Unveiling Cloud Vertical Structures over the Interior Tibetan Plateau through Anomaly Detection in Synergetic Lidar and Radar Observations

Cloud vertical structure(CVS)strongly affects atmospheric circulation and radiative transfer.Yet,long-term,groundbased observations are scarce over the Tibetan Plateau(TP)despite its vital role in global climate.This study utilizes ground-based lidar and Ka-band cloud profiling radar(KaCR)measurements at Yangbajain(YBJ),TP,from October 2021 to September 2022 to characterize cloud properties.A satisfactorily performing novel anomaly detection algorithm(LevelShiftAD)is proposed for lidar and KaCR profiles to identify cloud boundaries.Cloud base heights(CBH)retrieved from KaCR and lidar observations show good consistency,with a correlation coefficient of 0.78 and a mean difference of-0.06 km.Cloud top heights(CTH)derived from KaCR match the FengYun-4A and Himawari-8 products well.Thus,KaCR measurements serve as the primary dataset for investigating CVSs over the TP.Different diurnal cycles occur in summer and winter.The diurnal cycle is characterized by a pronounced increase in cloud occurrence frequency in the afternoon with an early-morning decrease in winter,while cloud amounts remain high all day,with scattered nocturnal increases in summer.Summer features more frequent clouds with larger geometrical thicknesses,a higher multi-layer ratio,and greater inter-cloud spacing.Around 26%of the cloud bases occur below 0.5 km.Winter exhibits a bimodal distribution of cloud base heights with peaks at 0-0.5 km and 2-2.5 km.Single-layer and geometrically thin clouds prevail at YBJ.This study enriches long-term measurements of CVSs over the TP,and the robust anomaly detection method helps quantify cloud macro-physical properties via synergistic lidar and radar observations.

Microphysical Characteristics of Rainfall Based on Long-Term Observations with a 2DVD in Yangbajain,Tibet

Raindrop size distribution(DSD)plays a crucial role in enhancing the accuracy of radar quantitative precipitation estimates in the Tibetan Plateau(TP).However,there is a notable scarcity of long-term,high-resolution observations in this region.To address this issue,long-term observations from a two-dimensional video disdrometer(2DVD)were leveraged to refine the radar and satellite-based algorithms for quantifying precipitation in the hinterland of the TP.It was observed that weak precipitation(R<1,mm h-1)accounts for 86%of the total precipitation time,while small raindrops(D<2 mm)comprise 99%of the total raindrop count.Furthermore,the average spectral width of the DSD increases with increasing rain rate.The DSD characteristics of convective and stratiform precipitation were discussed across five different rain rates,revealing that convective precipitation in Yangbajain(YBJ)exhibits characteristics similar to maritime-like precipitation.The constrained relationships between the slopeΛand shapeμ,D_(m)and N_(w)of gamma DSDs were derived.Additionally,we established a correlation between the equivalent diameter and drop axis ratio and found that raindrops on the TP attain a nearly spherical shape.Consequently,the application of the rainfall retrieval algorithms of the dual-frequency precipitation radar in the TP is improved based on the statistical results of the DSD.

