相控阵雷达扫描方式对回波强度测量的影响

有源数字阵列雷达的波束设计非常灵活,可变的波束宽度和多波束模式不仅可以满足不同任务的观测需求,还可有效节约扫描时间,但阵列天线参数随波位改变的特性对相控阵天气雷达回波强度的精确定标提出了挑战。2013年4—6月,中国气象科学研究院与安徽四创电子股份有限公司联合研发的X波段一维有源相控阵天气雷达 (X-PAR) 进行了装配后的首次测试观测。对比该雷达与相同位置的C波段双线偏振雷达 (C-POL) 观测资料发现,X-PAR不同宽度的扫描波束均能取得较合理的观测资料。但由于各模式的雷达方程及其标定方法不尽相同,并受到阵列天线照射体积、展宽波束和发射增益的影响,造成X-PAR回波强度存在一定的测量偏差。结合天线参数分析和实际数据统计将测量偏差订正至±1 dB内,为雷达的进一步调试和改进提供了依据。     

2010年6月中国南方持续性强降水过程：天气系统演变和青藏高原热力作用的影响

2010年6月中国南方发生持续性强降水,其强度与2008年6月相当,超过近年来其他年份。但是,与2008年6月相比,2010年6月对流层中低层低值系统活动在青藏高原至长江中下游地区异常频繁,副热带高压(副高)位置异常偏西、强度偏强,导致低层异常风场辐合区及强降水区域相对偏北。分析2010年6月14—24日中国南方连续出现的4次持续性强降水过程,发现南亚高压、对流层中层的中纬度槽脊和西太平洋副高以及低层切变线和东移低涡是造成持续性强降水的主要天气系统。利用WRF模式对2010年6月强降水过程实施显式对流集合模拟试验,在控制试验重现观测到的地面降水和天气系统特征的基础上,在敏感性试验中将青藏高原的地表短波反照率修改为1.0,对比两组模拟试验的结果表明:控制试验中青藏高原的地表感热加热作用使得高原及其周边地区的大气温度发生变化,相应的热成风平衡调整使得对流层低层至高层大气环流和天气系统特征发生显著变化,增强了中国南方的持续性降水。200 hPa青藏高原西部形成反气旋性环流异常,东部形成气旋性环流异常,青藏高原东部南下的冷空气加强,中国南方辐散增强;500 hPa青藏高原北部的脊加强,中国东部的槽加深,副高西北侧的西南风明显增强,从青藏高原向下游传播的正涡度也显著加强;850 hPa的低涡强烈发展并逐步东移,华南沿海的西南低空急流更为强盛,导致降水区的水汽辐合、上升运动及降水强度都增强。     

南海台风模式对“海燕”移动路径的预报

超强台风"海燕"是2013年最著名的台风。分析中国南海台风模式对"海燕"整个过程的预报,发现模式基本预报出"海燕"的快速穿过菲律宾和登陆越南后北翘东折的移动路径,但也存在一些不足,例如强度预报偏弱。通过高分辨率数值模拟分析了"海燕"的变化机理,发现高层暖心、高中低层一致东风气流是其超强发展和快速西移的主要特征。进一步的模式预报试验中,探讨了模拟技术对提高台风预报水平的影响作用。分析结果表明,准确的模式物理参数化(如边界层、积云对流和地形参数化)和模式初始大气构造等,以及提高模式分辨率有助于提高台风预报水平。     

An Overview of Nowcasting Development, Applications, and Services in the Hong Kong Observatory

