This study investigates the sensitivity of radiative transfer (RT) simulations to cloud microphysics (MP) assumptions. Experimental RT simulations were used to investigate the impact of various particle size distribution (PSD) and density assumptions for both rainwater and frozen hydrometeors incorporated into the scattering module of Radiative Transfer Models (RTMs). Input atmospheric fields from Weather Research and Forecasting (WRF) model simulations with the Predicted Particle Properties single free-ice category (P31ICE) scheme were held fixed. A plane-parallel, fast RTM with the delta-Eddington approximation was employed to simulate brightness temperatures (TBs) from the Global Precipitation Measuring Mission Microwave Imager (GMI) for five typhoon scenes. Rainwater PSD assumptions had a relatively low impact on radiative signatures due to compensating extinction from small and large particles and the offset between emission and scattering. For frozen hydrometeors, the impacts of PSD assumptions were mitigated by particle-size-dependent scattering compensation and the canceling effect of forward scattering. Density assumptions had the largest impact on radiative signatures. Additionally, sensitivity to variations in atmospheric fields was observed in physically consistent reference RT simulations using WRF outputs with six different MP schemes. Interactions between MP assumptions in the RTM scattering module and those in the WRF model either amplified or suppressed TB responses, underscoring the importance of physical consistency across the RT framework. Comparisons with GMI observations reinforced the need to reconcile both physical realism and consistency throughout RT simulations. These findings provide guidance for improving the application of RTMs in all-sky data assimilation and satellite-based cloud property retrievals.

Cloud-phase algorithms based on satellite infra- red (IR) observations typically use empirical thresholds and additional cloud properties to predict cloud phases, but these settings may not be consistently accurate under various conditions. This study introduces an unsupervised machine learning (ML) approach based on a Gaussian mixture model (GMM) to avoid the empirical determination of thresholds and auxiliary cloud products for cloud-phase estimation. The GMM-based cloud-phase algorithm proposed in the present study consists of three GMMs that distinguish cluster types representing water, ice, and undetermined phases using the brightness temperature (TB) at$11.2 \mu \text{m}$and the difference in TB between 8.6 and$11.2 \mu \text{m}$from Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, GK2A) satellite. The first GMM initially classifies four clusters, while the second and third GMMs regroup the initial clusters to distinguish supercooled water, mixed-phase, and optically thin cirrus clouds. The GMM-derived estimates are compared with operational cloud-phase products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Infrared Pathfinder Satellite Observations (CALIOP). Results show that the water and ice phases estimated using the GMM-based algorithm are in good agreement with both the MODIS and CALIOP products. The GMM-based algorithm also significantly reduces the misidentified area for undetermined phases observed in the GK2A operational product. Water and ice phases are also effectively estimated in warm regions, resulting in distributions similar to those derived from MODIS and CALIOP products. Unlike most IR cloud-phase algorithms that utilize thresholds and other cloud parameters, the GMM-based cloud-phase algorithm has the advantage of using only TB, thus avoiding auxiliary cloud properties.

Accurately representing ice clouds in passive microwave radiative transfer models (RTMs) is considerably challenging as these clouds include numerous ice particles with complex shapes. Current RTMs often oversimplify this complexity by assuming spherical or singular non-spherical habits for these particles. This study improves the representation of ice clouds in RTMs, shifting from the oversimplified “one-shape-fits-all” approach to a more realistic approach describing the inhomogeneous distribution of ice habits. This improved representation is facilitated by the Predicted Particle Properties (P3) microphysics parameterization scheme, which provides natural variability of the ice-phase hydrometeors’ microphysical properties. The impact of this improved representation on microwave scattering is evaluated by comparing the simulated brightness temperatures (TBs) with actual measurements from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) at scattering frequencies between 89 and 183 GHz, mainly focusing on tropical cyclone events in the Northwestern Pacific Ocean. The results show that the improved representation effectively describes the spatiotemporal variability of ice habits, improving the accuracy of TB simulations across the scattering channels. Moreover, investigations are conducted till the frequency of 664 GHz, emphasizing the potential importance of realistic ice habit distribution. Although some limitations exist, primarily relating to the model’s dependency on the P3 scheme and the limited range of the available ice habits, especially for rimed particles, this study takes a significant step toward improving the realism and accuracy of RTMs, providing a deeper understanding of ice clouds and their influence on RTMs.

