天气雷达反射率数据方位分辨率提高

天气雷达反射率数据方位分辨率的提高,对中小尺度强对流天气过程的精细探测有重要的意义。以雷达气象积分方程为基础,通过对天气雷达反射率方位数据的过采样,建立雷达测量的低分辨反射率数据与真实气象目标高分辨率数据之间的矩阵方程,提出使用截断奇异值分解算法优化超分辨反演方程逆求解及改善解稳定性。实验结果验证,该方法对反射率数据方位分辨率的提高是行之有效的。     

基于非表面参考技术的机载双频降水反演结果分析

双频降水测量雷达是我国计划发射的风云三号降水测量卫星上的主载荷。双频测量能够较好地估计粒子谱分布参数,提高降雨率定量反演的精度,对双频降水反演算法的研究是降水产品业务化的基础。首先介绍基于非SRT的双频降水反演算法的主要原理,随后采用模拟数据对反演算法的精度进行检验,并选取机载雷达外场校飞试验中的实测数据,分析了机载雷达降雨探测的合理性。结果表明:双频反演的DSD参数基本合理,能够准确地反映出层云降水中亮带的峰值信息,并且和在轨同步观测的星载降水测量雷达PR的探测结果相近。此外,还探讨了随机选取的机载反射率因子廓线的双频反演结果以及可能引起反演误差的原因,结果发现相态转换的高度以及水的体积比可能对4 km附近及其以上的区域有一定的影响,对4 km以下的部分则影响不大。同时温度对反演结果的影响主要集中在3.5 km附近,在其他高度层,温度的影响基本可以忽略。同时降雨率的精度还受到雷达系统误差的制约。总体而言,机载双频降水反演结果具有一定的精度,基本符合当时机载外场试验的实际天气状况,从而验证了机载雷达降雨探测的合理性。     

GPM资料在分析“彩虹”台风降水垂直结构中的应用

全球降水测量计划GPM(Global Precipitation Measurement)是新一代全球卫星降水产品,其核心观测平台搭载的微波扫描辐射仪增加的高频通道可以探测小雨和雪,双频降水雷达将Ku波段和Ka波段数据结合,可获取更多云与降水粒子信息。为探测台风降水的云雨结构,利用GPM的1C-GMI产品和2A-DPR产品,分析了2015年10月初的1522号台风"彩虹"的降水分布、垂直结构、雨顶高度、层云和对流云降水的结构,并与地基雷达的观测进行对比。结果表明:双频雷达的Ku波段擅长探测降水和零度层亮带,Ka波段探测云顶小粒子的效果更佳。台风在成熟阶段近地面降水率集中在20mm/h以下,部分在20~60mm/h,最大值达到88.68mm/h;雨顶高度集中在5~10km,最大高度超过15km。层云降水的面积在台风中所占比例达到63.4%,但单位面积平均降水率比对流降水低37%。双频降雨雷达DPR((Dual-frequency Precipitation Radar)和地基S波段雷达探测"彩虹"台风的结果十分相近,证明了DPR数据质量的可靠性。     

武夷山热带降雨测量卫星降水产品降尺度研究

热带降雨测量卫星(tropical rainfall measuring missionsatellite,TRMM)虽可测得大范围降水,但其空间分辨率较低,不能满足各种模型研究。以武夷山及周边地区为研究区,基于TRMM降水数据融合多源数据,对TRMM进行降尺度,从而得到高分辨率的降水产品。对2001—2010年的TRMM3B43月降水产品进行降尺度处理,将其空间分辨率由0.25°×0.25°(约28km×28km)提高到1km×1km,并利用验证站点对降尺度结果进行精度检验。结果表明,多源数据融合的降尺度方法在中国武夷山及周边地区具有较好的适用性。降尺度结果与验证站点降水量的相关系数R均在0.9以上,平均相对误差(MRE)及均方根误差(RMSE)较降尺度前都有所减小。与气象站点实测数据相比,降尺度结果能较好地模拟降水的时空分布及局地特征,且能够反映地形降水的差异性分布。     

