| 08 Apr 2026
A Hybrid Ice Hydrometeor Retrieval Algorithm for (Sub)millimeter-Wave Radiometers in Support of the PolSIR and PMM Missions
Abstract. This paper presents an ice hydrometeor retrieval algorithm for submillimeter-wave radiometers in support of the upcoming PolSIR (Polarized Submillimeter Ice Cloud Radiometer) and PMM (Precipitation Measuring Mission) satellite missions. The algorithm employs a hybrid Bayesian Monte Carlo Integration (BMCI) and Optimal Estimation Method (OEM) approach, which leverages the strengths of the BMCI method but extends the retrieval capability beyond the limitations of the a priori database when BMCI alone fails to identify enough database cases matching the observations. To address the highly non-Gaussian nature of the a priori statistics, a method using cumulative distribution functions (CDFs) and empirical orthogonal functions (EOFs) is applied to enable effective implementation of the OEM algorithm. With the CDFs/EOFs method, the OEM can maximize the posterior probability density function using the a priori constraint that is largely consistent with that used in the BMCI step, ensuring that the entire retrieval operates under a nearly uniform prior constraint.
The algorithm is first applied to evaluate the PMM-C2OMODO (Convective Core Observations through MicrOwave Derivatives in the trOpics) radiometer using simulated observations. Retrieval accuracies for key microphysical parameters are presented. Also, the retrieval diagnostics, including the vertical resolution, Degrees of Freedom (DoF), and Shannon information content, are analyzed. The algorithm is further applied to real CoSSIR (Configurable Scanning Submillimeter-wave Instrument/Radiometer) observations during the IMPACTS (Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms) campaign, and the results are evaluated against the collocated triple-frequency radar retrievals from CRS (Cloud Radar System) and HIWARP (High-altitude Imaging Wind & Rain Airborne Profiler) observations. Multiple ice particle habits are examined, and both layer-resolved and column-integrated mass and size parameters are evaluated. Both simulated experiments and real-observation retrievals demonstrate that the hybrid BMCI-OEM method is highly effective in reducing simulated and observed brightness temperature (TB) discrepancies. With an appropriate selection of ice cloud particle habits, the TB simulations closely reproduce the observations. Using the logarithmic difference as the quantitative metric, the CoSSIR-retrieved ice water content, layer-resolved particle diameter, ice water path, and column-averaged mean mass diameter with the hybrid BMCI–OEM algorithm differ from triple-frequency radar retrievals by 3.75, 0.82, 2.53, and 0.55 dB, respectively, representing reductions of 0.21, 0.02, 0.85, and 0.13 dB relative to BMCI-only retrievals. The hybrid Bayesian framework also demonstrates high extensibility to other remote-sensing observations. As more information becomes available through multi-sensor integration or the use of hyperspectral measurements, the hybrid Bayesian algorithm shows increasing potential to better constrain cloud microphysical properties.
General comments
This manuscript presents an ice hydrometeor retrieval algorithm for sub-mm radiometers in support of the upcoming PolSIR and PMM satellite missions. The study proposes a hybrid BMCI-OEM retrieval framework that effectively integrates the advantages of both methods and evaluates its performance using both simulated and real observations. The topic is relevant to ongoing efforts in cloud and precipitation remote sensing, and the manuscript provides a detailed assessment of retrieval performance through a range of diagnostics and comparisons with independent radar retrievals.
Overall, I would like to commend the authors for the quality of this work. The manuscript is clearly written, the methodology is comprehensively described, and the analyses are thorough. I particularly appreciated the inclusion of the information-content analysis and retrieval diagnostics, especially the discussion based on the Shannon information content metric. I only have a few minor comments listed below and recommend publication after minor revisions.
Specific comments
(1) The manuscript highlights the advantages of the hybrid BMCI-OEM framework over a BMCI-only approach. Could the authors comment on the computational performance of the hybrid retrieval framework? While BMCI is described as computationally efficient, the OEM component requires iterative optimization and numerical Jacobian calculations. It would be useful to know how often the OEM step is triggered in practice and what additional computational cost it introduces compared to a BMCI-only retrieval.
(2) Please consider including the spatial resolution/footprint size of the various instruments used in the study, as well as the expected characteristics of the future missions (e.g., C²OMODO footprints <5 km have been reported in the literature). In addition, please provide the radar sensitivity (minimum detectable reflectivity, dBZ), as this information is essential for understanding the sensitivity of the reference retrievals.
(3) The manuscript demonstrates a notable sensitivity of the simulated brightness temperatures to the assumed ice particle habit. All simulations further assume randomly oriented particles and use only the first Stokes component. While polarization differences vanish at nadir, particle orientation can still modify the bulk scattering and extinction properties and therefore affect the simulated brightness temperatures. Given that the selected particle models are adopted from May et al. (2024), where orientation effects are also discussed, could the authors briefly comment on the potential implications of neglecting particle orientation for the retrieved ice microphysical properties? In addition, errors associated with neglecting particle orientation and polarization effects may become increasingly important at larger viewing angles, which are relevant for the planned cross-track satellite observations, particularly for future dual-polarization conically scanning radiometers.
(4) The current implementation assumes pencil-beam radiative transfer simulations and evaluates only nadir observations. Please provide information on the horizontal resolution of the simulations and discuss the extent to which the reported performance may be affected by the neglect of finite instrument footprints and off-nadir viewing geometries. Given that both PolSIR and PMM-C2OMODO are cross-track scanning radiometers, beam-filling effects may become increasingly important at submillimeter frequencies.
In this respect, please delete the word "slightly" from the following statement:
"All results presented here assume nadir-only observations: we acknowledge that both the PolSIR and PMM-C2OMODO radiometers are cross-track scanners, and results at off-nadir angles can be slightly different"
as differences are to be expected.
Suggestions for future work
The paper reports the largest residual brightness temperature discrepancies for the channels closest to 325 GHz and for the most strongly scattering cloud scenes. For future developments of the retrieval framework, the authors may wish to explore alternative particle model selections. For example, McEvoy et al. (2026, EGUsphere preprint) adopted the same particle habit framework as May et al. (2024), but introduced an additional ICON-hail particle model after identifying discrepancies between simulated and observed submillimeter brightness temperatures in strongly scattering deep convective conditions. In this context, analysing the joint behaviour of the 183 and 325 GHz channels (e.g., through scatterplot diagnostics) may provide a useful tool for evaluating and refining the choice of particle models used in the retrieval framework.
McEvoy, P., May, E., and Eriksson, P.: The Arctic Weather Satellite, introducing a new wavelength range for ice hydrometeor retrievals, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-2456, 2026.