The Ku-band scatterometer called CSCAT onboard the Chinese–French Oceanography Satellite (CFOSAT) is the first spaceborne rotating fan-beam scatterometer (RFSCAT). A new algorithm for classification of Arctic sea ice types on CSCAT measurement data using a random forest classifier is presented. The random forest classifier is trained on the National Snow and Ice Data Center (NSIDC) weekly sea ice age and sea ice concentration product. Five feature parameters, including the mean value of horizontal and vertical polarization backscatter coefficient, the standard deviation of horizontal and vertical polarization backscatter coefficient and the copol ratio, are innovatively extracted from orbital measurement for the first time to distinguish water, first-year ice (FYI) and multi-year ice (MYI). The overall accuracy and kappa coefficient of sea ice type model are 93.35% and 88.53%, respectively, and the precisions of water, FYI, and MYI are 99.67%, 86.60%, and 79.74%, respectively. Multi-source datasets, including daily sea ice type from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF), NSIDC weekly sea ice age, multi-year ice concentration (MYIC) provided by the University of Bremen, and SAR-based sea ice type released by Copernicus Marine Environment Monitoring Service (CMEMS) have been used for comparison and validation. It is shown that the most obvious difference in the distribution of sea ice types between the CSCAT results and OSI SAF sea ice type are mainly concentrated in the marginal zones of FYI and MYI. Furthermore, compared with OSI SAF sea ice type, the area of MYI derived from CSCAT is more homogeneous with less noise, especially in the case of younger multiyear ice. In the East Greenland region, CSCAT identifies more pixels as MYI with lower MYIC values, showing better accuracy in the identification of areas with obvious mobility of MYI. In conclusion, this research verifies the capability of CSCAT in monitoring Arctic sea ice classification, especially in the spatial homogeneity and detectable duration of sea ice classification. Given the high accuracy and processing speed, the random forest-based algorithm can offer good guidance for sea ice classification with FY-3E/RFSCAT, i.e., a dual-frequency (Ku and C band) scatterometer called WindRAD.

In April 2023, China launched its first precipitation measurement satellite, FengYun-3G (FY-3G), to accurately measure the spatial and vertical structures of precipitation in the middle and lower latitudes of Earth. Equipped with advanced instruments, including a dual-frequency precipitation radar (DPR), a microwave radiometer, a visible and infrared (IR) imager, and a radio occultation sounder with reflectometry, FY-3G is designed for simultaneous measurements of precipitation, atmospheric temperature/moisture profiles, and cloud properties while capturing land and sea imaging at the same time. Additionally, it carries a polarized multiangle imager (PMAI) as an experimental payload to enhance the understanding of cloud microphysics. Six months after its launch, FY-3G has successfully completed the in-orbit commissioning phase. The detailed evaluation of the performance and the functionality of the platform as well as the instruments is presented in this article. Calibration and validation processes are also discussed. Since the end of 2023, sensor-dependent data records have been carefully validated and made available for public use. As the first of two planned precipitation missions in the FengYun satellite series, FY-3G offers independent and high-quality data on the occurrence, type, and intensity of precipitation, complementing the current observations made by the Global Precipitation Measurement satellite. This mission is expected to significantly advance the global understanding of precipitation dynamics, mechanisms, and the water cycle.

The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical models established in one region may not apply to other areas. This study employs a machine learning method to build a hail size estimation model without assuming relations in advance. It uses FY-4A AGRI data to provide cloud-top information and ERA5 data to add vertical environment information. Before training the model, we conducted a principal component analysis (PCA) to analyze the highly influential factors on hail sizes. A total of 18 features, composed of four groups, namely brightness temperature (BT), the difference in BT (BTD), thermodynamics, and dynamics groups, were chosen from 29 original features. Dynamic and BTD features show superior performance in identifying large hail. Although the selected features are more closely correlated to hail sizes than unselected ones, the relationships are complicated and nonlinear. As a result, a two-layer regression back propagation neural network (BPNN) model with powerful fitting ability is trained with selected features to predict maximum hail diameter (MHD). The linear fitting R² between predicted and observed MHDs is 0.52 on the test set, which signifies that our model performs well compared with other hail size estimation models. We also examine the model concerning all three hail cases in Shanghai, China, between 2019 and 2021. The model attained more satisfactory results than the radar-based maximum estimated hail size (MEHS) method, which overestimates the MHDs, thus further supporting the operational applications of our model.

Tropopause‐penetrating convection (or “overshooting convection” [OC]) is defined as a meso‐to‐microscale stratosphere–troposphere exchange process caused by deep convection. OC plays an important role in stratosphere–troposphere exchange and in producing turbulence. Compared with the tropics, the characteristics of OC in the extratropics are poorly understood. To this end, a high‐resolution and objective satellite‐based OC identification and classification algorithm was developed. In the algorithm, a machine learning method was utilized to derive tropopause height directly from satellite observations to ensure a high level of consistency between tropopause and cloud top height variations. By studying the climatology of OCs, including geographic distribution, vertical extent, and seasonal and diurnal variation, as well as their linkage with turbulence in the extratropics in 2018 based on Fengyun‐4A geostationary meteorological satellite data, it was found that most OCs in the extratropics have relatively shallow vertical extents and are more likely to occur over complex topography within the cyclonic curvature side of subtropical jets near the exit region, with a total frequency lower than 0.2%. They showed significant seasonal and diurnal variation. OCs in the Northern Hemisphere occurred most frequently during spring and summer and peak in the afternoon, while those in the Southern Hemisphere were more likely to occur in winter and spring, and with the highest likelihood in the early morning. Typical synoptic conditions for the occurrence of OCs in the mid–high latitudes of the Northern Hemisphere in the summer months included low‐level easterly flows, increased moisture symmetric instability, upper cold trough (or cold vortex), and a subtropical westerly jet. Under this large‐scale flow pattern, moderate‐to‐greater turbulence was often found to occur within or about 200 km away from the core convective area. The stronger the penetrating convection, the greater the probability of moderate‐to‐greater turbulence.