Abstract. The phase state of cloud particles and their transformation processes are fundamental to understanding cloud and precipitation formation, variations in radiative energy budgets, and the evolution of severe weather systems. At present, for small-scale cloud particles smaller than 50 μm, conventional identification methods based on morphological features are readily constrained by imaging resolution and feature overlap, making high-accuracy phase discrimination challenging. To address this issue, we propose a polarization tomographic imaging method that integrates digital holography and polarimetric imaging, enabling the simultaneous acquisition of morphological parameters of individual particles within a particle ensemble, including three-dimensional coordinates, particle size, area, perimeter, the major and minor axes of the minimum-area bounding rectangle, and circularity, together with polarization parameters. Through observation experiments in an ice cloud chamber, liquid droplet and ice crystal samples smaller than 50 μm were analyzed. By combining morphological and polarization parameters, accurate identification of liquid droplets and ice crystals in mixed-phase particles was achieved, with an accuracy of 98.33 %, representing an improvement of 4.16 percentage points over the conventional circularity-based identification method. In addition, a continuous 10 s observation window during the late stage of mixed-phase cloud glaciation in the ice cloud chamber experiment was selected to calculate liquid water content, the number concentrations of liquid droplets and ice crystals, and their particle-number fractions. The results show that the droplet number concentration and liquid water content decrease with time, whereas the relative fraction of ice crystals gradually increases, reflecting the microphysical process of liquid water conversion into ice in a mixed-phase environment. This method can not only effectively improve the accuracy of phase identification for small-scale cloud particles but also provide information on the microphysical evolution of cloud particles, offering important support for studies of cloud microphysical processes, improvement of model parameterizations, weather modification, and early warning of severe weather events.

The retrieval of cloud particle effective radius(re)from satellite remote sensing is a critical technique for studying cloud microphysical properties and precipitation processes.It has significant applications in aerosol-cloud interactions,severe convective weather monitoring and early warning,and weather modification.Accuracy validation of the retrieved re is essential for these applications. Based on improved satellite retrieval algorithm for cloud particle effective radius using 3.7 μm channel data,this study derives cloud particle effective radius(re_o)from MODIS and AVHRR observations.The retrieved re_o are systematically compared with in-situ re measurements by aircraft from 22 cases of continental cumulus cloud,and the algorithm's reliability and accuracy is evaluated. The comparisons show that the error of particle effective radius between the retrieval and airborne measurements is less than 2.4 μm,which is very close to 2 μm of international verification results within marine stratus.The distribution of re with temperature/height(vertical structure)is quite consistent with that detected by aircraft measurement.The retrieved re from the 3.7 μm has a high correlation with the airborne measurement,with a correlation coefficient of 0.79 and a linear fitting slope of 0.81.However,the re from MODIS cloud product has a low correlation with the airborne measurement,and the correlation coefficient and linear fitting slope are 0.43 and 0.32,respectively.All these results suggest high accuracy of the retrieved particle effective radius of clouds and the high reliability of the retrieved methodologies which demonstrate that improved algorithm can provide a reliable data foundation for applications.

Cloud seeding models are essential for understanding seeding mechanisms, yet their reliability remains insufficiently verified due to limited cases with confirmed seeding effects. On 19 March 2017, significant seeding signals were observed by multiple instruments following airborne cloud seeding over a stratiform cloud system with abundant supercooled water in northern China. This study performed an ensemble simulation of the case using two cloud microphysics schemes and three silver iodide (AgI) nucleation parameterizations, successfully replicating the vertical structure and evolution of the seeding‐induced cloud. The simulated seeding impact area, precipitation intensity, and changes in raindrop spectra closely aligned with observations. Results indicate that cloud seeding increased ice crystal amounts primarily through the deposition nucleation of AgI particles, activated the auto‐conversion of ice crystals to snow, enhanced snow deposition and riming processes, and ultimately increased surface precipitation through enhanced snow melting.

