利用普洱和文山多普勒天气雷达资料以及FY-2G云图数据,结合NCEP再分析资料和天气实况,分别对2017年5月12日发生在勐海县和2021年5月31日发生在广南县的龙卷天气进行分析.结果表明:两次龙卷出现区域的地形均为平坦坝子,坝子周围为高山环绕,且在龙卷发生地附近均有水库.从地面图看,两次非超级单体龙卷均是上升气流遇到地面辐合线触发的涡旋所导致的,龙卷天气出现时温度降低且气压下降,垂直结构呈"干-湿-干"的分布特征,对流有效位能值分别为1676.3 J·kg?1、2074.2 J·kg?1,0～3 km垂直风切变最大为11.1 m/s,形成绝对不稳定的大气层结,具备发生龙卷的有利条件.从云图看,两次龙卷天气过程均由中尺度对流云团引发,龙卷发生区域云顶亮温梯度较大.从雷达图看,两次龙卷天气均表现出"低层辐合、高层辐散"的环流特征.

利用云南省125个国家级自动气象站及3042个区域站降水数据、FY-2E/G云图数据以及探空观测数据,统计2015—2019年由切变线系统影响的云南短时强降水过程,对短时强降水时空分布、中尺度对流系统(Mesoscale?Convective?System,?MCS)系统特征、MCS系统发生发展的环境特征以及对流云系演变特征进行分析.?结果表明,云南切变线类短时强降水频次有4个大值中心,分别是云南南部边缘地区、曲靖南部至文山北部、华坪、德宏西部,傍晚至凌晨是强降水发生的主要时段;云南切变线类短时强降水对流云系分成新生对流云团、MαCS和MβCS和带状MCS共4类,75％的切变线类短时强降水是由MαCS和MβCS系统造成,MαCS和MβCS系统中低于–32?℃冷云区呈椭圆形,平均面积分别为1.8万km2、10.4万km2,存在1个或2个中心,中心云顶亮温低于–52?℃.?MαCS持续平均时间为3.3?h,其中最长时间为6?h;MβCS持续平均时间为2.3?h,其中最长时间是5?h.?短时强降水位于MαCS和MβCS强中心附近或者发展方向梯度大值区.?综合分析了高低空系统配置的结合、对流发生的条件、MCS的发生及消退和降水特征,旨在建立由切变线引起的4种短时强降水过程的概念模型,从而为云南省短时强降水的预测提供关键技术支持.

2017年6月24日和2019年9月30日云南省昭通市盐津县出现了两次严重的地质灾害,造成重大人员伤亡和财产损失.本文利用NCEP再分析资料、地面自动站观测资料及多普勒天气雷达探测资料,对比分析了两次灾害的天气诱因.结果表明:"6.24"滑坡灾害发生在夏季主汛期,是由冷暖空气长时间交汇造成的持续性降水天气导致,期间伴有几次区域性强降水过程,雷达回波以大范围层积混合云回波特征为主,对流性暴雨特征不明显."9.30"泥石流灾害发生在大气环流突变的秋季,冷暖空气势力较弱,天气尺度系统造成的区域降水强度不大,而由局地热力作用造成的单点对流性强降水才是灾害发生的主要诱因.

统计分析了2014-2016年5-10月国家站共出现的219站次有效短时强降水及云南省7部多普勒天气雷达资料,将云南省副热带高压(以下简称副高)外围的短时强降水进一步细分为两高(青藏高压和西太平洋副高)辐合类、单纯副高外围类及副高西侧配合西风槽类.初步得出以下结论:8月为云南省副高外围类短时强降水的高发期且降水时段集中在午后及前半夜;两高辐合类降水沿着辐合区呈显著的带状分布特征,降水强度强、落区相对集中.单纯副高外围类降水主要位于滇南地区,存在三个强降水中心.副高西侧配合西风槽类降水主要位于云南省的边缘地区,落区较为分散;三类降水回波主体平均强度均在35～45 dBz,平均持续9个体扫;近1/4的回波出现回波倾斜及强回波梯度特征,且降水明显强于未出现的回波,一定程度上可以作为判断降水强度的参考依据;三类降水的最强雷达回波顶高及垂直累积液态含水量出现的时间均同最强回波出现的时间基本一致或略有滞后;两高辐合类的垂直风廓线中有近一半的个例在低层存在西南风或西风气流,对应明显的暖平流输送特征.随着降水发展与副高外围晴空区相对应的无资料区的逐渐消失则是单纯副高外围类降水的垂直风廓线表现最为明显的特征.副高西侧配合西风槽类降水开始前后均存在高空西北气流入侵及中层风切变特征,与低槽后部带来的冷平流及冷暖气流交汇相对应.

