A new cloud detection algorithm for nighttime AVHRR/HRPT data

A new cloud detection algorithm for nighttime Advanced Very High Resolution Radiometer (AVHRR) data has been developed and applied to a large number of images from various locations around Japan. The algorithm is characterized by a recovery function and the use of a two-dimensional histogram. Results obtained after applying the algorithm are presented and compared with those of previous algorithms. The comparison reveals that the new algorithm appears to be more successful than the previous algorithms.     

Algorithm and validation of sea surface temperature observation using MODIS sensors aboard terra and aqua in the western North Pacific

A regional algorithm to estimate SST fields in the western North Pacific, where small oceanographic disturbance are often found, has been developed using Moderate Resolution Imaging Spectroradiometers (MODIS) aboard Terra and Aqua. Its associated algorithm, which includes cloud screening and SST estimation, is based on an algorithm for the Global Imager (GLI) aboard Advanced Earth Observing Satellite-II (ADEOS-II) and is tuned for MODIS sensors. For atmospheric correction, we compare Multi-Channel SST (MCSST), Nonlinear SST (NLSST), Water Vapor SST (WVSST) and Quadratic SST (QDSST) techniques. For NLSST, four first-guess SSTs are investigated, including the values for MCSST, climatology with two different spatial resolutions, and near-real-time objective analysis. The results show that the NLSST method using high-resolution climatological SST as a first-guess has both good quality and high efficiency. The differences of root-mean-square error (RMSE) between the NLSST models using low-resolution climatology and those using high-resolution climatology are up to 0.25 K. RMSEs of the new algorithm are 0.70 K/0.65 K for daytime (Aqua/Terra) and 0.65 K/0.66 K for nighttime, respectively. Diurnal warming and the stratification of the ocean surface layer under low wind are discussed.     

面向国产高分辨率卫星的浒苔绿潮遥感监测算法对比

利用高空间分辨率卫星影像准确监测浒苔绿潮对灾害早期发现、动态跟踪以及沿岸防御具有重要应用价值。目前面向高分辨率遥感影像已有多种浒苔提取方法,但不同遥感算法受到海水背景(浑浊和清澈海水)和外界观测环境(如云层、太阳耀斑、观测几何条件)等干扰,其监测效果可能受到不同程度影响。为此,以国产高分辨率GF-WFV和HJ-CCD影像为例,充分对比了归一化差值植被指数(normalized difference vegetation index,NDVI)、漂浮藻类高度虚拟基线指数(virtual baseline floating macroalgae height,VB-FAH)和绿度指数(tasseled cap greenness,TCG)在常见多种环境背景下提取浒苔的优势和不足。研究结果表明:在清澈海水、浑浊海水和弱太阳耀斑背景下,NDVI、VB-FAH和TCG三种算法均有较好的浒苔识别能力,其精度评价指标F1-score和总体分类精度(overall accuracy, OA)分别超过95.6%和95.2%。对于几何观测条件,VB-FAH和TCG算法对观测几何角度的变化不敏感并表现较高的稳定性,要优于NDVI方法。在云层覆盖和强太阳耀斑背景下,TCG算法的浒苔判识能力最好,并可有效排除云覆盖和强太阳耀斑的干扰,其精度评价指标F1-score和OA分别超过95.2%和95.0%。     

Systematic errors in estimation of insolation by empirical formulas

Systematic errors in the estimation of surface, insolation,Q, by two popular empirical formulas are investigated statistically by using coincident measurements of the global solar radiation and the total cloud cover at JMA observatories over Japan. The results show that Reed’s (1977) widely-accepted formula remarkably overestimatesQ under overcast conditions. The overestimation is particularly evident in the summer months. The formula also overestimatesQ in cloud-free conditions, which may be due to an overestimation of the clear-sky transmittance by Seckel and Beaudry, (1973). By contrast, Kondo and Miura’s (1985) formula underestimatesQ under overcast conditions, whereas it slightly overestimates in partially cloudy conditions. It is shown that these systematic errors can explain some, of the published differences between the estimation ofQ by the two formulas. The users of these formulas should be careful since these cloudiness-dependent errors can contaminate not only the absolute values but also the temporal anomalies or the spatial variability of the insolation, predicted by them. In particular, it can be serious in regions of dense cloud cover such as the northern North Pacific, the northern North Atlantic and the Southern Ocean. It is also shown that the ratio ofQ to the insolation at the top of the atmosphere,Q _TOA, takes on a range of values, particularly under dense cloud cover. This implies an inherent difficulty in estimation ofQ by a simple empirical formula utilizing only readily-available observables such as cloudiness.     

