Comparison of Satellite Cloud Image and Radar of Precipitation Process on July 31st,2007

Based on the data of satellite cloud image and Doppler radar,the rainstorm process from July 31st,2007 to August 1st,2007 in Liaoning was analyzed.The precipitation in Fushun and Haicheng was more than 100 mm,and 6 h precipitation in Fushun and Dandong was more than 50 mm.Through the analysis of strong precipitation period,the structure of clouds had a little decline from the stage of development to maturity.The gray value and gradient degree around were both larger in the center of heavy precipitation.

High Performance of Imaging Extraction for Infrared Satellite Cloud Image

The isotherm is an important feature of infrared satellite cloud images (ISCI), which can directly reveal substantial information of cloud systems. The isotherm extraction of ISCI can remove the redundant information and therefore helps to compress the information of ISCI. In this paper, an isotherm extraction method is presented. The main aggregate of clouds can be segmented based on mathematical morphology. T algorithm and IP algorithm are then applied to extract the isotherms from the main aggregate of clouds. A concrete example for the extraction of isotherm based on IBM SP2 is described. The result shows that this is a high efficient algorithm. It can be used in feature extractions of infrared images for weather forecasts.

Cloud Detection and Centroid Extraction of Laser Footprint Image of GF-7 Satellite Laser Altimetry

The laser altimeter loaded on the GaoFen-7(GF-7)satellite is designed to record the full waveform data and footprint image,which can obtain high-precision elevation control points for stereo image.The footprint camera equipped on the GF-7 laser altimetry system can capture the energy distribution at the time of laser emission and the image of the ground object where the laser falls,which can be used to judge whether the laser is affected by the cloud.At the same time,the centroid of laser spot on the footprint image can be extracted to monitor the change of laser pointing stability.In this manuscript,a data quality analysis scheme of laser altimetry based on footprint image is presented.Firstly,the cloud detection of footprint image is realized based on deep learning.The fusion result of the model is about 5%better than that of the traditional cloud detection algorithm,which can quickly and accurately determine whether the laser spot is affected by cloud.Secondly,according to the characteristics of footprint image,a threshold constrained ellipse fitting method for extracting the centroid of laser spot is proposed to monitor the pointing stability of long-period lasers.Based on the above method,the change of laser spot centroid since GF-7 satellite was put into operation is analyzed,and the conclusions obtained have certain reference significance for the quality control of satellite laser altimetry data and the analysis of pointing angle stability.

Cloud detection from visual band of satellite image based on variance of fractal dimension

Cover ratio of cloud is a very important factor which affects the quality of a satellite image, therefore cloud detection from satellite images is a necessary step in assessing the image quality. The study on cloud detection from the visual band of a satellite image is developed. Firstly, we consider the differences between the cloud and ground including high grey level, good continuity of grey level, area of cloud region, and the variance of local fractal dimension (VLFD) of the cloud region. A single cloud region detection method is proposed. Secondly, by introducing a reference satellite image and by comparing the variance in the dimensions corresponding to the reference and the tested images, a method that detects multiple cloud regions and determines whether or not the cloud exists in an image is described. By using several Ikonos images, the performance of the proposed method is demonstrated.

A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering

In this paper,the clustering analysis is applied to the satellite image segmentation,and a cloud-based thunderstorm cloud recognition method is proposed in combination with the strong cloud computing power.The method firstly adopts the fuzzy C-means clustering(FCM)to obtain the satellite cloud image segmentation.Secondly,in the cloud image,we dispose the‘high-density connected’pixels in the same cloud clusters and the‘low-density connected’pixels in different cloud clusters.Therefore,we apply the DBSCAN algorithm to the cloud image obtained in the first step to realize cloud cluster knowledge.Finally,using the method of spectral threshold recognition and texture feature recognition in the steps of cloud clusters,thunderstorm cloud clusters are quickly and accurately identified.The experimental results show that cluster analysis has high research and application value in the segmentation processing of meteorological satellite cloud images.

