Precipitation Retrieval from Himawari-8 Satellite Infrared Data Based on Dictionary Learning Method and Regular Term Constraint

In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.

Comparison of Cloud Type Classification with Split Window Algorithm Based on Different Infrared Band Combinations of Himawari-8 Satellite

Cloud detection and classification form a basis in weather analysis. Split window algorithm (SWA) is one of the simple and matured algorithms used to detect and classify water and ice clouds in the atmosphere using satellite data. The recent availability of Himawari-8 data has considerably strengthened the possibility of better cloud classification owing to its enhanced multi-band configuration as well as high temporal resolution. In SWA, cloud classification is attained by considering the spatial distributions of the brightness temperature (BT) and brightness temperature difference (BTD) of thermal infrared bands. In this study, we compare unsupervised classification results of SWA using the band pair of band 13 and 15 (SWA13-15, 10 and 12 μm bands), versus that of band 15 and 16 (SWA15-16, 12 and 13 μm bands) over the Japan area. Different threshold values of BT and BTD are chosen in winter and summer seasons to categorize cloud regions into nine different types. The accuracy of classification is verified by using the cloud-top height information derived from the data of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). For this purpose, six different paths of the space-borne lidar are selected in both summer and winter seasons, on the condition that the time span of overpass falls within the time ranges between 01:00 and 05:00 UTC, which corresponds to the local time around noon. The result of verification indicates that the classification based on SWA13-15 can detect more cloud types as compared with that based on SWA15-16 in both summer and winter seasons, though the latter combination is useful for delineating cumulonimbus underneath dense cirrus

Hourly land surface temperature retrieval over the Tibetan Plateau using Geo-LightGBM framework: Fusion of Himawari-8 satellite, ERA5 and site observations

The Tibetan Plateau(TP)is highly sensitive to even minor fluctuations in land surface temperature(LST),which can result in permafrost melting and degradation of alpine grasslands,leading to serious ecological consequences.Therefore,it is crucial to have high-temporal-resolution and seamless hourly estimating and monitoring of LST for a better understanding of climate change on the TP.Here,we employed Himawari-8 satellite,Digital Elevation Model(DEM),ERA5 reanalysis and meteorological station observations data to develop a new LightGBM framework(called Geo-LightGBM)for estimating LST on the TP,and then analyzed the spatiotemporal variations of those LST.Geo-LightGBM demonstrated excellent LST estimation accuracy,with an R2(coefficient of determination)of 0.971,RMSE(root-mean-square error)of 2.479℃,and MAE(mean absolute error)of 1.510℃.The estimated LST values for the year 2020 agreed well with observed values,with remarkable differences in hourly LST variations.Meanwhile,the estimated LST was more accurate than that from FY-4A.Spatially,there were two high LST centers,located in the Yarlung Zangbo River Basin and the Qaidam Basin,and a low LST center located in the central TP.The SHAP(SHapley Additive exPlanations)and correlation analyses revealed DSCS(the mean ground downward shortwave radiation under clear-sky conditions)to be the most importantly input variable for estimating LST.Spatiotemporal dummy variables(e.g.,longitude,latitude,DEM)were also found to be crucial for model accuracy improvement.Our findings indicate the potential for constructing a high-precision and seamless 24-h LST real-time retrieval and monitoring platform for the TP by combining satellite and China's independently developed CLDAS(China Land Data Assimilation System)data in future.

利用Himawari-8资料分析京津冀地区冬季雾的空间分布和演变过程

京津冀地区冬季雾霾过程频发,然而确定雾的空间分布一直是难点.本研究利用高分静止卫星Himawari-8的可见光和红外通道资料,分析2021年11月3-5日一次典型的京津冀地区雾的分布和演变过程.结果表明:①通过11.2μm与3.9μm通道的亮温差值(-8~-3 K)反演夜间雾的区域分布以及对应的雾顶高度,效果较好.②11月3-5日每天发生的雾,其类型、区域分布和演变过程都呈现不同的特征,3日清晨的雾为平流雾,覆盖范围为3 d中最大,几乎涵盖京津冀地区的平原地带;受西南水汽输送影响,北京市以南地区的雾发展较充分、持续时间最长,其中天津市北部的雾甚至在整个白天仍能维持.③11月4日和5日清晨均出现呈三角形区域分布的辐射雾,雾区的北缘和西缘分别因太行山和燕山山脉的阻挡形成,雾区南缘受低层风场作用位于天津市-石家庄市一线,呈平直的东北-西南走向,与非雾区界限分明;区域性静稳条件下,城市热岛效应对雾的分布和演变有重要影响.研究显示,京津冀地区冬季雾的分布和演变会受到水汽平流、低层风场、地形、热岛效应等多方面影响.