雾和霾都是低能见度天气,生成条件相似.利用安徽78个地面站逐时观测资料,基于雾、霾发生物理条件,建立了不同等级雾日和重度霾日的观测诊断方法,重建了不同等级雾和重度霾的时序资料.根据各站强浓雾发生的同步性,将安徽分为5个雾、霾分布特征不同的区域,探讨了各区域不同等级雾及重度霾出现时地面气象条件的异同.结果表明:(1)安徽省强浓雾主要是辐射雾.强浓雾、浓雾和大雾空间分布形势大体一致,淮河以北东、西部和江南都属于强浓雾高发区,但各地强浓雾的时、空分布特征和影响系统不同;重度霾有明显的北多、南少、山区最少的分布特征.(2)强浓雾年变化呈双峰型分布,峰值在1月和4月,日变化为单峰型,峰值在06时;而重度霾年变化为单峰型,峰值在1月,日变化为双峰型.(3)在强浓雾的高发时段(02—08时),强浓雾时降温幅度最大,比重度霾平均高1℃,风速显著偏低,超过75％的样本风速低于1.5 m/s,且无明显主导风向;而重度霾时,风速比雾时明显要大,个别区域有超过75％的样本风速大于1.5 m/s,且以西北风到东北风为主.说明重度霾能否演变为强浓雾的关键地面气象因子是风速、风向和降温幅度.

利用云南省2325个国家级台站和区域自动观测站逐小时降水数据,分析了2014～2018年云南雨季和干季的降水量、降水频次和降水强度的空间分布特征以及关键区域的降水日变化演变特征.结果表明:受复杂地形影响,云南不同区域降水特征差异显著,且与我国东部地区显著不同.年均降水量大体呈西南高、西北低的分布特征.对于云南西北部的怒江河谷地区,干、雨季降水均为夜间峰值,降水频次高,但强度较弱.对于云南最西部(99°E以西)的保山德宏地区,该地区累计降水量为云南最大,这一区域各台站日变化峰值均较为一致地出现在上午,在陆地地区较为少见.相邻的普洱和元江河谷位于云南南部(23°N以南),雨季两区域降水相当,但元江河谷在干季与雨季均为突出的夜间至清晨降水峰值,普洱地区雨季则是明显的午后降水峰值.云南中部地区降水量较周边地区明显偏小,该地区降水频次在雨季主要表现为清晨峰值,而在干季却是午后峰值更为突出,这也与我国东部地区降水日变化特征差异明显.

The performance of the forecasts of the European Centre for Medium-Range Weather Forecasts' (ECMWF) Integrated Forecasting System (IFS) with a horizontal resolution of 0.125 degrees is assessed through comparison with station rain-gauge data in the warm season (May-October) in the period 2017-2018 over southwestern China. The mean state of rainfall amount, frequency and intensity, as well as the diurnal cycle of precipitation, are involved in the evaluation. The IFS can capture well the spatial distributions of rainfall amount, frequency and intensity in the gauge data, but the rainfall frequency is generally larger and the intensity is weaker in the IFS. The rainfall events of the IFS usually start and peak much earlier, and the afternoon rainfall peaks are overestimated. The discrepancy between the gauge data and the IFS is larger over the western part of southwestern China. The results indicate that the IFS forecasts show considerable uncertainty when moving to the sub-daily scale, especially over areas with complex topography. When the IFS forecasts are applied operationally, the deviations (as revealed in this study) should be paid more attention.

