Diagnosing snow accumulation errors in a rain‐snow transitional environment with snow board observations

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process‐based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014–2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research.     

Rain on snow induced urban floods in cold maritime climate: Risk,indicators and trends

Urban floods pose a societal and economical risk. This study evaluated the risk and hydro-meteorological conditions that cause pluvial flooding in coastal cities in a cold climate. Twenty years of insurance claims data and up to 97 years of meteorological data were analysed for Reykjavík, Iceland (64.15°N; <100 m above sea level). One third of the city's wastewater collection system is combined, and pipe grades vary from 0.5% to 10%. Results highlight semi-intensive rain (<7 mm/h; ≤3 year return period) in conjunction with snow and frozen ground as the main cause for urban flood risk in a climate which undergoes frequent snow and frost cycles (avg. 13 and 19 per season, respectively). Floods in winter were more common, more severe and affected a greater number of neighbourhoods than during summer. High runoff volumes together with debris remobilized with high winds challenged the capacity of wastewater systems regardless of their age or type (combined vs. separate). The two key determinants for the number of insurance claims were antecedent frost depth and total precipitation volume per event. Two pluvial regimes were particularly problematic: long duration (13–25 h), late peaking rain on snow (RoS), where snowmelt enhanced the runoff intensity, elongated and connected independent rainfall into a singular, more voluminous (20–76 mm) event; shorter duration (7–9 h), more intensive precipitation that evolved from snow to rain. Closely timed RoS and cooling were believed to trigger frost formation. A positive trend was detected in the average seasonal snow depth and volume of rain and snowmelt during RoS events. More emphasis, therefore, needs to be placed on designing and operating urban drainage infrastructure with regard to RoS co-acting with frozen ground. Furthermore, more detailed, routine monitoring of snow and soil conditions is important to predict RoS flood events.     

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

东北地区春季首场透雨的变化特征及与青藏高原地面加热场强度的关系

利用1958—2012年4—5月东北地区(39°~55°N、118°~135°E)101个站点逐日降水资料、青藏高原地区(25°~40°N、73.75°~103.75°E)JRA-55的地面感热和潜热通量月平均再分析资料以及NCEP/NCAR-I大气环流场的月平均再分析资料,分析了春播期首场透雨出现日期的时空变化特征及其与透雨量和播种期降水量间的关系,以及对青藏高原地面加热场强度异常的响应及其可能机制。结果表明:透雨日期自1958年以来在东北地区的西北和东南大部分区域呈现略微偏晚的趋势;中部有略微偏早的趋势。春播期首场透雨出现时间偏早(晚)的地方,首场透雨量小(大),春播期总降水量多(少)。同时,4月青藏高原地面加热场强度增强(减弱),有利于(不利于)来自北方的冷空气和南方的暖湿气流在东北上空交汇,且上升气流增强(减弱),水汽输送充沛(减少),导致该地区春季首场透雨出现的时间偏早(晚)。     

Logistic判别模型在强降水预报中的应用

利用Logistiv判别模型进行强降水预报,并设计3种方案进行对比分析。方案1直接使用14个影响因子进行判别预报,受因子共线性作用及噪音信号影响,虽然拟合效果较好,但预报效果明显下降。方案2对14个影响因子进行主成分分析,利用前6个主成分建模,虽然拟合效果较方案1降低,但由于消除了因子共线性作用以及噪音信号影响,预报效果较方案1提高。方案3运用Bootstrap抽样技术得到符干样本并建模计算模型参数,打乱了原有时间序列中的波动,仪保留平稳信息,拟合自由度进一步降低,导致拟合效果较方案案2下降,但预报效果却是3种方案中最好且最稳定的。在上述研究基础上,利用欧洲中心数值预报模式的预报场资料,建立基于Logistic判别模型的强降水客观预报系统,并在中央气象台业务运行。2013和2014年连续两年汛期预报检验结果表明,概模型对强降水预报的TS评分高于数值模式本身,具有一定的业务参考价值。     

中国东部7类暴雨异常环流型

近年来的研究发现,瞬变扰动天气图上的扰动场天气系统对区域暴雨的落区指示能力强于传统天气图上的总场天气系统。为供预报员在业务预报中参考,本文划分1998年发生在中国东部地区的41日次区域暴雨为7类扰动场天气系统。与区域暴雨相联系的7类异常环流型分别是:华南切变线、华南涡旋、华南倒槽、长江切变线与槽、沿江涡旋、华北涡旋和东北涡旋。无论是在对流层的垂直剖面上,还是在850 hPa水平分布上,扰动天气图上位势高度低值和风扰动辐合处并配合大的水汽扰动对应有区域暴雨,而传统天气图上的低值系统和高水汽区与暴雨之间存在位置上的偏移。由此建议,用实况大气变量和中期数值模式产品绘制扰动天气图有助于预报员确定区域暴雨落区。     