东亚地区植被光合日内变化特征--融合OCO-3和Himawari-8的分析

日光诱导叶绿素荧光(SIF)是植物在太阳光照射条件下发出的光谱信号,直接反映了植被光合作用的强度,对于陆地植被观测至关重要。然而,目前全球SIF传感器的时空分辨率有限,难以捕捉光合作用的日内变化。利用搭载在国际空间站的轨道碳观测者三号卫星和静止卫星向日葵8号的数据,结合随机森林回归算法,重建了东亚地区2019—2022年生长季内7、8、9月时间分辨率为1h、空间分辨率为0.05°的SIF数据集。结果显示,东亚地区植被SIF值在上午逐渐增加,中午达到峰值,下午逐渐减小。不同月份的SIF日内变化呈现出明显的季节性特征,与夏季阳光充足和气温较高相关。不同植被类型间的SIF日内变化差异反映了它们在光合作用响应性上的特征。这些结果有助于深入理解东亚地区植被的时空动态,为生态系统管理、农业生产及气候变化响应策略提供了重要的科学依据和参考。

基于Himawari-8台湾海峡海温时空变化分析及应用

台湾海峡具有重要的战略地位和研究价值,开展台湾海峡及其周边海域的海洋表面温度(sea surface temperature,SST)时空变化研究有助于了解台湾海峡海陆环境与洋流变化,特别是对于充分了解台湾海峡复杂的海洋锋面系统有重要的意义。以台湾海峡及其周边海域为研究区,利用2016—2020年Himawari-8卫星数据,通过计算获取研究区年平均、季平均、旬平均的SST遥感数据,开展SST时空变化研究,并以此为基础,研究SST与福建内陆降水、福建沿海海雾之间存在的相关关系,研究结果表明:台湾海峡及其周边海域年平均SST由西北向东南呈带状分布并逐渐升高,按季节特点可分为等温线近似与岸界平行分布的冬季型及等温线近似均匀分布的夏季型,旬SST资料能更精细地刻画台湾海峡SST的时空变化情况;内陆月降水量与月平均SST总体存在较弱的负相关,但越远离外海海域负相关性越高;SST与沿海海雾存在着较强的负相关关系,并且随着SST的升高,海雾发生次数呈现减弱趋势。

基于Himawari-8静止卫星多通道资料的重庆地区短时强降水反演研究

收集了2021年重庆地区8次区域性暴雨天气过程中地面降水强度大于等于20mm/h的数据,匹配了相应时间空间范围的Himawari-8静止气象卫星多通道资料,建立了降水强度与辐射特征一一对应的数据集。在此基础上采用单位特征空间归类分析法,选择0.64μm、1.6μm、3.9μm、6.2μm、7.3μm、10.4μm等多个通道组成二维光谱空间,反演了强降水像素点的分布范围和集群特征,并基于反演结果开展了重庆地区短时强降水识别研究。提出了一种基于卫星多通道资料的强降水落区阈值判识方案,并利用该方案对2021~2022年重庆地区的16个强对流天气个例进行了检验。结果表明,该方案能够实现全天24h、逐10min的强降水落区连续动态监测,整点及整点前10min、前30min的卫星资料对降水落区的反演准确率达到70%~80%。

Estimation of Land Surface Temperature from Landsat-8 OLI Thermal Infrared Satellite Data. A Comparative Analysis of Two Cities in Ghana

