Accurate global wind observations are crucial for improving numerical weather prediction (NWP) and climate reanalysis. With the successful operation of Aeolus, spaceborne Doppler wind lidar (SDWL) has emerged as a promising technique for observing vertical profiles of the global wind field. In the first part of this study, we developed a hydrometeor–laser optical property parameterization framework by integrating outputs from the Weather Research and Forecasting (WRF) model with ERA5 reanalysis data, and applied this framework to simulate SDWL observations for Typhoon Bebinca (2024). Building on this work, the present study further investigates the potential value of SDWL observations for tropical cyclone (TC) analysis and prediction, using Typhoon Bebinca as a representative high-impact case.,An observing system simulation experiment (OSSE) was conducted, in which three internally consistent SDWL wind products—Rayleigh-clear winds, Mie-cloudy winds, and a combined all-sky product—were continuously assimilated into the WRF model using a four-dimensional data assimilation (FDDA) framework. The results show that, despite the limited spatial and temporal coverage of SDWL observations, their assimilation can still improve tropical cyclone track and intensity forecasts. Rayleigh-clear winds, which sample the middle and upper troposphere, effectively constrain the large-scale circulation and thereby improve the environmental steering flow that governs TC motion. In contrast, Mie-cloudy winds, which are concentrated in the lower troposphere, provide critical information for improving the storm inner-core structure. The combined all-sky assimilation, which integrates both Rayleigh and Mie observations, performs best, yielding significant improvements in both track and intensity forecasts across all forecast periods, as well as a marked enhancement in precipitation prediction. In addition, the inclusion of SDWL observations improves the thermodynamic and moisture fields, indicating a synergistic enhancement of the model’s physical consistency. Overall, this study highlights the considerable potential of next-generation SDWL missions to enhance operational forecasting capability and improve predictions of tropical cyclones and other tropical convective systems.

Landfalling tropical cyclones (LTCs) undergo rapid structural adjustments and complex nonlinear interactions in coastal regions, making short-term prediction of heavy rainfall and damaging winds particularly challenging. Conventional intermittent data assimilation often introduces dynamical imbalances into the analysis fields, which may further deteriorate subsequent forecasts. This study investigates the landfall process of Typhoon Bebinca (2024) and systematically evaluates a set of ensemble-based assimilation experiments conducted within an Incremental Analysis Update (IAU) framework, incorporating multiple observation types, including radar reflectivity, Doppler radial velocity, and surface measurements. The results show that the IAU technique, through the gradual application of analysis increments within a four-dimensional time window, effectively suppresses initialization shocks, alleviates spurious dynamical imbalance, and preserves flow-dependent coordination. The IAU-based framework efficiently retains observational information, optimizes vortex structure, intensifies the warm core, and promotes the formation of a vertically coherent subsidence column within the eye region, thereby strengthening the secondary circulation. In addition, the IAU scheme also helps establish a more consolidated and axisymmetric moisture core, accompanied by a sea-level pressure field with smoother and dynamically coherent gradient structures, indicating a more physically balanced thermodynamic-dynamic coupling. These balanced analyses translate into more accurate forecasts of track, intensity evolution, and landfall-induced precipitation. Overall, the IAU-enhanced ensemble assimilation system substantially improves the physical consistency of storm analyses and significantly increases the short-term predictability of LTC track, rainfall, and wind hazards over coastal urban regions.