利用Ka波段云雷达对青藏高原三类重要天气系统云宏观参数日变化特征的研究

青藏高原上空云宏观参数的日变化受大尺度环流、当地太阳辐射和地表过程的联合作用,对辐射收支、辐射传输及感热、潜热的分布等有重要影响。由于缺乏持续定量的观测,对各类天气系统云宏观参数日变化特征的了解还十分不足。多波段多大气成分主被动综合探测系统APSOS(Atmospheric Profiling Synthetic Observation System)的Ka波段云雷达是首部在青藏高原实现长期观测云的雷达。本文基于2019年全年APSOS的Ka波段云雷达资料,采用统计和快速傅里叶变换方法研究了西风槽、切变线和低涡三类重要天气系统影响下的有云频率、单层非降水云或者降水云非降水时段的云顶高度、云底高度和云厚日变化的时域和频域特征,得到了统计回归方程。主要结论有:(1)西风槽系统日均有云频率为56.9%,切变线系统为50.8%,低涡系统达73%。(2)尽管西风槽和切变线系统的成因不同,但两类系统云宏观参数的日变化趋势和主要谐波周期相似:日变化趋势基本为单峰单谷型,日出前最低,日落前最高。有云频率表现为日变化和半日变化,单层云云顶高度、云底高度和云厚主要表现为日变化。(3)低涡系统云宏观参数的日变化特征与前两类系统明显不同:日变化趋势表现为多峰多谷型,虽然有云频率和单层云云顶高度、云底高度主要谐波中均以日变化振幅最大,但频谱分布分散,云厚主要变化中振幅最大的是周期为4.8 h的波动。(4)得到了各系统有云频率、单层云云顶高度、云底高度和云厚日变化的统计回归方程。

Cloud Classification and Distribution of Cloud Types in Beijing Using Ka-Band Radar Data

A cloud clustering and classification algorithm is developed for a ground-based Ka-band radar system in the vertically pointing mode. Cloud profiles are grouped based on the combination of a time–height clustering method and the k-means clustering method. The cloud classification algorithm, developed using a fuzzy logic method, uses nine physical parameters to classify clouds into nine types: cirrostratus, cirrocumulus, altocumulus, altostratus, stratus, stratocumulus, nimbostratus,cumulus or cumulonimbus. The performance of the clustering and classification algorithm is presented by comparison with all-sky images taken from January to June 2014. Overall, 92% of the cloud profiles are clustered successfully and the agreement in classification between the radar system and the all-sky imager is 87%. The distribution of cloud types in Beijing from January 2014 to December 2017 is studied based on the clustering and classification algorithm. The statistics show that cirrostratus clouds have the highest occurrence frequency(24%) among the nine cloud types. High-level clouds have the maximum occurrence frequency and low-level clouds the minimum occurrence frequency.

Understanding the dynamical-microphysical-electrical processes associated with severe thunderstorms over the Beijing metropolitan region

The Dynamical-microphysical-electrical Processes in Severe Thunderstorms and Lightning Hazards(STORM973)project conducted coordinated comprehensive field observations of thunderstorms in the Beijing metropolitan region(BMR)during the warm season from 2014 to 2018.The aim of the project was to understand how dynamical,microphysical and electrical processes interact in severe thunderstorms in the BMR,and how to assimilate lightning data in numerical weather prediction models to improve severe thunderstorm forecasts.The platforms used in the field campaign included the Beijing Lightning Network(BLNET,consisting of 16 stations),2 X-band dual linear polarimetric Doppler radars,and 4 laser raindrop spectrometers.The collaboration also made use of the China Meteorological Administration’s mesoscale meteorological observation network in the Beijing-Tianjin-Hebei region.Although diverse thunderstorm types were documented,it was found that squall lines and multicell storms were the two major categories of severe thunderstorms with frequent lightning activity and extreme rainfall or unexpected local short-duration heavy rainfall resulting in inundations in the central urban area,influenced by the terrain and environmental conditions.The flash density maximums were found in eastern Changping District,central and eastern Shunyi District,and the central urban area of Beijing,suggesting that the urban heat island effect has a crucial role in the intensification of thunderstorms over Beijing.In addition,the flash rate associated with super thunderstorms can reach hundreds of flashes per minute in the central city regions.The super(5%of the total),strong(35%),and weak(60%)thunderstorms contributed about 37%,56%,and 7%to the total flashes in the BMR,respectively.Owing to the close connection between lightning activity and the thermodynamic and microphysical characteristics of the thunderstorms,the lightning flash rate can be used as an indicator of severe weather events,such as hail and short-duration heavy rainfall.Lightning data can also be assimilated into numerical weather prediction models to help improve the forecasting of severe convection and precipitation at the cloud-resolved scale,through adjusting or correcting the thermodynamic and microphysical parameters of the model.