Objective As an important component of the cloud droplet spectrum, ice crystals exert important effects on global radiation budget balance, global climate change, hydrological cycle, and weather modification. Due to the limitation of observation means, the understanding about the microphysical characteristics of ice crystal particles is not perfect till now. At present, it is difficult to identify the mixed phase of 2-100 mu m ice crystals from droplets, and there is a bottleneck to provide microphysical parameters of ice crystals. The lack of sufficient ice crystal detection data can cause large differences in the mean value of ice water paths in different models, especially in mixed-phase clouds. Therefore, we study the microphysical parameters of ice crystals. Methods For these two problems, based on the digital holography theory, we propose to employ the global digital image fusion method, the local Tenengrad variance method, and the rotating caliper method for identifying mixed phase states of droplets and ice crystals in the cloud by combining the roundness concept of solid and liquid particles. Combined with optical image recognition technology, we obtain the area, perimeter, convex hull, and minimum enclosing rectangle data of ice crystals. Finally, the microphysical parameters of ice crystals are acquired by adopting the above data. The microphysical parameters of plate, dendritic and hexagonal ice crystals are obtained by observation experiments in low-temperature cloud chambers. Results and Discussions Some obvious conclusions can be obtained by adopting the proposed method. 1) By leveraging the rotating caliper method and the specific geometric parameter roundness F, the mixed phase identification of droplets and ice crystals in clouds is realized. Under the specific roundness threshold, the recognition rate of droplets and ice crystals is greater than 93% (Fig. 8). 2) Combined with optical image recognition technology, morphological data (the area, perimeter, convex hull, and minimum enclosing rectangle data) of ice crystal particles are obtained (Table 1). 3) The microphysical parameters of ice crystals are acquired by morphological data of ice crystals (Table 2). 4) When the digital hologram of ice crystal particles is obtained with the frequency of 30 frame/s, the three-dimensional kinetic velocity of ice crystal particles can also be acquired by this method (Fig. 11). Conclusions An ice crystal detection method based on a pulse-modulated laser, high-resolution optical system, and coaxial digital holography (DH) is presented. The local Tenengrad variance method, the rotating caliper method, and the specific geometric parameter roundness F are adopted for phase state identification of particles. To verify the validity of the detection method and identification algorithm, we observe the mixed particles of droplets and ice crystals in the cloud chamber. Additionally, the three-dimensional motion velocity and trajectory of ice crystals can be obtained from the sampling interval time and the three-dimensional coordinates and equivalent diameters of the center of mass at different time. This method solves the bottleneck problem that the existing observation technology cannot identify the phase states and obtain the microphysical parameters of ice crystals. Meanwhile, the method is of significance to improve the accuracy of numerical weather prediction and weather modification operation.

Robust water management is in intense demand in many water scarcity areas, such as arid and semi-arid regions in the world. As part of the regional water management strategy, rain enhancement is vital to replenish groundwater reservoirs, and the key challenge is how to assess its effectiveness. Some recent weather modification experiments attained cloud seeding effect through advanced in situ measurement coupled with accurate numerical simulation. However, there is still a lack of an objective and scientific approach to quantitatively evaluate the rain enhancement effect, especially for many non-randomized operational cloud seeding activities in China. In this study, we proposed a composite evaluation approach by analyzing two operational aircraft cloud seeding cases in stratus clouds in Shaanxi, China. By calculating the aircraft cloud seeding agent plumes, the target areas (as well as the control areas) of cloud seeding were dynamically and roughly determined. Physical properties, such as radar reflectivity and precipitation, were individually quantified in these areas. The cloud seeding effect was then evaluated by calculating the difference in parameter variation between target and control areas. This approach can be applied to qualitative analysis in a single aircraft cloud seeding operation and can also provide quantitative statistical results from multiple cloud seeding cases. We found that the average precipitation enhancement percentage of 18 operational aircraft cloud seeding cases is ~4.84%. Note that the homogeneity hypothesis of the seeding cloud, the error in the calculation of the target area, and the selection of control areas are the major uncertainties likely in the evaluation of the cloud seeding effect by this approach.