RESEARCH ON SATELLITE IMAGE PROCESSING AND RECOGNITION WITH PARALLEL ALGORITHM

Using the method of mathematical morphology,this paper fulfills filtration,segmentation and extraction of morphological features of the satellite cloud image.It also gives out the relative algorithms,which is realized by parallel C programming based on Transputer networks.It has been successfully used to process the typhoon and the low tornado cloud image.And it will be used in weather forecast.

Discussion Measurement Models and Algorithms of the Wind Vector Field Based on Satellite Images

This article aims to discuss the strike two-dimensional wind vector on geostationary satellite imageries. The magnitude and direction of the wind vector are decided by the moving speed of the clouds. First, based on the features of the cloud map, we extract the characteristics of clouds and establish matching model for the clouds image. Maximum correlation coefficient between the target modules and tracking module is obtained by using infrared brightness temperature cross-correlation coefficient method. Then, the beginning and end of the wind vector can be ascertained. Using the spherical triangles of the law of cosines, we determine the magnitude and direction of the wind vector.

Satellite-based Observational Study of the Tibetan Plateau Vortex:Features of Deep Convective Cloud Tops

In this study, an east-moving Tibetan Plateau vortex(TPV) is analyzed by using the ERA-5 reanalysis and multi-source satellite data, including FengYun-2 E, Aqua/MODIS and CALIPSO. The objective is to demonstrate:(i) the usefulness of multi-spectral satellite observations in understanding the evolution of a TPV and the associated rainfall, and(ii) the potential significance of cloud-top quantitative information in improving Southwest China weather forecasts. Results in this study show that the heavy rainfall is caused by the coupling of an east-moving TPV and some low-level weather systems [a Plateau shear line and a Southwest Vortex(SWV)], wherein the TPV is a key component. During the TPV's life cycle, the rainfall and vortex intensity maintain a significant positive correlation with the convective cloud-top fraction and height within a 2.5?radius away from its center. Moreover, its growth is found to be quite sensitive to the cloud phases and particle sizes. In the mature stage when the TPV is coupled with an SWV, an increase of small ice crystal particles and appearance of ring-and U/V-shaped cold cloud-top structures can be seen as the signature of a stronger convection and rainfall enhancement within the TPV. A tropopause folding caused by ageostrophic flows at the upper level may be a key factor in the formation of ring-shaped and U/V-shaped cloud-top structures. Based on these results, we believe that the supplementary quantitative information of an east-moving TPV cloud top collected by multi-spectral satellite observations could help to improve Southwest China short-range/nowcasting weather forecasts.

Estimation of Cloud Motion Using Cross-Correlation

This paper describes the estimation of cloud motion using lag cross-correlation. In order to compute the lag cross correlation, the Bayes Decision method is used first to identify cloud and surface of earth. Then cloud motion vectors are retrieved at a subset of points through multiple applications of a cross-correlation analysis. An objective analysis is used to define displacement at every satellite pixel throughout the domain and smooth the local inconsistencies. Cloud motions are then produced with a backward trajectory technique by using these displacement vectors.

Infrared Radiance Simulation and Application under Cloudy Sky Conditions Based on HIRTM

An algorithm based on hyperspectral infrared cloudy radiative transfer model (HIRTM) is introduced and a simulation method for infrared image of the generation geostationary meteorological satellite is proposed. Based on the parameters from weather research and forecast (WRF), such as the water content, atmospheric temperature, and humidity profile, the simulation data for the advanced Himawari imager (AHI) infrared radiative (IR) channels of Himawari-8 are obtained. Simulated results based on HIRTM agree well with the observed data. Further, the movement, development, and change of the cloud are well predicated. And the simulation of IR cloud image for the weather forecast has been obtained. This paper provides an improved method for evaluation and improvement of regional numerical model for weather forecast.