Data assimilation (DA) aims to achieve consistent atmospheric analyses with observations and numericalmodel forecasts. However, the increasing trend in forecast resolution and observation richness places an increasing compu-tational burden on DA. To address this challenge, we develop a novel latent space DA (LSDA) framework that performsefficient DA in a reduced-dimensional latent space learned by an autoencoder from numerical atmospheric states. Distinctfrom previously reported LSDA methods, our approach introduces an extra neural network, observation to latent spacemapping network (O2Lnet), trained on simulated observations derived from model states, to map real observations ontothe autoencoder (AE) latent space. The observation-derived latent state obtained by O2Lnet can then be directly decodedto obtain the analysis in model space using the decoder component of the autoencoder. In Part I, we aim to demonstratethe feasibility of this observation-only analysis method, denoted as LSDA-OOA, by inferring 2-m temperature (T2) analy-ses on 1-km grids with both idealized and real T2 observations. The idealized experiments demonstrate that given sufficientobservations, LSDA-OOA can yield high-quality analyses while exhibiting a favorable resiliency to random observation er-rors. When applied to analyze real T2 observations, LSDA-OOA produced T2 analyses with an accuracy comparable tothe Weather Research and Forecasting (WRF) Model in four-dimensional DA (FDDA) method. In particular, it greatlyoutperforms WRF-FDDA for the cases containing larger errors in forecasts (backgroundfields). Finally, we replace thetraining data from WRF-FDDA analyses with the forecasts instead andfind that this only results in a small increase in theerror in the LSDA-OOA analyses.

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models.

This two-part study introduces a novel latent space data assimilation (LSDA) framework comprised of an autoencoder-observation to latent space, referred to as the AE-O2L network. This network allows observation-only analysis (LSDA-OOA) as demonstrated in Part I. The present work (Part II) extends AE-O2L to incorporate background fields into the data assimilation together with observations, referred to as observation and background assimilation (LSDA-OBA). As in Part I, the 2-m temperature (T2m) of a 1-km-grid numerical weather prediction (NWP) system over a complex surface in eastern China is used to train and test the AE-O2L-based LSDA-OBA framework. The result shows that assimilating backgrounds through the latent space improves LSDA performance. LSDA-OBA also outperforms the variational LSDA (LSDA-Var) method, especially when observations are sparse. By assimilating 40 real observations, LSDA-OBA achieves analyses of 933 test cases with an MAE of 0.72 K as verified against the seven data-withheld stations versus 0.76 K for LSDA-Var. Furthermore, LSDA-OBA runs two orders of magnitude faster than LSDA-Var. Sensitivity experiments show that the increment of each element of the latent vector corresponds to a mode perturbation in the NWP model space, and this relationship is roughly linear. We also demonstrate that the space spanned by these modes approximates the decoding space of the autoencoder. When performing an LSDA process, different modes are activated for different weather scenarios. Furthermore, the accumulated effect of the most active modes can approximate the final analysis with proper structures and intensity, which explains how LSDA works with such a small latent space.

Rapid-update data assimilation (DA) cycles, particularly during the early stages of the assimilation process, often suffer from physical imbalances that degrade the quality of analyses and lead to a rapid decline in forecast skill. This study evaluates the impact of combining the incremental analysis update (IAU) method with the ensemble Kalman filter (EnKF) on the assimilation of observations from a Multi-Parameter Phased Array Weather Radar. A series of experiments were conducted for two convective precipitation cases using a numerical weather prediction model with a 500-m horizontal grid resolution and a 1-min DA interval. The results show that the IAU strategy effectively mitigates the imbalances introduced by intermittent EnKF assimilation. Moreover, IAU maintains a slightly higher ensemble spread while still effectively constraining the analysis toward observations, enhancing ensemble diversity without sacrificing accuracy. The time-continuous, four-dimensional assimilation provided by IAU enables the model to gradually develop and refine convective structures during the forward integration, resulting in a more pronounced surface cold pool and deeper updrafts, thereby slowing down the rapid decline of forecast skills, particularly in high-reflectivity regions. This study indicates that for convective-scale rapid cycling assimilation at minute intervals, combining IAU with EnKF is a superior approach for improving precipitation forecasts.

