Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

HOG-VGG:VGG Network with HOG Feature Fusion for High-Precision PolSAR Terrain Classification

This article proposes a VGG network with histogram of oriented gradient(HOG) feature fusion(HOG-VGG) for polarization synthetic aperture radar(PolSAR) image terrain classification.VGG-Net has a strong ability of deep feature extraction,which can fully extract the global deep features of different terrains in PolSAR images,so it is widely used in PolSAR terrain classification.However,VGG-Net ignores the local edge & shape features,resulting in incomplete feature representation of the PolSAR terrains,as a consequence,the terrain classification accuracy is not promising.In fact,edge and shape features play an important role in PolSAR terrain classification.To solve this problem,a new VGG network with HOG feature fusion was specifically proposed for high-precision PolSAR terrain classification.HOG-VGG extracts both the global deep semantic features and the local edge & shape features of the PolSAR terrains,so the terrain feature representation completeness is greatly elevated.Moreover,HOG-VGG optimally fuses the global deep features and the local edge & shape features to achieve the best classification results.The superiority of HOG-VGG is verified on the Flevoland,San Francisco and Oberpfaffenhofen datasets.Experiments show that the proposed HOG-VGG achieves much better PolSAR terrain classification performance,with overall accuracies of 97.54%,94.63%,and 96.07%,respectively.

Efficient Vision Transformers for Autonomous Off-Road Perception Systems

The development of autonomous vehicles has become one of the greatest research endeavors in recent years. These vehicles rely on many complex systems working in tandem to make decisions. For practical use and safety reasons, these systems must not only be accurate, but also quickly detect changes in the surrounding environment. In autonomous vehicle research, the environment perception system is one of the key components of development. Environment perception systems allow the vehicle to understand its surroundings. This is done by using cameras, light detection and ranging (LiDAR), with other sensor systems and modalities. Deep learning computer vision algorithms have been shown to be the strongest tool for translating camera data into accurate and safe traversability decisions regarding the environment surrounding a vehicle. In order for a vehicle to safely traverse an area in real time, these computer vision algorithms must be accurate and have low latency. While much research has studied autonomous driving for traversing well-structured urban environments, limited research exists evaluating perception system improvements in off-road settings. This research aims to investigate the adaptability of several existing deep-learning architectures for semantic segmentation in off-road environments. Previous studies of two Convolutional Neural Network (CNN) architectures are included for comparison with new evaluation of Vision Transformer (ViT) architectures for semantic segmentation. Our results demonstrate viability of ViT architectures for off-road perception systems, having a strong segmentation accuracy, lower inference speed and memory footprint compared to previous results with CNN architectures.

Effect of the off-road terrains on the ride comfort of construction vehicles

In order to evaluate the impact of off-road terrains on the ride comfort of construction vehicles,a nonlinear dynamic model of the construction vehicles interacting with the terrain deformations is established based on Matlab/Simulink software.The weighted root mean square(RMS)acceleration responses and the power spectral density(PSD)acceleration responses of the driver s seat heave,the pitch and roll angle of the cab in the low-frequency region are chosen as objective functions under different operation conditions of the vehicle.The results show that the impact of off-road terrains on the driver s ride comfort and health is clear under various conditions of deformable terrains and range of vehicle velocities.In particular,the driver s ride comfort is greatly affected by a soil terrain while the comfortable shake of the driver is strongly affected by a sand terrain.In addition,when the vehicle travels on a poor soil terrain in the frequency range below 4 Hz,more resonance peaks of acceleration PSD responses occurred than that on a rigid road of ISO 2631-1 level C.Thus,the driver s health is significantly affected by the deformable terrain in a low-frequency range.

Using video imagery to reconstruct the 3D intertidal terrain along a beach with multiple cusps

A high-frequency,high-resolution shore-based video monitoring system(VMS)was installed on a macrotidal(tidal amplitude>4 m)beach with multiple cusps along the Quanzhou coast,China.Herein,we propose a video imagery-based method that is coupled with waterline and water level observations to reconstruct the terrain of the intertidal zone over one tidal cycle.Furthermore,the beach cusp system(BCS)was precisely processed and embedded into the digital elevation model(DEM)to more effectively express the microrelief and detailed characteristics of the intertidal zone.During a field experiment conducted in January 2022,the reconstructed DEM was deemed satisfactory.The DEM was verified by RTK-GPS and had an average vertical root mean square error along corresponding RTK-GPS-derived intertidal profiles and corresponding BCS points of 0.134 m and 0.065 m,respectively.The results suggest that VMSs are an effective tool for investigating coastal geomorphic processes.

