This study utilizes nearly synchronous Sentinel-1 Synthetic Aperture Radar (SAR) data and dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement mission (GPM) to analyze the effect and sensitivity of a significant precipitation event in the Amazon region on the normalized radar cross-section (NRCS) attenuation for HH/HV. Additionally, by comparing the changes in NRCS obtained from two consecutive observations, the rainfall intensity was also estimated. The results reveal that the maximum attenuation of HH/HV NRCS caused by heavy precipitation exceeded 8 dB, and the sensitivity of HH polarization and HV polarization showed significant consistency in response to rainfall attenuation. Furthermore, the rainfall rate derived from SAR signal attenuation is highly consistent with the GPM DPR rainfall rate.

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core observatory possesses unique capabilities for delineating the three-dimensional structures of Heavy Ice Precipitation (HIP). This study employs a preliminary statistical analysis of 10 years of GPM DPR measurements from 2014 to 2023 to examine the spatial variability of HIP over various topographical regions in eastern China. The findings indicate that the extent and vertical depth of HIP are greater over plains compared to mountainous areas. Moreover, the seasonal distribution patterns of HIP in the southern hilly terrain and adjacent oceanic zones exhibit marked distinctions from other areas, with the most substantial coverage and thickness occurring during spring. The "high occurrence and coverage" of HIP is most likely related to the applicability of the HIP detection algorithm of GPM DPR in Eastern China. The underlying dynamic and thermodynamic factors potentially influencing these observations--particularly those relating to the monsoonal cycle and the topography of Eastern China--warrant further investigation in subsequent research.

Researchers attempt to use synthetic aperture radar (SAR) for retrieving of rainfall over sea surface, but the mechanism of rainfall on the sea surface is too complex to quantitatively describe, such as the relationship between the NRCS of SAR and rainfall rates. In this article, we matched Radarsat-2 SAR and SFMR observation dataset for 35 tropical cyclones. Based on the existing empirical models of rainfall attenuation, the volume scattering model and the CMOD5 model, we indirectly obtain the sea surface backscattering by subtracting them from the NRCS of SAR. Correlate it with meteorological elements such as wind and rain, we found that the sea surface contribution to the signal is strongly influenced by the radar incidence angle. A fitting model of the rain-induced sea surface backscatter coefficient affected by the angle of incidence and wind direction was established. We find the attenuation is found to decrease with increasing angle of incidence, and the rate of change is faster at small angles.

While a variety of methods have been developed for estimating single-layer cloud base height (CBH), few studies have been introduced for retrieving overlapping CBH. To enhance the characterization of the vertical structure of overlapping clouds, which account for approximately a quarter of global clouds, this study presents an extrapolation algorithm for estimating overlapping CBH from passive radiometers. The algorithm relies on the continuity of cloud boundaries within a given region and develops four tests to identify appropriate single-layer cloud pixels for accurately inferring overlapping CBHs. The algorithm was applied to data from the aqua moderate-resolution imaging spectroradiometer (MODIS), and the results were validated against active cloud profiling radar (CPR)-cloud-aerosol Lidar with orthogonal polarization (CALIOP) measurements. The results indicate that the CBH retrievals derived from a single-layer cloud assumption are significantly biased in overlapping cloud cases. In contrast, the extrapolation algorithm provides more accurate retrievals of both upper layer ice CBH and lower layer water CBH. Specifically, the mean CBH bias for upper layer ice clouds is reduced from −2.4 to −0.9 km, while for lower layer water clouds, it is reduced from 3.8 to 1.6 km. By accurately extracting the vertical structure of overlapping clouds, this approach shows potential for improving cloud radiative forcing estimates, weather modification, and climate modeling.

This study develops a machine learning (ML)-based multilayer cloud detection algorithm for the passive Advanced Himawari Imager (AHI) aboard the geostationary Himawari-8 satellite. AHI measurements in the 0.64-, 1.6-, 2.3-, 3.9-, 7.3-, 8.6-, 11.2-, and 12.4-$\mu \text{m}$channels, their combinations, geolocations, and observational geometries are used as predictors, and collocated active CloudSat and CALIPSO data are used to accurately label multilayer cloud pixels as the reference/truth of the predictand. We develop an ML-based daytime model (ML-Day) that utilizes all the aforementioned predictors and an all-time one (ML-All) that excludes the solar channel-dependent variables. Among four ML algorithms, the random forest (RF) performs slightly better than the artificial neural network, K-nearest neighbor, and support vector machines. By comparing with the merged CloudSat and CALIPSO product, the ML-Day model correctly identifies ~89% single-layer clouds and ~70% multilayer clouds, outperforming the Moderate Resolution Imaging Spectroradiometer (MODIS) operational multilayer cloud product (~80% and ~40% given by Marchantet al.). The success rates of ML-All for single-layer and multilayer clouds also reach ~85% and ~64%, respectively. The misclassification of our algorithm is mostly caused by missing optically thin clouds, a drawback of most radiometers without the 1.38-$\mu \text{m}$channel. Furthermore, with multilayer cloud pixels well detected by our algorithm, the AHI operational cloud top height retrievals are found to be larger biased due to multilayer cloud occurrence and might be improved by considering cloud vertical structures.