This study investigates the statistical characteristics of raindrop size distributions (DSDs) in monsoon season with observations collected by the second-generation Particle Size and Velocity (Parsivel²) disdrometer located in Zhuhai, southern China. The characteristics are quantified based on convective and stratiform precipitation classified using the rainfall intensity and total number of drops. On average of the whole dataset, the DSD characteristic in southern China consists of a higher number concentration of relatively small-sized drops when compare with eastern China and northern China, respectively. In the meanwhile, the D_m and log₁₀N_w scatter plots prove that the convective rain in monsoon season can be identified as maritime-like cluster. The DSD is in good agreement with a three-parameter gamma distribution, especially for the medium to large raindrop size. Using filtered data observed by Parsivel² disdrometer, a new Z–R relationship, Z = 498R^1.3, is derived for convective rain in monsoon season in southern China. These results offer insights of the microphysical nature of precipitation in Zhuhai during monsoon season, and provide essential information that may be useful for precipitation retrievals based on weather radar observations.

The China Meteorological Administration has deployed the China New Generation Weather Radar (CINRAD) network for severe weather detection and to improve initial conditions for numerical weather prediction models. The CINRAD network consists of 217 radars comprising 123 S-band and 94 C-band radars over mainland China. In this paper, a high-resolution digital elevation model (DEM) and beam propagation simulations are used to compute radar beam blockage and evaluate the effective radar coverage over China. Results show that the radar coverage at a height of 1 km above ground level (AGL) is restricted in complex terrain regions. The effective coverage maps at heights of 2 km and 3 km AGL indicate that the Yangtze River Delta, the Pearl River Delta, and North China Plain have more overlapping radar coverage than other regions in China. Over eastern China, almost all areas can be sampled by more than 2 radars within 5 km above mean sea level (MSL), but the radars operating in Qinghai-Tibet Plateau still suffer from serious beam blockage caused by intervening terrain. Overall, the radars installed in western China suffer from much more severe beam blockage than those deployed in eastern China. Maps generated in this study will inform users of the CINRAD data of their limitations for use in precipitation estimation, as inputs to other weather and hydrological models, and for satellite validation studies.

This study assesses the performance of the latest version 05B (V5B) Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Early and Final Runs over southern China during six extremely heavy precipitation events brought by six powerful typhoons from 2016 to 2017. Observations from a dense network composed of 2449 rain gauges are used as reference to quantify the performance in terms of spatiotemporal variability, probability distribution of precipitation rates, contingency scores, and bias analysis. The results show that: (1) both IMERG with gauge calibration (IMERG_Cal) and without gauge correction (IMERG_Uncal) generally capture the spatial patterns of storm-accumulated precipitation with moderate to high correlation coefficients (CCs) of 0.57–0.87, and relative bias (RB) varying from −17.21% to 30.58%; (2) IMERG_Uncal and IMERG_Cal capture well the area-average hourly series of precipitation over rainfall centers with high CCs ranging from 0.78 to 0.94; (3) IMERG_Cal tends to underestimate precipitation especially the rainfall over the rainfall centers when compared to IMERG_Uncal. The IMERG Final Run shows promising potentials in typhoon-related extreme precipitation storm applications. This study is expected to give useful feedbacks about the latest V5B Final Run IMERG product to both algorithm developers and the scientific end users, providing a better understanding of how well the V5B IMERG products capture the typhoon extreme precipitation events over southern China.

Rapidly shrinking Arctic sea ice has had substantial impacts on the Earth system. Therefore, reliably estimating the Arctic sea-ice thickness (SIT) using a combination of available observations and numerical modeling is urgently needed. Here, for the first time, we assimilate the latest CryoSat-2 summer SIT data into a coupled ice-ocean model. In particular, an incremental analysis update scheme is implemented to overcome the discontinuity resulting from the combined assimilation of biweekly SIT and daily sea-ice concentration (SIC) data. Along with improved estimates of sea-ice volume, our SIT estimates corrected the overestimation of SIT produced by the reanalysis that assimilates only SIC in summer in areas where the sea ice is roughest and experiences strong deformation, e.g., around the Fram Strait and Greenland. This study suggests that the newly developed CryoSat-2 SIT product, when assimilated properly using our approach, has great potential for Arctic sea-ice simulation and prediction.