Improvement of X-Band Polarization Radar Melting Layer Recognition by the Bayesian Method and ITS Impact on Hydrometeor Classification

Using melting layer(ML)and non-melting layer(NML)data observed with the X-band dual linear polarization Doppler weather radar(X-POL)in Shunyi,Beijing,the reflectivity(ZH),differential reflectivity(ZDR),and correlation coefficient(CC)in the ML and NML are obtained in several stable precipitation processes.The prior probability density distributions(PDDs)of the ZH,ZDR and CC are calculated first,and then the probabilities of ZH,ZDR and CC at each radar gate are determined(PBB in the ML and PNB in the NML)by the Bayesian method.When PBB>PNB the gate belongs to the ML,and when PBBPNB the gate belongs to the ML,and when PBB相似文献   

两次秸秆焚烧导致辽宁重污染过程的气象条件对比分析

利用环境监测站大气污染物数据、地面自动气象站观测资料、L波段加密探空资料和0.125°×0.125°的EC再分析资料,结合MODIS遥感火点监测和HYSPLIT4后向轨迹模拟结果,对比分析了2015年11月8日和2016年11月5日的两次由于东北地区秸秆焚烧导致辽宁重污染天气过程的大气边界层特征、气象扩散条件和大气污染物输送来源等。结果表明:两次过程地面PM_(2.5)浓度均出现快速上升和下降,其中2015年11月8日重污染过程的污染强度较2016年11月5日强,且持续时间更长。2015年11月8日重污染过程的混合层高度较低,其上层的中性层结转变为逆温层结,抑制混合层高度的发展。同时低层冷平流不断侵入到暖平流下方,使得大气层结稳定性增强,维持时间较2016年11月5日重污染过程更长,低层下沉运动和黑龙江西南部、吉林西部污染物的远距离输送增强使得辽宁地面污染物浓度快速累积。而2016年11月5日重污染天气过程主要受深厚冷空气影响,东北地区西部污染物的区域输送和地面风场辐合是地面污染物浓度快速上升的主要原因。     

近30a台湾非台风暴雨研究回顾

从1987年开始,台湾暴雨研究进入一个新纪元,由早期定性描述分析进入定量计算与模拟,其中包括新观测设施的建设与数值天气预报系统的建立。近年来,随着全球气候变暖,台湾极端降雨事件有所增加,其中不少极端降雨事件是由非台风暴雨所致,往往给当地社会经济和人民生命财产造成严重影响,这就迫切需要不断提高强降雨定量预报业务水平。本文通过对近30 a台湾在非台风暴雨研究发展方面取得的主要进展的回顾,重点介绍了台湾气象部门为了提高强降雨定量预报业务水平所做出的努力,同时对未来台湾非台风暴雨研究规划与方向作了简要介绍。     

1981-2008年中国陆地植被NPP对气候变化响应的敏感性试验

在验证CENTURY模型对中国陆地植被净初级生产力(Net Primary Productivity,NPP)模拟能力的基础上,利用该模型探讨了1981-2008年中国陆地植被NPP的年际变异和变化趋势对CO₂浓度、温度和降水变化的响应。结果表明,中国陆地植被NPP对不同气候因子的响应程度存在明显不同。其中,CO₂浓度变化对植被NPP年际变异的影响不显著,但能够引起中国大部分地区植被NPP趋势系数增大;温度对中国中高纬度地区植被NPP的年际变化影响显著,但就全国范围而言,植被NPP年际变异对温度变化的响应程度总体低于对降水变化的响应程度;降水变化是对中国植被NPP变化趋势起主导作用的气候因子。此外,综合考虑温度和降水变化的影响发现,植被NPP变化趋势的响应特征类似于降水单独变化时植被NPP变化趋势的响应特征。     

引发强降水的一次东移高原云团的能量演变特征研究

利用日本气象厅葵花-8卫星亮温资料、欧洲中心ERA5（the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis）再分析资料,根据时间尺度分解的局地能量诊断方法,本文从能量学多个角度研究了2016年6月5日00时（协调世界时,下同）至6日15时（持续40小时）一次东移并引发强降水的高原对流云团,得到了以下主要结论。本次事件中,高原东移对流云团在不同阶段的主要影响系统有所不同。移出高原前,其主要受高原涡和高原短波槽的共同影响,随着云团移出高原,高原涡消亡,而高原短波槽则随时间发展加强,成为东移云团的最主要影响系统。高原东移对流云团具有显著的深对流特征,自西向东引发了一系列的降水,移出高原后,其对流重心显著降低,降水达到最强。不同阶段高原东移对流云团的能量转换特征显著不同。云团位于高原上时（第一阶段）,背景场通过动能的降尺度能量级串为造成强降水的扰动流直接提供能量,这是此阶段扰动流动能维持的主要方式;云团移出高原过程中（第二阶段）,降水凝结潜热明显增强,由此制造的扰动有效位能也显著增强。在垂直运动配合下,扰动有效位能斜压释放所制造的动能是本阶段造成强降水扰动流动能维持的最主要能量来源;云团移出高原后（第三阶段）,背景场对造成强降水扰动流的影响再次增强,但是不同于第一阶段的直接影响方式,该阶段背景场的作用是以一种间接的影响方式出现。其首先通过有效位能的降尺度级串将背景场的有效位能转换为扰动流的有效位能,然后通过扰动有效位能的斜压能量释放为扰动流的动能维持不断地提供能量。此外,本阶段内还出现了扰动流向背景场动能的升尺度级串供给（即扰动流的反馈）,但其强度不足以对背景场的演变产生显著影响。     