Passive microwave radiative transfer models are strongly influenced by the cloud and precipitation hydrometeor properties. Particularly, they can sensitively interact with frozen hydrometeors through multiple high-frequency channels. However, frozen hydrometeors are one of the most difficult parameters to comprehend due to the lack ofin situdata. Until recently, studies have attempted to describe more reasonable hydrometeor distributions using various microphysics parameterizations coupled with the weather research and forecasting (WRF) models. Herein, we aim to apply the proposed methods to passive microwave radiative transfer simulations. We implemented a passive microwave radiative transfer simulation that considers various microphysical assumptions by creating a new Mie scattering lookup table. Furthermore, we evaluated the bulk microphysics parameterizations [WDM6, Morrison (MORR), Thompson (THOM), and P3 schemes] for the tropical cyclone Krosa (2019) that were observed by the global precipitation measurement microwave imager instrument, specifically concentrating on the rimed and aggregated ice categories (snow, graupel, and P3 ice). Based on the evaluation results, we concluded the following: WDM6 graupel and MORR snow afford excessive scattering signals at 37 GHz. However, at 166 GHz, none of the parameterizations produces sufficient scattering signals for comparison with the observations. The P3 ice affords significantly underestimated scattering signals at 89 GHz and above, despite its sophisticated assumptions. On the contrary, THOM snow affords scattering signals similar to the observations, despite a shape-related error. In summary, this study introduced a method for implementing a microphysical-consistent radiative transfer computation and successfully showed how various microphysical assumptions of clouds can change the passive microwave radiative signatures.

The accuracy of a physically based passive microwave precipitation retrieval algorithm is affected by the quality of the a priori knowledge it employs, which indicates the relationship between the precipitation information obtained from cloud-resolving models (CRMs) and the simulated brightness temperatures (TBs) from radiative transfer models. As various microphysical assumptions reflecting a wide variety of sophisticated microphysical properties are applied to the CRMs, the TBs simulated based on the model-driven 3-D precipitation fields are determined by the selected microphysical assumption. In this article, we developed a prototype precipitation retrieval algorithm that incorporates various cloud microphysics schemes in its a priori knowledge (i.e., databases). In the retrieval process, a specific a priori database is selected for every target precipitation scene by comparing the similarities of the simulated and observed microwave emission and scattering signatures. The prototype algorithm was tested through application to precipitation retrieval for tropical cyclones at various intensity stages, which occurred over the northwestern Pacific region in 2015. The a priori databases constructed using the weather research and forecasting double-moment (WDM6) and Thompson Aerosol Aware schemes are superior when used for weak-to-moderate rainfall systems, whereas the databases constructed with the other schemes are superior within strong rain rate regions. The retrieval results obtained using the best-performing database are generally superior for all rain rate regions. Furthermore, we confirm that the database quality is more important than the number of databases. In comparison with the data from the dual-precipitation radar, the retrieval's correlations, bias, and root mean square are 0.75, 0.14, and 5.62, respectively.

Satellite-based microwave sensors that respond to the vertical distribution of hydrometeors have been continuously employed in the investigation of precipitation systems characteristics. Rain/no-rain classification (RNC) methods often are either applied before retrieving precipitation information from a number of algorithms based on passive microwave measurements or adopted to build the precipitation event-based databases. As a simple rain indicator, the polarized corrected temperature (PCT) at 89-GHz (PCT89) method using the global precipitation measurement (GPM) microwave imager (GMI) has been employed by many researchers, because it can estimate the scattering intensity while minimizing the effects of the surface emissivity at high resolution. This article presents a new consideration using the PCT89-based RNC method through statistical verification. Precipitating clouds were subdivided into 11 types (three stratiform types and four convective types) by the GPM dual frequency precipitation radar (DPR) precipitation classification algorithm. Quantitative comparison of verification results was performed in the tropics from January to December 2015 and major sources of uncertainty were analyzed from the perspective of the precipitation mechanism. Results showed a tendency of false identification for stratiform types except for those located near the convective core, and thus the method was susceptible to failure in the identification of convective types. Consequently, this method leads to an increase of 70% and 54% in the number of two significant stratiform types compared to DPR, while the convective types decreased by up to 53%. This article suggests that the precipitations identified by the PCT89 have features that enhance the bias toward the stratiform type.