武夷山热带降雨测量卫星降水产品降尺度研究

热带降雨测量卫星(tropical rainfall measuring missionsatellite,TRMM)虽可测得大范围降水,但其空间分辨率较低,不能满足各种模型研究。以武夷山及周边地区为研究区,基于TRMM降水数据融合多源数据,对TRMM进行降尺度,从而得到高分辨率的降水产品。对2001—2010年的TRMM3B43月降水产品进行降尺度处理,将其空间分辨率由0.25°×0.25°（约28 km×28 km）提高到1 km×1 km,并利用验证站点对降尺度结果进行精度检验。结果表明,多源数据融合的降尺度方法在中国武夷山及周边地区具有较好的适用性。降尺度结果与验证站点降水量的相关系数R均在0.9以上,平均相对误差（MRE）及均方根误差（RMSE）较降尺度前都有所减小。与气象站点实测数据相比,降尺度结果能较好地模拟降水的时空分布及局地特征,且能够反映地形降水的差异性分布。     

基于SPOT-VGT可见光/短波红外波段数据对AMSR-E土壤湿度的降尺度研究

基于SPOT-VGT数据,由短波红外、红和蓝波段反射率计算了表征地表土壤湿度的可见光—短波红外干旱指数(VSDI),通过对1km空间分辨率的VSDI影像进行空间升尺度处理,采用多种函数建立了25km空间分辨率AMSR-E土壤湿度数据与VSDI指数的关系,发现二者关系最符合S型曲线模型,拟合残差在空间上呈现随机分布的特征。基于S曲线函数关系下的1km预测土壤湿度和残差值,对AMSR-E土壤湿度进行降尺度模拟,得到1km空间分辨率的土壤湿度。将原始AMSR-E土壤湿度和实测数据对降尺度结果分别比较验证后,表明基于该方法获得的土壤湿度模拟精度较高。     

基于流体动力学的探空温度传感器太阳辐射误差研究

针对太阳辐射加热导致的误差显著限制了温度测量的准确度的问题,提出了基于流体动力学的太阳辐射误差的修正方法--数值分析法.建立从地面到32 km高空不同气压条件下珠状热敏电阻器探空温度传感器的误差热分析模型,通过计算流体动力学对其进行太阳辐射误差数值模拟分析.着重研究了太阳辐射方向、传感器表面涂层反射率、传感器尺寸等物理参数对太阳辐射误差的影响.研究结果表明:太阳辐射引起的温度测量误差随海拔高度的上升呈现非线性单调递增的变化趋势.当太阳辐射方向垂直于传感器正面时误差最大,增大传感器表面涂层反射率、减小传感器尺寸都能有效降低太阳辐射误差.     

8毫米云雷达与探空观测确定云底和云顶高度的对比分析

利用地基Ka波段云雷达和无线电探空仪数据进行云边界识别和对比分析研究。结果表明,8毫米波云雷达探测的云底高度比无线电探空仪观测偏低约300m,大多数情况下二者识别的云底高度接近;而判定的云顶高度偏差较大。雷达与探空云底高度判别偏差较大时,在云下常存在大气"干层",此时雷达探测更为灵敏;探空仪水平漂移及其湿度传感器的探测误差随高度增加是造成两者偏差的主要原因。通过计算和对比雷达反射率的时空变化率,给出了云雷达确定云底和云顶高度的一个可信度判据。     

不同分辨率下风场的数值模拟仿真研究

文中利用新一代中小尺度数值预报模式WRF模式,针对发生在广州白云机场附近的一次风切变过程进行了数值模拟,模拟中采用四层嵌套的方式,在物理过程方案等参数完全一致的情况下,研究只有水平分辨率的不同对风场模拟效果造成的影响.实验中分别采用了9km、3km、1km和0.33km的水平分辨率,分别从水平风场和垂直风场两个角度对模拟效果的不同进行了对比分析,结果表明水平分辨率不同的模拟结果中风场的分布和变化趋势是一致的,但较高的分辨率对风场的模拟更加细致;当水平分辨率高于1km及其以上时,可以模拟出水平风场的细微变化;随着高度的增加,分辨率的不同对水平风场的模拟结果影响减小;水平分辨率的提高使模拟结果中垂直风场的变化更加剧烈,垂直气压场的变化幅度更大     

酒精五塔蒸馏工艺过程模拟

针对典型的酒精蒸馏五塔工艺流程及蒸馏过程各物流的组成、压力、温度等工艺条件,分析模拟计算的热力学方法和热力学数据,结果表明,本文所选用的热力学方法SRKM、IDEAL、NRTL及物流组分交互作用参数均适用于酒精蒸馏五塔工艺流程,各工艺物流的模拟数据与设计数据最大误差均小于4.5%,模拟值与设计值比较吻合,不超出设计误差(10%)的限度.     

