台风艾云尼非对称降水及动热力结构演变特征分析

台风艾云尼（1804号）第2次登陆广东过程中降水表现出显著的非对称分布,强降水主要位于其路径前进方向的右侧（简称台风右侧）。利用欧洲中期天气预报中心ERA5再分析资料、广东风廓线雷达观测资料以及降水观测资料,对造成非对称降水的环流背景和动力、热力结构演变特征进行了分析。结果表明:艾云尼左右两侧水汽输送及动力、热力条件差异是造成降水非对称的主要原因。加强的低空急流以及台风马力斯（1805号）水汽的输送为台风右侧强降水的产生提供了更好的水汽背景,而低空急流的加强配合高空强的辐散抽吸使得右侧垂直上升运动也明显大于左侧。边界层内强盛的低空急流以及珠江三角洲地区下垫面强摩擦辐合作用导致艾云尼右前侧径向入流强度更强、强入流层厚度更厚、边界层高度更高,且由于距离台风眼墙越近风速越大,上述现象越明显,为强降水的产生提供的动力和水汽条件越好。强降水期间艾云尼右侧低层大气维持不稳定状态,分析表明强低空急流携带的θ_se平流及其随高度的减弱弥补了强降水造成的能量损耗,是不稳定能量维持的重要原因。      

利用星载测雨雷达探测结果对夏季中国南方对流和层云降水气候特征的分析

本文利用热带测雨卫星(TRMM)上搭载的测雨雷达(PR)十年的探测结果, 对夏季中国南方对流降水和层云降水的气候特征进行了分析。研究结果表明:夏季中国南方层云降水频次较对流降水频次高出两倍以上, 而对流降水强度至少是层云降水强度的4倍; 就整个中国南方而言, 这两种类型的降水对总降水量贡献相当。日变化分析表明夏季中国南方大部分地区的对流降水主要出现在午后, 层云降水出现时间并不集中, 但这两类降水的频次日变化均显示了明显的地域性特征; 对降水廓线日变化的分析结果表明, 对流降水和层云降水廓线的日变化主要表现在“雨顶”高度的日变化, 即对流降水云的厚度有明显的日变化变化特征, 不同地区的降水廓线存在明显的差异。降水率剖面分析结果显示了对流降水的“雨顶” 高度日变化较层云降水剧烈, 降水率的日变化则相反, 且层云降水率的地域性特征更强。     

超低仰角扫描改进高山雷达降水估测

利用2004年5月云南大暴雨过程的26次昆明雷达超低仰角对比观测体扫资料,分析了昆明雷达0°和0.5°仰角数据以及对比了定量估测降水的情况。结果表明：0°仰角探测到的降水分布范围更大;与0.5°仰角相比,通过定量计算,探测降水范围能提高约9.96%,估测的降水总量约为11.6%。从探测到的降水量和范围来看,0°仰角较0...     

Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific

Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution(0.1°) North Pacific Ocean model(LICOM_np). The performance of the regional coupled model,WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regional Climate Model(RegCM4) with LICOM_np. The analysis focused on the 2005 western North Pacific summer monsoon rainfall. The results showed that the regional coupled models with either RegCM4 or WRF4 as their atmospheric model component simulated the broad features over the WNP reasonably well. Quantitative intercomparison of the regional coupled simulations exhibited different biases for different climate variables.RegCM4_LICOM exhibited smaller biases in its simulation of the averaged June–July–August SST and rainfall, while WRF4_LICOM better captured the tropical cyclone(TC) intensity, the percentage contributions of rainfall induced by TCs to the total rainfall, and the diurnal cycle of rainfall and stratiform percentages, especially over land areas. The different behaviors in rainfall simulated by the two models were partly ascribed to the behaviors in the simulated western North Pacific subtropical high(WNPSH). The stronger(weaker) WNPSH in WRF4_LICOM(RegCM4_LICOM) was driven by overestimated(underestimated) diabatic heating, which peaked at approximately 450 hPa over the region around the Philippines in association with different condensation–radiation processes. Coupling of WRF4 with LIOCM is a crucial step towards the development of the next generation of regional earth system models at the Chinese Academy of Sciences.     