Terrain-influenced wind flow of the Qitai radio telescope site

The wind environment of a site is one of the important factors affecting the observation performance of large aperture and high-performance radio telescopes.Exploring the relationship between the effects of different terrains on wind flow is important to optimize the wind environment of the site.The terrain of the Qitai radio telescope(QTT)site located in east Tianshan Mountains at an elevation of about 1800 m was used to study the wind flow in the adjacent zone of antenna based on numerical simulation.The area from 600m south to 600m north of the antenna is defined as the antenna adjacent zone,and three groups of boundaries with different terrains are set up upstream and downstream,respectively.Since the zone where the antenna is located is a slope terrain,in order to verify the influence of terrain on the wind flow and to clarify the relationship between the influence of boundary terrain on the wind flow,a control group of horizontal terrain is constructed.The simulation results show that the wind flow is mainly influenced by the terrain.The highest elevation of the upstream and downstream boundary terrains affects the basic wind speed.The upstream boundary terrain has a greater impact on wind flow than the downstream boundary terrain.In addition,the wind speed profile index obtained by numerical simulation is smaller than the actual index for the wind from south.Therefore,the wind speed at the upper level(about 100 m)obtained by inversion based on the measured wind speed at the bottom(about 10 m)is also smaller than the actual wind speed.

Single-element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration.The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data.To map anomalies in a discrete field model of such data,two corrections are required:background correction and downstream dilution correction.Topography and geomorphology are important factors in variations of element content in stream sediments.However,few studies have considered,through the use of digital terrain analysis,the influence of geomorphic features in downstream dilution correction of stream sediment geochemical data.This study proposes and demonstrates an improvement to the traditional downstream dilution correction equation,based on the use of digital terrain analysis to map single-element anomalies in stream sediment geochemical landscapes.Moreover,this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area,Tibet.The efficiency of the proposed methodology was validated against known mineral occurrences.The results indicate that catchment-based analysis outperforms interpolation-based analysis of stream sediment geochemical data for anomaly mapping.Meanwhile,the proposed modified downstream dilution correction equation proved more effective than the original equation.However,further testing of this modified downstream dilution correction is needed in other areas,in order to investigate its efficiency further.

Correction of CMPAS Precipitation Products over Complex Terrain Areas with Machine Learning Models

Machine learning models were used to improve the accuracy of China Meteorological Administration Multisource Precipitation Analysis System(CMPAS)in complex terrain areas by combining rain gauge precipitation with topographic factors like altitude,slope,slope direction,slope variability,surface roughness,and meteorological factors like temperature and wind speed.The results of the correction demonstrated that the ensemble learning method has a considerably corrective effect and the three methods(Random Forest,AdaBoost,and Bagging)adopted in the study had similar results.The mean bias between CMPAS and 85%of automatic weather stations has dropped by more than 30%.The plateau region displays the largest accuracy increase,the winter season shows the greatest error reduction,and decreasing precipitation improves the correction outcome.Additionally,the heavy precipitation process’precision has improved to some degree.For individual stations,the revised CMPAS error fluctuation range is significantly reduced.

Strategies for Advancing Road Construction Slope Stability: Unveiling Innovative Techniques for Managing Unstable Terrain