Cold clouds are the main operation target of artificial precipitation enhancement, and its key is to find a supercooled cloud water area where the catalyst can be seeded to promote the formation of precipitation particles and increase precipitation to the ground. Based on the multi-spectral characteristics of the Fengyun-4A (FY-4A) satellite, a methodology for identifying supercooled cloud water is developed. Superimposed by a cloud top brightness temperature of 10.8 µm, a combination of 0.46 µm, 1.6 µm, and 2.2 µm red–green–blue (RGB) composites are used to identify the cloud phase and to obtain the real-time supercooled cloud water distribution every 5 min and in a 2 km resolution for the whole coverage of China. Based on the RGB composition, the supervised machine learning method K-mean clustering was applied to classify the cloud top phase. The results were validated extensively with Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP). It is worthwhile to highlight that the corresponding hit rate reached 87% over the full disk domain for both the summer and winter seasons. Furthermore, on 29 November 2019, microphysical properties were measured, and the data of supercooled cloud droplets and ice crystals were obtained using YUN-12 transport aircraft in Taiyuan. After simultaneously matching the satellite with an airborne track, the cloud particle image data were obtained near the cloud top and within the clouds during the climb and descending stages of the flight. The phase obtained from the microphysical properties of supercooled cloud droplets and ice crystals was compared with cloud phase results identified by FY-4A and Moderate Resolution Imaging Spectroradiometer (MODIS) cloud phase products. The case study and comparison show that (1) the supercooled water clouds and ice particles identified by FY-4A are in good agreement with those from the airborne measurement at the cloud top and within the cloud and (2) the positions and shapes of water clouds and ice clouds identified by FY-4A correspond well with MODIS cloud phase products. However, there is a small deviation in the extent of ice clouds, which is mainly located in the transition area between ice clouds and water clouds. The extent of ice clouds identified by FY-4A is slightly larger than that of MODIS products. Combined with airborne detection, the comparison shows that the ice clouds identified by the FY-4A satellite are consistent with aircraft detection. The supercooled cloud water identified by FY-4A can meet the needs of the operational precipitation enhancement of cold clouds, improve operational effectiveness, and promote the application of satellite technology for weather modification.

On 29 November 2019， there was a snowfall process in Shanxi affected by the reflux inverted trough system.Based on the airborne cloud physics data， the evolution characteristics and ice crystal particle growth of the snowfall process were analyzed.The results show that snowfall radar echo was embedded with 15~25 dBZ block echo in the uneven sheet echo with intensity of 5~15 dBZ.In the first detection the liquid water content was mainly distributed in the upper part of the cloud.When the cloud top temperature was -10 ℃， the ice and snow crystals were plate， and the ice and snow crystals observed in the cloud were mainly plate and needle.The maximum ice crystal number concentration was 297 L-1， which generated by deposition growth to less than 200 μm.The maximum diameter of snowflakes was 1700 μm located at 2000 m.Below 2000 m， most snowflakes did not reach the ground due to the decrease of relative humidity.In the second detection there was basically no liquid water in the cloud.When the cloud top temperature was -12.5 ℃， the ice and snow crystals were dendrite.There were a large number ice and snow crystals of plate， needle， dendrite and aggregate observed in the cloud.The maximum N200（Ice and snow crystal number concentration which particle diameter greater than 200 μm）and maximum diameter of ice and snow crystals appeared at -11 ℃ and -2 ℃ respectively through condensation and aggregation growth.The second peak appeared in the crystal spectrum of ice and snow.The maximum diameter of snowflakes near the ground was 2800 μm.Through the observation of massive snowfall， it was found that the horizontal distribution of microphysical characteristics of particles in cloud was uneven.The cloud with high ice water content and ice snow crystal number concentration were dominated by large dendritic ice and snow crystals and aggregates.Aggregation growth was an important way of snow growth.The phase states of the particles in the cloud retrieved by the FY-4A satellite were consistent with those measured by the aircraft.

The identification of raindrops and hailstones is of great significance to the study of precipitation characteristics from the aspect of microphysics and can provide important data support for weather modification. In this paper, an identification method of raindrops and hailstones based on digital holographic interference is proposed. The grayscale gradient variance method is used to obtain the focus position of the particles. By means of binarization and morphological processing, digital holograms are processed to obtain clear profiles of the particles. Then the contour parameters of the particles are used to obtain the equivalent volume diameter and roundness. Finally, according to the equivalent volume diameter, roundness and lens-like effect of the particles, the phase states of the raindrop and hailstone are identified by the algorithm. Experiments show that the method proposed in this paper has a good identification effect on raindrops and hailstones. The research results can provide reference for the research of the identification method of raindrops and hailstones and the acquisition of accurate characteristic parameters.