Positioning accuracy of the Data Collection Platform through the translocation and orbit correction method

A new algorithm has been developed to determine the position of a Data Collection Platform (DCP) that is used for the ARGOS Localization and Data Collection System. The algorithm uses an estimation scheme to correct the orbital parameters to achieve accurate positioning of the NOAA meteorological satellite. It is proved that the positioning accuracy of a DCP by the method of orbital Paramater correction is superior to the translocation method.Positioning of a moving DCP was also attempted by installing a DCP on board a vessel.     

机载LiDAR点云的Delaunay三角网快速生成算法

为了提高机载激光点云的Delaunay三角网生成效率及稳定性,基于分块算法的思想,改进了点云数据的分块方式,利用点云的凸包及重心点进行数据分块,并结合子块中激光点个数阈值进行三角形子块的细分,从而避免了传统子块合并过程中复杂的相邻三角形搜索及优化处理,仅需简单的一步优化即可完成相邻子块的合并,大大提高了算法的效率。经实验证明,该算法简单、高效且稳定性好,尤其对于大数据量的机载LiDAR点云Delaunay三角网生成具有明显的优势。     

山东国家级气象观测站综合判识观测项目评估分析

基于全国综合气象信息共享系统(CIMISS)数据库,以一致率(冻土深度用误差绝对值)作为检验指标.对山东国家级气象观测站15项自动综合判识数据,与同期台站观测结果做对比检验,并进行评估分析.结果 表明:1)总云量判识一致率为65.8％,云量偏差绝对值为1的占比51.1％;大部分不一致的情况出现在实况为阴天到满云时,判识...     

面向地形匹配的多波束测深数据抽稀方法研究

基于多波束测深的地形定位是水下潜器导航技术研究和发展的重点,多波束测深数据的高精度快速重采样是水下地形匹配定位的前提。传统的实时抽稀方法因对多波束测深数据模型的过分简化而效果欠佳。参考Douglas-Peucker算法和点云数据抽稀方法,采用角度-弦高联合准则对多波束每ping数据进行抽稀处理,参考导航地形图对抽稀后的多ping数据基于点云离散度进行二次抽稀处理,从而实现多波束测深数据的高精度快速抽稀处理。典型的数学仿真地形和实测多波束条带数据实验表明:文中提出的抽稀方法数据抽稀率仿真地形在85%以上,实测地形在90%以上,数据抽稀前后点云构成的曲面DEM误差在3%以内,并且算法实时性较好。     

基于改进DBSCAN算法的ICESat-2海面点数据去噪处理及精度评估

ICESat-2卫星搭载先进的光子计数式激光雷达ATLAS,使用全新的测量方式获取海量的光子点云数据.光子计数激光点云由于受海洋表面噪声以及水中穿透性较强等因素影响,现有的去噪算法在浅海区域海面去噪上存在一定的局限性.针对上述问题,本文提出了一种基于改进DBSCAN参数的去噪算法,通过利用点云数据集自身分布特性生成候选...     

Retrieval of Suspended Sediment from Advanced Hyperspectral Sensor Data in the Scheldt Estuary at Different Stages in the Tidal Cycle

Hyperspectral airborne remote sensing images and in-situ data are combined to assess the spatial and temporal sediment dynamics in the tidal Scheldt river. A log-linear empirical relationship has been developed between a near-infrared reflectance difference and total suspended matter. The relationship was shown to be relatively insensitive to the varying cirrus cloud cover occurring during data acquisition. The produced sediment maps show good agreement with known variations of turbidity over the tidal cycle: maximum turbidity around high water, gradual settling of the sediment in the succeeding slack water and resuspension at the onset of the ebb flow stage.     

Retrieval of Suspended Sediment from Advanced Hyperspectral Sensor Data in the Scheldt Estuary at Different Stages in the Tidal Cycle

Hyperspectral airborne remote sensing images and in-situ data are combined to assess the spatial and temporal sediment dynamics in the tidal Scheldt river. A log-linear empirical relationship has been developed between a near-infrared reflectance difference and total suspended matter. The relationship was shown to be relatively insensitive to the varying cirrus cloud cover occurring during data acquisition. The produced sediment maps show good agreement with known variations of turbidity over the tidal cycle: maximum turbidity around high water, gradual settling of the sediment in the succeeding slack water and resuspension at the onset of the ebb flow stage.     

