the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Mar 2025
Synergy of millimeter-wave radar and radiometer measurements for retrieving frozen hydrometeors in deep convective systems
Abstract. Satellite remote sensing of frozen hydrometeors in deep convective systems is essential for understanding precipitation systems and the formation of upper-level clouds. To reduce uncertainties in ice cloud microphysical properties inside convective clouds, a combined use of millimeter-wave sensors sensitive to frozen particles in deep convective clouds is a promising strategy. This study uses the CloudSat Cloud Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) to retrieve the vertical profiles of ice water content (IWC), number concentration (Nt) and mass-weighted diameter (Dm). A new retrieval method is developed by a combination of Deep Neural Network (DNN) and Optimal Estimation Method (OEM). In the first step of the algorithm, an initial guess is estimated by DNN based on an a priori database, followed by the next step where OEM seeks a more optimal frozen hydrometer profile.
The retrieval performance is evaluated against selected match-up observations of CloudSat and GPM. The combined use of CPR and GMI observations reduce retrieval errors compared to the CPR-only observations. The retrieved frozen hydrometer profiles excellently reproduce CPR reflectivity and GMI brightness temperatures (Tb) when computed by forward simulations. The dual-frequency precipitation radar (DPR) reflectivity is also reasonably reproduced, indicating some ability to retrieve large snow and graupel particles detectable by the low-frequency radars. Among different ice habit models tested, the optimal models for this synergistic algorithm are dendrite snowflake and soft sphere for the ice density model used in this algorithm. The combined algorithm developed by this work implies the potential of passive and active millimeter-wave instruments for retrieving multiple aspects of the cloud ice properties when combined in tandem. Future work will incorporate new satellite missions, including EarthCARE Doppler millimeter-wave radar and submillimeter-wave radiometers such as Ice Cloud Imager.
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RC1: 'Comment on egusphere-2025-173', Anonymous Referee #1, 11 Apr 2025
This manuscript introduces a new algorithm combining Deep Neural Networks and Optimal Estimation to retrieve vertical profiles of ice water content, number concentration, and mass-weighted diameter using CloudSat CPR and GPM GMI data. The combined CPR and GMI observations retrievals are characterized by reduced uncertainties compared to those from CPR-only data and accurately reproduce radar reflectivity and brightness temperatures, demonstrating the potential of combined millimeter-wave instruments for cloud ice property retrieval. Future work will integrate data from upcoming satellite missions like EarthCARE and Ice Cloud Imager.
The manuscript is well written and within the scope of AMT. Nevertheless, there are three aspects that need to be presented/discussed in a broader perspective:
1) The "a priori" covariance S_a. It is unclear why the authors use a formulation used in previous studies when the DNN model provides the "a priori" estimates. Given that the DNN retrievals were developed using simulations, the authors could evaluate retrieval errors using an independent simulated dataset (or setting aside a fraction of the existing simulated dataset for evaluation) and calculate the associated S_a. This should be discussed in the manuscript.
2) The interpretation of results via Eq. (14). Specifically, the authors state that matrix S in Eq. (14) provides the error of the estimated variables. While this may be considered true at some general (and approximate) levels, S is more rigorously the posterior error covariance. If the "a priori" error covariance S_a is correctly estimated and the forward modelling errors are correctly specified, S is indeed the true error covariance. However, given that both S_a and the modeling errors may not be accurately estimated, covariance S given by Eq. (14) could be significantly different from the actual error covariance. Moreover, theoretically, the inclusion of observations always results in a smaller S, but practically the reduction in S depends on how accurate the forward models are. Therefore, the authors should clarify that the results shown in Fig. 8 are not errors in the true sense (estimate-true) because the true values are unknown. Instead, these results are theoretical estimates derived using Eq. (14), and this limitation should be discussed.
3) The performance of the soft-sphere electromagnetic calculations is somewhat surprising. While soft-sphere calculations have been shown to work in some cases, it has also been shown that it is generally difficult (or impossible) to find assumptions about the density of hydrometeors that work for a wide range of frequencies (Kuo et al., 2016; Olson et al., 2016). The backscattering properties of snow particles at W-band differ significantly from those of soft spheroids except for an equivalent density of 0.3 g/cm^3. Therefore, the fact that soft spheroids result in the best agreement should not be construed as a general indication that the soft-spheroid approach works in all cases. This is especially true given that the largest discrepancies occur at the low end of the brightness temperatures and that the radar model does not account for multiple scattering. This limitation needs to be acknowledged and discussed.