大气物理过程方案对PM2.5预报的敏感性试验

利用WRF-Chem大气化学模式,选择2015年12月中旬发生在我国的大范围空气污染过程,在采用同样化学方案条件下,针对模式中不同物理过程及其参数化方案开展了地面PM_(2.5)预报的敏感性试验。结果表明:该模式能较好展示此次PM_(2.5)污染的演变过程,与实况也较接近,但对青海经宁夏至内蒙的PM_(2.5)高值区出现了漏报现象,这可能是模式外边界未对污染物做更新所致。对地面PM_(2.5)的预报,各物理过程的敏感度不同,边界层(含近地面层)过程的影响要明显大于积云对流及微物理过程的影响,不同的参数化方案会造成不同的预报误差。边界层过程QNSE和与其配套的近地面层方案的组合是预报的较佳组合;而TEMF和与其配套的组合以及ACM2和Pleim-X的组合则不佳。合理的物理过程参数化方案有助于提高PM_(2.5)预报质量。模式预报对排放源也有适应过程,其Spin-up时间较气象要素长。     

杭州市典型雨转雪天气成因及预报模型

利用2008—2018年的NCEP(1°×1°)再分析资料、常规气象观测资料和降雪加密观测资料,选出杭州地区10次典型的雨转雪天气过程,从大尺度环流背景和动力、水汽以及热力因子等物理量场结构方面展开研究,最终得出杭州冬季典型雨转雪天气的预报模型:①大尺度环流配置需满足能为雨转雪天气的形成提供有利的水汽、动力抬升以及中低层上暖下冷的逆温或等温层结条件;②水汽和动力因子等物理量须满足产生纯雪的特定条件;③杭州温度层结须为T_(2m)≤1.5℃、T_(925)≤-4.0℃、T_(850)≤0℃、T_(700)≤-1.0℃和T_(500)≤-10.0℃。此外,进一步补充了杭州可能产生大雪甚至暴雪量级降雪的特定条件。最终选取2019年初的2次典型降水过程进行预报回报检验。     

Meteorology in Canada: A reaction to the rose report

The methods used in an earlier study focusing on the province of Ontario, Canada, were adapted for this current study to expand the study area over eastern Canada where the infrastructure is at risk of being impacted by freezing rain. To estimate possible impacts of climate change on future freezing rain events, a three-step process was used in the study: (1) statistical downscaling, (2) synoptic weather typing, and (3) future projections. A regression-based downscaling approach, constructed using different regression methods for different meteorological variables, was used to downscale the outputs of eight general circulation models to each of 42 hourly observing stations over eastern Canada. Using synoptic weather typing (principal components analysis, a clustering procedure, discriminant function analysis), the freezing rain-related weather types under historical climate (1958–2007) and future downscaled climate conditions (2016–2035, 2046–2065, 2081–2100) were identified for all selected stations. The potential changes in the frequency of future daily freezing rain events can be projected quantitatively by comparing future and historical frequencies of freezing rain-related weather types.

The modelled results show that eastern Canada could experience more freezing rain events late this century during the coldest months (i.e., December to February) than the averaged historical conditions. Conversely, during the warmest months of the study season (i.e., November and April in the southern regions, October in the northern regions), eastern Canada could experience less freezing rain events late this century. The increase in the number of daily freezing rain events in the future for the coldest months is projected to be progressively greater from south to north or from southwest to northeast across eastern Canada. The relative decrease in magnitude of future daily freezing rain events in the warmest months is projected to be much less than the relative increase in magnitude in the coldest months.     

C‐band SAR backscatter characteristics of Arctic sea and land ice during winter

Abstract

Synthetic Aperture Radar (SAR) data has become an important tool for studies of polar regions, due to high spatial resolution even during the polar night and under cloudy skies. We have studied the temporal variation of sea and land ice backscatter of twenty‐four SAR images from the European Remote Sensing satellite (ERS‐1) covering an area in Lady Ann Strait and Jones Sound, Nunavut, from January to March 1992. The presence of fast ice in Jones Sound and glaciers and ice caps on the surrounding islands provides an ideal setting for temporal backscatter studies of ice surfaces. Sample regions for eight different ice types were selected and the temporal backscatter variation was studied. The observed backscatter values for each ice type characterize the radar signatures of the ice surfaces. This time series of twenty‐four SAR images over a 3‐month period provides new insights into the degree of temporal variability of each surface. Ice caps exhibit the highest backscatter value of ‐3.9 dB with high temporal variability. Valley glacier ice backscatter values decrease with decreasing altitude, and are temporally the most stable, with standard deviations of 0.08–0.10 dB over the 90‐day period.

First‐year ice and lead ice show a negative trend in backscatter values in time and a positive correlation of up to 0.59 with air temperature over the 90‐day period. For first‐year ice and lead ice, episodes of large temperature fluctuations (±12°C) are associated with rapid changes in backscatter values (±2 dB). We attribute the backscatter increase to a temperature‐induced increase in brine volume at the base of the snow pack. Multi‐year ice, conglomerate ice and shore ice are relatively stable over the 3‐month period, with a backscatter variation of only a few dBs. An observed lag time of up to three days between backscatter increase/decrease and air temperature can be attributed to the insulation effect of the snow cover over sea ice. The net range of the backscatter values observed on the most temporally stable surface, valley glacier ice, of about 0.30 dB indicates that the ERS‐1 SAR instrument exceeds the 1 dB calibration accuracy specified for the Alaska SAR Facility processor for the three winter months.