Examination of the Quality of GOSAT/CAI Cloud Flag Data over Beijing Using Ground-based Cloud Data

It has been several years since the Greenhouse Gases Observing Satellite （GOSAT） began to observe the distribution of CO2 and CH4 over the globe from space. Results from Thermal and Near-infrared Sensor for Carbon Observation-Cloud and Aerosol Imager （TANSO-CAI） cloud screening are necessary for the retrieval of CO2 and CH4 gas concentrations for GOSAT TANSO-Fourier Transform Spectrometer （FTS） observations. In this study, TANSO-CAI cloud flag data were compared with ground-based cloud data collected by an all-sky imager （ASI） over Beijing from June 2009 to May 2012 to examine the data quality. The results showed that the CAI has an obvious cloudy tendency bias over Beijing, especially in winter. The main reason might be that heavy aerosols in the sky are incorrectly determined as cloudy pixels by the CAI algorithm. Results also showed that the CAI algorithm sometimes neglects some high thin cirrus cloud over this area.

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product （collection 6_L2） obtained from the Moderate Resolution Imaging Spectroradiorneter （MODIS） sensors on board the Terra （MOD06） and Aqua （MYD06） satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 （12 years） with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.

基于数据集蒸馏的光伏发电功率超短期预测

云是影响太阳直接辐射变化的主要因素,由于各类云的透光率不同,导致到达光伏电站的太阳辐射会随之产生波动。为解决各类云遮挡下的光伏发电功率波动大、预测模型个数多的问题,提出一种基于卫星云图和数据集蒸馏的光伏发电功率超短期预测模型。首先,基于待测场站上方的历史云图,采用Farneback光流法预测出云图;然后,根据卫星云分类标签数据建立各类云的样本库,利用数据集蒸馏算法训练样本库得到云类判别图,将预测云图与云类判别图匹配计算,获得云类聚合匹配特征;最后,利用上述特征、云量特征以及数值天气预报数据建立长短期记忆网络模型,对光伏发电功率进行超短期预测。利用某光伏电站数据进行验证,结果显示,该文所提模型能准确描述云层的各项特征,有效提升光伏功率预测精度。

Evaluation of Cloud Top Height Retrievals from China’s Next-Generation Geostationary Meteorological Satellite FY-4A

To evaluate the validity of cloud top height (CTH) retrievals from FY-4A, the first of China's next-generation geostationary meteorological satellite series, the retrievals are compared to those from Himawari-8, CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS) operational products from August to October 2017. Regarding CTHs from CloudSat, CALIPSO, and MODIS as truth, the results show that the performance of FY-4A CTH retrievals is similar to that of Himawari-8. Both FY-4A and Himawari-8 retrieve reasonable CTH values for single-layer clouds, but perform poorly for multi-layer clouds. The mean bias error (MBE) shows that the mean value of FY-4A CTH retrievals is smaller than that of Himawari-8 for single-layer clouds but larger for multi-layer clouds. For ice crystal clouds, both FY-4A and Himawari-8 obtain the underestimated CTHs. However, there is a tendency for FY-4A and Himawari-8 to overestimate the CTH values of CloudSat and CALIPSO mainly for low level liquid water clouds. The temperature inversion near the tops of water clouds may result in an overestimation of CTHs. According to the MBE change with altitude, FY-4A and Himawari-8 overestimate the CTHs mainly for clouds below 3 km, and the overestimation is slightly more apparent in Himawari-8 data than that in FY-4A values. As the cloud optical thickness (COT) increases, the CTH bias of FY-4A CTH retrievals gradually decreases. Two typical cases are analyzed to illustrate the differences between different satellites' CTH retrievals in detail.

融合光谱阈值与图像技术的静止卫星夜间云检测方法

针对夜间云检测问题,本文基于静止气象卫星Himawari-8影像数据,分析了云像元光谱特征与图像特征,提出了融合光谱阈值与图像技术的静止卫星夜间云检测方法,实现了静止卫星夜间云的快速、准确检测。利用MODIS云产品和CALIPSO雷达数据,对云检测结果进行定性分析与定量验证。结果表明:①云检测结果与MODIS的云产品MYD06分布基本一致;②算法夜间平均云检测精度达到80.3%;③不同季节夜间的云检测精度随季节变化较明显,夏季最高达到83.3%,可以区分不同季节夜间的云与非云区域。因此,融合光谱阈值与图像技术的静止卫星夜间云检测方法能有效实现夜间云检测,为夜间云检测应用提供了新思路。

