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Evaluating the NOAA Coastal and Marine Ecological Classification Standard in estuarine systems: A Columbia River Estuary case study

A common first step in conservation planning and resource management is to identify and classify habitat types, and this has led to a proliferation of habitat classification systems. Ideally, classifications should be scientifically and conceptually rigorous, with broad applicability across spatial and temporal scales. Successful systems will also be flexible and adaptable, with a framework and supporting lexicon accessible to users from a variety of disciplines and locations. A new, continental-scale classification system for coastal and marine habitats—the Coastal and Marine Ecological Classification Standard (CMECS)—is currently being developed for North America by NatureServe and the National Oceanic and Atmospheric Administration (NOAA). CMECS is a nested, hierarchical framework that applies a uniform set of rules and terminology across multiple habitat scales using a combination of oceanographic (e.g. salinity, temperature), physiographic (e.g. depth, substratum), and biological (e.g. community type) criteria. Estuaries are arguably the most difficult marine environments to classify due to large spatio-temporal variability resulting in rapidly shifting benthic and water column conditions. We simultaneously collected data at eleven subtidal sites in the Columbia River Estuary (CRE) in fall 2004 to evaluate whether the estuarine component of CMECS could adequately classify habitats across several scales for representative sites within the estuary spanning a range of conditions. Using outputs from an acoustic Doppler current profiler (ADCP), CTD (conductivity, temperature, depth) sensor, and PONAR (benthic dredge) we concluded that the CMECS hierarchy provided a spatially explicit framework in which to integrate multiple parameters to define macro-habitats at the 100 m² to >1000 m² scales, or across several tiers of the CMECS system. The classification's strengths lie in its nested, hierarchical structure and in the development of a standardized, yet flexible classification lexicon. The application of the CMECS to other estuaries in North America should therefore identify similar habitat types at similar scales as we identified in the CRE. We also suggest that the CMECS could be improved by refining classification thresholds to better reflect ecological processes, by direct integration of temporal variability, and by more explicitly linking physical and biological processes with habitat patterns.     

大倾角时的声学多普勒测速公式误差分析

国内外在声学多普勒测速误差分析和修正算法方面已作了大量的研究工作.一般认为JANUS配置方式下,多普勒频移公式的简化误差可忽略.文中从理论上对多普勒测速的原理公式的简化过程进行分析,研究了大倾角状态下的测速公式的相对误差,认为在高速、大倾角工况下公式简化误差不可忽略,并提出了修正公式.     

单点GPS浮标测波方法与数据质量控制研究

首先对GPS浮标测波原理进行分析,介绍了GPS接收器测量运动参数的多普勒方法;然后对于在现场测量中可能发生的采样丢失所导致的数据间断进行探讨,并提出了间断接续和冗余采样的解决方法,通过数值模拟实验证明了数据质量控制方法的有效性.     

An algorithm on convective weather potential in the early rainy season over the Pearl River Delta in China

This paper describes the procedure and methodology to formulate the convective weather potential （CWP） algorithm. The data used in the development of the algorithm are the radar echoes at 0.5° elevation from Guangzhou Doppler Radar Station, surface observations from automatic weather stations （AWS） and outputs of numeric weather prediction （NWP） models. The procedure to develop the CWP algorithm consists of two steps： （1） identification of thunderstorm cells in accordance with specified statistical criteria; and （2） development of the algorithm based on multiple linear regression. The thunderstorm cells were automatically identified by radar echoes with intensity greater than or equal to 50 dB（Z） and of an area over 64 square kilometers. These cells are generally related to severe convective weather occurrences such as thunderstorm wind gusts, hail and tornados. In the development of the CWP algorithm, both echo- and environment-based predictors are used. The predictand is the probability of a thunderstorm cell to generate severe convective weather events. The predictor-predictand relationship is established through a stepwise multiple linear regression approach. Verification with an independent dataset shows that the CWP algorithm is skillful in detecting thunderstorm-related severe convective weather occurrences in the Pearl River Delta （PRD） region of South China. An example of a nowcasting case for a thunderstorm process is illustrated.     