为提高对短时局地强降水系统演变的认识，探索短时强降水天气特征及成因，利用FY-4A气象卫星及天气雷达遥感产品，并借助地面及探空资料，对2019年7月27日冀南平原一次局地大暴雨过程进行中尺度时空演化特征分析。结果表明，本次过程是在副热带高压外围，受高空槽以及低层切变线影响生成的局地强降水天气。降水系统内的对流组织主要经历了“新生发展-合并加强-降水维持-移出消散”的演化过程。本次冀南平原地区大暴雨的成因主要包括：两个初期发展的对流系统的合并加强，前期外围对流活动提供了充沛的水汽环境，强对流系统形成了低层旋转、中层强辐合的中尺度垂直环流结构，各层辐合中心空间位置基本一致，有利于水汽供应并维持云中液滴的高效碰并增长。研究结果对冀南平原短时强对流天气的临近预报预警提供了科学依据。

Planetary boundary layer (PBL) is formed by the interaction of the atmosphere and the surface and surface conditions have an important influence on the PBL state. This study developed a method to retrieve temperatures in the lower troposphere using temperature reanalysis at 2 m (T2) with a U-net based deep learning model (briefly, lower troposphere retrieval with U-net, LTRUnet). High-resolution four-dimensional data assimilation (FDDA) reanalysis data over Southern China was selected to demonstrate the ability of the deep-learning network. The normalization method and the hyper-parameters of LTRUnet were determined through experiments. The results show that LTRUnet can project the temperatures in the layers below about 1.3 km with good accuracy, with a Mean Absolute Error (MAE) <0.5 K on the test set compared to the reanalysis. The performance of LTRUnet deteriorates with height and has a strong diurnal variation. By utilizing auxiliary physical information of the terrain height and the first-guess temperature (Tb) as additional channels of the LTRUnet input, the error (MAE) of the projected temperature was further reduced by 19.5%. Using the first guess background fields also allows LTRUnet to achieve the same prediction performance with much less training data. Sensitivity tests of the input variables indicate that the topographic information helps the model to better predict the topography-induced temperature distribution, and Tb plays a more significant role in higher levels than T2. Finally, by training LTRUnet independently for different phases of PBL with a physics-guided data grouping strategy, the model results are further improved.

A summer convective precipitation case, occurring in eastern China on 16-17 July 2020, is selected to investi-gate the impact of joint assimilation of ground-based profiling platforms and weather radars on forecasting convective storms using observational system simulation experiments (OSSEs). The simulated profiling platforms include the Doppler wind lidar (DWL), a wind profiler (WP), and a microwave radiometer (MWR). Results show that joint assimilation of WP and radar data produces a better analysis of convective dynamical structure than joint assimilation of DWL and radar data, since WP detects deeper layer winds. Joint assimilation of MWR and radar data enables rapid adjustment of temperature and humidity and thus, avoids the potential errors introduced by the latent heat term of the radar diabatic initialization in the early stage. Profiling observations in a horizontal spacing of 80 km provide fewer benefits for convective forecasting, while reducing the spacing to 40 km can dramatically improve model analysis and forecasts. Joint assimilation of multiple profiling observations in a 20-km horizontal spacing with radar data exhibits a beneficial synergistic effect and mitigates "the ramp-down issue" during the forecast stage. Assimilating profiling observations with an update interval less than 30 min does not have as pronounced an effect on convective forecasts as horizontal spacing. Furthermore, assimilating profiling ob-servations at a 20-km horizontal spacing can obtain accurate mesoscale background environment and forecast storms with an ability comparable to radar data assimilation. This work emphasizes the need to consider implementing a joint mesoscale detection system that incorporates weather radars and profiling observations for leveraging convective storm forecasting.

