A New Prediction Model of Rain Attenuation That Separately Accounts for Stratiform and Convective Rain

An improved version of the exponential cell (EXCELL) rain attenuation model is presented here. Analogously to the original one, it predicts attenuation through a cellular representation of precipitation, but, in addition, is able to discriminate between stratiform and convective rain by means of an embedded algorithm. Accordingly, two separate physical rain heights, derived from the ERA-15 database, are used to calculate stratiform and convective rain attenuation and, when considering stratiform precipitation, the melting layer contribution to attenuation is added. Eventually, the predicted cumulative distribution function (CDF) of excess attenuation is the combination of the contributions due to stratiform and convective precipitation types. Some input parameters of the prediction model, such as those defining the melting layer process or the rain plateau embedding rain cells, can be modified in order to account for the local meteorological characteristics.      

北京一次强降水过程雨滴谱特征

雨滴谱是云及降水物理学中重要的研究对象,基于北京2015年8月7日的强降水雨滴谱观测数据,计算得到该过程的雨滴谱、质量加权直径、平均直径等雨滴尺寸分布特征,由雨滴谱观测数据计算得到降水强度、液态水含量和雷达反射率,分析这些降水参数在此次降水过程中的变化特征。综合分析雨滴谱参数和S波段天气雷达反射率表明此次降水过程具有飑线特征,可划分为前端的引导对流性降水、过渡降水和尾部层状降水三个阶段。最后讨论不同降水阶段雨滴谱参数及降水参量的变化及其对雷达定量降水估测Z-R关系的影响。结果表明对流性降水阶段的雨滴数浓度（大于4600个/m3）、质量加权直径（2.69 mm）、最大直径（6.3 mm）均远高于过渡和层状降水阶段,对流降水阶段和过渡阶段雨滴直径分布范围类似（标准差均大于0.3 mm）,层状降水分布较窄（标准差0.24）。不同降水阶段拟合得到的Z-R关系明显不同,拟合得到此次降水整个过程的Z-R指数经验关系系数a=674.91,指数b=1.39。     

Precipitation type determination from spaceborne radar observations

The Tropical Rainfall Measuring Mission (TRMM) program is dedicated to observing and understanding the impact of tropical rainfall. Two important products of the TRMM precipitation radar include the classification of precipitation into convective and stratiform type as well as determination of the height of the brightband. Currently, TRMM uses an algorithm to arrive at these products based on a characterization of horizontal and vertical variability of reflectivity. This paper presents a new algorithm for classifying precipitation type into convective or stratiform as well as detect the height of brightband developed using wavelet analysis. The results obtained from wavelet analysis are compared against simultaneous ground validation products from the TRMM program. The results show that the wavelet-based algorithms provide fairly accurate results for both convective/stratiform classification as well as brightband determination.     

雷达图像中的对流云识别算法研究

对流云是形成暴雨、冰雹、龙卷风等灾害性天气的主要原因。基于准确识别对流云从而达到对灾害天气进行预警的目的,本文采用了模板匹配这一创新性的对流云识别的方法。结合对流云和层状云的物理特性以及雷达回波水平场结构,根据雷达反射率数据的强度、峰值和半径三方面的指标来进行对流识别。通过采用京津地区雷达站的雷达数据进行大量试验,排除了亮带对对流云识别的干扰,提高了对流云识别的准确度。     

Estimating Gaussian Markov random field parameters in anonstationary framework: application to remote sensing imaging

In this paper, we tackle the problem of estimating textural parameters. We do not consider the problem of texture synthesis, but the problem of extracting textural features for tasks such as image segmentation. We take into account nonstationarities occurring in the local mean. We focus on Gaussian Markov random fields for which two estimation methods are proposed, and applied in a nonstationary framework. The first one consists of extracting conditional probabilities and performing a least square approximation. This method is applied to a nonstationary framework, dealing with the piecewise constant local mean. This framework is adapted to practical tasks when discriminating several textures on a single image. The blurring effect affecting edges between two different textures is thus reduced. The second proposed method is based on renormalization theory. Statistics involved only concern variances of Gaussian laws, leading to Cramer-Rao estimators. This method is thus especially robust with respect to the size of sampling. Moreover, nonstationarities of the local mean do not affect results. We then demonstrate that the estimated parameters allow texture discrimination for remote sensing data. The first proposed estimation method is applied to extract urban areas from SPOT images. Since discontinuities of the local mean are taken into account, we obtain an accurate urban areas delineation. Finally, we apply the renormalization based on method to segment ice in polar regions from AVHRR data.     

