The First Global Map of Atmospheric Ammonia(NH_(3)) as Observed by the HIRAS/FY-3D Satellite

Atmospheric ammonia(NH_(3)) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH_(3) concentration based on the absorption lines of NH_(3) in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH_(3)column from the Hyperspectral Infrared Atmospheric Sounder(HIRAS) onboard the Chinese Feng Yun(FY)-3D satellite and present the first atmospheric NH_(3) column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH_(3) hotspots around the world, e.g., India, West Africa, and East China, where large NH_(3) emissions exist. The HIRAS NH_(3) columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer(IASI)measurements, and we find that the two instruments observe a consistent NH_(3) global distribution, with correlation coefficient(R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH_(3) retrieval are discussed.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

基于FY-3D/MERSI2的内蒙古草地净初级生产力反演

净初级生产力(Net Primary Productivity,NPP)不仅是估算生态系统固碳释氧、衡量陆地碳循环的主要参数,也是评价生态系统健康状况的主要指标。针对目前国产卫星对草地净初级生产力遥感监测应用较少的情况,本文基于FY-3D/MERSI2资料构建一套内蒙古草地净初级生产力反演模型,结合光能利用率模型与生态过程模型,以遥感数据产品和中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)资料为驱动,通过较严格的云检测算法得到晴空条件下内蒙古草地NPP。研究中引入分辨率较高的格点化气象数据,提升了反演结果的精细化程度;同时还基于观测数据及MODIS产品构建了内蒙古草地生育期不同月份(5—8月)地上生物量及光合有效辐射吸收比率(Fraction Photosynthetic Active Radiation Absorption Ratio,FPAR)与归一化植被指数(Normalized Differ⁃ence Vegetation Index,NDVI)的多种关系模型,基于FY-3D数据直接估算叶面积指数(Leaf Area Index,LAI)及FPAR等过程参数。将反演的关键生态过程参数与MODIS对应产品对比,发现二者具有较好相关性和空间一致性。最后利用2021年6月18个生态气象观测站牧草观测资料与估算结果进行对比验证,二者具有较好的一致性,相关系数为0.86。本研究利用FY-3D/MERSI2反演的NPP能够完整呈现内蒙古地区植被生产力的普遍状态。

FY-3D MERSI-Ⅱ反射太阳波段长期响应衰减估计及订正方法

遥感仪器的长期辐射响应衰减估计与订正是提高卫星遥感数据稳定性和准确性的关键。利用MERSI-Ⅱ在沙漠和DCC稳定目标上空的长时间观测数据,建立仪器辐射响应与时间的函数关系模型,并通过逆方差加权平均方法融合两种稳定目标的结果,获取仪器辐射响应退化量和辐射定标系数的序列估计。辐射响应衰减跟踪监测结果表明,MERSI-Ⅱ自在轨开始业务运行到2022年9月,波长小于500 nm的3个蓝光波段(通道1,通道8—9)和波长大于1000 nm的4个近红外-短波红外波段(通道5—7,通道19)存在明显衰减,年衰减率为1.47%~4.32%。L1级产品精度检验也发现,7个明显衰减通道的业务定标偏差均超过±5%,其中通道5和8偏差最大,约为-20%。应用本研究获取的辐射定标系数序列后,L1产品辐射定标精度随时间变化平稳,定标偏差维持在±3%以内,同时L2级产品精度也明显提升,表明了该研究建立的辐射衰减估计与订正方法的有效性。

基于FY-3D卫星的微波与光学陆表温度融合研究

目前还没有基于国产卫星的1 km分辨率的全天候陆表温度(LST)产品,FY-3D卫星提供了中分辨率成像仪(MERSI)Ⅱ型1 km分辨率晴空LST产品与微波成像仪(MWRI)25 km全天候LST产品,因此可结合两者优势开展全天候1 km分辨率LST的融合研究。基于地理加权回归(GWR)方法,选择海拔、FY-3D归一化植被指数和归一化建筑指数等建立GWR模型对FY-3D/MWRI 25 km LST降尺度到1 km,并与MERSI 1 km LST进行融合;同时针对MWRI轨道间隙,利用前后1天融合后的云覆盖像元1 km LST进行补值,可以得到接近全天候下的1 km LST。基于以上融合算法,选择了中国区域多个典型日期FY-3D/MERSI和MWRI LST官网产品进行了融合试验,并利用公开发布的全天候1 km LST产品(TPDC LST)对FY-3D 1 km LST融合结果进行了评估。研究结果表明,基于GWR法的LST降尺度方法,可以有效避免传统微波LST降尺度方法中存在的“斑块”效应和局地温度偏低等问题;LST融合结果有值率从融合前的22.4%~36.9%可提高到融合后69.3%~80.7%,融合结果与TPDC LST的空间决定系数为0.503~0.787,均方根误差为3.6~5.8 K,其中晴空为2.6~4.9 K,云下为4.1~6.1 K;分析还表明目前官网产品FY-3D/MERSI和MWRI LST均存在缺值较多与精度偏低等问题,显示其存在较大改进潜力,这有利于进一步改进FY-3D LST融合质量。