层状云和对流云的雷达识别及在估测雨量中的应用

参考“峰值法”建立了利用新一代多普勒天气雷达回波强度识别混合型降水过程中层状云和对流云的方法,分析了反射率阈值和影响半径的变化对识别效果的影响,检验了识别效果,并分析该方法在雷达估测降水中的应用。表明本文所建立的暴雨中层状云和对流云识别方法基本合理,配合云的垂直结构分析大部分地区识别的效果较好,同时通过该识别方法对合肥2002年6月19日一次强降水天气过程的估测降水的应用,表明只按层状云的Z-I关系估测的雨强,相对于识别对流云和层状云后分别按各自的Z-I关系估测的单点雨强,其最大差别约达37%,相应的面雨量也有一定的差别;对流云的多少对发展的中尺度对流系统的雨强有很大影响,这次混合型降水过程以对流云降水为主。对流云和层状云是形成暴雨的重要因素,准确地识别两者,对估测降水的精度会有积极作用。     

基于新型观测算子的双偏振雷达雨滴谱变分反演

提出一种基于变分理论的双偏振多普勒天气雷达反演雨滴谱方法,最优反演雨滴谱的同时可实现衰减订正。反演过程中使用一种新型观测算子,利用滴谱仪实测数据计算状态变量和双偏振参量,使观测算子更能代表本地降水特性。将新型观测算子、误差协方差矩阵和状态变量的先验估计用于代价函数中,基于高斯-牛顿迭代方法求解代价函数得到最优雨滴谱。利用理想模拟试验和南京信息工程大学C波段双偏振多普勒天气雷达实测个例对算法进行验证和评估。结果表明：算法反演得到的状态变量(液态水含量LWC和质量权重平均直径D_m)与滴谱仪数据计算结果的相关系数达到了0.96和0.80,相对偏差为25.19%和10.63%,均方根误差比常规反演结果改善了50%左右,比基于模拟观测算子的变分反演结果改善了30%左右,最优反演雨滴谱得到的降雨率R和雨量计数据相关系数达到0.89,相对偏差为14.78%。     

雷暴天气在多普勒雷达资料上的前兆特征分析

通过多普勒雷达资料分析,得到：雷暴天气的形成过程通常在多普勒雷达图上往往反映为一个双层干区的迅速破坏和基本消失的过程。并且雷暴天气结束后,如果双层干区再次重新建立、破坏和消失．那么意味着再次雷暴天气过程的发生。依据上述分析通过简单统计归纳得到的合肥雷暴临近预报指标的预报效果是令人满意的。     

河北省廊坊“20200625”强超级单体雷达回波特征分析

摘 要：利用常规观测、北京和天津多普勒天气雷达探测等资料，对2020年6月25日夜间发生在河北省廊坊市一次冰雹、雷暴大风和短时强降水等强对流天气过程进行分析。结果表明：此次超级单体风暴发生的背景条件为高空冷涡前部高空槽叠加地面冷锋系统，高CAPE值、强垂直风切变以及适当的0 ℃和-20 ℃高度等为其发展维持提供了有利的环境条件。超级单体属于右移强风暴，发展演变过程中回波形状不断变化，中气旋特征持续存在，回波垂直结构呈现出回波墙-回波悬垂和有界弱回波区-三体散射和旁瓣回波等典型超级单体雷达回波特征。在本次过程中，55 dBz及以上强回波迅速向高层伸展后迅速下降并配合较低的强回波质心高度，预示地面将出现大冰雹和灾害性大风；VIL最大值达到55 kg?m-2可以作为本地发布冰雹预警的指标，发布冰雹预警时间可以提前12 min；将VIL值升到40 kg?m-2作为地面灾害大风预警指标，发布雷暴大风预警的提前量为24 min。三维空间图像可以直观地展现出超级单体的空间结构。     

降雨强度对激光雷达测风精度的影响研究

多普勒激光雷达在大气、环境以及风能等领域得到越来越广泛的应用，但对于复杂天气下多普勒激光雷达的适用性仍然有待深入研究。为此本研究采用2020年8—10月期间位于福建三沙的地基多普勒激光雷达与边界层高塔所搭载的超声风温仪观测的风场数据进行对比，发现多普勒激光雷达在水平风速、风向方面具有稳定的高精度探测性能，与超声风温仪之间相关系数达到了0.948和0.984。相比之下，激光雷达垂直风速的探测误差较大，与超声风温仪之间相关系数仅有0.353。研究发现，降雨强度与激光雷达垂直风速误差呈正相关关系，强降雨下垂直风速偏差最大可达到9 m/s。