribution that is expected to foster and facilitate the use of PRO data from wider users. The paper is also very well organized and well written. I have only minor suggestions that the authors can choose to incorporate or not at their discretion.Minor comments:
- line 156 " only that in the PAZ case...": maybe you meant to write "the only difference bing that ..."- line 158 and elsewhere: The use of the word "differentitian" to mean the subtraction of V-pol excess phase from H-pol excess phase is confusing. Please consider using different wording, like "differencing" for example.
- Paragraph starting at line 165: It would be useful to include some short explanation about how geometric and wave optics are different here.
- The first paragraph of section 4.1: I assume, from the description in this paragraph, that the 1D (vertical) refractivity profile from the UCAR retrieval is used to compute the ray tracing assuming that the profile is horizontally uniform. If so, making this point explicit in the text and, if possible, discussing any limitation from this approach would be useful for the data users.
- line 219 "...one each 0.1km": Put "apart" after 0.1km.
- line 244 "Likewise": Replace with "Like" or "As with".
- line 254 "sense": Replace with "sensed"
- line 284, 341 and elsewhere, "specially": Replace with "especially" (or rephrase).
- line 307 "equal to" : consider replacing this with "close to" because, even when there is no precipitation at the ground, there can be hydrometeors aloft.
- line 328 "arount" : replace with "around"
- Figure 6: Please give precise definition of the "Height". I assume it is the tangent height which is also the lowest height of the ray. If so, please explicitly state so.
- Paragraph starting at line 343: In this paragraph the mean and standard deviation of delta Phi is used, but the statistics is taken over which samples are not clearly stated. Please clarify.
- line 406 "maximum WC": not clear it is maximum with respect to what. I guess it is the largest values of WC over the vertical direction within each colmn?
- Section 7: An important caveat in interpreting the results shown is section is that, in ERA5, hydrometeor water content associated with cumulus parametrization is not provided. As a result, the KDP equivalent computed solely from large-scale clouds will inevitably underestimate the observed KDP (and hence delta Phi). This will justify the model's underestimation shown in Figure 8d.
Citation: https://doi.org/10.5194/essd-2024-150-RC2 - AC2: 'Reply on RC2', Ramon Padullés, 14 Oct 2024
Data sets
resPrf [dataset] Ramon Padullés, Estel Cardellach, and Santi Oliveras https://doi.org/10.20350/digitalCSIC/16137
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