the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Cluster Analysis of Vertical Polarimetric Radio Occultation Profiles and Corresponding Liquid and Ice Water Paths From GPM Microwave Data
Abstract. The polarimetric phase difference between the horizontal and vertical components of GNSS radio signals is correlated with the presence of ice and precipitation in the propagation path of those signals. This study evaluates the ability of k-means clustering to find relationships among polarimetric phase difference, refractivity, liquid water path (LWP), ice water path (IWP), and water vapor pressure using over two years of data matched between the Global Precipitation Measurement (GPM) mission and Radio Occultations through Heavy Precipitation demonstration mission onboard the Spanish Paz spacecraft (ROHP-PAZ). A cluster hierarchy is introduced across these variables. A potential refractivity model for polytropic atmospheres is introduced to ascertain how different types of vertical thermodynamic profiles that can occur during different precipitation scenarios are related to changes in the polytropic index and thereby vertical heat transfer rates. The clustering analyses uncover a relationship between the amplitude and shape of deviations from the potential refractivity model and water vapor pressure and confirm the expected positive correlation between polarimetric phase difference and both LWP and IWP. For certain values, the coefficients of the potential refractivity model indicate when a profile has little to no moisture, and the study reveals a similar relationship between the clustering for these coefficients and different water vapor pressure profiles. The study also confirms the relationship between the integrated polarimetric phase difference and water vapor pressure columns, known as the "precipitation pickup," globally (ρs=0.971 after averaging) and over different latitudinal ranges (>50°, ≥20°, and <20°, with different ρs for each).
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RC1: 'Comment on egusphere-2024-1278', Anonymous Referee #1, 09 Oct 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1278/egusphere-2024-1278-RC1-supplement.pdf
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AC1: 'Reply on RC1', Jonas Katona, 23 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1278/egusphere-2024-1278-AC1-supplement.pdf
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AC1: 'Reply on RC1', Jonas Katona, 23 Nov 2024
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RC2: 'Comment on egusphere-2024-1278', Anonymous Referee #2, 15 Oct 2024
The manuscript presents a study that investigates the relationship between polarimetric phase difference, precipitation, water vapor pressure and other related variables using the k-means clustering with two types of datasets, the GPM and ROHP-PAZ. The research addresses a scientifically significant topic and has the potential to make valuable contributions to the use of PRO data (e.g., for numerical weather prediction and climate studies) in the future, as well as to the broader RO, PRO, and precipitation data communities.
On the other hand, the flow of the presentation in the results section is sometimes difficult to follow for the reader. Specifically: 1) the order of the figures does not always align with how the content is presented. For instance, Fig. 5 is mentioned in Line 221 before Figs. 3 and 4, and there is barely any description of Fig. 5. Similarly, Tables 2,3, and 4 is brought up much earlier than Table 1. I suggest the authors introduce the Tables/Figures in the first place they refer to and give the readers a broad idea what those figures/tables are indicating. 2) Some figure captions should be shortened, and the descriptive statements should be moved to the main body of the text. Please also see the specific comments made as “Line 235 and Fig. 3”.
Overall, I believe this manuscript is of good quality, and my specific comments below mostly focus on how the paper is presented rather than the work that has been done. I suggest a minor revision.
Line 27: better to have units for k1 and k2.
Line 40: 1DVAR (e.g., UCAR/COSMIC) should be briefed here when talking about “wet” profiles, i.e., temperature and humidity.
Line 53, I think there is another important paper by Padullés et al. (2022) in which they discussed the sensitivity of PRO simulation to frozen hydrometeors based. Could the authors provide any insights into the separation of frozen hydrometeors rather than the two water phases (ice and precipitation) used? Perhaps a few sentences could be added to the discussion or introduction.
Additionally, what are the authors' thoughts on how their results can be generalized to high-resolution NWP model states? Specifically, could the authors offer perspectives on the potential application of their methods using NWP model data, rather than the GPM data used in this study?
Line 60: “Part of the challenge is that a given ΔΦ at a specific height may be caused by both ice or precipitation”. Any other potential causes? Could the authors provide some discussion about the challenges in representing ΔΦ by model states?
Line 66: The k-means cluster analysis is present without introduction. Could the authors provide a brief introduction. What are the major advantages using this analysis? Why do the authors use this method? Any references?
Line 90: It is better to provide a brief introduction about the GPM data. For instance, whether it is gridded data. What are the spatial and temporal resolution? How the matching is being done?
Line 95 and Fig. 1b: What caused the missing period in Jan/Feb 2019? If the monthly coverage is not equal and the seasonality is not studied in this paper, Fig. 1b does not seem useful and can be removed.
Line 197: Do the authors really mean 300hpa? 300hpa of water vapor pressure is way beyond the quality. How does this the QC threshold of “nonphysically high water vapor pressure values” come from?
Line 228: “Bretherton et al. (2004) showed a relationship between precipitation and total column water vapor over the tropics”. What did this paper say? Could the authors add the main findings of such “relationship”?
Line 231: Could the authors add a bit more details about the “pickup”? was there any particular pickup values discussed in these papers?
Line 235 and Fig. 3: 1) Besides caption, a brief description should be given for each figure in the content before they are being discussed. 2) if the authors really think they want to talk about Panel d first, they may want to re-arrange the panels so they can discuss by alphabetic orders in the content. 3) As the authors state that “we look for the precipitation pickup pattern.” The pattern is not actually being discussed.
Line 238: “There is also an apparent total column water vapor threshold after which ΔΦ, the PRO signature of precipitation, starts increasing at a faster rate…”. I suggest the authors merge/reorganize this sentence with the previous paragraph where they discussed the relationship in literatures between total column water vapor threshold and ΔΦ.
Line 279: Again, this paragraph descripting what Fig. 4 presents should have appeared much earlier.
Line 57: what does “CloudSat” mean particularly here? Any description?
Line 79: model (3)?
Citation: https://doi.org/10.5194/egusphere-2024-1278-RC2 -
AC2: 'Reply on RC2', Jonas Katona, 23 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1278/egusphere-2024-1278-AC2-supplement.pdf
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AC2: 'Reply on RC2', Jonas Katona, 23 Nov 2024
Data sets
PAZ CALIBRATED POLARIMETRIC PRODUCTS Ramon Padullés, Chi O. Ao, F. Joseph Turk, Manuel de la Torre Juárez, Byron Iijima, Kuo-Nung Wang, and Estel Cardellach https://genesis.jpl.nasa.gov/ftp/paz_pol/
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