基于多种纹理特征的全色图像云雪区特征提取

针对全色图像云检测与雪检测的问题,文中提出了一种基于多种纹理特征的特征提取方法。首先,利用自适应的大津阈值分割算法提取云、雪区域。然后,通过分形维数、灰度共生矩阵、小波变换等方法提取云、雪区域的多种纹理特性。最后,利用径向基核函数的支持向量机(Support Vector Machine,SVM)分类器进行云雪自动检测。典型遥感数据的实验结果验证了本文算法的有效性。     

Three-site space-diversity experiment at 20 GHz using ACTS in theEastern United States

A three-site rain-fade space-diversity measurements experiment at 20 GHz has been in near continuous operation since September 1, 1994. Two receiver sites (at the Applied Phys. Laboratory [APL] of The Johns Hopkins University and COMSAT Laboratories) are located in Central Maryland, and the other is located in Virginia. The APL and COMSAT locations are separated by 33 km. The Virginia site is separated from APL by 44.5 km and from COMSAT by 30.5 km. Receivers at each of the sites measure the 20-GHz CW signal level from the radiating beacon onboard the geostationary Advanced Communications Technology Satellite (ACTS). Because of the lateral variability of rain, the likelihood is diminished that intense rain cells will simultaneously intersect all three Earth-satellite paths. There is also a reduced likelihood that intense rain will simultaneously intersect two Earth-satellite paths associated with different two-site scenarios. Hence, a substantially smaller rain-fade margin is required when the sites operate in a diversity mode (e.g., connected together such that the largest signal is used) vis-a-vis uncoupled single terminal operation. In this effort, we examine the efficacy of employing three-site and two-site space-diversity systems to reduce the required fade margin. Single- and joint-terminal rain-fade distributions are calculated for both the three-site and different combinations of two-site scenarios. Diversity gains, which give a measure of the reduced fade margin, are determined from these distributions. The distributions and diversity gains are also individually determined for two six-month periods of the year for which precipitation is predominantly convective or stratiform, respectively. Comparisons between measured diversity gains and those estimated using the model of the radiocommunications sector of the International Telecommunications Union are made     

Steady Convective Diffusion in a Bifurcation

In a study of the development of atherosclerotic lesions, we investigate convective diffusion along the wall of a modeled arterial bifurcation. Under boundary layer assumptions, the resulting set of equations is transformed into a form to which we can apply Laplace transforms. The result is two integral relationships between the species flux through the wall and its concentration at the wall, one of which appears to be new.     

An Artificial-Neural-Network-Based Integrated Regional Model for Rain Retrieval Over Land and Ocean

An integrated regional model is proposed for rain-rate retrievals over land/ocean from the brightness temperature (Tb) values of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The polarization-corrected temperature calculated from the 85.5-GHz channels is also considered as one of the inputs along with the nine channel Tb values. This model is applicable over the region between and . For this purpose, an artificial neural network is utilized. The collocated precipitation radar (PR) near-surface rain rates as given by a 2A25 data product is considered as a target value. The methodology consists of the separation of land and ocean pixels, the separation of stratiform and convective pixels over land/ocean, and the selection of important features (inputs) for the multilayer perceptron network by the feature selection technique for each group. For the separation of land/ocean pixels, the Tb values of the 10.65-GHz vertical channel are utilized. The values are utilized to separate the stratiform and convective pixels both over land and ocean. The rain retrieval from the developed model is validated with TRMM PR. Overall result shows the better agreement of the model-retrieved rain rate with the PR observation compared to the TMI (2A12) rain rate particularly over land. The rain retrieved from the developed model is further validated with Doppler weather radar. A reasonably good agreement is observed between these two estimations.     

Multimodal hand gesture recognition combining temporal and pose information based on CNN descriptors and histogram of cumulative magnitudes

In this paper, we present a new approach for dynamic hand gesture recognition. Our goal is to integrate spatiotemporal features extracted from multimodal data captured by the Kinect sensor. In case the skeleton data is not provided, we apply a novel skeleton estimation method to compute temporal features. Furthermore, we introduce an effective method to extract a fixed number of keyframes to reduce the processing time. To extract pose features from RGB-D data, we take advantage of two different approaches: (1) Convolutional Neural Networks and (2) Histogram of Cumulative Magnitudes. We test different integration methods to fuse the extracted spatiotemporal features to boost recognition performance in a linear SVM classifier. Extensive experiments prove the effectiveness and feasibility of the proposed framework for hand gesture recognition.     

