Mesoscale convective clouds often develop rapidly and with complex structure. They interact in multiple ways with the environmental background as well as cloud microphysical processes. Highly unstable atmospheric conditions and triggering mechanisms that lead to ascending motion are prerequisites for the development of convective clouds. Clouds and precipitation are primarily composed of discrete liquid or solid particles, including cloud and rain droplets, snow crystals, snowflakes, sleet and hails. The formation and growth of these particles is actually the result of interactions between thermodynamic processes and microphysical processes in the atmosphere (Reisner et al., 1998; Zhang et al., 2009; Zheng et al., 2013). The development of strong convective cloud is often accompanied by heavy rain, hail and other mesoscale disastrous weather. Our understanding of cloud physics and precipitation processes is insufficient due to their complexity and diversity, which leads to inappropriate descriptions of the physical processes involved in clouds and precipitation in numerical models. This is one of the major reasons for the uncertainty in numerical modeling studies (IPCC, 2001).
In order to reduce the spin-up problem in numerical models, it is necessary to introduce atmospheric water vapor content and other hydrometeors into the initial conditions of numerical models (Xue et al., 2003; Barker et al., 2004; Liu et al., 2008). However, regular atmospheric observations cannot provide the hydrometeor fields required as the input for data assimilation systems. For this reason, a cloud analysis system is often applied to produce the hydrometeor fields involved in cloud physics and rainstorms. Currently, several widely used cloud analysis systems include LAPS (Local Analysis and Prediction System), ADAS (ARPS Data Analysis System; ARPS refers to Advanced Regional Prediction System) and GRAPES-MESO (Global/Regional Assimilation and Prediction System mesoscale numerical forecast system). Hu et al. (2006a, b) implemented the ADAS cloud analysis system to assimilate observations from operational WRS-88D Doppler radars in their simulation of clusters of tornadic thunderstorms using the mesoscale modeling system ARPS. It was found that the experiment using the improved ADAS cloud analysis procedure with reflectivity could capture important characteristics of the main tornadic thunderstorms more accurately. With assimilation of radar observations, the spin-up problem in storm prediction can be significantly reduced and the storm cluster can be forecasted two hours earlier than without assimilation. (Qu et al., 2012) designed the GRAPES-MESO cloud analysis system and applied it to the assimilation of the blackbody temperature and total cloud amount derived from satellite images provided by the Feng-Yun II geostationary satellite. The results of typhoon simulations indicated that the GRAPES-MESO cloud analysis system can retrieve three-dimensional cloud cover information reasonably well. LAPS, developed by NOAA since the 1990s, was the first cloud analysis system to implement a multiple iteration successive corrections method. LAPS can assimilate regular soundings, surface observations, radar reflectivity analysis, satellite image products of all channels, standard aviation routine weather reports, multi-layered CO2 products etc., to produce high spatiotemporal resolution cloud fields, including three-dimensional cloud views, two-dimensional cloud coverage, cloud base and cloud top heights, and cloud water/ice mixing ratios (Albers et al., 1996). (Shaw et al., 2001) conducted hot-start numerical studies using the LAPS cloud analysis products in their initialization, and improved the first three hours' forecast skill scores.
The implementation of a cloud analysis system can provide cloud parameters such as cloud water content and cloud ice content for the initial conditions of numerical models. However, the accuracy of cloud analysis systems depends on multisource and high spatiotemporal resolution observations, such as satellite remote sensing data and radar observations. With the development of the weather observation network in China, an increasing number of new detection instruments, mainly composed of operational weather Doppler radar, have been deployed to obtain real-time and high spatiotemporal resolution observations. The unique advantages of Doppler radar in high-resolution mesoscale detection, which includes the detection of three-dimensional clouds, precipitation and wind, provide the possibility for successful cloud analysis. LAPS combines data from different sources to analyze clouds and hydrometeors to produce thermodynamically balanced initial conditions for numerical models, and thereby improves short-range precipitation forecasts (Albers et al., 1996; Xie et al., 2013). Researchers in China have implemented LAPS to integrate multiple sources of observations and found that the accuracy of the cloud analysis depends on the accuracy of the high-resolution radar reflectivity and the reliability of the satellite remote sensing data (Li et al., 2009; Liu et al., 2014).
Various types of Doppler radars with different horizontal and vertical detection ranges are deployed. How to improve the quality of data from different types of radars and effectively assimilate these data in the LAPS cloud analysis system to provide reasonable initial conditions of cloud fields is critical for the improvement of mesoscale model forecasts of local rainstorms. Southwest China is characterized by complex terrain with mountains and rolling hills. Heavy precipitation is frequent in Southwest China, which often leads to mudslides, landslides and other secondary geological disasters, and subsequent serious losses to human life and the economy. For example, Wangmo County in Guizhou Province has experienced mountain torrents and mudslides many times in the past, and short-term intense rains are the main trigger of the mudslides. Previous studies have indicated that heavy rainstorms able to induce secondary geological disasters (e.g., mudslides) share common features; for instance, they all last for a short time, their rainfall intensity is high, and their heavy rainfall is highly localized. These heavy rainstorms often appear as local abnormal precipitation on small scales (Xu et al., 2005; Zhao and Cui, 2010; Li et al., 2014). So far, studies on data quality control, especially for radar observations in Southwest China, have been conducted. However, the unique landscape in Southwest China imposes limitations on the effective radar detection range in both the horizontal and vertical direction. The uncertainties of the strong three-dimensional radar signals in the radar mosaic add difficulty to the application of LAPS in the reanalysis of the cloud physical processes involved in micro- and mesoscale rainstorms and mudslides.
The regional ground rain gauge stations of the China Mesoscale Observation Network can compensate well for the missing information in radar blind zones. Continuous hourly precipitation can be directly measured by ground rain gauges with high accuracy at each individual station. However, ground rain gauge measurements often miss the heavy precipitation center due to the inhomogeneous distribution of weather stations and precipitation. Radar can detect real-time clouds and precipitation structure and the evolution of the precipitation system, and quickly provide the real-time spatial distribution of the precipitation within a certain area (Zhang et al., 2007). The relationship between rain rate and radar reflectivity is the basis for the quantitative radar measurement of precipitation. Using the variational method, the three-dimensional radar mosaic can be corrected based on real-time precipitation observations. This method is effective for solving the problems caused by blind zones of various types of radar, and problems related to the quality of the radar mosaic. As a result, the fusion effects of radar reflectivity in the LAPS cloud analysis can be improved, leading to a more realistic description of cloud microphysical parameters in the initial conditions of numerical models, and improvement in the simulation of local heavy precipitation on the micro- and mesoscale.
In the present study, the LAPS cloud analysis system and the Weather Research Forecast (WRF) model are applied to simulate local heavy precipitation events that occurred in Wangmo County, Guizhou Province. A variational method is implemented to correct the three-dimensional radar reflectivity based on ground rain gauge observations. The improvement in the simulation of local heavy precipitation events of this type through application of radar radial velocity and reflectivity data and the variational method is investigated. The purpose of the study is to more effectively assimilate radar reflectivity information into the initial conditions of models, besides radial velocity, and improve the performance of the numerical simulation of mesoscale rainstorms.