MM5 and WRF were run daily for December of 2006 and December of 2007 and the results at ground level and in PBL were assessed and compared by calculating a set of common used statistics measures using the ground-level observations of the China Meteorology Agency routine meteorological network,and the high resolution sounding data at observatories of Nanjing and Anqing. Generally,the simulated ground level temperature and humidity by both MM5 and WRF were reliable,but the simulated wind speed was a little worse. Both models performed better during daytime than during nighttime. In addition,the validation results show ed evident regional distribution,e. g.,the results changed worse from east to west for temperature,from southeast to northwest for humidity,from plain area to hill and mountain areas for wind speed. According to correlation coefficient(R) and mean absolute error(MAE),WRF performed better than MM5 for temperature and humidity at the ground level. Taking Nanjing and Anqing for examples,the modeled sounding in PBL at both 08: 00 and 20: 00 were acceptable,except for the wind speed below 150 m in Nanjing. The results at 20: 00 were better than those at 08: 00,and improved with increasing height for both models. In general,WRF performed better for temperature and humidity,while MM5 performed better for wind speed. Both models could reproduce the near surface temperature inversion,with overestimated the occurring frequency. For the near surface temperature inversion,M M5 outperformed WRF for the frequency,while WRF outperformed MM5 for the thickness and intensity.

Accurate forecasts of fog and visibility are very important to air and high way traffic, and are still a big challenge. A 1D fog model (PAFOG) is coupled to MM5 by obtaining the initial and boundary conditions (IC/BC) and some other necessary input parameters from MM5. Thus, PAFOG can be run for any area of interest. On the other hand, MM5 itself can be used to simulate fog events over a large domain. This paper presents evaluations of the fog predictability of these two systems for December of 2006 and December of 2007, with nine regional fog events observed in a field experiment, as well as over a large domain in eastern China. Among the simulations of the nine fog events by the two systems, two cases were investigated in detail. Daily results of ground level meteorology were validated against the routine observations at the CMA observational network. Daily fog occurrences for the two study periods was validated in Nanjing. General performance of the two models for the nine fog cases are presented by comparing with routine and field observational data. The results of MM5 and PAFOG for two typical fog cases are verified in detail against field observations. The verifications demonstrated that all methods tended to overestimate fog occurrence, especially for near-fog cases. In terms of TS/ETS, the LWC-only threshold with MM5 showed the best performance, while PAFOG showed the worst. MM5 performed better for advection–radiation fog than for radiation fog, and PAFOG could be an alternative tool for forecasting radiation fogs. PAFOG did show advantages over MM5 on the fog dissipation time. The performance of PAFOG highly depended on the quality of MM5 output. The sensitive runs of PAFOG with different IC/BC showed the capability of using MM5 output to run the 1D model and the high sensitivity of PAFOG on cloud cover. Future works should intensify the study of how to improve the quality of input data (e.g. cloud cover, advection, large scale subsidence) for the 1D model, particularly how to eliminate near-fog case in fog forecasting.

To assess the performance of MM5in simulating the meteorological parameters in the planetary boundary layer(PBL),Meteorological elements of PBL in east China in December of 2006and 2007 were simulated by MM5.The simulation results were assessed objectively by the ground convention observation data and daily sounding data at Nanjing and Anqing stations at 08:00and 20:00.Some common used statistical parameters of temperature,relative humidity,wind direction and wind speed at surface and different levels in PBL were calculated.And simulation effect of sounding in PBL before fog and during fog were assessed,respectively.The results show that:(1)the simulated temperature and relative humidity were reliable at the ground level,but the error of simulated wind speed was more.Frequency distributions of the biases between simulated and observation temperature,relative humidity and wind speed are nearnormal distribution,and peak values are-1.52℃,4.59% and 1.92m·s-1,respectively.The simulation effect in daytime is better than in nighttime.(2)Take Nanjing and Anqing stations for example,the simulated sounding in PBL at 08:00and 20:00were acceptable,and the effect at 20:00was better than that at 08:00.Simulation effect of temperature and relative humidity in PBL at Nanjing station was better than that at Anqing station,but wind speed at Anqing was better.(3)Take Nanjing as an example,simulation effect of temperature and relative humidity during the fog and before fog days were worse than normal,but wind speed had no significant difference.(4)The frequently of temperature inversion near surface were more than 40%in Nanjing and 30%in Anqing in winter.Temperature inversion near surface reappeared but overrated by model,and simulation effect in middle-to-upper boundary layer was not good.In addition,According the results of numerical sensitivity test,the high vertical resolution near surface was not necessary the bias of negative temperature at the ground level in modeling scheme.