This study employs Landsat-8 Operational Land Imager (OLI) thermal infrared satellite data to compare land surface temperature of two cities in Ghana: Accra and Kumasi. These cities have human populations above 2 million and the corresponding anthropogenic impact on their environments significantly. Images were acquired with minimum cloud cover (<10%) from both dry and rainy seasons between December to August. Image preprocessing and rectification using ArcGIS 10.8 software were used. The shapefiles of Accra and Kumasi were used to extract from the full scenes to subset the study area. Thermal band data numbers were converted to Top of Atmospheric Spectral Radiance using radiance rescaling factors. To determine the density of green on a patch of land, normalized difference vegetation index (NDVI) was calculated by using red and near-infrared bands i.e. Band 4 and Band 5. Land surface emissivity (LSE) was also calculated to determine the efficiency of transmitting thermal energy across the surface into the atmosphere. Results of the study show variation of temperatures between different locations in two urban areas. The study found Accra to have experienced higher and lower dry season and wet season temperatures, respectively. The temperature ranges corresponding to the dry and wet seasons were found to be 21.0985oC to 46.1314oC, and, 18.3437oC to 30.9693oC respectively. Results of Kumasi also show a higher range of temperatures from 32.6986oC to 19.1077oC during the dry season. In the wet season, temperatures ranged from 26.4142oC to -0.898728oC. Among the reasons for the cities of Accra and Kumasi recorded higher than corresponding rural areas’ values can be attributed to the urban heat islands’ phenomenon.

Achievements of Space Scientific Experiments Aboard SJ-8 Satellite

As scientific experiment payloads,microgravity experiments of fluid physics,life science,combustion science,physics and accelerator measurement were conducted on board the Chinese recoverable satellite SJ-8 during 18-day orbital flight.The experimental payloads and an experiment support system constituted the microgravity experiment system of the flight mission.This article has presented the briefs of the scientific achievements of these space experiments,the composition and performance of the Microgravity Experimental System(MES) and the general picture of the overall flight mission,respectively.

Himawari-8静止气象卫星草原火监测分析

Himawari-8(向日葵8号)静止气象卫星具有高空间分辨率、高观测频次和高时效特点,在草原火灾监测具有很强优势。提出利用Himawari-8数据的自适应阈值火点监测算法,并以2016年4月内蒙古自治区呼伦贝尔市边界境外草原火灾为例,重点利用19~20日的22个小时期间的132个时次Himawari-8数据,对火情进行连续动态监测。结果显示,本次火灾共持续22 h,过火区域约1 500km2,火点像元最长持续时间约6 h。通过分析多时次连续观测影像和火点信息可知,火场在一段时间内从西向东的蔓延速度为5.4 km/h,达到急进地表火程度。分析表明,Himawari-8的10 min一次密集观测频次可实现快速获取火点位置、面积、温度等火情信息。可跟踪火场动态发展变化,估算火场蔓延速度和方向以及确定火场类型,提高卫星遥感草原火监测和预警能力。

Himawari-8气象卫星黄海浒苔动态监测

基于Himawari-8气象卫星数据,研究了利用归一化植被指数提取浒苔信息方法,并提出了浒苔强度和移动速度估算方法。对2016年5-7月黄海海域浒苔信息进行监测,获得了浒苔暴发的时间、地点、面积、强度、影响范围、漂移路径及移动速度。结果表明:2016年5月19日Himawari-8气象卫星首次监测到黄海海域出现浒苔信息;6月中下旬进入暴发期,浒苔面积、影响范围及强度达到最大值;7月上旬,伴随着浒苔大面积登陆青岛、烟台、威海等地,浒苔进人缓慢消亡阶段。多时次浒苔强度合成产品显示:2016年浒苔在黄海中部海域、烟台以东海域覆盖强度较大,在初始位置一带覆盖强度较小。浒苔漂移路径整体为从东南外海逐渐开始向西北近海海域靠近,日移动速度不断变化。浒苔的动态变化与水文气象环境密切相关,适宜的温度是浒苔生长和发展的基础,浒苔出现后,盛行风向是浒苔漂移方向的主要驱动力,2016年5-7月强劲的南风使浒苔一直向北漂移,并最终抵达威海,浒苔的移动与风向大致相同。