Forecasts of numerical weather prediction models unavoidably contain errors, and it is a common practice to post-process the model output and correct the error for the proper use of the forecasts. This study develops a grid-to-multipoint (G2N) model output error correction scheme which extracts model spatial features and corrects multistation forecasts simultaneously. The model was tested for an operational high-resolution model system, the precision rapid update forecasting system (PRUFS) model, running for East China at 3 km grid intervals. The variables studied include 2 m temperature, 2 m relative humidity, and 10 m wind speed at 311 standard ground-based weather stations. The dataset for training G2N is a year of historical PRUFS model outputs and the surface observations of the same period and the assessment of the G2N performance are based on the output of two months of real-time G2N. The verification of the real-time results shows that G2N reduced RMSEs of the 2 m temperature, 2 m relative humidity, and 10 m wind speed forecast errors of the PRUFS model by 19%, 24%, and 42%, respectively. Sensitivity analysis reveals that increasing the number of the target stations for simultaneous correction helps to improve the model performance and reduces the computational cost as well indicating that enhancing the loss function with spatial regional meteorological structure is helpful. On the other hand, adequately selecting the size of influencing grid areas of the model input is also important for G2N to incorporate enough spatial features of model forecasts but not to include the information from the grids far from the correcting areas. G2N is a highly efficient and effective tool that can be readily implemented for real-time regional NWP models.

Satellite visible radiance data that contain rich cloud and precipitation information are increasingly assimilated to improve the forecasts of numerical weather prediction models. This study evaluates the Data Assimilation Research Testbed (DART, Manhattan release v9.8.0), coupled with the Weather Research and Forecasting (WRF) model (ARW v4.1.1) and the Radiative Transfer for TOVS (RTTOV, v12.3) package, for assimilating the simulated visible imagery of the FY-4A geostationary satellite located over Asia in an Observing System Simulation Experiment (OSSE) framework. The OSSE was performed for the tropical storm Higos that occurred in 2020 and contains multi-layer mixed-phase cloud and precipitation processes. The advantages and limitations of DART for assimilating FY-4A visible imagery were evaluated. Both single-observation experiments and cycled data assimilation (DA) experiments were performed to study the impact of different filter algorithms available in DART, variables being cycled, observation outlier thresholds, observation errors, and observation thinning. The results show that assimilating visible radiance data significantly improves the analysis of the cloud water path (CWP) and cloud coverage (CFC) from first-guess forecasts. The rank histogram filter (RHF) allows WRF to more accurately simulate CWP and CFC compared with the ensemble adjustment Kalman filter (EAKF) although it took roughly twice as long as the latter. By cycling both cloud and non-cloud variables, specifying large outlier threshold values, or setting smaller observation errors without thinning of observations, WRF achieved a better simulation of CWP and CFC. With model integration, DA of the visible radiance data also generated a slightly positive impact on non-cloud variables as they were adjusted through the model dynamics and physics related to cloud processes. In addition, the DA improved the representation of precipitation. However, the impact on the rain rate is limited by the inabilities of the DA to improve cloud vertical structures and cloud phases. A negative impact of the DA on cloud variables was found due to the nature of the non-linear forward operator and the non-Gaussian distribution of the prior. Future works should explore faster and more accurate forward operators suitable for assimilating FY-4A visible imagery, techniques to reduce the non-linear and non-Gaussian errors, and methods to correct the location errors which correspond to the clouds underestimated by the first guess.

