基于时间序列的InSAR相干性量级估计

本文提出了一种适用于InSAR数据处理的自适应相干性量级估计方法,该方法能够满足复信号随机平稳的假设前提,并兼顾运算效率与估计精度.此方法生成的相干图具有很好的分布特征,避免了影像空间分辨率的损失.提出的算法分为两个步骤:(1)根据地物后向散射特性,对时间序列SAR影像进行聚类分析,选择具有同分布的样本,保证SAR影像质地平稳条件;(2)对干涉图进行条纹频率估计,采用极大似然(ML)条纹频率估计方法去除系统相位引起的复信号非平稳性,并根据Cramer-Rao边界条件改善条纹频率的估计精度.以美国南加州洛杉矶地区的ENVISAT ASAR数据集为例,本文将新方法与现有方法进行了量化分析.结果表明,较传统方法而言,基于时间序列的相干性估计方法能够得到更可靠、精度更高、空间特征更鲜明的干涉相干图.     

基于MSAVI-WI特征空间的新疆渭干河-库车河流域绿洲土壤盐渍化研究

土壤盐渍化是造成干旱区土地荒漠化及生态恶化的重要原因,及时获取大尺度高精度土壤盐渍信息是防治工作的基础。选取新疆塔里木盆地北缘渭干河—库车河流域三角洲绿洲为研究区,利用Lansat-TM数据与野外实测数据分析盐渍化土壤与修改型土壤调整植被指数(MSAVI)、湿度指数(WI)之间的关系,在此基础上提出了MSAVI-WI特征空间概念,构建了土壤盐渍化遥感监测指数模型(MWI)。结果表明:MWI与土壤表层含盐量相关性较高,其相关性为0.844,精度高于土壤盐渍监测常用的盐分指数与实测数据的相关性。MWI能较好的反映盐渍化土壤地表植被及土壤水分的组合变化,具有明确的生物物理意义,并且特征参量简单,理论上易于理解,实践上易于实现,MWI模型的构建有利于干旱区大尺度土壤盐渍化定量监测与评价工作的开展。     

洪水淹没分析中的自适应逐点水位修正算法

洪涝灾害损失评估是防洪减灾科学决策的基础,其中洪水淹没分析是准确提取洪水淹没范围、水深及历时等灾情信息的关键。洪水淹没分析主要采用数字高程模型数据,由于DEM的格网分辨率与高程精度有限,常出现异常的洼地或平地,导致难以可靠地计算每个格网点处的流向,而传统方法采用统一高程的洼地填平处理又使得容易出现洪水演进过程中复杂起伏地形水面爬坡以及平坦地形水位断流的问题,为此提出顾及流速和淹没时间的自适应逐点水位修正算法,即在DEM坡面流模拟的基础上,根据洪水水流特性、地形、边界变化、水流速度、水深变化以及淹没点的淹没时间,计算水位修正值,对洪水演进过程中每个格网点的水位进行修正,采用多种地貌类型的DEM数据进行试验,证明洪水演进的淹没范围、水深及历时的实时计算结果准确可靠,可为快速评估灾害损失与防洪决策服务提供更为科学的依据。     

基于Illustrator标图的大型挂图制作技巧

随着大型挂图的广泛应用,对大型挂图制作的研究也越来越重要。本文结合具体的任务实践,主要介绍了Illustrator下标图的大型挂图的制作流程,总结了标图过程中的一些技巧,为以后的大型挂图的标图制作提供参考依据。     

卡尔曼滤波粗差探测在GPS变形监测中的应用

在GPS变形监测中,由于受到周跳、SA政策、整周模糊度解算误差及其多路径效应的影响,致使观测数据中含有粗差。用标准卡尔曼滤波方法进行数据处理时,观测数据中的粗差会在卡尔曼滤波预测残差中得到反映。通过结合标准卡尔曼滤波模型与拟准检定法检测,找出并剔除粗差,处理得到正确的结果,并列举了实例证明该方法的实用性。     

基于蚁群算法优化正射影像镶嵌线

本文针对正射影像镶嵌线优化需求,本文提出了一种利用蚁群算法优化正射影像镶嵌线的方法。该方法将待镶嵌的相邻正射影像对根据地面坐标计算出它们在重叠区域的差值图像,通过外方位元素生成一条初始镶嵌线,并根据蚁群算法在重叠区域的差值图像上沿着初始镶嵌线在起点至终点选择一条最佳的路径避开房屋等高大地物,保存为最优镶嵌线。试验结果证明该算法能快速、有效选择正射影像的镶嵌线,实现大比例尺与城市地区的正射影像智能镶嵌。     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.     

1961-2020年郑州主城区大气自净能力变化特征分析

利用郑州市主城区1961—2020年气象观测资料和2014—2018年空气质量监测数据,分析了郑州主城区大气自净能力指数的长期变化趋势与影响因子以及2014—2018年主城区大气自净能力与PM_(2.5)的关系。结果表明:郑州主城区大气自净能力指数30 a气候均值为4.42 t·(d·km^(2))^(-1),春季大气自净能力最强,为5.20 t·(d·km^(2))^(-1);秋季大气自净能力最弱,为3.88 t·(d·km^(2))^(-1),不利于对大气污染物的清除。1961—2020年郑州主城区大气自净能力呈显著的减弱趋势,其中1969年最强为6.85 t·(d·km^(2))^(-1),2020年最弱为3.06 t·(d·km^(2))^(-1)。影响因子中,1961—1980年混合层厚度与大气自净能力指数呈正相关;日平均风速≥2.5 m·s^(-1)的日数和小风日数与大气自净能力分别呈正、负显著相关;大气自净能力指数与降水日数显著相关,2015年后偏强降水日数的增加对大气自净能力在同时期的增强有一定影响。此外,研究还表明主城区大气自净能力和PM_(2.5)浓度存在显著的负相关,说明大气自净能力强时,对应的PM_(2.5)浓度低,环境空气质量趋好。     

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term,ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region.The fixed station was established in 1989,and conventional radiation observations started much later in 2008.In this study,a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data,and a highprecision,long-term DGSR dataset is constructed.Then,the trend of DGSR from 1990 to 2019 at Zhongshan Station,Antarctica is analyzed.The RF model,which performs better than other models,shows a desirable performance of DGSR hindcast estimation with an R~2 of 0.984,root-mean-square error of 1.377 MJ m~(-2),and mean absolute error of 0.828 MJ m~(-2).The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004.Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station.In addition to clouds and water vapor,bad weather conditions (such as snowfall,which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station.The high-precision,longterm estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow,ice,and atmosphere.