CWRF模式在中国夏季极端降水模拟的误差订正

利用1980—2015年6—8月我国逐日降水观测数据评估CWRF模式（Climate-Weather Research and Forecasting model）多种参数化方案对我国夏季日降水的模拟能力,并考察累积概率变换偏差订正法（CDFt）的订正效果。通过将广义帕累托分布（GPD）引入到偏差订正模型中,提出针对极端降水的累积概率变换偏差订正法（XCDFt）,检验和评估其对极端降水订正的适用性。结果显示:CWRF模式微物理过程选用Morrison-aerosol参数化方案组合对我国降水场的模拟较好,CDFt订正效果良好;XCDFt偏差订正模型能够较好地提取模式建模与验证时期变化信号,订正后相比订正前与观测极端降水的概率分布更为接近;经过XCDFt订正后华南、华中和华北地区20年一遇的极端降水重现水平较模拟值更接近观测值,可为CWRF模式提高极端降水的业务预测水平提供参考。     

基于最优概率的中期延伸期过程累计降水量分级订正预报

采用ECMWF集合预报降水量资料和中国降水量观测资料,研发了基于最优概率的过程累计降水量分级订正预报(OPPF)技术,并在遵循总体技术思路的基础上设计出三种不同的OPPF计算方案(OPPF1、OPPF2、OPPF3),继而选用2015—2017年汛期(5—9月)中国91次区域性强降水过程进行回报试验和预报效果对比评估,结果表明:(1)在中期延伸期预报时效(96~360小时),对强降水和有无降水的预报效果,三种OPPF均明显优于集合平均(EMPF)和控制预报(CTPF);对中等以上或较强以上强度降水的预报效果,OPPF1和OPPF3明显优于CTPF、与EMPF基本接近。(2)三种OPPF相比,OPPF3的预报效果较OPPF1总体略胜一筹,两者均好于OPPF2。(3)预报效果存在明显的地域差异,南方地区强降水预报的TS评分明显大于北方地区,且OPPF3预报效果明显优于EMPF;在96~240小时预报时效,东北地区东部OPPF3强降水的预报效果也明显好于EMPF。      

Atmospheric radiative transfer simulation for atmospheric correction of remote sensing data

The radiance leaving the earth-atmosphere system which can be sensed by a satellite borne radiometer is the sum of radiation emission from the earth surface and each atmospheric level that are transmitted to the top of the atmosphere. The radiation emission from the earth surface and the radiance of each atmospheric level can be separated from the radiance at the top the atmospheric level measured by a satellite borne radiometer. However, it is very difficult to measure the atmospheric radiance, especially the synchronous measurement with the satellite. Thus some atmospheric radiative transfer models have been developed to provide many options for modeling atmospheric radiation transport, such as LOWTRAN, MODTRAN, 6S, FASCODE, LBLRTM, SHARC, and SAMM. Meanwhile, these models can support the detailed detector system design, the optimization and evaluation of satellite mission parameters, and the data processing procedures. As an example, the newly atmospheric radiative transfer models, MODTRAN will be compared with other models after the atmospheric radiative transfer is described. And the atmospheric radiative transfer simulation procedures and their applications to atmospheric transmittance, retrieval of atmospheric elements, and surface parameters, will also be presented.     

控制点影像库在遥感影像几何纠正中的应用

影像几何纠正是遥感图像处理的重要技术。为了克服现有方法在实际应用中的不足,提出利用控制点影像库进行影像自动匹配,然后进行几何变化的方式。这种方法简单有效,快速稳定,匹配得到的同名点数量足够且分布均匀,并通过引入最小二乘匹配使配准达到子像素精度,提高几何纠正的速度与可靠性。     

CSAMT的静态效应校正及应用

CSAMT方法在地下近地表存在电性横向不均匀时,在双对数坐标系中视电阻率曲线会出现沿着视电阻率轴平行移动的现象,称之为静态效应.静态效应对深部电性结构处理解释结果影响重大,需对其进行合适的校正.本文介绍了目前通常使用的几种静态校正方法,并且分析了各方法的优缺点.在新疆某地进行的CSAMT铜矿勘探中,应用了曲线平移法进行静态校正,取得了较好的效果.     