The determination of the three-dimensional distribution of rain from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar

This paper describes a recent development in rainfall estimation using satellite-flown and ground-based radars. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), its algorithms and data processing are discussed. The ground validation algorithms and processing of the ground-based radar reflectivity data are explained. The estimates of attenuation-corrected radar reflectivity factor and rainfall rate are given at each resolution cell of the PR. The estimated near-surface rainfall rate and average rainfall rate at the altitudes of 2 km are calculated for each beam position. The TRMM PR profiling algorithm and processing of the PR reflectivity for rain distribution are explained. The TRMM rain products and their geophysical parameters are derived from the measurements from the satellite and ground-based radar. The derived geophysical parameters include vertical rain and hydrometeor profile, rain type, radar back-scatter cross-section, raindrop size distribution, rain gauge rain rates and 5-day and monthly average rain rates. For validation purposes the instantaneous and climatological comparison of the rain estimates from both the Precipitation Radar and ground-based radar at Melbourne, Florida, was carried out on the basis of rain type; i.e. convective/stratiform, vertical structure and rain maps. The error sources in rain profile retrieval from space-borne radar; i.e. the PR and ground-based radar with their algorithm limitations are discussed. A second set of data, this time for an area where no simultaneous ground data are available has also been analysed; the data were chosen for the three-dimensional rain distribution over some parts of India. The issues such as discrimination of rain from surface clutter, calibration accuracy and sensitivity of precipitation radar and discrimination of rain echo from noise are discussed.     

Uncertainty analysis of radar rainfall estimates over two different climates in Iran

Accuracy of rainfall quantification is one of the most important concerns in meteorological and hydrological modelling. Satellites and weather radars can provide meteorological information with higher temporal and spatial resolution than ground stations. Rain gauges measure rain rate directly; however, weather radars estimate rain rate by converting radar reflectivity aloft to rain rate at ground level. This conversion with a power law relation between radar reflectivity and rain rate could be altered from place to place or in various precipitation types. This variety may be the source of errors and uncertainty of radar rainfall estimates. One way to assess the uncertainty of radar rainfall is simulation of rainfall fields. In this article, after calibrating two radars located in the south-western and northern parts of Iran, uncertainty of rainfall estimates of these radars has been analysed using the Gaussian Copula model. Reliability of this model was examined for 10% of the rainfall events that were not included in the simulation process. Obtained results of the current research indicate that recalibration of radars can considerably reduce bias and root mean error. In addition, the Copula-based model can generate rainfall fields with similarly spatial structures to those of observed rainfall data.     

Automated Bayesian quality control of streaming rain gauge data

Radar-rainfall data are being used in an increasing number of real-time applications because of their wide spatial and temporal coverage. Because of uncertainties in radar measurements and the relationship between radar measurements and rainfall on the ground, radar-rainfall data are often combined with rain gauge data to improve their accuracy. However, while rain gauges can provide accurate estimates of rainfall, their data are sometimes corrupted with errors caused by the environment in which the gauges are deployed. This study develops a real-time method for identifying measurement errors in rain gauge data streams. This method employs a dynamic Bayesian network (DBN) model of the rain gauge data stream to sequentially forecast the next rain gauge measurement from both the rain gauge and weather radar data streams and a decision rule-based classifier to identify data errors. Because of the uncertainty in the relationship between the radar and rainfall measurements, this method uses a statistical learning method (expectation maximization) to determine the best parameters for this relationship, given an adaptively sized moving window of previous measurements. The performance of the error detector developed in this study is demonstrated using a precipitation sensor network composed of five telemetered tipping bucket rain gauges and a WSR-88D weather radar. Through an analysis using synthetic errors, the false alarm rate and false negative rate were calculated to be 0.90% and 1.5%, respectively.     