The Hong Kong Observatory （HKO） has been developing a suite of nowcasting systems to support op- erations of the forecasting center and to provide a variety of nowcasting services for the general public and specialized users. The core system is named the Short-range Warnings of Intense Rainstorm of Localized Systems （SWIRLS）, which is a radar-based nowcasting system mainly for the automatic tracking of the movement of radar echoes and the short-range Quantitative Precipitation Forecast （QPF）. The differential, integral （or variational）, and object-oriented tracking algorithms were developed and integrated into the nowcasting suite. In order to predict severe weather associated with intense thunderstorms, such as high gust, hail, and lightning, SWIRLS was enhanced to SWIRLS-II by introduction of a number of physical models, especially the icing physics as well as the thermodynamics of the atmosphere. SWIRLS-Ⅱ was further enhanced with non-hydrostatic, high resolution numerical models for extending the forecast range up to 6h ahead. Meanwhile, SWIRLS was also modified for providing nowcasting services for aviation community and specialized users. To take into account the rapid development of lightning events, ensemble nowcasting techniques such as time-lagged and weighted average ensemble approaches were also adopted in the nowcasting system. Apart from operational uses in Hong Kong, SWIRLS/SWIRLS-Ⅱ was also exported to other places to participate in several international events such as the WMO/WWRP Forecast Demon- stration Project （FDP） during the Beijing 2008 Olympics Games and the Shanghai Expo 2010. Meanwhile, SWIRLS has also been transferred to various regional meteorological organizations for establishing their nowcasting infrastructure. This paper summarizes the history and the technologies of SWIRLS/SWIRLS-Ⅱ and its variants and the associated nowcasting applications and services provided by the HKO since the mid 1990s.     

Weather extremes and street life in India—Implications of Fuzzy Cognitive Mapping as a new tool for semi-quantitative impact assessment and ranking of adaptation measures

The potential impacts of progressing climate change are alarming. Some adverse consequences are now unavoidable and adaptation measures are increasingly needful. This poses enormous challenges for emerging megacities in the Global South, which barely manage in current weather conditions. This paper introduces Fuzzy Cognitive Mapping as a new tool for structured, semi-quantitative assessments of climate change impacts and adaptation measures.Fuzzy Cognitive Mapping is used to evaluate differences in sensitivities to heatwaves and rainstorms across socio-economic groups and for the ranking of useful adaptation options, based on 188 individual interviews to the impacts of extreme weather events in Hyderabad, India. The results of this multi-stakeholder assessment indicate that rainstorms affect low-income residents more than heatwaves, while the opposite is true for medium-income respondents. The latter are also less seriously affected by extreme weather in general. Profession, though, not income determines the kind of impact that people feel most affected by. Individual characteristics like age and gender do not significantly explain differences in the data, but religion does. This is because, in Hyderabad, Muslims live in the older, less serviced and more affected parts of the city. However, semi-quantitative scenario analyses suggest that, under future climate change, many parts of the city will become increasingly exposed to the effects of extreme weather. Planned investments in urban infrastructure will be seriously challenged by climate change and preventive adaptation measures are urgently needed to at least maintain the current level of quality of life. Investments in the health infrastructure appear to be most effective in reducing the impact of heatwaves and investments in the traffic infrastructure most effective in reducing the impact of rainstorms. However, looking at heat and rain events together—which is realistic as they are both projected to increase and often occur in the same year—reveals that investments in water infrastructure and management have greatest potential to reduce impacts across all localities and on all social groups, particularly the lower-income classes. This is because first-order impacts caused by inadequate water infrastructure often give rise to second- or third-order impacts. Addressing the root cause is the most effective way to break cause-and-effect chains and prevent proliferation of negative consequences. Similar studies are suggested in other cities in order to support adaptation mainstreaming in complex urban environments. Fuzzy Cognitive Mapping proved a useful, semi-quantitative tool for climate change impact and adaptation assessments.     