我国风云四号气象卫星与日本Himawari-8/9卫星比较分析

从观测仪器种类、观测波段设置、观测时空分辨率、仪器灵敏度、仪器定标精度、数据服务能力等六个方面比较和分析了我国风云四号(FY-4)试验星与日本Himawari-8/9卫星的水平和差距。从观测仪器种类方面来看,Himawari-8/9卫星只装载了辐射成像仪(AHI),FY-4试验星除装载一台与AHI性能基本相当的扫描辐射成像仪(AGRI)外,更将首次在静止轨道上同时实现红外干涉式高光谱探测和闪电探测;观测波段设置方面,FY-4试验星搭载的AGRI与Himawari-8/9 AHI基本相当;时空分辨率方面,AHI略优于AGRI;仪器灵敏度方面,AHI和AGRI在发射波段基本一致,AHI在反射波段明显优于AGRI;仪器定标精度方面,AHI和AGRI基本相当;数据服务能力方面,FY-4试验星仍然采用传统的卫星直接广播和借助通信卫星转发两种模式,而Himawari-8/9以互联网云服务为主,可按需灵活调配服务内容和性能且运维成本较低。

利用Himawari-8高频次监测太湖蓝藻水华动态

新一代静止气象卫星Himawari-8以其10 min/次的高观测频次,为连续动态监测蓝藻水华提供了有力的数据支持.基于太湖实地光谱测量资料,提出Himawari-8卫星资料太湖蓝藻水华动态监测方法.以2015年10月2日蓝藻水华发生过程为例,利用连续、多时次Himawari-8资料,动态监测了太湖蓝藻水华的发展变化,分析蓝藻水华的出现、发展和消失,计算蓝藻水华强度的动态变化,认识蓝藻水华程度及变化特征,估算蓝藻水华的动态变化速度.上述分析为研究蓝藻水华的生长消亡过程提供了支持.进一步探讨蓝藻水华动态变化与气象要素的关系,发现在相同的温湿条件下,风场对蓝藻水华的形成、运动和消失有直接的驱动作用.

基于Himawari-8静止卫星森林火灾识别技术研究

新一代静止气象卫星Himawari-8/9 AHI(Advanced Himawari Imager)传感器采用全圆盘扫描观测方式,常用极轨卫星Day/Night双模式火点算法不适用于AHI火点识别。本文以MODIS MOD14(MODIS Fire algorithm)火点识别算法为基础,通过分析静止卫星观测下的火点像元辐射特性和太阳中红外反射特征,确定适用于AHI的MIR/LIR火点识别因子及其动态检测阈值,并利用林业部门提供的大量实测历史火点数据校正识别因子和动态检测阈值,最终建立一种基于Himawari-8/9AHI传感器的采用中红外动态阈值的静止卫星火点识别算法模型。利用NPP VIIRS VNP14(NPP VIIRS Fire products)和JAXA EORC WLF (Wild Land Fire)火点数据,对一次兴安岭森林火灾案例中288个时次AHI全圆盘火点判识结果进行对比验证,结果表明该AHI火点识别算法准确率超过95%。根据新一代静止气象卫星Himawari-8/9AHI传感器提供的10分钟密度遥感观测数据,即可以实现森林火灾实时跟踪监测。

基于Himawari-8卫星的逐时次海表温度融合

Himawari-8卫星是日本气象厅发射的新一代地球同步静止气象卫星,为获取逐时次海表温度产品提供了有力数据支持。本文以Himawari-8 AHI海表温度为基础,利用最优插值法融合GCOM-W1 AMSR2海表温度和NERA-GOOS现场观测资料,生成逐时次海表温度融合产品。为了充分利用邻近时刻的海表温度观测资料,利用Himawari-8 AHI海表温度和欧洲中期天气预报中心海面风速数据建立匹配数据集,研究建立海表温度日变化模型,实现邻近时刻海表温度的订正;为了消除多源海表温度间的系统偏差,以Himawari-8 AHI海表温度为目标数据,利用泊松方程对GCOM-W1 AMSR2海表温度进行偏差订正。实验验证结果表明,利用逐时次海表温度融合产品计算的日增温情况与海面风速具有较好的相关性,间接证实了逐时次海表温度融合产品的准确性;另外,逐时次海表温度融合产品与现场观测海表温度的偏差为0.09℃、均方根误差为0.89℃,二者具有较好的一致性,说明逐时次海表温度融合产品具有较高的精度。