无锡梅雨期湿沉降综合分析

利用2008—2014年梅雨期间酸雨观测资料及2014年6月16—27日降水个例加密采样资料,结合大气污染物资料分析了近7 a无锡梅雨期酸雨特征,研究降水过程中空气污染物、p H值、电导率的变化及降水对污染物的清除作用。结果表明:无锡市梅雨期酸雨年平均p H值呈现逐年递增趋势。降水过程中,颗粒物质量浓度显著降低;气体浓度变化受其自身日变化及排放源影响大于雨水的清除作用;样品的p H值、K值每个过程变化并不一致,K值变化与颗粒物质量浓度变化大致保持一致。降水、风对颗粒物质量浓度影响大于对气体浓度的影响。长时间连续降水时,降水对颗粒污染物的清除存在极限。小时雨量在0~0.5 mm时,降水对颗粒物浓度做负清除,其值反而略有增加;小时雨量在0.6~5.0 mm时,降水对颗粒物质量浓度做正清除;小时雨量达到5.1 mm及以上时,对PM_(2.5)和PM_(2.5-10)做正清除,对PM_(10)做负清除。降水对SO_2有稀释清除作用;对NO_2的稀释作用取决于其开始浓度值;对CO、O_3的清除作用不显著。     

天气尺度波列对长江中下游6月梅雨的影响

采用1979—2007年6月NCEP/NCAR2.5°×2.5°逐日再分析资料和中国743站逐日降水资料,利用相关分析、合成分析等方法,分析了天气尺度波列的特征及其对长江中下游6月梅雨的影响。结果表明:当长江中下游6月梅雨较少时,东亚及西太平洋区域存在一个天气尺度波列;该波列的延伸距离较短(从黄河河套地区经过长江中下游至南海、菲律宾海一带),维持时间也很短,且仅仅在500 h Pa以下较强。诊断及个例分析表明,当该波列异常显著时,长江中下游梅雨降水明显减少,而其南部区域降水则增多,说明该波列对预报长江中下游降水具有重要的指示意义。     

星载SAR对雨团催生海面风场的观测研究

雨团或对流雨是热带与亚热带地区的主要降雨形式,较易被高分辨率星载合成孔径雷达（SAR）探测到。SAR图像上的雨团足印是由大气中雨滴的散射与吸收、下沉气流等共同导致形成的。本文以RADARSAT-2卫星100 m分辨率的SAR图像上雨团引起的海面风场及其结构反演与解译作为实例进行分析。使用CMOD4地球物理模式函数,分别以NCEP再分析数据、欧洲MetOp-A卫星先进散射计（ASCAT）和中国HY-2卫星微波散射计的风向为外部风向,进行了SAR图像的海面风场反演。反演的海面风速相对于NCEP、ASCAT和HY-2的均方根误差（RMSE）分别为1.48 m/s,1.64 m/s和2.14 m/s。SAR图像上一侧明亮另一侧昏暗的圆形信号图斑被解译为雨团携带的下沉气流对海面风场（海面粗糙度）的改变所致。平行于海面背景风场其通过雨团圆形足印中心的剖面上的风速变化可拟合为正弦或余弦曲线,其拟合线性相关系数均不低于0.80。背景风场的风速大小、雨团引起的风速大小以及雨团足印的直径可利用拟合曲线获得,雨团足印的直径大小一般为数千米或数十千米,本文的8例个例解译与分析均验证了该结论。     

两次强降水过程雨滴谱特征及差异

利用寿县观测站内的Parsivel激光雨滴谱仪结合观测站雨量数据及雷达基数据,分析了发生在2015年6月26—30日梅雨期间和2015年8月7—10日超强台风"苏迪罗"影响期间2次强降水过程的雨滴谱结构特征及其差异,拟合了雨强与雷达反射率因子之间的关系。结果表明:雨强的大小直接影响雨滴谱的特征参量,且随着雨强增大而增大;梅雨锋暴雨中1.0mm直径≤1.5mm的粒子所占比例最多,雨强贡献率最大;台风雨中0.75mm直径≤1.0mm的粒子所占比例最多,但1.0mm直径≤1.25mm的粒子对雨强的贡献最大,说明较大粒子对强降水的贡献较大。