This comprehensive review paper explores various aspects of geotechnical engineering, with a focus on the management of unstable terrains, numerical methods for solving complex soil and consolidation problems, rheological analysis of suspensions and muddy soils, and stability analysis of slopes. It begins by examining the unique physicochemical properties of cohesive sediments, including cohesion and specific surface area. The temporal evolution of deposit concentration and average bed concentration in unstable terrains is discussed, along with settling behavior of isolated particles and hindered settling using empirical equations. Key sedimentation theories, such as Kynch’s theory, and geotechnical consolidation theories, including Terzaghi’s consolidation equation and Gibson’s theory, are presented. The investigation interrelates these theories and principles to offer a holistic view of managing unstable terrains. It also addresses the challenges associated with experimental determination of constitutive relationships and presents alternative simplification methods proposed by researchers. Additionally, it delves into numerical methods for solving nonlinear partial differential equations governing soil behavior, emphasizing the need for numerical frameworks and discussing various techniques and associated challenges. The rheological analysis section covers material flow behavior, rheological behavior models, and the rheological properties of water and cohesive sediment mixtures. Fundamental geotechnical calculations, constitutive laws, and failure criteria are explained, highlighting their relevance in geotechnical engineering applications. This paper provides a multidimensional perspective on geotechnical engineering, offering valuable insights into soil properties, consolidation processes, numerical methods, rheological analysis, and slope stability assessment for professionals in the field.

中国典型复杂地形风能资源特性及其形成机制

为提高对复杂地形风资源特性及其形成机制的认识,改进复杂地形风场数值模拟方法,分别选取代表极大起伏山地与宽谷地形的藏南谷地、代表极大起伏山地与深谷地形的横断山区以及代表中、小起伏丘陵山地的山西高原开展风资源特性观测实验,分析不同典型复杂地形条件下天气背景风场、局地大气环流、地形动力强迫、地表摩擦与热力作用对风资源特性形成的贡献,结果表明:山西高原局地大气环流的作用较小;藏南谷地和横断山区的山谷风环流对其风能资源特性的形成起主要作用,尤其是横断山区还存在多尺度的局地大气环流,传统的风电场风资源CFD数值模拟不足以描述如此复杂的风场。因此,在局地大气环流作用明显的复杂地形地区,需要采用中尺度与CFD结合的风电场选址风资源数值模拟方法。

Intensified haze formation and meteorological feedback by complex terrain in the North China Plain region

华北平原地区冬季雾霾污染频发,本研究结合地面观测,卫星反演和大气动力-化学耦合模拟发现,该地区复杂地形加剧了细颗粒物污染及其与大气边界层之间的相互作用.一方面,复杂地形导致污染在山麓平原积聚,加强迎风坡上升气流.在污染事件中,污染层易被抬升至1-2公里高度,高空较强的氧化能力利于二次气溶胶生成;另一方面,地形导致的污染层抬升和二次生成进一步加强气溶胶-边界层相互作用,削弱垂直扩散并加剧近地面大气污染.

2022年8月四川盆地持续性极端高温特征及不同模式预报误差分析

本文基于2022年8月四川盆地104站逐时温度、降水数据和1971—2021年历史同期数据,及EC、CMA-GFS、CMA-MESO模式的2 m气温预报等数据,运用统计学相关方法分析了此次极端高温过程的特征及预报误差。结果表明:①2022年8月四川盆地极端高温过程范围大、强度强、持续时间长,有87.5%站最高气温超过该站历史同期极值,且高温最强盛时段较历史同期明显推后。②2022年8月最高气温分布为东高西低,最高气温与历史同期极值差分布则相反,其中最高气温随站点海拔增大而减小,而极值差则随站点海拔先增大再减小。另外,受热岛效应影响,极值差大值站点主要集中在龙泉山脉附近。③高温期间,最高、最低气温平均值高、距平大,且累计降水量和雨日数也明显低于历史同期。④相较而言,EC模式的预报优势主要在盆地低海拔地区。而CMA-MESO模式在盆地周边陡峭地形区域的平均绝对误差则更小。另外,EC模式预报的最高气温峰值出现时间更接近于实况,而CMA-MESO模式预报高温持续日数更接近实况。

基于非监督对比学习的火星地形特征提取方法

火星表面地形智能识别对火星车自主探测具有重要意义,火星地形图像的特征提取方法目前主要分为传统的浅层视觉特征提取和基于监督学习的深层特征提取2类。找回丢失图像信息、获取大量带标签数据是要解决的关键问题。为此,提出一种基于非监督对比学习的火星地形特征识别方法,通过建立图像字典数据集,用“问询”和“编码”2组神经网络分别将单个图像与“字典”数据集中其他图像进行对比,用相似度泛函作为损失函数对网络进行训练,从而实现对火星地形图像的特征识别。所提方法还具有对训练数据集之外的新类型地形图像识别能力,后续识别分类优越性突出。仿真结果表明:所提方法识别准确率为85.4%,对新类型地形图像的识别准确率为84.5%。