甘肃河东地区基于Himawari-8卫星多通道数据的降水反演

应用Himawari-8卫星多通道数据和甘肃河东地区三次区域性降水天气观测数据,利用支持向量机(SVM)、云顶亮温变温和降水二次曲线拟合方法进行降水反演及对比分析。结果表明:(1)SVM反演降水能力随降水量级增大逐渐降低,反演结果整体偏小,尤其对20mm·h^-1以上降水反演结果明显偏小;(2)云顶亮温变温分布随小时降水强度增大向低值区转移,负变温越大对应着越强的小时降水量。云顶亮温变温与小时降水的关系近似为二次曲线,当变温为负值时其与降水量呈显著线性负相关;(3)SVM反演结果优于二次曲线拟合,前者具有更高的相关系数、更小的相对误差及均方根误差。

基于动态背景场沙尘覆盖度指数的Himawari-8沙尘判识及强度估算方法

针对沙尘遥感监测开展的研究很多,但对于一些强度较弱的沙尘,在不同地表状态下呈现的复杂状况,传统方法识别效果较差。采用Himawari-8卫星数据,提出一种针对性的识别方法,基于动态亮温差底图引入了沙尘覆盖度指数(fractional dust cover index,FDCI)。该指数在一个较确定范围内对不同强度沙尘都可进行有效判识,并能提升弱沙尘识别效果。在此基础上构建了基于指数判识结果的沙尘强度指数(enhanced dust intensity index,EDII)估算方法。文中列举了4次沙尘过程的判识结果,并结合地面观测数据进行了验证。其中对2017年5月3日沙尘强度的地面验证表明,位于观测区的27个站中有22个站的地面观测沙尘强度与估算结果一致。对于各种地表及强度条件下的沙尘监测,该方法是对分裂窗亮温差法的有效改进。

基于Himawari-8卫星的中国东北秸秆焚烧监测及其对空气质量的影响研究

利用Himawari-8卫星的AHI成像仪于2016年3月下旬对中国东北进行秸秆焚烧火点监测,结合气溶胶光学厚度(AOD)与地面空气质量数据分析了秸秆焚烧对空气质量的影响。结果显示,研究期间共监测到秸秆焚烧火点425次。齐齐哈尔市、哈尔滨市、呼伦贝尔市、黑河市、绥化市和大庆市火点数较多,分别为116、75、52、50、41、20次。火点数早晚少、中午多。秸秆焚烧对空气质量有很大影响,火点及其下风向的空气质量指数(AQI)和AOD往往较高。秸秆焚烧产生的主要空气污染物是CO和PM10,但它们的峰值滞后于秸秆焚烧的时间。

基于Himawari-8卫星的沙尘监测

Himawari-8卫星宽波段覆盖、高频次观测的特点可以在沙尘暴动态监测中发挥重要作用。基于Himawari-8卫星的可见光红外辐射计(advanced himawari image,AHI)的波段设计,通过对典型地物以及多类型沙尘光谱特性和多波段亮温差均值单倍标准差的分析,提出中红外和热红外通道亮度温度差值的组合阈值法进行沙尘遥感监测。运用多个区域多个时相的Himawari-8卫星数据开展了沙尘监测试验,使用臭氧探测仪(ozone monitoring instrument,OMI)监测的结果进行验证分析。结果表明,沙尘监测结果与臭氧监测仪监测的紫外气溶胶指数产品空间分布基本一致,可以达到较高的精度。

利用葵花8号(Himawari-8)高时空分辨率的红外亮温资料估计台风莫兰蒂的短时强降水及其演变

为探究高时空分辨率卫星资料在我国登陆台风降水预报中的作用,本文针对2016年14号超强台风莫兰蒂,采用Himawari-8亮温资料通过定量降水估计(quantitative precipitation estimation,QPE)方法估算降水强度,并利用国家气象信息中心全国综合气象信息共享系统(China Integrated Meteorological Information Sharing System,CIMISS)提供的台站降水资料进行质量评估。结果表明:(1)卫星估算与地面实测的降水落区有较好的对应关系,但是卫星高估了弱降水,而低估了强降水(尤其在高海拔地区);(2)卫星估算与地面实测的降水强度并非同位相变化,而是实测降水滞后于卫星估算的降水,该滞后时间在台风登陆后的移动区较长(约2～2. 5 h);(3)整体而言,卫星估算的某时刻降水强度与未来一段时间内(约2～2. 5 h)该地区的实际总降水量有很强相关性,说明Himawari-8卫星估算的降水可在台风降水的预警预报中提供指示作用。