甘肃北部短时强降水中尺度特征及物理量配置

利用甘肃北部27个国家级自动气象站及635个区域气象站降水资料,结合常规高空、地面和欧洲中期天气预报中心（ECMWF）再分析物理量场资料,选取了2016—2019年5—9月104个典型短时强降水个例,对甘肃北部短时强降水天气发生发展的环境条件进行了中尺度综合分析,揭示了区域内短时强降水的一些特征和规律。结果表明：（1） 甘肃北部短时强降水集中出现在6—8月,短时强降水的强度多为10~20 mm。（2） 甘肃北部短时强降水天气的典型特征,分为副高边缘型、低压槽型、西北气流型和河套阻高型4种流型。（3） 通过分析不同天气形势、不同类别、不同物理量参数间的联系与区别,总结出各类短时强降水天气的环流特征和物理量要素指标和阈值。（4） 地面辐合线（冷锋）是甘肃北部触发强对流天气的关键系统,地面辐合线（冷锋）的分析对短时临近预报至关重要。（5） 低空偏南风急流（显著流线）在110°E左右北上及在37°N左右产生辐合是判断甘肃北部能否产生短时强降水的重要依据。并对2020年短时强降水预报效果进行检验,预报准确率达63.6%,说明建立的短时强降水预报指标预报能力较强,为提高短时强降水预报预警能力提供了一种新途径。     

台风水汽稳定同位素组成与天气过程的关系——以2015 年13 号台风“苏迪罗”为例

基于台风“苏迪罗”（1513）影响前后南京实时高频监测的水汽稳定同位素数据,并结合再分析资料、HYSPLIT后向轨迹模型分析了大气水汽δ¹⁸O与天气过程之间的关系以及大气水汽过量氘所指示的水汽来源。结果表明,1）整个台风影响过程水汽δ¹⁸O先保持基本不变后一直下降的趋势,而水汽过量氘则呈现完全相反的变化趋势。2）根据台风“苏迪罗”影响前后南京水汽δ¹⁸O变化特征,将其划分为3个阶段：Ⅰ阶段水汽δ¹⁸O较高与南京地区较为稳定的大气条件相对应,水汽过量氘值较低指示南京地区主要受海洋水汽影响;Ⅱ阶段台风环流及其残压和北方南下冷空气相互作用造成南京地区强降水,水汽凝结和降雨蒸发的共同作用导致水汽δ¹⁸O不断贫化,较高的水汽过量氘表明南京地区主要受海洋和局地混合水汽的影响;Ⅲ阶段可能是中尺度下沉气流导致南京地区极端偏负的δ¹⁸O和高水汽过量氘。     

我国雷暴活动对宇宙线福布希下降的短期响应

本文研究了1971－1980年间51个大于3％的宇宙线福布希下降对我国185个气象站上空雷暴活动的短期影响.结果表明，雷暴活动对福布希下降的短期响应存在明显的空间差异．在一些区域中福布希下降后雷暴活动明显增强，而在另一些区域中明显减弱，在这些明显响应区中，雷暴活动在福布希下降前后的差异显著性分别通过了10-2－10－6置信度检验．这些响应区所在的地理位置与雷暴活动对耀斑爆发的响应区相互对应，但响应性质恰恰相反．这些结果给太阳活动与地球短期天气相关机理研究提出了新的挑战，也为进一步的研究提供了新的事实根据．     

Implementing quality control on a random number stream to improve a stochastic weather generator

For decades, stochastic modellers have used computerized random number generators to produce random numeric sequences fitting a specified statistical distribution. Unfortunately, none of the random number generators we tested satisfactorily produced the target distribution. The result is generated distributions whose mean even diverges from the mean used to generate them, regardless of the length of run. Non‐uniform distributions from short sequences of random numbers are a major problem in stochastic climate generation, because truly uniform distributions are required to produce the intended climate parameter distributions. In order to ensure generation of a representative climate with the stochastic weather generator CLIGEN within a 30‐year run, we tested the climate output resulting from various random number generators. The resulting distributions of climate parameters showed significant departures from the target distributions in all cases. We traced this failure back to the uniform random number generators themselves. This paper proposes a quality control approach to select only those numbers that conform to the expected distribution being retained for subsequent use. The approach is based on goodness‐of‐fit analysis applied to the random numbers generated. Normally distributed deviates are further tested with confidence interval tests on their means and standard deviations. The positive effect of the new approach on the climate characteristics generated and the subsequent deterministic process‐based hydrology and soil erosion modelling are illustrated for four climatologically diverse sites. Copyright © 2007 John Wiley & Sons, Ltd.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.