Empirical models to predict the volumes of debris flows generated by recently burned basins in the western U.S.

Recently burned basins frequently produce debris flows in response to moderate-to-severe rainfall. Post-fire hazard assessments of debris flows are most useful when they predict the volume of material that may flow out of a burned basin. This study develops a set of empirically-based models that predict potential volumes of wildfire-related debris flows in different regions and geologic settings.The models were developed using data from 53 recently burned basins in Colorado, Utah and California. The volumes of debris flows in these basins were determined by either measuring the volume of material eroded from the channels, or by estimating the amount of material removed from debris retention basins. For each basin, independent variables thought to affect the volume of the debris flow were determined. These variables include measures of basin morphology, basin areas burned at different severities, soil material properties, rock type, and rainfall amounts and intensities for storms triggering debris flows. Using these data, multiple regression analyses were used to create separate predictive models for volumes of debris flows generated by burned basins in six separate regions or settings, including the western U.S., southern California, the Rocky Mountain region, and basins underlain by sedimentary, metamorphic and granitic rocks.An evaluation of these models indicated that the best model (the Western U.S. model) explains 83% of the variability in the volumes of the debris flows, and includes variables that describe the basin area with slopes greater than or equal to 30%, the basin area burned at moderate and high severity, and total storm rainfall. This model was independently validated by comparing volumes of debris flows reported in the literature, to volumes estimated using the model. Eighty-seven percent of the reported volumes were within two residual standard errors of the volumes predicted using the model. This model is an improvement over previous models in that it includes a measure of burn severity and an estimate of modeling errors. The application of this model, in conjunction with models for the probability of debris flows, will enable more complete and rapid assessments of debris flow hazards following wildfire.     

Raman激光雷达探测对流层中上部大气温度分布

介绍了一台氮分子(N2)Raman激光雷达系统.利用N2分子Raman散射和气溶胶及分子的Mie-Rayleigh散射信号,通过同时订正分子、气溶胶和臭氧的衰减,反演出对流层中上部大气密度和温度的垂直分布,其结果与常规球载无线电探空仪探测资料对比,在8～18 km范围内表现了较好的一致性.其中,二者测量的温度在9～15 km高度内相对差别小于4 K.     

基于粒子群优化的多指标组合算子的大气污染预报模型

根据多指标组合算子法建立了大气污染物浓度预报的参数模型,并采用一种新颖的粒子群优化算法对大气污染物浓度预报模型中的参数进行优化。通过实例计算,该模型同线性回归、模糊模式识别、参数化组合算子方法进行了结果比较,结果表明,所建立的模型比前三种方法平均误差率小,吻合度好,具有较好的预测效果。     

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature(SAT) series at 32 stations in China back to the 19 th century had previously been developed based on the RHtest method by Cao et al.,but some inhomogeneities remained in the dataset.The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization(MASH) method.The MASH procedure detects 33 monthly temperature records as erroneous outliers and152 meaningful break points in the monthly SAT series since 1924 at 28 stations.The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series.The new data show significant warming trends during 1924–2016 at all the stations,ranging from 0.48 to 3.57?C(100 yr)~(-1),with a regional mean trend of 1.65?C(100 yr)~(-1);whereas,the previous results ranged from a slight cooling at two stations to considerable warming,up to 4.5?C(100 yr)~(-1).It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century.     

逆散射理论的发展及应用前景

逆散射理论作为量子力学发展的产物,目前在地震数据处理方面得到了较为广泛的应用.逆散射成像是通过散射体外部场的探测来获得散射体内部的信息,它是近年来发展比较迅速的一种成像方法,其突出的应用是运用逆散射序列中的一阶近似项进行深度成像.本文对逆散射理论在国内外的发展历程进行较为系统的概括和总结,在此基础上,对逆散射理论的应用前景进行了分析.逆散射理论在多次波的压制、界面奇性反演、大扰动深度成像等方面具有广阔的应用前景.     

纳米碳纤维红外消光数值计算

为探讨纳米碳纤维作为烟幕粒子的红外消光特性,基于电磁场理论建立了细直的纳米碳纤维感应电流积分方程,并利用矩量法进行了求解,导出了纳米碳纤维散射场及吸收、散射和消光截面的计算式.通过与变分法的计算结果相比较,表明了该方法的有效性.利用该方法数值分析了纳米碳纤维红外消光截面与入射场波长、入射角、纤维长度和半径的关系,计算结果为纳米碳纤维用于红外烟幕干扰提供了理论依据.     

青蛤(Cyclina sinensis)贝壳形态性状对软体部重的影响分析

随机抽取中国及日本海域的1龄青蛤(Cyclina shiensis)245个,分别测量其壳高、壳长、壳宽、韧带长、壳重及软体部重等形态学和生产性状,采用相关分析、通径分析、决定系数分析和多元回归分析方法,讨论了贝壳各形态性状对软体部重的影响.结果表明,青蛤的壳高对软体部重相关系数r1y和直接影响通径系数Pi分别达到了0.953和0.938,统计检验均达到了极显著水平(P<0.01).此外,壳高对软体部重的决定系数R12为0.9080,多元回归分析亦显示出青蛤的壳高与软体部重具有显著的相关性(P<0.01).实验结论认为,壳高对青蛤的生产性状影响最大,可以作为青蛤种质选育的重要甄别性状.     

基于微扰法的改进一维分形模型分层海面电磁散射研究

运用微扰法研究了平面波入射分层介质粗糙面的电磁散射,采用改进的一维分形海面模型模拟实际的分层海面,推导出了平面波入射时的散射系数计算公式。通过数值计算得到了HH极化双站散射系数随散射角的变化曲线,讨论了中间介质介电常数和厚度、摩擦风速和入射波频率对双站散射系数的影响,得到改进的一维分形分层海面散射系数的基本特征、分区特征和随频率变化的特征,结果表明散射系数近似具有"量子化"特征。这些结果在诸如海洋遥感、无线电传播与通信、粗糙面重构等方面中有着广泛的应用。     

郯庐断裂带中南段地壳介质散射强度

本研究运用远震扰动场方法,利用134个数字地震观测台记录的10次远震记录对郯庐断裂带中南段地壳介质散射强度进行研究.结果表明,研究区上、下地壳介质散射强度呈现强烈的横向非均匀性特征,上、下地壳介质散射强度空间分布特征基本一致,散射强度高值区主要分布在华北断块区及秦岭—大别褶皱系的东部地区,散射强度低值区主要分布在下扬子断块区,散射强度峰值区主要分布在六安、莒南、嘉山、烟台等地.地壳介质散射强度与构造块体具有较强的相关性,构造块体不同,地壳介质散射强度则存在一定的差异;沿郯庐断裂带地壳介质散射强度呈现分段性特征,可分为潍坊至临沂段、临沂至嘉山段以及嘉山至九江段三段;地震活动与地壳介质散射强度有较强的相关性,地震多发生在地壳介质散射强度梯度带上,郯庐断裂带的潍坊至临沂段、嘉山至九江段地壳介质散射强度梯度较大,此段中小地震活动较强,而临沂至嘉山段地壳介质散射强度梯度小,中小地震活动相对较弱.地壳介质散射强度与构造块体、断裂构造及地震活动之间的相关性,除反映了深部介质物性可能存在差异外,也可能与深部介质物理形态、物质运移以及深部应力应变环境的变化有关.

     

Application of a Neural Network to Store and Compute the Optical Properties of Non-Spherical Particles

Radiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (~127 GB) to that of the DNN parameters (~20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.