Abstract To accurately identify ideal operating conditions for scientific cloud seeding and to collect evidence of the effects of seeding, a set of three flights, with figure‐8‐like paths, was designed for supercooled liquid water detection. These flights were conducted near the cloud top before seeding, during seeding, and upon re‐entry. Using both in situ aircraft and satellite remote sensing observations, a comprehensive analysis was conducted to understand the response of cloud properties to seeding within supercooled cloud tops. Satellite observations showed clear icing cloud tracks after seeding with abundant supercooled liquid water near the cloud top. Radar observations confirmed enhanced radar reflectivity echoes in cloud‐seeding regions. For regions cloud seeded with supercooled liquid water, the liquid droplets quickly froze into ice crystals, which further grew into large ice crystals via vapor deposition, riming, and collision‐coalescence processes. With these processes, large ice crystals formed and rapidly fell downward, causing the sinking of cloud tops and precipitation to fall to the ground. By contrast, there were no such phenomena in the regions without supercooled liquid water or in those regions with supercooled liquid, but otherwise without seeding. In addition, radar reflectivity echoes quickly decreased in all regions without seeding. The evolutionary characteristics of radar echoes and in situ observed cloud and precipitation properties suggest that the response of cloud microphysical characteristics to seeding varies with time and location due to various seeding potentials, leading to different formation conditions of both cloud‐top icing and cloud tracks.

利用陕西、山东、贵州和新疆等地近十年日间降雹记录和对应的极轨卫星数据,采用卫星云微物理反演技术,定量分析冰雹云微物理特征,比较不同地区间差异,并利用FY-4A静止卫星定量分析一次冰雹过程云微物理特征演变,探讨冰雹云卫星识别预警应用潜力.结果 表明:(1)陕西、山东等地冰雹云微物理特征具有一致性,卫星早期识别指标为:晶化温度(Tg)较冷,均值为-33℃;全部冰晶化时Tg对应的云粒子有效半径re(表征为reg)未饱和(＜40 μm),均值36.9 μm,且reg越小冰雹云越强;云顶呈现re随高度减小带.(2)各地冰雹云早期识别指标在数值上存在一定差异,实际应用时应针对各地进行相应调整.(3)在静止卫星上,冰雹云微物理特征与极轨卫星相一致,将早期识别指标应用于FY-4A静止卫星,跟踪云团发展演变,实现自动预警.(4)经过4次降雹过程中应用,FY-4A卫星自动预警与实况吻合22次,漏报2次,自动预警平均提前约2小时.FY-4A卫星自动预警对及时有效组织实施人工防雹作业具有重要现实意义.

利用微波辐射计分析了秦岭南北的水汽、液态水含量、湿度、云底高度等特征,结果表明:秦岭北垂直积分水汽量年平均为18.52 kg·m-2,秦岭南为20.94 kg·m-2,90％以上水汽秦岭北平均高度为4.26 km,秦岭南为3.87 km;垂直积分液水含量,秦岭南年平均为0.13 kg·m-2,秦岭北年平均为0.12 kg·m-2,两者相差不多;秦岭腹地的空气湿度大,秦岭南年平均相对湿度75.3％,秦岭北年平均相对湿度为59.8％,秦岭南比秦岭北平均相对湿度大15.6％;云底高度,秦岭南年平均为3817.5 m,秦岭北年平均为4396 m,中云云底高度年平均差异不大;降雨时秦岭南云底年平均高度为323.3 m,较秦岭北低,二者相差42.2 m.

The entrainment rate (λ) is difficult to estimate, and its uncertainties cause a significant error in convection parameterization and precipitation simulation, especially over the Tibetan Plateau, where observations are scarce. The λ over the Tibetan Plateau, and its adjacent regions, is estimated for the first time using five-year satellite data and a reanalysis dataset. The λ and cloud base environmental relative humidity (RH) decrease with an increase in terrain height. Quantitatively, the correlation between λ and RH changes from positive at low terrain heights to negative at high terrain heights, and the underlying mechanisms are here interpreted. When the terrain height is below 1 km, large RH decreases the difference in moist static energy (MSE) between the clouds and the environment and increases λ. When the terrain height is above 1 km, the correlation between λ and RH is related to the difference between MSE turning point and cloud base, because of decreases in specific humidity near the surface with increasing terrain height. These results enhance the theoretical understanding of the factors affecting λ and pave the way for improving the parameterization of λ.