GPM卫星和地面雷达对江苏盐城龙卷风强降水估测的对比

为综合评估卫星和天气雷达在2016年6月23日盐城龙卷风期间的强降水过程的降水估测精度,以国家级雨量站观测数据为基准,结合相关系数(CC)、相对误差(RB)、均方根误差(RMSE)以及分级评分指标,利用S波段的天气雷达定量降雨估测产品(RQPE)和全球降水观测计划多卫星融合产品(IMERG_FRCal,IMERG_FRUncal,IMERG_ERCal)进行比较。结果表明,雷达和卫星的累积降水量与雨量站的空间相关性很强(相关系数大于0.9),基本上能捕捉到整个降水过程的空间分布。降水主要分布在江苏省北部,但卫星高估了江苏省东北部强降水中心的降水量;对于小时时序区域平均降水,卫星高估了降水,而雷达低估了累积降水量。综合降水中心区域分析,IMERG的强降水区域降水量与雨量站的时间序列的偏差显著;RQPE在降水峰值达到之前及峰值之后与地面雨量站的变化趋势基本一致,但对降雨量峰值有明显的偏低。RQPE能较为准确地在时间上捕捉到降雨强度的变化趋势,但对于大雨及暴雨的估测能力不佳;RQPE的POD、SCI值都远远高于IMERG, FAR也较小。IMERG几乎未能监测到强降水的发生。总体上,RQPE对此次龙卷风强降水量的估测表现优于3种IMERG产品,特别是在捕捉强降水区域的空间分布方面,但对于强降水的估测能力仍需进一步改善。     

Raindrop Size Distribution Parameters Retrieved from Xinfeng C-Band Polarimetric Radar Observations

The objective of this research was to acquire a raindrop size distribution (DSDs) retrieved from C-band polarimetric radar observations scheme for the first time in south China.An observation period of the precipitation process was selected,and the shape-slope (μ-Λ) relationship of this region was statistically analyzed using the raindrop sample observations from the two-dimensional video disdrometer (2DVD) at Xinfeng Station,Guangdong Province.Simulated data of the C-band polarimetric radar reflectivity Z_(HH)and differential reflectivity Z_(DR)were obtained through scattering simulation.The simulation data were combined with DSD fitting to determine the Z_(DR)-Λand log_(10)(Z_(HH)/N_0)-Λrelationships.Using Xinfeng C-band polarimetric radar observations Z_(DR)and Z_(HH),the raindrop Gamma size distribution parametersμ,Λ,and N_0were retrieved.A scheme for using C-band polarimetric radar to retrieve the DSDs was developed.This research revealed that during precipitation process,the DSDs obtained using the C-band polarimetric radar retrieval scheme are similar to the 2DVD observations,the precipitation characteristics of rainfall intensity (R),mass-weighted mean diameter (D_m) and intercept parameter (N_w) with time obtained by radar retrieval are basically consistent with the observational results of the 2DVD.This scheme establishes the relationship between the observations of the C-band polarimetric radar and the physical quantities of the numerical model.This method not only can test the prediction of the model data assimilation system on the convective scale and determine error sources,but also can improve the microphysical precipitation processes analysis and radar quantitative precipitation estimation.The present research will facilitate radar data assimilation in the future.     

两次西行热带气旋影响云南降水对比分析

利用常规观测资料、中国气象局上海台风研究所(CMA-STI)热带气旋最佳路径数据和FY-2C卫星云图观测资料,以0608台风派比安和0809强热带风暴北冕两个西行热带气旋影响云南降水为例,通过其路径、降水量、移动速度、环境场和物理量场的对比分析,结果表明:两次台风源地、移动路径及登陆地点、影响时段、最大降水落区相同,但影响时间长度、影响范围和造成的灾害程度后者强于前者;西南季风与热带辐合带(ITCZ)较活跃,副热带高压西伸增强,并有低空急流、辐合区配合台风低压环流共同作用是热带气旋导致云南强降水的重要天气背景;云图中尺度分析发现,多种系统的共同作用,导致台风环流持久不消,进而易激发多个α-中尺度对流系统(MαCS)和β-中尺度对流系统(MβCS)云团生成并持久维持,是台风低压强降水发生的直接原因;物理量场的诊断分析表明,活跃的季风系统,使孟加拉湾和南海构成强大的水汽通道,伴随低空急流的建立和增强,致使大量不稳定能量和水汽向云南输送,在云南形成条件性对称不稳定(CSI)和深厚斜压性的正反馈机制,是导致云南强降水的重要物理机制。     

山东不同云系降水微物理参数特征

研究不同云系降水的微物理参数特征,对研究降水机制、人工影响天气、雷达定量测量降水、数值预报模式中微物理参数化方案的选择等都有一定意义。本文针对2015年济南地区的液态降水过程,基于微降水雷达(Micro Rain Radar,简称MRR)资料,研究不同云系降水的微物理参数。在400 m高度上,层状云降水0.02~0.2 mm h-1雨强样本数很大,但对累计降水量的贡献很小。混合云和对流云降水在大粒子端数浓度较高。在垂直方向上,层状云降水中的粒子的尺度较集中,中值体积直径D0平均在1 mm左右,随高度的变化不大。对流云降水在雨强大于20 mm h-1时,强垂直气流(包括上升气流和下沉气流)对粒子直径的影响较大,进而影响空中微降水雷达反演降水参数的数据质量。而垂直气流的影响对层状云降水影响较小,在层状云降水时,微降水雷达可以用来分析零度层亮带以下雨滴谱在垂直方向上的演变。     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

华南区域高分辨率数值模式前汛期预报初步评估

基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。