Minor Comments:
i) Eqs. (1) and (2). Delanoe et al. (2014) use a different formulation in which the shape (mu) dependence of the integrated properties is not a important as that of the generalized intercept that can be parameterized as a function of temperature. The normalized PSD approach is likely to explain better variability in the PSD with a reduced number of parameters.
ii) How is H in Eq. (13) calculated (i.e. finite-difference or automatic differentiation)?
References
Delanoë, J.M.E., Heymsfield, A.J., Protat, A., Bansemer, A. and Hogan, R.J., 2014. Normalized particle size distribution for remote sensing application. Journal of Geophysical Research: Atmospheres, 119(7), pp.4204-4227.
Kuo, K., and Coauthors, 2016: The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part I: An Extensive Database of Simulated Pristine Crystals and Aggregate Particles, and Their Scattering Properties. J. Appl. Meteor. Climatol., 55, 691–708, https://doi.org/10.1175/JAMC-D-15-0130.1.
Olson, W. S., and Coauthors, 2016: The Microwave Radiative Properties of Falling Snow Derived from Nonspherical Ice Particle Models. Part II: Initial Testing Using Radar, Radiometer and In Situ Observations. J. Appl. Meteor. Climatol., 55, 709–722, https://doi.org/10.1175/JAMC-D-15-0131.1.
Citation: https://doi.org/10.5194/egusphere-2025-173-RC1 -
RC2: 'Comment on egusphere-2025-173', Joe Turk, 14 Apr 2025
This manuscript makes use of the collection of 3-frequency (Ku, Ka and W)-band spaceborne radar in combination with (10-183 GHz) passive MW sensing capabilities afforded by the overlapping (2014-2020) period of GPM and CloudSat science operations. The goal of the analysis is to capitalize upon this sensing capability to improve estimates of the ice water path, and characteristics of its associated microphysical structure, namely the profile of the DSD number concentration and mass-weighted mean diameter. Given the recent deployment of the EarthCARE radar and the fact that GPM is (hopefully) operating into the early 2030s, these results can be applied to the EarthCARE-GPM combination, to further expand the record of observations that sample deep convective clouds. The paper is well-intentioned, very relevant and within the scope of AMT.
As pointed out by the authors, the primary sources of differences in current IWP products are mainly due to the uncertainties in the cloud microphysical properties and differences in the type of sensor (radar, radiometer) sensitivity to the profile of ice particles. I have two comments.
At at these higher (89 GHz and higher) frequencies, the attenuation due to water vapor is significant. In the tropical regions, the attenuation due to water vapor is significant (up to 2-way path attenuation exceeding 8 dB; see Josset et al. 10.1109/TGRS.2012.2228659). And for radiative transfer at 89, 166 and the various 183 GHz water vapor bands, the amount and vertical extent of the water vapor can reduce the overall scattering albedo and impact simulation of TB at these channels. My question is: How “accurate” is the specification of the ancillary data used (ECMWF-AUX)? In your figure 2, these data appear to be used as a one-time “fixed” input, indicating that the water vapor profile stays fixed while you vary the ice particles in the forward OEM simulations. Would you expect the water vapor profile to be the “same” across different types of ice particle shapes (dendrite, long column, etc.)? While I am no expert in this topic, in nature water vapor and ice particle processes are likely correlated to some extent.
The GMI has dual-polarized (V and H) capabilities at 89 and 166 GHz. Previous studies have indicated polarization difference especially at 166 GHz (Gong et al. 2017, https://doi.org/10.5194/acp-17-2741-2017). In your forward simulation, were polarized TB calculations performed? The extent of V-H polarization difference may provide additional independent information to identify and/or constrain the type of ice particles appropriate for certain deep convective clouds.
Just FYI- The CloudSat-GPM (and CloudSat-TRMM) dataset has recently been updated to cover all current Release-5 CloudSat data. While the data products themselves remained unchanged, the data cover up thru mid-2020. Details are available at NASA’s Precipitation Processing System (https://arthurhou.pps.eosdis.nasa.gov) and details at: https://arthurhou.pps.eosdis.nasa.gov/Documents/CSAT_TRMM_GPM_COIN_ATBD_V05.pdf.
Citation: https://doi.org/10.5194/egusphere-2025-173-RC2
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