When comparing the consistency of the cloud vertical structure detected by L-band radiosonde and millimeter-wave cloud radar， the observation error caused by the drift deviation of radiosonde balloon shouldn’t be ignored.Therefore， the principle of time matching and space matching of screening the samples is put forward which can effectively reduce it.By analyzing a total of 406 pairs of cloud base and top height samples observed by radiosonde and cloud radar at Xi’an Jinghe Meteorological Station over a period of 508 days from August 17， 2017 to December 31， 2018.The results showed that when using the principle of time and space matching to screen cloud height samples， the correlation coefficients of them were significantly improved and the errors caused by the balloon drifting were effectively reduced.The correlation coefficients of the cloud base/top height samples increased from 0.70/0.66 to 0.98/0.97 by using the time matching and space matching principle， and the mean square root errors decreased from 2009 m/2148 m to 602 m/708 m， and the mean absolute percentage errors reduced from 47%/47% to 14%/9%.Using only the time matching principle can greatly improve the correlation coefficients of cloud base height samples， which average value increased from 0.35 to 0.86.When filtering the cloud top heights， the principle of time and space matching must be used at the same time to effectively improve the consistency of the two observations.Whether or not the time and space matching principle are used to select the samples， the cloud base and top heights observed by radiosonde are higher than those observed by cloud radar.In addition， the correlation coefficients of the cloud base and top height samples do not decrease with the increase of their relative distances.

Operational cloud seeding has been implemented to alleviate local precipitation shortages in China for over half a century. Here, we present quantitative evidence for the effect of AgI seeding on supercooled layer clouds with a top cloud temperature of −15°C in China, as documented for the first time by a combination of radar, satellite, and disdrometer observations. A radar signature appeared 18 min after seeding, shortly followed by a visible glaciated seeding track. The seeding signature expanded horizontally at a rate of ∼1.4 and ∼0.3 m s −1 before and after 04:25 UTC. The radar signature descended to the surface 40 min after seeding. A disdrometer captured the precipitation of the first seeded raindrops that reached maximum diameter of 2.75 mm compared to the maximum diameter of 1 mm of the light background rain. The enhanced surface rainfall was observed within the subsequent 100 min. A conceptual model for the formation and expansion of the seeding track is presented. Although the precipitation was light, it is a promising step toward the goal of quantifying the impact of cloud seeding in China.

The ice crystal habits, distributions and growth processes in two snowfall cloud cases on 29 November 2009 and 3 March 2012 in northern China were compared and analyzed with aircraft data. The results showed that ice crystal habits were affected by the height of ice clouds. Ice crystals in clouds with cloud top temperatures of −12.6 °C were predominantly needle, plate, dendrite and irregular. When the cloud top temperature was lower than −19.5 °C, plates, dendrites and irregular ice crystals were observed in addition to needles, capped-column crystals were observed in the lower and middle layers of clouds, and column crystals were observed in the upper layer of clouds. The liquid water content of the two snowfall processes was lower than 0.1 g·m−3. Ice particles grew mainly via deposition, riming and aggregation processes. On 29 November, the liquid water content of the stratospheric mixed snowfall cloud was distributed in the lower part of the cloud. The maximum values of particle concentration and ice water content detected by a cloud imaging probe were 187 L−1 and 1.05 g·m−3, which were at −8.7 °C, and the ice water content was higher. On 3 March, the liquid water content of snowfall in stratiform clouds was located in the middle layer, and the maximum ice water was low, which was only 0.052 g m−3. The ice water value on 29 November was higher, which was mainly due to the convective zone embedded in the cumulus mixed cloud containing a large number of riming and aggregated snow crystals. Using an exponential function to fit the crystal spectrum of the two snowfall processes, N0 and λ were 109−1011 m−4 and 108−1010 m−4 and 103−104 m−1 and 104 m−1, respectively. Compared with 3 March, N0 on 29 November was larger and the variation range of λ was one more order of magnitude. N0 and λ conformed to a power function distribution. By analyzing the scatter plot of the correlation coefficient and slope, it was found that the exponential function can accurately express the crystal spectrum of snow clouds.