HY1200声速剖面仪计算测深仪声速改正数方法

介绍了HY1200声速剖面仪平均声速的定义及应用,以及用平均声速进行测深仪声速改正数的方法和公式。     

共转换点道集的抽取与转换波时变静校正

纵横波速度比固定时,同一地震道中各时间采样点对应不同的转换点位置,因此,常规的整体抽道方法只能抽取特定深度或特定层位条件下的共转换点道集,它不能保证所有P-SV转换波的反射点位置都在同一水平位置处。提出了一种精确抽取共转换点道集的新方法,运用这种方法抽取的共转换点道集中所有数据对应的转换点在地面的投影都对应于同一位置,因此,该道集是一种真正意义上的共转换点道集,在此基础上提出了一种解决短波长问题的转换波时变静校正方法,实际资料处理取得了好的效果。     

基于Kongsberg EM多波束的Snippet数据处理方法

多波束声呐记录的海底后向散射片段(Snippet)数据处理成角度响应曲线和地理编码(Mosaic)图像可以 帮助识别海底底质类型和反映地貌形态,这一过程包括辐射校正、角度响应改正(AVG)和几何地理编码,但不同的多波束系统硬件在辐射校正和角度响应改正方法上存在差异且传统处理方法忽略了声呐系统本身的指向性模型随时间变化的事实。以声呐方程为基础,针对Kongsberg EM 多波束系统提出了一套完整的Snippet数据处理流程,并分析了各步骤中存在的可变性,给出了每一步的处理建议,最后将此方法应用于EM2040浅水多波束实测数据,并验证了该方法的有效性和实用性。     

Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

Coffee berry necrosis is a fungal disease that, at a high level, significantly affects coffee productivity. With the advent of surface mapping satellites, it was possible to obtain information about the spectral signature of the crop on a time scale pertinent to the monitoring and detection of plant phenological changes. The objective of this paper was to define the best machine learning algorithm that is able to classify the incidence CBN as a function of Landsat 8 OLI images in different atmospheric correction methods. Landsat 8 OLI images were acquired at the dates closest to sampling anthracnose field data at three times corresponding to grain filling period and were submitted to atmospheric corrections by DOS, ATCOR, and 6SV methods. The images classified by the algorithms of machine learning, Random Forest, Multilayer Perceptron and Naive Bayes were tested 30 times in random sampling. Given the overall accuracy of each test, the algorithms were evaluated using the Friedman and Nemenyi tests to identify the statistical difference in the treatments. The obtained results indicated that the overall accuracy and the balanced accuracy index were on an average around 0.55 and 0.45, respectively, for the Naive Bayes and Multilayer Perceptron algorithms in the ATCOR atmospheric correction. According to the Friedman and Nemenyi tests, both algorithms were defined as the best classifiers. These results demonstrate that Landsat 8 OLI images were able to identify an incidence of the coffee berry necrosis by means of machine learning techniques, a fact that cannot be observed by the Pearson correlation.     

山地叶面积指数反演理论、方法与研究进展

叶面积指数LAI（Leaf Area Index）是表征叶片疏密程度和冠层结构特征的重要植被参数,在气候变化、作物生长模型以及碳、水循环研究中发挥着重要作用。遥感是获取区域及全球尺度LAI的一个重要手段,当前LAI产品主要基于遥感数据反演得到,但是多数LAI产品算法并未考虑地形特征的影响,导致山地LAI遥感反演精度不确定性大。提高山地LAI遥感反演精度亟需考虑地形因子对冠层反射率的影响,其中山地冠层反射率模型和遥感数据地形校正是提升山地LAI遥感反演精度的关键。本文围绕山地LAI遥感反演理论与方法,综合分析了国内外山地冠层反射率模型和地形校正模型的研究进展,总结了目前山地LAI遥感反演存在的问题,并讨论了未来研究的发展趋势。