Detection and assessment of cloud cover and precipitation parameters using data of scanning radiometers of polar–orbiting and geostationary meteorological satellites

Two multispectral threshold techniques have been developed and tested for the automatic classification of AVHRR/NOAA and SEVIRI/Meteosat-10 data. They provide day-and-night detection and the assessment of cloud-cover parameters, as well as the discrimination of precipitation zones and severe weather phenomena. The validation of output information products, which has been performed with ground-based conventional meteorological observations and radar data, as well as with independent satellite-based estimates of cloud cover and precipitation parameters, confirms the feasibility of developed techniques and reasonable accuracy of output products. Therefore, the technique is concurrent to those implemented in current foreign satellite centers.     

Integrative algorithm of determining ice conditions in Polar Regions by data of satellite microwave radiometry (VASIA2)

In this paper, a new algorithm for determining the concentration of the ice cover in Polar Regions by data of satellite microwave radiometry is considered. The technique of its construction is described in detail; it cardinally differs from the technique of creating present-day algorithms. The new algorithm demonstrates good results in determining the concentration of the ice cover in Polar Regions. The algorithm permits one not only to obtain maps of ice concentration, but also to determine areas of puddles covering the ice-cover surface in summer months. The algorithm is easy-to-use and requires no additional or fitting parameters. At the end of the work, advantages and disadvantages of the new algorithm are discussed.     

FY-4A卫星海域云图图像增强融合方法研究

FY-4A卫星是我国目前最先进的气象卫星,为缓解光照对其可见光和红外云图影响,增强图像质量和显示细节,通过分析中央气象台发布的FY-4A卫星云图中可见光和红外云图资料,指出云图可见光和红外云图存在极强互补性的特征,提出一种基于灰度均值的自适应非线性Gamma图像增强融合方法。仿真实验结果指出利用本文方法得到的融合云图拥有显示云的更多细节和识别云层云类的特点。该方法有效性的验证,为21世纪海上丝绸之路气象预报保障提供了新的云图识别处理技术。     

基于Himawari-8数据的夜间海雾识别

海雾是一种发生在海面的灾害性天气现象,掌握海雾的分布与生消变化,能有效地减少海雾带来的危害。卫星遥感观测具有近实时、大范围覆盖、连续观测等特点,特别是高时间分辨率的静止卫星观测系统,能够对海雾的发生?发展?消亡过程进行动态跟踪观测。本文以2018?2019年黄、渤海发生的海雾事件为样例,利用日本静止气象卫星Himawari-8（H-8）红外辐射数据,分析海雾的多通道红外亮温辐射特性,通过不同波段差和波段比组合,定义海雾和晴空水体分离指数、海雾和一般云系分离指数、多通道亮温差斜率指数以及中红外亮温纹理指数,提出基于多指数概率分布的夜间海雾监测算法;算法分别应用于H-8和韩国静止气象卫星GEO-KOMPSAT2A（GK-2A）数据,对2020年2?6月发生的6次海雾事件多时次卫星观测识别出的海雾位置分布和覆盖面积进行对比实现互验证,结果表明,本文提出的夜间海雾监测算法能有效地实现夜间海雾的识别;选择2020年4月29日夜间H-8和GK-2A 每10 min一次连续观测数据的监测结果,对海雾的发生区域进行跟踪分析,清晰地展现出此次海雾事件的发生、发展演变过程,说明算法能清楚地监测出各时段海雾的分布,跟踪海雾的发展变化,可为海上大雾的防灾减灾提供科学依据和决策基础。     

Fatigue damage induced by nonGaussian bimodal wave loading in mooring lines

Catenary mooring lines are typically subjected to bimodal loads, comprising of a wave frequency (WF) component due to the first-order wave forces and a low frequency (LF) component induced by the second-order wave forces. For moored vessels, the LF forces due to current and wind also play a role. Only dynamic wave loads are considered herein, while current and wind loads are modeled as constant forces. Because of the nonlinearities of the mooring line characteristics, the dynamic line tension and the second-order responses, both the WF and LF line tensions are in principle nonGaussian. These facts make it difficult to estimate the combined fatigue damage of mooring lines in the frequency domain. A fatigue combination rule based on the Jiao and Moan’s theory has been extended to cover the nonGaussian case. The purpose of this paper is to improve and validate the frequency-domain method by time-domain analysis based on a simplified, but accurate mechanical model of the dynamic line tension. Improvements on the LF and combined fatigue damage estimation have been made by considering the nonsymmetrical property of the LF line tension distribution. Both the WF and LF mooring line tensions due to wave loading have been simulated in the time domain for different sea states and the combined fatigue damage has been estimated by using the rainflow cycle counting algorithm. The accuracy of the frequency-domain method for estimating the bimodal nonGaussian fatigue damage of mooring lines has been verified by the time-domain simulations and is considered to be acceptable.     

