A virtual globe-based time-critical adaptive visualization method for 3D city models

3D city models, which are important items of content on the virtual globe, are characterized by complicated structures and large amounts of data. These factors make the visualization of 3D city models highly dependent upon the performance of computer hardware. Thus, achieving the efficient rendering of 3D city models using different hardware performance levels represents one of the key problems currently facing researchers. This paper proposes a time-critical adaptive visualization method that first estimates the possible rendering time for each model according to the data structure of the model in addition to the CPU/GPU performance of the computer. It then dynamically adjusts the rendering level for each model based on the results of an estimation of the rendering time to ensure that the final scene can be completed within a given time. To verify the effectiveness and flexibility of this method, it is applied using different computers. The results show that the adaptive visualization method presented in this paper not only can adapt to computers with different levels of performances but also demonstrates an obvious improvement in the time estimation precision, visual effects, and optimization speed relative to existing adaptive visualization methods.     

A comparative analysis of the NDVIg and NDVI3g in monitoring vegetation phenology changes in the Northern Hemisphere

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived from the NDVI3g (SOS3g) based on all the methods, for the whole northern hemisphere. In addition, SOSg and SOS3g both showed an advancing trend during 1982–2006, but that trend was more significant with SOSg than with SOS3g in the results from all three methods. In summary, the difference between SOSg and SOS3g (in the multi-year mean SOS, SOS change slope and the turning point in the time series) varied among the methods and was partly related to latitude. For the multi-year mean SOS, the difference increased with latitude intervals in the low latitudes (0–30°N) and decreased in the mid- and high-latitude intervals. The GIMMS NDVI3g data-sets seemed more sensitive than the GIMMS NDVIg in detecting information about the ground, and the SOS3g data were better correlated both with the in situ observations and the SOS derived from the Moderate Resolution Imaging Spectroradiometer NDVI. For the northern hemisphere, previous satellite measures (SOS derived from GIMMS NDVIg) may have overestimated the advancing trend of the SOS by an average of 0.032 d yr^–1.     

形态参数下地表采动裂缝三维建模及可视化方法

实现对地表采动裂缝的三维建模及可视化能促进裂缝的形状、成因研究,并为开采沉陷监测和生态环境恢复提供科学支持。目前三维建模方法和软件主要面向建筑物、管道、地质体等地物地貌,若直接应用于地表裂缝的建模与表达会存在野外测绘工作量大、形态规律表达不准确、呈现不逼真等问题。本文结合三维地形建模、分形、空间插值等理论,提出了基于几何形态参数的地表裂缝三维建模方法,并利用ArcEngine平台实现了对裂缝的三维可视化;选取甘肃省东峡矿区为试验区,根据裂缝分布与形态预计参数,实现了对区域内裂缝的模拟,并通过与实测数据和高分辨率影像进行对比,验证了该方法能真实逼真呈现裂缝的形态、延伸与细节信息。     

徕卡三维激光扫描系统在建筑物精细建模中的应用

三维激光扫描技术作为一种新的测绘技术手段改变了传统的测量模式,利用三维激光扫描技术可以快速、高效、连续地获取海量的点云数据,通过处理可以快速得到被测目标的三维模型。本文主要结合徕卡Scan Station P40三维激光扫描系统介绍其在复杂建筑物精细建模中的应用,通过对点云数据及建模精度分析,得出了有益的结论。     

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.     

Spatiotemporal Variations of Microwave Land Surface Emissivity (MLSE) over China Derived from Four-Year Recalibrated Fengyun 3B MWRI Data

Microwave Land Surface Emissivity (MLSE) over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures (Tbs) from Fengyun-3B Microwave Radiation Imager (FY-3B MWRI), combined with cloud properties derived from Himawari-8 Advanced Himawari Imager (AHI) observations. The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer. The MLSEs at horizontal polarizations at 10.65, 18.7, and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren, or sparsely vegetated, area. The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau, which is highly consistent with MLSEs derived from AMSR-E. It demonstrates that the measurements of FY-3B MWRI Tbs, including its calibration and validation, are reliable, and the retrieval algorithm developed in this study works well. Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation, rainfall, and snow cover. In tropical and southern forest regions, the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall. In the boreal area, snow causes a significant decrease of MLSE at 36.5 GHz in winter. Meanwhile, the MLSE at lower frequencies experiences less suppression. In the desert region in Xinjiang, increases of MLSEs at all frequencies are observed with increasing snow cover.     