Land Cover Classification for Different Spatial Resolution Images from CNN

Based on convolutional neural networks and five different spatial resolution remote sensing images, the land use/land cover classification study was carried out on a small area in the eastern part of Xining City, aiming at exploring the differences of image classification by CNN with different spatial resolutions and CNN’s ability to extract different features. In order to improve the selection efficiency of the samples, a window sliding method was introduced to assist the samples selection. The research shows that the overall classification accuracy of the five different spatial resolution images is above 89%, the Kappa coefficient is above 0.86. The result further shows that within the resolution scale the higher the resolution, the performance of the CNN classification results for the details is better, and can maintain high classification accuracy, indicating that CNN is more suitable for high spatial resolution images; at the same time, the image spatial resolution is too high, the ground objects exhibit high intra-class variability and low inter-class variability, the classification accuracy tends to decrease. In comparison, CNN has the best classification effect on SPOT 6 images in this study, and window sliding is an effective sample-assisted selection method. This research has certain reference significance for similar research in the future.     

基于CNN的不同空间分辨率影像土地覆被分类研究

基于卷积神经网络（Convolutional Neural Networks, CNN）和5种不同空间分辨率的遥感影像,对西宁市东部一区域开展土地覆被分类研究,旨在探索CNN在不同空间分辨率下进行影像分类的差异性和对不同地物的提取能力。为提高样本的选择效率,引入了窗口滑动方法进行辅助选样。研究表明5种不同空间分辨率影像的总体分类精度均达89%以上,Kappa系数达0.86以上,分类精度较高。在所涉及的分辨率尺度范围内,空间分辨率越高,CNN分类结果越精细,并能保持较高的分类精度,表明CNN更适合高空间分辨率影像分类;但同时影像空间分辨率越高,地物表现出较高的类内变异性和低类间差异性,分类精度有降低的趋势。相比较而言,SPOT 6影像的分类精度最高,同时窗口滑动是一种有效的样本辅助选择方法。研究对今后同类工作具有一定的借鉴意义。     

多源数据融合高时空分辨率晴雨分类

高时空分辨率晴雨分类与交通、旅游、农业灌溉及人们日常出行都密切相关,然而"天有不测风云","东边日头西边雨",准确的高时空分辨率晴雨分类是极具挑战性的问题.提出了一种基于多源数据的多视角学习晴雨分类方法,其中,多源数据包括雷达、卫星及地面观测因子及晴雨观测数据.该方法表述如下：首先,依据雷达观测因子构造了VisCAPPI视角和VisPPI视角,依据葵花卫星资料构造了VisSat视角,依据地面观测因子构造了VisGround视角;然后,对这4个视角特征进行组合获得组合视角VisCAPPI_PPI,VisRadar_Sat,VisRadar_Groumd,VisSat_Ground,VisRadar_Sat_Ground,应用随机森林机器学习方法分别对这些视角进行样本学习,获得这些视角的晴雨分类模型;最后,对这些视角晴雨分类模型估计进行融合,获得晴雨分类结果.主要贡献在于：（1）提出了雷达、卫星和地面观测因子多视角构建方法,构建了VisCAPPI,VisPPI,VisSat和VisGround晴雨分类视角及其组合视角;（2）提出了一种多视角方法（multi-view weight random forest,简称MVWRF）,能够处理雷达、卫星和地面观测因子多源数据融合晴雨分类问题,提高1km×1km和6min时空分辨率晴雨分类准确率.在2016年10月7日和8日,泉州雷达覆盖的393个气象观测站上进行模型训练和测试,结果显示,该方法能够取得较高的晴雨分类准确率和较低的漏报率、空报率,优于对比方法.     