The terrain effect on atmospheric environment is poorly understood in particular for the polluted region with underlying complex topography. Therefore, this study targeted the Sichuan Basin (SCB), a deep basin with severe PM2.5 pollution enclosed by the eastern Tibetan Plateau (TP), Yunnan-Guizhou Plateaus (YGP) and mountains over Southwest China, and we investigated the terrain effect on seasonal PM2.5 distribution and the meteorological mechanism based on the WRF-Chem simulation with stuffing the basin topography. It is characterized that the three-dimensional distribution of topography-induced PM2.5 concentrations over the SCB with the seasonal shift of regional PM2.5 averages from approximately 30 mu g mxe213; 3 in summer to 90 mu g mxe213; 3 in winter at surface layer and from summertime 10 mu g mxe213; 3 to wintertime 30 mu g mxe213; 3 in the lower free troposphere. Such basin-forced PM2.5 changes presented the vertically monotonical declines concentrated within the lower troposphere below 3.6 km in spring, 2.3 km in summer, 2.6 km in autumn and 4.8 km in winter. Impacts of deep basin aggravated PM2.5 accumulation within the SCB and transport toward the surrounding plateaus contributing approximately 50-90% to PM2.5 levels over the regions of eastern TP and northern YGP. In the SCB, atmospheric thermal structure in the lower troposphere could build a vertical convergence layer between the boundary layer and free troposphere, acting as a lid inhibiting air diffusion, which was regulated by the terrain effects on interactions of westerlies and Asian monsoons, especially the wintertime strong warm lid deteriorating air pollution in the SCB. Furthermore, warm and humid air conditions within the basin prompted sulfur oxidation ratio by +0.02 and nitrogen oxidation ratio by +0.22 effectively producing the secondary PM2.5 in atmospheric environment.

Sea breezes are one of the most important weather processes affecting the environmental and climatic features over coastal areas, and the sea breeze from the Pearl River Estuary (PRE) has significant effects on the Pearl River Delta (PRD) region. We simulated a typical sea breeze process that occurred on 27 December 2020 in the PRD region using the Weather Research and Forecasting (WRF) model to quantify the effects of topography and city clusters on the development of the sea breeze circulation. The results show that: (1) the topography on the west coast of the PRD tends to block the intrusion of the sea breeze and detour it along the eastern part of the terrain in the southeast of Jiangmen. The depth of sea breeze along the position of the detour is increased by 120 m, the penetration distance is increased by 40 km, the maximum intensity of sea breeze decreases by ~0.4 m/s, and the time of maximum speed delays for 4 h. However, on the east coast, the topography promotes the sea breeze, resulting in an occurrence about 4 h earlier due to the heating effects. The depth and the speed of the sea breeze are increased by 466 m and 1.2 m/s, respectively. (2) Under the influence of Urban Heat Island Circulation (UHIC), the sea breezes reach cities near the coast an hour earlier and are later inhibited from propagating further inland. Moreover, a wind convergence zone with a speed of 3–5 m/s and a width of about 25 km is formed along the boundary of suburbs and cities in the PRD region. As a result, two important convergence areas: Foshan–Guangzhou, and Dongguan–Shenzhen are formed. (3) Overall, the topography has a more remarkable impact on the mesoscale wind field especially in the mountain and bay areas, resulting in an average speed disturbance of 2.8 m/s. The urban heat island effect is relatively small and on average it causes only ± 0.9–1.8 m/s wind speed perturbations in the periphery of two convergence areas and over PRE.

Abstract. Satellite visible (VIS) radiance data contain rich cloud information that are increasingly assimilated for improving cloud and precipitation forecasting of numerical weather prediction models. Recently, the Data Assimilation Research Testbed (DART), a widely used data assimilation resource that supports the Weather Research and Forecasting (WRF) model, was facilitated with an interface for the Radiative Transfer for TOVS (RTTOV), which supports radiance assimilation from visible (VIS) to microwave wavelength channels. This study evaluates the WRF (ARW v4.1.1)/DART (Manhattan release v9.8.0)-RTTOV (v12.3) system for assimilating the radiance data of channel 2 (0.55~0.75 μm) of the Advanced Geostationary Radiation Imager (AGRI) onboard FY-4. Observing System Simulation Experiments (OSSEs) were performed for a cyclone case. The results indicate that assimilating VIS radiance data improves cloud forecast skills in general. Best results were achieved for the data assimilation (DA) experiment with dense observations and high updating frequency. The best results could capture the “eye” structure of the cyclone system and significantly improves cloud water path and cloud coverage simulations. Nevertheless, three main problems were revealed. The first is its inability to improve cloud vertical distribution such as layered structures and cloud phases; The second is the its inability to influence atmosphere thermodynamic state variables positively; The third is a waste of up to 50 % observations during the filtering processes.