FY-3E: The First Operational Meteorological Satellite Mission in an Early Morning Orbit

Fengyun-3 E(FY-3E),the world’s first early-morning-orbit meteorological satellite for civil use,was launched successfully at the Jiuquan Satellite Launch Center on 5 July 2021.The FY-3E satellite will fill the vacancy of the global early-morning-orbit satellite observation,working together with the FY-3C and FY-3D satellites to achieve the data coverage of early morning,morning,and afternoon orbits.The combination of these three satellites will provide global data coverage for numerical weather prediction(NWP)at 6-hour intervals,effectively improving the accuracy and time efficiency of global NWP,which is of great significance to perfect the global earth observing system.In this article,the background and meteorological requirements for the early-morning-orbit satellite are reviewed,and the specifications of the FY-3E satellite,as well as the characteristics of the onboard instrumentation for earth observations,are also introduced.In addition,the ground segment and the retrieved geophysical products are also presented.It is believed that the NWP communities will significantly benefit from an optimal temporal distribution of observations provided by the early morning,mid-morning,and afternoon satellite missions.Further benefits are expected in numerous applications such as the monitoring of severe weather/climate events,the development of improved sampling designs of the diurnal cycle for accurate climate data records,more efficient monitoring of air quality by thermal infrared remote sensing,and the quasicontinuous monitoring of the sun for space weather and climate.     

适应数值模式垂直坐标的GPS掩星反演资料稀疏化方案

GPS掩星反演大气温湿资料具有高垂直分辨率、高精度、受云和降水影响小等优点,针对GRAPES同化预报系统,发展设计了一种既考虑预报模式高度-地形追随垂直坐标不均匀分层特点,又结合掩星反演资料特性的新适应性垂直稀疏化方案。通过个例试验和批量试验,探索了该适应性稀疏化方案对分析预报质量的影响。试验结果表明:选取合适的稀疏参数,新稀疏化方案的GPS掩星反演资料对背景场的调整更加有效,分析场质量更高;位势高度、比湿、温度和风场等预报场的均方根误差均更小;适应性稀疏化方案对改善台风路径预报具有积极作用。批量试验则进一步证实了适应性稀疏化方案对分析场质量有明显的改善作用。     

承德市臭氧污染气象条件预报方法研究

利用2014-2016年承德市环境监测站和气象站的数据,分析了气象条件对承德市O₃-8h浓度的影响,探讨了臭氧污染气象条件的预报方法。结果表明：4-7月是承德市O₃-8h浓度较高的月份,O₃浓度的日变化特征为午后浓度高而夜间浓度低;O₃污染的天气形势为500 hPa受高压脊和偏西气流影响,850 hPa有强暖平流和20℃以上的高温,地面受低压前部和高压后部之间的偏南气流影响;有利于O₃-8h出现高浓度的气象因子为日平均气温大于23℃、日最高气温大于28℃、日平均海平面气压995-1007 hPa、日平均水汽压18-28 hPa、偏南风大于1 m·s^-1。利用气象因子综合评分建立臭氧污染指数,与O₃-8h浓度的相关系数高达0.7553,说明臭氧污染指数能较好地预报臭氧污染天气。     

FY-3/MERSI卫星资料监测淮河水体方法及应用

在考虑水体与其它地物光谱特征差异及FY-3/MERSI通道光谱和空间分辨率特点的基础上,介绍了利用FY-3/MERSI进行水体识别及水体变化信息检测的方法。以淮河流域为例,利用FY-3/MERSI对2016年6月底至7月中旬降水过程前后水体变化信息进行了监测,并利用相近时间成像的50m空间分辨率的高分四号卫星对水体监测结果进行了对比分析,说明了本文方法的有效性。最后给出了利用我国卫星遥感资料进行水体变化监测需要进一步研究的方向。