基于GA-BP神经网络的临洪河口湿地土地覆盖分类算法研究

针对传统分类方法易受到"同物异谱"和"同谱异物"影响，致使河口湿地覆盖分类精度较低的问题，提出一种基于遗传算法优化BP神经网络分类算法。以江苏省临洪河口湿地为研究区，选用哨兵Sentinel-2影像，经辐射校正、大气校正和图像裁剪等预处理后，构建基于自适应遗传算法优化的BP神经网络算法开展临洪河口湿地土地覆盖分类研究，并与传统BP神经网络、支持向量机和随机森林算法进行精度比较。研究结果表明：遗传算法优化后的BP神经网络算法开展河口湿地土地覆盖分类的总精度为96.162 7%，Kappa系数为0.952 0；与传统BP神经网络、支持向量机和随机森林分类算法的分类总精度相比，分别提高了7.359 7%、11.677 9%和6.042 4%；对应的Kappa系数也相应提高了0.090 8、0.118 0和0.074 8；有效解决了河口湿地土地覆盖分类精度低的问题。遗传算法优化后的BP神经网络可实现河口湿地土地覆盖的高精度分类，促进湿地资源的合理开发和保护，为实现海洋生态文明建设提供技术支撑。     

Improved Algorithms for the Measurement of Total Precipitable Water and Cloud Liquid Water from SARAL Microwave Radiometer Observations

SARAL carried onboard a radar altimeter that provides very precise measurements of the sea surface height (SSH). Like other altimetric missions, SARAL carries a passive microwave radiometer (PMR) for wet tropospheric correction to SSH. In the present study, new algorithms are developed for the retrieval of cloud liquid water (CLW) and total precipitable water vapor (TPW) over the global oceans from PMR measurements of the brightness temperatures. A radiative transfer and genetic algorithm based retrieval scheme is proposed for the estimation of CLW and TPW from SARAL PMR. The comparisons of CLW from PMR with independent measurements from GPM-GMI and SSMIS within and outside ±40° latitudes show correlation (R) of 0.86 and 0.83, bias of 0.7 and ?3.61?mg/cm², and root mean square error (RMSE) of 8.42 and 8.07?mg/cm², respectively. Similarly, TPW from PMR with GPM-GMI and SSMIS show R of 0.99 and 0.98, bias of ?0.04 and ?0.03?g/cm² and RMSE of 0.17 and 0.17?g/cm², respectively. The retrieval accuracy of CLW and TPW from the new algorithms is compared with these parameters provided in the SARAL geophysical data records as finished products, which showed substantial improvement in the quality of the parameters from the new algorithm.     

Sea surface temperature observation by Global Imager (GLI)/ADEOS-II: Algorithm and accuracy of the product

A sea surface temperature (SST) retrieval algorithm for Global Imager (GLI) aboard the ADEOS-II satellite has been developed. The algorithm is used to produce the standard SST product in the Japan Aerospace Exploration Agency (JAXA). The algorithm for cloud screening is formed by combinations of various types of tests to detect cloud-contaminated pixels. The combination is changed according to the solar zenith angle, which enables us to detect clouds even in the sun glitter region in daytime. The parameters in the cloud-detection tests have been tuned using the GLI global observations. SST is calculated by the Multi-Channel SST (MCSST) technique from the detected clear pixels. Using drifting buoy measurements, match-up data are produced to derive the coefficients of the MCSST equations and to examine their performance. The bias and RMSE of the GLI SST are 0.03 K and 0.66 K for daytime and, −0.01 K and 0.70 K for nighttime, respectively.     

基于MODIS数据的黄渤海海域云检测方法及其应用研究

在处理MODIS数据、业务化监测黄渤海海冰过程中,云检测是重要内容之一,其结果直接影响海冰信息反演的精度。为了减少云在海冰监测过程中的影响,本文针对冬季黄渤海海域的特点,采用多波段阈值云检测算法对云进行检测,并用IDL语言图像处理技术实现该算法。在研究过程中,本文采用C#语言,借助ArcGIS Engine组件,将云检测算法与地理信息系统二次开发技术有机结合,实现了云检测过程的全自动化和可视化,提高了MODIS数据的处理效率。实验表明,该方法具有较高的云检测精度,检测效果理想,并为海冰监测过程的全自动化与可视化研究奠定了基础。