The model_codes.tar.gz file includes the source code of WRF-ARW (v4.1.1), WPS (v4.1), RTTOV (v12.3), and DART (Manhattan release v9.8.0). The input_file.tar.gz file includes the input namelist files for the WRF/WPS/DART models (tool) for the nature run, control run, and six data assimilation experiments. In addition, the input observation sequence file for the DART tool at each analysis time are provided for the six data assimilation experiments. More details are provided in the readme.txt file after unzipping input_file.tar.gz. The PROCESSED.tar.gz file includes the processed output files for the nature run, the control run, and six data assimilation experiments. More details are in the readme.txt file after unzipping PROCESSED.tar.gz. The Figure_script.tar.gz file includes the visualization scripts of figure 1~figure 12 in the manuscript. Each figure corresponds to a subdirectory which includes a readme.txt file for more detailed description.

The spatiotemporal statistical characteristics of warm-season deep convective systems, particularly deep convective systems initiation (DCSI), over China and its vicinity are investigated using Himawari-8 geostationary satellite measurements collected during April-September from 2016 to 2020. Based on a satellite brightness temperature multiple-threshold convection identification and tracking method, a total of 47593 deep convective systems with lifetimes of at least 3 h were identified in the region. There are three outstanding local maxima in the region, located in the southwestern, central and eastern Tibetan Plateau and Yunnan-Guizhou Plateau, followed by a region of high convective activities in South China. Most convective systems are developed over the Tibetan Plateau, predominantly eastward-moving, while those developed in Yunnan-Guizhou Plateau and South China mostly move westward and southwestward. The DSCI occurrences become extremely active after the onset of the summer monsoon and tend to reach a maximum in July and August, with a diurnal peak at 11–13 LST in response to the enhanced solar heating and monsoon flows. Several DCSI hotspots are identified in the regions of inland mountains, tropical islands and coastal mountains during daytime, but in basins, plains and coastal areas during nighttime. DCSI over land and oceans exhibits significantly different sub-seasonal and diurnal variations. Oceanic DCSI has an ambiguous diurnal variation, although its sub-seasonal variation is similar to that over land. It is demonstrated that the high spatiotemporal resolution satellite dataset provides rich information for understanding the convective systems over China and vicinity, particularly the complex terrain and oceans where radar observations are sparse or none, which will help to improve the convective systems and initiation nowcasting.

基于WRF四维资料同化和预报技术,初步发展了针对我国西北地区云微物理和播云催化技术的云解析人工影响天气模式系统(CR-WMM,Cloud-Resolvable Weather Modification Model).该模式耦合并改进了中国气象科学院发展的微物理方案(CAMS-MP)和碘化银(Agl)催化方案,并实现基于大涡模拟(LES)模式的飞机、地面烟炉等播撒源及毗邻区域Agl粒子扩散的精细模拟方法.选取降水案例对CR-WMM资料同化功能、CAMS-MP微物理参数化和Agl的催化数值模拟方案进行测试和评估,验证了该系统的资料同化能力、微物理参数化和Agl催化数值模拟方案的可靠性.CR-WMM具备连续同化常规和加密气象观测,特别是针对云微物理过程的新型卫星、云雷达和人工影响天气外场作业飞机和基地的特殊观测能力,能生成全面、精确的云和降水热力、动力和微物理分析场,支撑云和降水过程及云催化技术的理论研究及优化人工播云方案辅助决策.并提出为达到这一目标,CR-WMM模式在未来5—10年应集中攻克的五个方面的科学难题.