Spatial information of high-frequency brightness temperatures for passive microwave rainfall retrievals

The effect of rainfall inhomogeneity within the sensor field of view (FOV) affects significantly the accuracy of rainfall retrievals causing the so-called beam-filling error. Observational analyses of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) data suggest that the beam-filling error can be classified in terms of the mean rain rate and the rainfall inhomogeneity parameter or coefficient of variation (CV_R, standard deviation divided by mean). The dependence of the beam-filling error on the rain rate and CV_R has been confirmed quantitatively using a single channel at 19.4 GHz. It is also found significantly different beam-filling errors for the two different regions, the East and West Pacific, where the spatial and vertical distributions of rainfalls are different. It is also observed that the vertical distribution of rainfall is related to the spatial variability of rainfall (CV_R) and similarly to the spatial variability of TMI 85.5 GHz brightness temperature (CV _Tb). Based on these findings, this study exploits the CV _Tb to correct the beam-filling error in a direct inversion from a rainfall (R) and brightness temperature (T _b) curve at a single frequency, and to reduce the retrieval error in the context of a Bayesian-type inversion method for multi-frequency rainfall retrievals. Both the experiments suggest that the spatial variability of the high-frequency radiometer data appears to contain useful information for retrievals.     

A 3D drop-size distribution model based on the convolution of raindrops at terminal velocity

Using a matrix of drop size distributions (DSDs), measured by a microscale array of disdrometers, a method of spatial and temporal DSD interpolation is presented. The goal of this interpolation technique is to estimate the DSD above the disdrometer array as a function of three spatial coordinates, time and drop diameter. This interpolation algorithm assumes simplified drop dynamics, based on cloud advection and terminal velocity of raindrops. Once a 3D DSD has been calculated, useful quantities such as radar reflectivity Z and rainfall rate R can be computed and compared with corresponding rain gauge and weather radar data.     

卫星反演降水产品误差随时空分辨率和雨强的变化特征分析

卫星反演降水产品作为降水资料获取的一种重要途径,其精度往往因时空尺度和雨强的不同而变化。本文以美国CMORPH卫星反演降水产品为研究对象,以2008年～2010年夏季中国东部的降水格点分析场为参考值,以均方根误差RMSE作为误差分析指标,定量化地探究了CMORPH产品的误差随时间分辨率、空间分辨率和雨强的变化规律。结果表明:卫星反演降水产品的误差随空间分辨率和时间分辨率的增大呈现递减的趋势,随雨强的增大而呈现出递增的趋势。进一步的分析发现:空间分辨率对误差的影响规律可以采用y=a*e-b*x函数进行拟合,时间分辨率和雨强对误差的影响规律可以采用y=a*x-b函数进行拟合。在此基础上,构建了CMORPH产品随时空分辨率和雨强等3种因素而变化的误差模型σr=a*e-b*A*T-c*Rd。针对误差模型的适用性分析表明:误差公式在多数研究区均表现出良好的适用性。     

Estimating spatially downscaled rainfall by regression kriging using TRMM precipitation and elevation in Zhejiang Province,southeast China

Estimating regional daily rainfall accurately is of prime importance for many environmental applications, such as hydrology, meteorology, and ecology. The rainfall product from the Tropical Rainfall Monitoring Mission (TRMM) satellite is better able to estimate rainfall than rain gauge interpolation in some regions with coarse rain gauge spatial resolution. In the present article, analyses were made at 1379 rain gauge stations in Zhejiang Province, China, during January 2011 to July 2012 (536 days). A good relationship was found between the rain gauge data and the data analysis from the TRMM, especially for the precipitation that was between 2 and 10 mm day^–1. However, gaps exist between TRMM products and rain gauge records, which could be considered as uncertainty. To predict rainfall more precisely, four categories of daily rainfall and three regression kriging (RK) models were selected for analysis. TRMM and elevation data were used as auxiliary variables to construct RK₁. The auxiliary variable in RK₂ and RK₃ was TRMM and elevation data, respectively. Residuals (four rainfall categories × three RK models) of RK models showed spatial auto-correlation. Compared with RK₂, which has a 0.25° resolution, RK₁ and RK₃ are predicted at a finer 1 km spatial resolution. However, RK₁ has the best performance in rainfall prediction according to validation statistics. The root mean square error was decreased from 0.667 to 0.437 and the mean of error was improved from –0.250 to –0.007 in the prediction of mean daily rainfall. RK₁ may facilitate easy downscaling of precipitation and capture the trends in daily rainfall variability.