The impact of the three dimenstion (3D) effect on the passive microwave rainfall estimations is examined by synthetic retrievals employing a Bayesian methodology. The results showed that the uncertainty in the rainfall estimations due to the 3D effect depended on the viewing directions considered in the a-priori information. It was also found that taking more viewing angles or the azimuth angles in the a-priori information into consideration tended to moderate the retrieval difference that resulted from the different viewing directions. In addition, the retrieval uncertainty related to the 3D effect appeared to be more significant for heavy rainfall cases with large amounts of ice particles, as expected. We additionally performed the retrieval experiments with the databases constructed with the one dimensional slant path (1D-SP) calculation. In general, the 3D model-based experiments slightly outperformed the 1D-SP model-based experiments. However, the 1D-SP model may be considered as an alternative model for the full 3D radiative transfer model with limited computing resources and a required level of the retrieval accuracy.

Statistical approaches for quantitative precipitation nowcasts (QPNs) have emerged with recent advances in sensors in geostationary orbits, which provide more frequent observations at higher spatial resolutions. Advanced Meteorological Imager (AMI) onboard South Korea's second geostationary satellite (GEO-KOMPSAT-2A), scheduled for launch in early 2018, is an example of these sensors. This paper introduces operational prototype algorithms that attempt to produce QPN products for GEO-KOMPSAT-2A. The AMI QPN products include the potential accumulated rainfall and the probability of rainfall (PoR) for a 3-h lead time. The potential accumulated rainfall algorithm consists of two major procedures: 1) identification of rainfall features on the outputs from the GEO-KOMPSAT-2A rainfall rate algorithm; and 2) tracking of these rainfall features between two consecutive images. The potential accumulated rainfall algorithm extrapolates precipitation fields every 15 min. Rainfall rates at each time step are accumulated to yield the 3-hourly rainfall. In addition, the extrapolated precipitation fields at 15-min intervals are used as inputs for the PoR algorithm, which produces the probability of precipitation during the same 3-h period. The QPN products can be classified as extrapolated features associated with precipitation. The validation results show that the extrapolated features tend to meet the designated accuracy for the prototype development stage. We also confirm a tendency for decreasing accuracy of the QPN products with increasing forecast lead time. Mitigating the dependence on lead time may remain a challenge that can be incorporated into the next generation of QPN algorithms.

Vertically and horizontally inhomogeneous distributions of hydrometeors are often observed in precipitating clouds. The 3-D characteristics can then cause errors in the passive microwave rainfall measurements with the current off-nadir viewing sensors' specifications. This result is due to the fact that the same surface rainfall could be associated with different amounts of hydrometeors depending on the sensors' viewing paths. In this paper, we confirmed that the plane-parallel radiative treatment to the atmosphere leaves a notable deficiency in the microwave radiometric signatures, particularly at the higher frequency channels for different viewing directions when largely inhomogeneous precipitating clouds are accompanied by significant ice particles. The mean differences between the two brightness temperature fields with two opposite azimuthal viewing directions were up to approximately 40 °K for the vertically polarized channel at 85.5 GHz in the case study. The impact of the 3-D effect on the passive microwave rainfall estimations was also examined by synthetic retrievals employing a Bayesian methodology. The results showed that the uncertainty in the rainfall estimations due to the 3-D effect depended on the viewing directions considered in the a priori information. It was also found that taking more viewing angles or the azimuth angles in the a priori information into consideration tended to moderate the retrieval difference that resulted from the different viewing directions. In addition, the retrieval uncertainty related to the 3-D effect appeared to be more significant for heavy rainfall cases with large amounts of ice particles, as expected.