滇中及以东地区一次强降雪天气过程诊断分析

2022年2月19至23日,滇中及以东地区出现强降温降雪天气,其中曲靖出现了中到大雪局部暴雪或大暴雪。利用常规观测资料、物理量场、卫星云图等数据对此次天气过程进行诊断分析。结果表明:南支槽与昆明准静止锋(冷锋)是此次强降雪天气的主要影响系统;西南气流加强、水汽通量加大以及低层水汽辐合、高层辐散为此次降雪天气过程提供了较好的水汽条件;强烈的上升运动为降雪提供了必要的动力扰动;700 hPa上-4℃等温线和地温低于0℃的温度线与雪线位置相对应;卫星云图清晰的反映了南支槽与昆明准静止锋(冷锋)的移动、交汇和发展;多普勒雷达图上也进一步证实了不断补充加强的冷空气在此次降雪过程中的重要性。此次分析从天气系统、物理量场、卫星云图和雷达回波等各个维度进行,得出了滇中及以东地区出现降雪天气的指标,对以后降雪天气的预报具有明确的指示作用。

云技术在卫星图像生成中的应用与优势

文中主要研究了云技术在卫星图像生成中的应用及优势。首先,介绍了云技术的概念、特点和发展历程,然后详细阐述了云技术的原理,并分析了云技术的优势及存在的问题,最后提出了一种基于云平台的卫星图像生成方法,以有效解决传统卫星图像生成中存在的问题。

联合块匹配与稀疏表示的卫星云图修复

针对卫星云图在接收及传输过程中受噪声、大气湍流、太阳风暴及卫星轨道漂移等影响造成的云图数据破损,提出了一种联合块匹配与稀疏表示的卫星云图修复方法。首先,根据破损区域的优先权值确定待修复像素,对该像素的邻域进行分块处理。然后,利用待修复块与各匹配块之间的结构相似度,建立相应的冗余字典;通过求解稀疏表示问题修复该破损区域。最后,沿着等照度线不断更新优先权值,实现整幅图像的修复。实验结果表明,提出的方法不仅能避免传统偏微分方程(PDE)修复法所导致的结构丢失,也能很好地改善基于纹理填充修复方法所导致的修复不足及块效应现象。测试结果显示:在云图存在局部区域缺失时,修复后云图的峰值信噪比(PSNR)比匹配追踪法及总变分法的修复结果平均提高了8.50dB和0.28dB,而且在纹理细节及边缘区域具有更好的视觉效果。

多通道卫星云图云检测方法的研究

通过对2002年7、8、9三个月,范围为(8.65°N^59.65°N,73.22°E^134.42°E)的GMS-5卫星云图3000多次数字资料的取样,根据遥感原理和样本统计特征,探讨了常用的通道阈值法云检测方法,并尝试建立了红外分裂窗通道差值法和通道综合运算法的云检测方法。通过各种检测方法比较分析后发现:对通道阈值法,只要用红外一和可见光两个通道的阈值,就可得到较好的检测效果,但用这种方法阈值要随着太阳高度角和季节的变化发生相应的变化,虽阈值变化的幅度不大,却会对云检测工作带来很大的不便。此外,该方法云检测的结果存在地理位置的影响,即检测出的云量在中低纬度偏多而较高纬度偏少。作者建立的通道综合运算云检测方法,不仅改善了地理位置的变化对云检测带来的影响,而且通过红外分裂窗通道差值检测,可减弱太阳高度角的影响,减少了检测过程中阈值变化的繁琐,同时得到了更好的检测效果,检测结果与其可见光图像中的云区相比基本符合。

多光谱卫星图像的一种模糊聚类方法

基于二维光谱特征空间,用模糊C均值(FCM)聚类方法,对多光谱静止卫星(GMS-5)图像进行了云分类试验,得到了比较合理的分类结果。该方法利用不同光谱通道的卫星云图光谱特征构造出一个二维光谱特征空间,对云图在特征空间上的光谱特征点进行FCM聚类,然后与已知云类样本的特征进行比较,确定出各聚类域的类属,进而得到二维光谱空间的云分类图,实况接收的云图可通过查验特征像素点在分类图中的落区位置来实现云的分类。