基于激光SLAM多地形机器人的设计

为解决传统轮式机器人在复杂地形中受限与腿式机器人控制策略复杂的问题,提出一种多地形机器人。结合激光即时定位与地图构建(SLAM)方法和自适应式轮腿机构,将树莓派作为运算单元,搭载(ROS)机器人操作系统,应用激光SLAM技术实现环境地图构建和机器人导航,同时结合深度模型和摄像头完成图像任务。自适应式轮腿机械结构使机器人能够根据环境需求自动切换为轮式或腿式行进模式。底层控制器采用STM32F407,机器人通过PID算法能实现精准的移动和机械臂作业。结果表明:该多地形机器人控制方法简单高效,在坡地、草地、坑地、台阶障碍物等复杂地形中展现了灵活移动的能力,最大翻越障碍高度可达250 mm,爬坡角度可达45°,在稳定性和适应性方面具有显著优势。

六盘山地形及相对湿度对微波辐射计反演气温的影响

利用2018年4月至2019年11月的六盘山隆德站微波辐射计探测资料与平凉站探空资料,研究了微波辐射计反演温度(TM)对空气相对湿度的敏感性。首次提出湿度敏感性系数(HSR)和零度漂移度(ZDD)概念,利用这两个参量在不同高度层的分布特征,研究了六盘山地形对空气相对湿度和温度的影响,主要结论如下:(1)非降水天气条件下,空气相对湿度越大,HSR与1.0的差别越大,ZDD也越大。(2)六盘山地形对大气层的湿度垂直分布影响较大。爬坡气流或抬升气流明显增加了3 km以下气层湿度水平,晴天和阴天条件下空气相对湿度分别在山顶以上0.5 km气层和1.0~2.0 km气层达到最大,TM比实际温度最大能偏低2.7℃。(3)地形对气层湿度的影响进一步影响了空气温度的垂直分布,阴天条件下的大气逆辐射加热使得3 km以下气层平均升温2.3℃,因湿度敏感性产生的温度影响平均达到0.9℃,最高能达到1.7℃。

电大山区地物环境中电波传播的电磁计算

在无人驾驶与无人机等新兴行业中,信号覆盖范围的要求较高,不仅仅在城市,在人迹罕至的山地、沙漠、森林中也需要无线信号的覆盖才能真正完成远程操控,这些地区更多需要考虑的是地势变化对电磁传播所带来的影响。计算电磁学中的一致性几何绕射理论方法是分析电大环境电磁问题的有效方法,使用计算电磁学的方法研究电磁波在山区地物环境中的传播规律。给出了一种建立不规则地形模型的新方法,可以通过数字高程的网格数据生成电磁算法可用的三次多项式曲面,使用多个立方曲面对不规则地形进行拼接,使用平均均方根误差验证模型数据的准确性。基于所得的地形数据,完成了并行的几何光学算法,并对区域电磁场的分布进行了仿真计算。选取了实际山区地物环境进行了实地测量,测量结果与仿真结果对比趋势一致,验证了该方法在非规则地形中电磁波传播分析中的有效性。考虑环境电磁计算的规模,建立了相应的并行策略,100核测试的并行效率可以保持在80%以上。

秦巴山片区生态敏感性评价及地形梯度特征

[目的]秦巴山片区不仅是我国重要的生态功能区,也是国家集中连片特困区,加强生态敏感性评价,揭示其地形梯度特征,对于山区生态红线划定和生态高地建设具有重要价值。[方法]借助GIS技术,以土地利用/覆被、气候、地形和土壤等数据为基础,综合采用线性加权求和法、熵权法、空间叠置法等,评价区域生态敏感性,并进行敏感性功能区划。[结果](1)2000-2018年秦巴山片区生态敏感性呈现波动性变化,2010年以后生态敏感性有所降低;(2)水平方向上呈现南高北低的格局,巴山山区县生态敏感性高于秦岭山区县;(3)垂直方向上,随着地形的增加,生态敏感性呈现“先增加后降低再增加”的态势;在海拔900 m附近最高;重度以上生态敏感性在地表切割深度75~200 m和坡度15°~25°分布面积最广;(4)基于生态敏感性评价结果,将秦巴山片区分为重点建设区、适度开发区、水土保持区、一般生态区和重要生态功能区,归纳各功能区土地利用和生态敏感性特征,并提出生态保护对策。[结论]未来要持续优化土地利用格局,加强坡度15°以上和中低地形位区域的山水林湖田草综合整治与生态修复。