Physically-based retrieval algorithms for passive microwave radiometers have been used to monitor global rainfalls. The most important part of the algorithms may be accurate knowledge on the a-priori databases for the inversion of rainfalls. This study investigates the impacts of the uncertainties in a-priori databases associated with assumed cloud microphysics on rainfall measurements especially for extreme rain events. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) observations of Typhoon Sudal (2004) are used for the generation of rainfall databases and rainfall target scenes. Six different microphysics schemes are evaluated to better represent the rainfall structure of an intense tropical cyclone (TC).

Analysis of precipitation radar (PR) and Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data collected from the TRMM satellite shows that rainfall inhomogeneity, as represented by the coefficient of variation (CV), depends on a spatial scale, i.e., the CV appears to be nearly constant at all rain rates within the field of view (FOV) of the TMI 37-GHz channel, while it decreases with rain rate at lower spatial resolutions, such as the FOV sizes of the low-frequency TMI channels (10.7 and 19.4 GHz). It is known that the brightness temperature (Tb) for a low-frequency channel decreases with increasing rainfall inhomogeneity for a given rain rate. As such, more inhomogeneous rainfall at low rain rates leads to a lower Tbcompared with that of a FOV with homogeneous rainfall; however, less inhomogeneous rainfall at high rain rates tends to produce a Tbsimilar to that of homogeneous rainfalls. These results indicate that the observed radiometric signatures of low-frequency channels at low spatial resolutions are characterized by a larger response range and smaller variability than those at a higher spatial resolution. Based on the observational characteristics of the TMI and PR data sets, we performed synthetic retrievals of rainfalls, employing a Bayesian retrieval methodology at different retrieval resolutions corresponding to the FOV sizes of the TMI channels at 10.7, 19.4, and 37 GHz. Comparisons of the rainfalls retrieved at the different resolutions and their temporal and regional averages show that the systematic bias resulting from the rainfall inhomogeneity is smaller in the lower resolution data than in their higher resolution counterparts. We note that such low-resolution rainfall retrievals are not expected to describe the instantaneous features of rain fields; however, they could be useful for climatological estimates at large temporal and spatial scales.

Analyses of data collected by the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and precipitation radar (PR) show that the radiometric responses to rainfall profiles are sensitive to spatial inhomogeneities within the sensor field of view (FOV). The uncertainty, the so-called beam-filling error, is associated with the variability in horizontal and vertical rainfall structures within a sensor FOV coupled to the non-linear relationship between brightness temperature (Tb) and rain rate (R). This study classifies the beam-filling error as an inherent error because the sensor itself has the non-unique radiometric signatures associated with the rainfall inhomogeneity. The specific forward models hardly overcome the error inherent in the sensor. It is also shown that lower resolution retrievals are less sensitive to the inherent error than the retrievals at higher resolutions due to the less non-linearity in the Tb-R relationship at lower resolutions.

The effect of rainfall inhomogeneity within the sensor field-of-view (FOV), the so-called beam-filling error, affects significantly the accuracy of rainfall retrievals. Observational analyses of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data show that the beam-filling error can be examined in terms of the coefficient of variation (CV, standard deviation divided by mean rain rate) that provides a measure of the spatial variability. Furthermore, the CV of surface rainfall from PR is related to its vertical structure and has some correlation with the TMI 85 GHz brightness temperature (Tb), especially at the high rain rates. Based on these findings, we exploit the 85 GHz spatial variability in the context of a Bayesian-type inversion method for rainfall retrieval. The spatial variability at various domain sizes (thus CV in a vector form) is blended with the sets of multi-channel brightness temperatures (Tb vector) for the Bayesian inversion. The a-priori databases for the inversion are constructed from the collocated TMI and PR observations at the PR resolution. Through synthetic retrievals we demonstrated that the inclusion of CV information of the Tb remarkably improved the rainfall retrieval accuracy by reducing the effect of the rainfall inhomogeneity.