During the presummer rainy season (April–June), southern China often experiences frequent occurrences of extreme rainfall, leading to severe flooding and inundations. To expedite the efforts in improving the quantitative precipitation forecast (QPF) of the presummer rainy season rainfall, the China Meteorological Administration (CMA) initiated a nationally coordinated research project, namely, the Southern China Monsoon Rainfall Experiment (SCMREX) that was endorsed by the World Meteorological Organization (WMO) as a research and development project (RDP) of the World Weather Research Programme (WWRP). The SCMREX RDP (2013–18) consists of four major components: field campaign, database management, studies on physical mechanisms of heavy rainfall events, and convection-permitting numerical experiments including impact of data assimilation, evaluation/improvement of model physics, and ensemble prediction. The pilot field campaigns were carried out from early May to mid-June of 2013–15. This paper: i) describes the scientific objectives, pilot field campaigns, and data sharing of SCMREX; ii) provides an overview of heavy rainfall events during the SCMREX-2014 intensive observing period; and iii) presents examples of preliminary research results and explains future research opportunities.

The Mesoscale Heavy Rainfall Observing System (MHROS), supported by the Institute of Heavy Rain (IHR), Chinese Meteorology Administration, is one of the major systems to observe mesoscale convective systems (MCSs) over the middle region of the Yangtze River in China. The IHR MHROS consists of mobile C-POL and X-POL precipitation radars, millimeter wavelength cloud radar, fixed S-band precipitation radars, GPS network, microwave radiometers, radio soundings, wind profiler radars, and disdrometers. The atmospheric variables observed or retrieved by these instruments include the profiles of atmospheric temperature, moisture, wind speed and direction, vertical structures of MCS clouds and precipitation, atmospheric water vapor, and cloud liquid water. These quality-controlled observations and retrievals have been used in mesoscale numerical weather prediction to improve the accuracy of weather forecasting and MCS research since 2007. These long-term observations have provided the most comprehensive data sets for researchers to investigate the formation-dissipation processes of MCSs and for modelers to improve their simulations of MCSs. As the first paper of a series, we briefly introduce the IHR MHROS and describe the specifications of its major instruments. Then, we provide an integrative analysis of the IHR MHROS observations for a heavy rain case on 3-5 July 2014 as well as the application of IHR MHROS observations in improving the model simulations. In a series of papers, we will tentatively answer several key scientific questions related to the MCS and Meiyu frontal systems over the middle region of the Yangtze River using the IHR MHROS observations.

An improved approach to derive pseudo water vapor mass mixing ratio and in-cloud potential temperature was developed in this paper to better initialize numerical weather prediction (NWP) and build convective-scale predictions of severe weather events. The process included several steps. The first was to identify areas of deep moist convection, utilizing Vertically Integrated Liquid water (VIL) derived from a mosaicked 3D radar reflectivity field. Then, pseudo-water vapor and pseudo-in-cloud potential temperature observations were derived based on the VIL. For potential temperature, the latent heat initialization for stratiform cloud and moist adiabatic initialization for deep moist convection were used based on a cloud analysis method. The third step was to assimilate the derived pseudo-water vapor and potential temperature observations, together with radar radial velocity and reflectivity into a convective-scale NWP model during data assimilation cycles spanning several hours. Finally, 3-h forecasts were launched each hour during the data assimilation period. The effects of radar data and pseudo-observation assimilation on the prediction of rainfall associated with convective systems surrounding the Meiyu front in 2018 were explored using two real cases. Two sets of experiments, each including several experiments in each real case, were designed to compare the effects of assimilation radar and pseudo-observations on the ensuing forecasts. Relative to the control experiment without data assimilation and radar experiment, the analyses and forecasts of convections were found to be improved for the two Meiyu front cases after pseudo-water vapor and potential temperature information was assimilated.Atmosphere 2020, 11, 182 2 of 24 variables is nonlinear. Yet, the effective assimilation of reflectivity into convective-scale models is essential for properly initializing convective-scale NWP models. One simple way to assimilate reflectivity data for initializing the convective-scale NWP is the cloud analysis method [15][16][17][18][19]. For example, the Advanced Regional Prediction System (ARPS [20]) cloud analysis [18] can specify hydrometeor variables and adjust in-cloud temperatures for the initialization. Although this method has been proven useful, problems still remain. One problem is that the cloud analysis method relies on empirical algorithms to relate the hydrometeor variables and the reflectivity, and requires tuning of many uncertain parameters [7]. Another problem is that too much water vapor and latent heating are added to the cloud analysis, resulting in an increased false alarm rate and over-prediction, especially when many data assimilation cycles are involved [21][22][23]. To solve these problems, more data assimilation approaches have been used, such as latent heat nudging [24], variational techniques [1,[7][8][9][10][11]25,26], the ensemble Kalman filter (EnKF) [5,6,12,[27][28][29], and hybrid variational and ensemble approaches [13,14,30]. These studies have demonstrated that assimilation of reflect...

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.

Southwest China is with complex topography including basins, mountains, hills and plains, where abrupt heavy rainfall events (AHREs) occur frequently and are difficult to quantitatively estimate due to limited ground‐based observations. Using the data of ground rain gauges and GPM dual‐frequency precipitation radar during April to September in 2014–2020, this study investigates vertical structures of AHREs over southwest China. Both mass‐weighted mean diameter (Dm) and reflectivity (Ze) of AHREs increase rapidly in ice‐phase process but slowly in liquid‐phase process. For convective rainfall of AHREs, abundant water vapour and strong atmospheric convective motion cause higher Dm and Ze but lower generalized intercept parameter (dBNw) than those for stratiform rainfall. Moreover, ice‐phase process is active while liquid‐phase process is weak in the mountains, but the situation is opposite in the plains, which results in large‐size and low‐concentration raindrops in the mountains while small‐size and high‐concentration raindrops in the plains. Furthermore, statistical models of vertical profile of reflectivity (VPR) indicate that VPR patterns are affected by surface rain intensity and present different trends around the 0°C level between stratiform and convective rainfalls. In addition, higher rain top height in the mountains is conducive to ice‐phase process while lower terrain height in the plains is favourable for liquid‐phase process. Therefore, the VPR pattern depends on rain type, terrain and rainfall intensity, and its fine model is beneficial for understanding microphysical process of AHREs and improving quantitative precipitation estimation by ground‐based radars.