地形影响的水平相关模型在CMA-MESO中的应用

在背景误差水平相关模型中引入地形作用,研究复杂地形下近地面观测资料同化对分析和预报的影响。CMAMESO三维变分系统中背景误差水平相关关系采用高斯相关模型描述,观测信息在高度追随坐标的模式面上各向同性传播。然而在地形复杂的近地面层,观测信息传播受到山脉阻挡,因而其背景误差协方差非均匀且各向异性,观测信息传播应随地形高度变化。为此,采用美国国家气象中心NMC方法统计复杂地形下背景误差水平相关结构,构建包含地形高度和地形梯度影响的高斯相关模型,并将改进的水平相关模型应用于CMA-MESO三维变分分析。理想试验表明:考虑地形项的水平相关模型方案使观测信息以随地形高度变化的各向异性形式传播,越过大地形观测信息影响明显减弱,分析增量更加合理。我国北方一次强降水过程分析预报试验表明:随地形高度变化的水平相关模型方案使地面观测信息各向异性传播,削弱了大地形处近地面的分析增量,对降水预报略有正贡献。针对华东地区降水过程进行5 d逐小时快速更新分析预报循环试验结果表明,随地形变化的水平相关模型方案对10 m风场和24 h时效内降水预报有正贡献。

沙漠履带式运输车通过性仿真与试验

为提高沙漠地区车辆运输车能力,通过对沙漠土壤参数和履带车辆在松软地面通过性理论的分析,设计一种配备三角履带板的LY352J型沙漠履带式运输车。该车能够进行人员物资运输、车辆牵引、绞盘牵引作业,主要由三角履带板、绞盘和驾驶室等关键部位组成。通过理论分析和计算,确定各关键部件参数及沙漠履带式运输车在沙漠松软地面下的牵引通过性,应用Creo软件建立该履带车辆的虚拟样机模型,并采用RecurDyn(Recursive Dynamic)动力学仿真软件对车辆履带的最佳张紧力选择、直行、爬坡、转弯进行模拟仿真。结果显示,车重45%的条件下履带最佳张紧力为26.46 kN,能够保证车辆运行平稳且各项性能良好;上坡仿真得到的最大坡度为30°;转向动力学仿真中,车辆在路面上的转向稳定性较优,但在转弯时车辆容易失稳,会出现横向倾斜。最后在试验场地对试验样机进行直行、纵向爬坡和转向试验,验证该车型在沙漠地形实际行走时具有良好通过性能,能够克服轮式运输车在沙地沉陷的弊端,可满足现阶段沙漠地形运输作业的需求。

景观功能视角下的西南山区农业景观格局演变研究——以重庆市石柱县为例

鉴于中国西南山区农业生态环境的脆弱性与敏感性,解析该地区农业景观时空演变特征对合理利用农业景观资源和保护生态环境具有重要的理论意义.以重庆市石柱县为例,采用景观利用图谱、地形位分布指数、景观转移矩阵等方法,从景观功能视角开展西南山区农业景观格局演变特征分析.研究表明:①农业景观呈现出“三增一减”的变化趋势,即生活型和拓展型农业景观面积呈增长态势,生态型农业景观面积呈波动变化趋势;生产型农业景观面积持续下降.农业景观变化主要表现为生产型向生态型、生活型、拓展型农业景观转化;生态型向生活型、生产型、拓展型农业景观转化.②石柱县生态型农业景观主要分布在高等级地形位,生产型农业景观逐渐向高程和坡度较高的地形位移动,生活型农业景观主要分布在低、中低等级地形位上,拓展型农业景观逐渐向较高地形位转移.③坡度、高程等是导致研究区农业景观格局演变的主要环境因素;而农业人口、农民人均纯收入、第一产业占比等是农业景观格局演变重要的人文驱动因素.