Radar data are essential to convection nowcasting and nudging-based radar data assimilation through diabatic initialization is one of the most effective approaches for forecasting convective systems with numerical weather prediction (NWP) models, used at several advanced global weather centers. It is desired to assess the uncertainty and physical consistency of this assimilation process. This paper investigated impacts of relaxation coefficient, radar data update intervals and continuous assimilation time duration and addressed the key issues and possible solutions of the radar data assimilation based on the WRF hydrometeor and latent heat nudging (HLHN) developed at the National Center for Atmospheric Research (NCAR). It is revealed that excessively large relaxation coefficient forced the model to observations with a tendency greater than the physical terms of the convection, causing the dynamic imbalances and serious convection “ramp-down” right after the free forecast starts. Assimilating high update frequency radar data can make the tendency terms moderate and sustained thereby maintaining the assimilation effect and reducing fortuitous convection. HLHN requires a minimum continuous assimilation duration to contain the initial forced disturbance of the model. For a summer Meiyu precipitation case studied, the minimum duration is ~1 h. Appropriate selection of the HLHN parameters is able to effectively improve the temperature, humidity, and dynamic fields of the model. In addition, several issues still remain to be solved to further enhance HLHN.

Doppler spectra measured by vertically pointing radars are inherently linked to raindrop size distributions (DSDs). But, accurate estimation of DSDs remains challenging because raindrop spectra are broadened by atmospheric turbulence and shifted by vertical air motions. This paper presents a novel method to estimate vertical air motions that there is no need to assume a model for DSD at each range gate. The theory of the new method is that the spectral difference between the adjacent range gates is contributed by vertical air motions and the variability of DSDs. The contribution of the change of DSDs is estimated by looking up the prepared tables (LUTs) of raindrop velocity difference and shape function difference. Then, the vertical air motions can be estimated by minimizing the cost function of the two spectra between the adjacent range gates. The retrieval algorithm is applied to three cases including a stratiform and two convective observed by a C band vertically pointing radar in Longmen, Guangdong province of China in June 2016. Before that, the spectrum broadening effect is removed by the traditional deconvolution method with a wind profiler. The vertical profiles of precipitation parameters are also retrieved to investigate the microphysical process. The precipitation parameters retrieved near the surface are compared with the ground data collected by a two-dimensional video disdrometer(2DVD) and the results show good agreements.

The raindrop size distribution (DSD) characteristics during the precipitation season are analyzed using data collected by an OTT Particle Size Velocity (Parsivel) disdrometer and a Vertical Pointing Radar with C-band Frequency Modulation Continuous Wave (VPR-CFMCW) technology. The two datasets were collected at the same site in Longmen, Guangdong Province, which is the precipitation center of south China, from June to July in 2016 and 2017. We evaluate different fitting methods for the gamma model function and choose a nonlinear least-squares method to fit DSD. Based on the radar reflectance obtained by VPR-CFMCW, the precipitating clouds that occur during the summer precipitation season in south China are classified into four types (i.e., convective, stratiform, mixture, and shallow). The characteristic parameters and the gamma model parameters of different precipitation types are compared. Avoiding the limitations of rainfall classification at the surface, the new classification quantifies the characteristics of mixture and shallow precipitation. The results show that the stratiform precipitation makes up 43.1% of the summer precipitation process in south China, and the contribution of convective precipitation to total rainfall is 62.7%. The precipitation parameters of the four types of precipitation, such as the rain rate (R), the mass-weighted mean diameter (Dm), the radar reflectance (Z), and the liquid water content (LWC), follow the pattern: convective > mixture > stratiform > shallow. The DSD characteristics of the four precipitating cloud types are investigated. For the DSD of convective and mixture precipitation, the spectra width is similar but the rain drop concentration of the mixture is smaller. For the DSD of stratiform and shallow clouds, the rain drop concentrations are similar, but the spectra width of the shallow clouds are smaller. In addition, the relationships between μ-Λ, Dm-Nw, Dm-R, and Z-R are obtained. These new relationships will help improve the accuracy of precipitation estimation and deepen the understanding of the characteristics of surface precipitation microphysical parameters for different types of precipitating clouds in south China.