Maximum likelihood approach to image texture and acoustic signalclassification

The authors describe a method of classifying natural textures based on the maximum likelihood parameter estimation technique. The novelty of the technique lies in the use of textural features that are derived from the subbands of a wavelet transformed image via the co-occurrence matrices. A maximum likelihood classifier is designed using a set of training texture samples. Ten different Brodotz (1965) textures have been classified using this procedure with an average classification accuracy of 99.7%. The main emphasis is to apply this technique to the classification of underwater acoustic signals. A time-frequency plot is obtained for each segment of the acoustic signal and then converted to an intensity pattern. The textural classification scheme is then applied to the intensity patterns of the acoustic signals. Eight different underwater acoustic signals have been classified by this procedure with an average accuracy of 99.99%     

Global Millimeter-Wave Precipitation Retrievals Trained With A Cloud-Resolving Numerical Weather-Prediction Model, Part II: Performance Evaluation

This paper evaluates the performance of the global precipitation rate retrieval algorithm for the Advanced Microwave Sounding Unit (AMSU) that was described in Part I of this paper. AMSU is in polar orbit on several National Ocean and Atmospheric Administration (NOAA) operational weather satellites. Predicted rms retrieval errors based on a 15-km resolution 0.5-1.0-mm/h MM5 truth were 0.88, 0.83, 1.13, and 3.04 for stratiform, warm rain, ice-free rain, and convective rain, respectively, which were averaged over all view angles for land and sea up to 73deg latitude. For MM5 rates of 4-8 mm/h, these rms errors increased to 2.8, 3.4, 3.9, and 4.9 mm/h, respectively. The corresponding rms retrieval accuracies for MM5 hydrometeor water paths between 0.125 and 0.25 mm for rainwater, snow, and graupel were 0.19, 0.10, and 0.22 mm, respectively. The rms retrieval accuracy for the 0.125-0.25-m/s peak vertical wind was 0.08 m/s. Biases are small for cumulative precipitation estimates, although an upward correction factor of 1.37 is derived for convective precipitation rate probability distributions. Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are also characterized.     

Elevation angle dependence in tropical regions

The variation of path attenuation with elevation angle has been generally considered to approximate a cosecant law, certainly between elevation angles of about 10° and 50°. Such a theory relies on the assumption that the attenuation layer is statiform for most of the time, and is therefore probably reasonable for temperate regions. In tropical areas however the incidence of convective rain is much higher than in temperate regions, so that during rainy periods, particularly at times of high rain rate, the assumption that the attenuating layer is stratiform is no longer valid. The cosecant law does not therefore hold in such conditions. An experiment has been carried out in the tropical rain belt of northern Australia over several years to investigate the elevation-angle dependency. Four radiometers were co-sited, with the same azimuth. Three of these, with a half power beamwidth of 2.2°. were elevated to 15°, 45° and 85°. The fourth. with a beamwidth of 0·6° was elevated to 45° so as to allow an investigation into aperture effects. The antennae were all orientated for horizontal polarization. Rain-gauges were co-sited with the radiometers and placed along the look path. The results obtained clearly indicate that, in tropical regions. the cosecant law is inappropriate for predicting the elevation angle effects of path attenuation. particularly at high rain rates. The presence of rain cells with rates exceeding 300 mm/h ensures that the assumption of a stratiform attenuating layer is invalid.     

基于超像素和SVM的交互式联合分割算法研究*

联合分割是一类针对前景相同或相似的图像集进行处理的图像分割算法。本文将分割问题视为前背景像素的分类问题,提出了一种基于超像素和机器学习的联合分割算法, 其中使用支持向量机来实现超像素的分类。相比于其他联合分割算法,本文使用词袋(BOF)模型来描述每个超像素,并引入词频-逆向文件频率(Tf-idf)加权算法来优化提取到的特征。为了减少用户交互工作,通过只在一组前景相似的图像中使用一幅种子图像,并在训练分类器时采用样本抽取的方法来解决正负样本数量不平衡的问题。本文使用iCoSeg联合分割标准图像集来测试本文的算法,实验结果表明,相比其他联合分割算法,本文的方法在精确度和灵活性上都更有优势。     