西北区域人工影响天气能力建设项目预期利用已有和本项目即将建设的装备设施,通过科学设计的专项研究,开展针对西北区域地形云的人工增雨(雪)试验研究.在工程项目建设中设立研究试验内容,旨在通过项目建设中同步实施试验研究,充分体现科技支撑能力在工程项目中的重要作用.总结了西北区域人工影响天气能力建设项目中研究试验的设计和实施过程,依据建设经验,提出提高工程项目效益的建议,为相关工程项目建设提供参考.

西北人影工程在西北地区选取重点区域建设人工影响天气试验示范基地,合理设计观测仪器设备布局,建立了涵盖中尺度水汽、风场监测、云降水宏观场监测、云降水微观场探测的大气、云和降水宏微观三维结构及湿热力、动力综合监测网;在基地科学设计外场试验区,开展外场作业试验,开展新型催化作业装备和催化剂研发.基地的建设能够有效地促进我国人影业务自主创新,为西北区域和全国人影业务发展提供有力的科技支撑,提高作业效率和水平,提高西北区域人工影响天气作业实际效益.目前该基地已基本完成观测系统以及业务平台建设,观测资料已在研究试验中得到有效应用.

最近60多年,全球范围内广泛开展了人工增雨作业,但人工增雨效果检验一直是个难题.传统上,利用雨量计和目标/对比区统计数据评估人工增雨效果,结果大多不确定.对一次人工增雨作业而言,从科学上给出令人信服的效果检验更是没有好的解决方案.2017年3月19日,陕西省实施业务飞机冷云增雨作业播撒含有750 g碘化银(AgI)的催化剂,播撒线长125 km.作业后卫星、雷达观测到一条与播云线对应的清晰的云迹线,地面雨滴谱仪观测到相应的雨强、雨滴数浓度、雨滴直径增大,表明播云使云体产生了增雨响应.针对这次增雨过程,从连片雷达回波中分离增雨作用造成的回波增强带(增雨影响回波)和确定了自然降水回波强度,建立增雨影响回波强度(Z)与地面雨强(I)的拟合关系(Z-I关系),定量研究人工增雨的时、空演变.结果表明:(1)增雨影响时间约4 h,增雨影响回波区域(增雨影响区)面积为5448 km2.该区累计降雨总量和增雨总量分别为1.518×106 m3和8.04×105 m3,增雨影响区内增雨率达53％.(2)总降雨量、增雨量、自然降雨量随时间先增后减,总降雨量与增雨量的峰值同步,两者峰值都早于自然降雨峰值;催化后146 min(04时47分,世界时,下同),每6 min增雨量达到最大,为4.9×104 m3;催化后174 min(05时15分),增雨雷达回波面积达到最大(1711 km2),面积峰值滞后增雨量峰值出现.(3)增雨影响区位于播撒线下游,呈条带状;区域内总降雨量空间分布为中间大边缘小,与增雨量空间分布一致.(4)此次增雨作业改变了降雨时、空分布,促进降雨形成,增加了地面降雨量.

简要阐述了开展陕西渭北果业区防雹技术研究试验的重要意义,讨论了渭北果业区冰雹研究在气候、雷达回波、雹云探空、冰雹微物理、数值模拟和防雹效果等方面的进展,并对研究试验中存在的问题进行了讨论.这些研究加深了渭北冰雹形成过程的认识,对研究高效冰雹防御方法和提高冰雹防御效果有重要意义.

根据2013—2014年5—10月西安地区观测得到的雨滴谱数据,结合C波段新一代多普勒天气雷达的观测资料,对西安地区43次积层混合云降水的平均雨滴谱分布、微物理特征量及雷达反射率因子Z和雨强R的关系进行统计分析.结果表明:积层混合云降水的平均雨滴谱呈单峰型,Gamma分布对降水大粒子的拟合明显优于M-P分布;积层混合云中雨滴数浓度最大值及对雨强贡献最大值均出现在雨滴直径小于1 mm的范围内;利用最小二乘法建立了西安地区积层混合云的Z-R关系Z=168R1.43;当雨滴谱数据计算的回波强度小于(大于)30 dBz,雷达对回波强度有明显高估(低估)现象,针对此现象提出了积层混合云雷达回波的5档修正方案;利用Z=168R1.43估算西安积层混合云降水个例的降雨量更接近实测降雨量,估算降雨量的相对误差从51.3％减小到25.4％.