We investigated the cloud liquid water (CLW) and rain liquid water (RLW) during weak precipitations (rain rate below 12 mm/h) using microwave radiometer and microrain radar measurements collected by the Integrative Monsoon Frontal Rainfall Experiment over central China in 2018. The CLW path increased sharply from 0.6 to 4.1 mm for precipitation clouds. RLW path presented a similar trend, although it had a larger correlation coefficient with rain rate. Precipitation efficiency reached up to ~50% and then clearly decreased as precipitation weakened. Because weak precipitation is mostly formed in stable nimbostratus, CLW content (CLWC) during precipitation tends to has a quasi‐normal distribution with mode at 0.38 g/m³, whereas RLW content (RLWC) shows a positively skewed distribution with mode at 0.06 g/m³. Normalized CLWC initially increases then decreases with height in nonprecipitation clouds but varies slightly in precipitation clouds due to relatively monodispersed droplets in the weaker convective motion. CLWC derived from millimeter‐wave cloud radar (MMCR) shows similar vertical distribution but with larger values. The mean normalized CLWCs are 0.06 and 0.38 g/m³ for nonprecipitation and precipitation clouds, respectively. RLWC varies slightly with height with a mean of 0.22 g/m³ because both the collision and breakup of raindrops are weak. A case study showed different distributions and vertical structures of CLWC and RLWC in various stages of precipitation. Thicker clouds result in larger CLWC and RLWC, which will cause greater rain rate. This qualitatively explains relationships among cloud thickness, CLW, RLW, and rain rate in precipitation during the monsoon.

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds.

Hubei Province Region (HPR), located in Central China, is a concentrated area of severe convective weather. Three severe convective processes occurred in HPR were selected, namely 14–15 May 2015 (Case 1), 6–7 July 2013 (Case 2), and 11–12 September 2014 (Case 3). In order to investigate the differences between the three cases, the temporal and spatial distribution characteristics of cloud–ground lightning (CG) flashes and precipitation, the distribution of radar parameters, and the evolution of cloud environment characteristics (including water vapor (VD), liquid water content (LWC), relative humidity (RH), and temperature) were compared and analyzed by using the data of lightning locator, S-band Doppler radar, ground-based microwave radiometer (MWR), and automatic weather stations (AWS) in this study. The results showed that 80% of the CG flashes had an inverse correlation with the spatial distribution of heavy rainfall, 28.6% of positive CG (+CG) flashes occurred at the center of precipitation (>30 mm), and the percentage was higher than that of negative CG (−CG) flashes (13%). Moreover, the quantity of thunderstorm cells in Case 1 was more than other cases, the peak time of +CG flashes was prior to that of total CG flashes in Case 2 and Case 3, and the time of +CG flashes’ peak in Case 2 was prior to that of precipitation at about 2 h. Based on the analysis of the cloud environment, there are three main reasons for the differences of CG flashes and precipitation. Firstly, the structure of the LWC vertical profile and the height of the LWC peak are different, and high LWC makes it difficult for the collision of ice particles to generate electricity. Secondly, the differences between convective available potential energy (CAPE), precipitation, and CG flashes is caused by the sudden increase of VD from 1.5 km to 3 km, and thirdly, the production of CG flashes is very sensitive to RH at the surface layer and the total CG flashes increase as the RH increasing.

Heat wave is serious natural disaster that can harm human health and affect social economy, transportation and ecological environment. This paper investigates the long term trends of high temperature events in three major cities (Beijing, Tianjin and Shijiazhuang) of northern China during 1970–2019, and quantifies the contributions of main influencing factors to the variability of high temperature days. High temperature events in Beijing–Tianjin–Shijiazhuang cities mainly occur from June to July and account for 60–65% of the annual total, showing a significant upward trend in the interannual change. There is a trend of high temperature events starting early and ending late in recent 50 years, and this trend is intensified by the increasing urbanization. Due to geographical location and city scale, the frequency and intensity of high temperature events show difference among Beijing, Tianjin and Shijiazhuang cities. Variance analysis shows that climate warming contributes 20.2–25.5% to the variability of high temperature days, while urban heat island accounts for 14.7–24.2%, equal to the sum of the contributions from atmospheric circulation and solar activity.

Satellite quantitative precipitation estimation (QPE) can make up for the insufficiency of ground observations for monitoring precipitation. Using an Advanced Geosynchronous Radiation Imager (AGRI) on the FengYun-4A (FY-4A) satellite and rain gauges (RGs) for observations in the summer of 2020. The existing QPE of the FY-4A was evaluated and found to present poor accuracy over the complex topography of Western China. Therefore, to improve the existing QPE, first, cloud classification thresholds for the FY-4A were established with the dynamic clustering method to identify convective clouds. These thresholds consist of the brightness temperatures (TBs) of FY-4A water vapor and infrared channels, and their TB difference. Then, quantitative cloud growth rate correction factors were introduced to improve the QPE of the convective-stratiform technique. This was achieved using TB hourly variation rates of long-wave infrared channel 12, which is able to characterize the evolution of clouds. Finally, the dynamic time integration method was designed to solve the inconsistent time matching between the FY-4A and RGs. Consequently, the QPE accuracy of the FY-4A was improved. Compared with the existing QPE of the FY-4A, the correlation coefficient between the improved QPE of the FY-4A and the RG hourly precipitation increased from 0.208 to 0.492, with the mean relative error and root mean squared error decreasing from −47.4% and 13.78 mm to 8.3% and 10.04 mm, respectively. However, the correlation coefficient is not sufficiently high; thus, the algorithm needs to be further studied and improved.