夏季热带及副热带降水云可见光/红外信号气候分布特征

通过融合热带测雨卫星(TRMM)测雨雷达(PR)和可见光/红外扫描仪(VIRS)十年的探测结果,利用PR对降水云的直接识别,分析研究了夏季热带和副热带地区降水云及层状降水云和对流降水云的可见光0.63μm与红外10.8μm通道信号的气候特征。研究结果表明热带辐合带(ITCZ)、南半球辐合带(SPCZ)、亚洲季风区、热带非洲、北美及南美热带地区降水云的0.63μm通道平均反射率均大于0.5,最大平均反射率可达0.7以上,且降水云平均反射率在陆面高于洋面;热带和副热带非降水情况的0.63μm通道的平均反射率量值变化于0.1至0.4之间。夏季热带和副热带95%以上的对流降水平均反射率超过0.5,而只有50%层云降水的平均反射率超过0.5。夏季亚洲季风区、ITCZ、SPCZ、热带非洲降水区域的10.8μm通道平均辐射温度低于250K,而非降水情形的平均辐射温度高于280K;热带非洲、青藏高原、中美洲及热带东太平洋对流降水的10.8μm通道平均辐射温度多低于230K,而在东北和东南太平洋、北部非洲和南部非洲、南美陆面的大部分地区层云降水该通道的平均温度在280K~290K之间。研究结果还表明降水情形下,热带东半球热辐射温度比西半球温度偏低约10K,相应东半球的0.63μm通道平均反射率也高于西半球。     

基于支持向量机的说话人辨认研究

支持向量机是统计学理论的一个重要的学习方法，也是解决模式识别问题的一个有力的工具，本文提出了用支持向量机来解决说话人辨认问题。结合语音信号的特点，解决了大数据量情况下支持向量机的训练问题。支持向量机对两类的分类问题有着突出的优势，本文用两种判决规则将两类问题应用到多类的识别问题。用支持向量机实现了一个与文本无关的说话人辨认系统，实验表明，本方法有良好的效果。     

A support vector machine approach for detection of microcalcifications

In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive enhancement learning scheme for improved performance. SVM is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. We formulate MC detection as a supervised-learning problem and apply SVM to develop the detection algorithm. We use the SVM to detect at each location in the image whether an MC is present or not. We tested the proposed method using a database of 76 clinical mammograms containing 1120 MCs. We use free-response receiver operating characteristic curves to evaluate detection performance, and compare the proposed algorithm with several existing methods. In our experiments, the proposed SVM framework outperformed all the other methods tested. In particular, a sensitivity as high as 94% was achieved by the SVM method at an error rate of one false-positive cluster per image. The ability of SVM to out perform several well-known methods developed for the widely studied problem of MC detection suggests that SVM is a promising technique for object detection in a medical imaging application.     

ARMAR observations of the melting layer during TOGA COARE

The NASA/JPL Airborne Rain MApping Radar (ARMAR) was operated on the NASA DC-8 aircraft during TOGA COARE in early 1993. On 12 flights ARMAR observed stratiform precipitation associated with mesoscale convective systems. The statistics of 16 melting layer parameters, including maximum reflectivity, cooling rate, Doppler velocity, LDR, and the HH-VV correlation coefficient are presented and discussed     

一种改进的Laplacian SVM的SAR图像分割算法

当有标识的样本数量有限时,Laplacian SVM算法需要加入尽量多的无标识样本,以提高分类精度.但同时当无标识样本数很大时,算法的时间和空间复杂度将难以接受.为了将Laplacian SVM应用于SAR图像分割这样的大规模分类问题中,提出了一种改进的Laplacian支持向量机算法(Improved Laplaci...     

应用SVM算法进行TM多光谱图像地物分类

SVM算法是近年来统计模式识别领域流行的算法。因为有统计学习理论（SLT）作为基础,SVM算法具有良好的计算有效性、健壮性和统计稳定性,被广泛地应用在图像识别、语音识别、文字识别等多种模式识别领域。TM多光谱遥感图像的地物分类一直是遥感图像处理领域比较困难的问题。现尝试应用SVM算法对北京市怀柔水库附近地区的地物进行分类,收到了较好的效果。