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.     

Inverse modelling using PS‐InSAR data for improved land subsidence simulation in Las Vegas Valley,Nevada

Las Vegas Valley has had a long history of groundwater development and subsequent surface deformation. InSAR interferograms have revealed detailed and complex spatial patterns of subsidence in the Las Vegas Valley area that do not coincide with major pumping regions. This research represents the first effort to use high spatial and temporal resolution subsidence observations from InSAR and hydraulic head data to inversely calibrate transmissivities (T), elastic and inelastic skeletal storage coefficients (S_ke and S_kv) of the developed‐zone aquifer and conductance (CR) of the basin‐fill faults for the entire Las Vegas basin. The results indicate that the subsidence observations from InSAR are extremely beneficial for accurately quantifying hydraulic parameters, and the model calibration results are far more accurate than when using only groundwater levels as observations, and just a limited number of subsidence observations. The discrepancy between distributions of pumping and greatest levels of subsidence is found to be attributed to spatial variations in clay thickness. The Eglington fault separates thicker interbeds to the northwest from thinner interbeds to the southeast and the fault may act as a groundwater‐flow barrier and/or subsidence boundary, although the influence of the groundwater barrier to this area is found to be insignificant. Copyright © 2016 John Wiley & Sons, Ltd.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Effects of surface flux parameterization on the numerically simulated intensity and structure of Typhoon Morakot (2009)

The effects of surface flux parameterizations on tropical cyclone(TC) intensity and structure are investigated using the Advanced Research Weather Research and Forecasting(WRF-ARW) modeling system with high-resolution simulations of Typhoon Morakot(2009).Numerical experiments are designed to simulate Typhoon Morakot(2009) with different formulations of surface exchange coefficients for enthalpy(C_K) and momentum(C_D) transfers,including those from recent observational studies based on in situ aircraft data collected in Atlantic hurricanes.The results show that the simulated intensity and structure are sensitive to C_K and C_D,but the simulated track is not.Consistent with previous studies,the simulated storm intensity is found to be more sensitive to the ratio of C_K/C_D than to C_K or C_D alone.The pressure-wind relationship is also found to be influenced by the exchange coefficients,consistent with recent numerical studies.This paper emphasizes the importance of C_D and C_K on TC structure simulations.The results suggest that C_D and C_K have a large impact on surface wind and flux distributions,boundary layer heights,the warm core,and precipitation.Compared to available observations,the experiment with observed C_D and C_K generally simulated better intensity and structure than the other experiments,especially over the ocean.The reasons for the structural differences among the experiments with different C_D and C_K setups are discussed in the context of TC dynamics and thermodynamics.     

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。     

华南暖区暴雨研究进展

华南前汛期暴雨预报一直是我国大气科学界的一个研究热点,特别是发生在锋前的暖区暴雨,由于其天气尺度斜压性强迫不明显,环境大气水汽含量丰富,热动力不稳定性强,边界层触发机制复杂,以及特殊的地形和海陆热力差异的外强迫作用,导致暴雨突发性强,地域性特征显著,也是困扰预报业务人员的难点问题。目前我国预报业务中使用的全球数值预报模式对暖区暴雨的预报能力十分有限,高分辨率中尺度数值模式的预报效果也不尽人意。该文回顾了近40年华南前汛期暴雨大部分研究成果,针对华南暖区暴雨的提出及典型背景场、暖区暴雨与低空急流的关系、暖区中尺度对流系统的形成及传播、暖区暴雨触发机制等独特的天气动力学特征进行了系统梳理与分析,并依据前人研究成果及中央气象台预报实践经验,总结提炼了3类华南暖区暴雨类型——边界层辐合线型、偏南风风速辐合型,以及强西南急流型的天气系统配置及触发因子。最后提出针对华南暖区暴雨需要进一步研究的科学问题。