Smart 3D数据的三维模型重建

针对Smart 3D生成的实景三维模型是一个三角网集合,并不能直接应用于数字城市建设、土地利用管理与城市规划的问题,该文提出了Smart 3D数据三维模型重建的方法:研究了基于分割过滤方法,提取高精度数字高程模型;基于参数化建模、纹理重建方法构建了建筑物三维模型。利用敦化市的倾斜影像进行了实验。实验结果表明:三维重建模型精度高、效果好、提高了自动化的程度,实现了城市建筑物模型的单体化。     

模式水平扩散参数化的数值分析与试验

为了研究整个对流层中水平扩散系数（Ｋｙ）随高度变化的参数化形式以及平流格式的数值扩攻对物质浓度分布的影响及二者对模式结果影响的相互关系,本文用“高分辨对流层物质交换模式”（ＥＭ３）对三咱不同ＫＹ参数化型能对行星边界层（ＰＢＬ）内受下垫面影响的高湍流ＰＢＬ以上整个对流层受水平风切变产生的切变湍流分层结构具有良好的描述能力;模式采用几乎无数值扩散二阶矩守恒的Ｐｒａｔｈｅｒ平流差分格式代替模式Ｓｍｏｌａ     

论地球空间信息科学的形成

从分析地球空间信息科学与全球变化监测和社会可持续发展的关系出发,回顾了地球空间信息科学产生的背景,提出地球空间信息科学的定义及内涵,概括了学科当前的发展状况,提出了现阶段学科研究的主要内容,最后,展望了地球空间信息科学的发展。     

无人机影像三维重建在沙丘形态监测中的应用

沙丘形态监测对开展土地沙化防治工作具有重大意义。应用无人机,分别在2016年7月、2017年3月、7月和9月对青海湖克土沙区4个沙丘进行影像采集,借助PhotoScan软件进行三维重建生成高分辨率的沙丘正射影像和DEM数据,使用ArcGIS软件获取沙丘形态参数并分析变化。实验结果表明：基于无人机影像监测沙丘形态,具有可行性和准确性,正射影像精度较高,DEM数据通过校正后可正确反映沙丘形态,为逐天高精度监测变化提供了可能。     

高分辨率青藏高原历史月降水数据重建

青藏高原对全球气候研究具有重要意义,而降水数据对水文、气象和生态等领域的研究也至关重要,且随着研究内容和尺度的变化,对高时空分辨率的历史降水数据的需求越发迫切。本文基于TRMM 3B43降水数据,采用随机森林算法,引入归一化植被指数(AVHRR NDVI)、高程(SRTM DEM)、坡度、坡向、经度、纬度6个地理因子,建立历史降水重建模型,获得1982-1997年分辨率为0.0833°的青藏高原年降水数据,然后根据比例系数法计算出月降水数据。为提高精度,利用站点数据对月降水数据进行校正。结果表明,该方法能简单有效地获得高时空分辨率的历史降水数据,决定系数R²大部分在0.4~0.9之间,平均值为0.6767,其中夏季效果最好,冬季效果最差;均方根误差RMSE和平均绝对误差MAE均在50 mm以下,RMSE均值为22.66 mm,MAE均值为15.97 mm;偏差Bias较小,基本在0.0~0.1之间。     

三小时负变压异常指数及对强对流天气的预报意义

低层暖平流强迫类强对流发生前,地面经常伴有低于日变化的3 h变压。结合常规地面观测资料,定义低于日变化的3 h变压异常（超过一个标准差定义为异常）指数PCR（Pressure Change Range）,讨论了中国中东部地区3 h变压标准差的气候分布特征;最后以3次强对流天气过程为例说明PCR指数的预报价值和时效。结果表明,与3 h变压均值相比,中国中东部地区的3 h变压标准差的日变化较小,PCR更适合作为变压异常程度的标准。东北、华北、华东-华中区域PCR冬春季节出现站次数偏多,夏秋季节偏少;华南区域除了冬春季外,夏季也偏多,秋季偏少。PCR主要集中在低级别强度上,但PCR级别越高,越有可能出现强对流天气。东北区域出现PCR的首要原因是受东北气旋的影响,且可能有TBB≤-52℃的云系相对应;华北、华东-华中、华南出现PCR的首要原因是冷高压变性或迅速东移,没有TBB≤-52℃的云系相对应;地面倒槽中出现的PCR全部有TBB≤-52℃的云系对应。3次强对流天气过程均发生在地面倒槽中;在发生前3 h左右,地面气压场上有较明显的负PCR中心出现,强对流天气中尺度云团有向负PCR中心移动的趋势。      

Assimilation of Feng-Yun-3B Satellite Microwave Humidity Sounder Data over Land

The ECMWF has been assimilating Feng-Yun-3B(FY-3B) satellite microwave humidity sounder(MWHS) data over ocean in an operational forecasting system since 24 September 2014. It is more difficult, however, to assimilate microwave observations over land and sea ice than over the open ocean due to higher uncertainties in land surface temperature, surface emissivity and less effective cloud screening. We compare approaches in which the emissivity is retrieved dynamically from MWHS channel 1 [150 GHz(vertical polarization)] with the use of an evolving emissivity atlas from 89 GHz observations from the MWHS onboard NOAA and EUMETSAT satellites. The assimilation of the additional data over land improves the fit of short-range forecasts to other observations, notably ATMS(Advanced Technology Microwave Sounder) humidity channels, and the forecast impacts are mainly neutral to slightly positive over the first five days. The forecast impacts are better in boreal summer and the Southern Hemisphere. These results suggest that the techniques tested allow for effective assimilation of MWHS/FY-3B data over land.     

A Prototype Regional GSI-based EnKF-Variational Hybrid Data Assimilation System for the Rapid Refresh Forecasting System:Dual-Resolution Implementation and Testing Results

A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.     

不同同化方案在一次梅雨锋暴雨预报中的应用研究

基于ATOVS辐射率资料和GTS常规观测资料融合,通过不同化任何观测资料的控制试验、3DVAR同化试验和ETKF-3DVAR混合同化试验,对2013年7月4—5日一次梅雨锋暴雨天气过程进行模拟分析。结果表明:混合同化方案能够明显地提高暴雨预报准确率,且有效改善了风场、温度场和相对湿度场等各个气象要素的初始场结构,而这些气象要素对于暴雨的预报准确性具有重要作用。且混合同化方案对于中低层大气改进效果较为明显。在数值积分过程中,随着时间的推移,混合同化方案的优势逐渐减弱。     

A 3D multi-threshold,region-growing algorithm for identifying dust storm features from model simulations

Dust storms cause significant damage to health, property, and the environment worldwide every year. To help mitigate the damage, dust forecast models simulate and predict upcoming dust events, providing valuable information to scientists, decision makers, and the public. These simulation outputs are in four-dimensions (i.e., latitude, longitude, elevation, and time) and represent spatially heterogeneous dust storm features and their evolution over space and time. This research investigates and proposes an automatic multi-threshold, region-growing-based algorithm to identify critical dust storm features from 3D dust storm simulations. A multi-threshold scheme is defined for the identification of dust storm features with different dust concentrations. Based on the multi-thresholds, dust storm features are iteratively identified by developing a region-growing algorithm that splits a clustered dust storm feature into multiple sub-features. The proposed approach is compared with three commonly used methods in image processing and thunderstorm identification. The proposed approach outperforms the other three methods in sensitivity and quantitative/qualitative accuracy. This research approach may also be slightly adjusted to identify critical 3D features from simulation outputs for other severe weather and geographical phenomena.