四川盆地及川西高原地区FY-3D/VASS温湿度廓线检验

利用2020—2021年探空站及ERA5再分析资料,对FY-3D大气垂直探测系统(VASS)温湿度廓线在四川盆地及川西高原地区的精度进行检验。结果表明:与ERA5数据相比,FY-3D/VASS温度在盆地和高原地区不同季节的平均偏差分别为-0.631~0.500℃和-2.230~-1.234℃,均方根误差分别为2.117~3.222℃和3.077~3.460℃,高原比盆地地区大,在垂直高度上,盆地地区的精度在800~700 hPa较低,高原地区的精度随高度的减小逐渐降低;FY-3D/VASS比湿呈现一致的负偏差,平均偏差在盆地和高原地区分别为-0.775~-0.525 g·kg^(-1)和-1.096~-0.347 g·kg^(-1),均方根误差分别为1.251~2.367 g·kg^(-1)和0.696~1.991 g·kg^(-1),在700~600 hPa的精度较低;通过将两组检验的平均偏差相加,间接建立FY-3D/VASS基于探空温湿度数据的检验结果,其中FY-3D/VASS温度在盆地地区的平均偏差较小,高原地区平均偏低2.440℃左右,比湿在盆地和高原地区平均偏低0.612 g·kg^(-1)左右。上述结果可提升FY-3D/VASS温湿度反演产品在四川盆地及川西高原地区的天气气候应用能力。

Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite

Water vapor plays a key role in weather, climate and environmental research on local and global scales. Knowledge about atmospheric water vapor and its spatiotemporal variability is essential for climate and weather research. Because of the advantage of a unique temporal and spatial resolution, satellite observations provide global or regional water vapor distributions. The advanced Medium Resolution Spectral Imager (MERSI) instrument-that is, MERSI-II-onboard the Fengyun-3D (FY-3D) meteorological satellite, has been one of the major satellite sensors routinely providing precipitable water vapor (PWV) products to the community using near-infrared (NIR) measurements since June 2018. In this paper, the major updates related to the production of the NIR PWV products of MERSI-II are discussed for the first time. In addition, the water vapor retrieval algorithm based on the MERSI-II NIR channels is introduced and derivations are made over clear land areas, clouds, and sun-glint areas over the ocean. Finally, the status and samples of the MERSI-II PWV products are presented. The accuracy of MERSI-II PWV products is validated using ground-based GPS measurements. The results show that the accuracies of the water vapor products based on the updated MERSI-II instrument are significantly improved compared with those of MERSI, because MERSI-II provides a better channel setting and new calibration method. The root- mean-square error and relative bias of MERSI-II PWV products are typically 1.8-5.5 mm and −3.0% to −14.3%, respectively, and thus comparable with those of other global remote sensing products of the same type.

FY-3D MWRI在轨交叉辐射定标和海表温度反演

海表温度(sea surface temperature,SST)在气候监测、海洋学研究、渔业经济研究和污染监测等多个方面起着重要作用。本文利用风云三号D星(FY-3D)微波成像仪(Microwave Radiation Imager,MWRI)数据对海表温度进行了反演。首先,以全球降水测量(Global Precipitation Measurement,GPM)卫星微波成像仪(GPM Microwave Imager,GMI)作为参考传感器,采用基于海洋微波辐射传输模型(radiative transfer model,RTM)的双差异方法对FY-3D MWRI进行交叉辐射定标,消除了其在轨辐射定标偏差,为精确反演海表温度奠定了基础;然后,在波段选择的基础上提出耦合微波海表发射率的海表温度多波段反演新算法,与传统的多波段反演算法相比,新反演算法的均方根误差从0.74 K减少至0.69 K;最后,用新反演算法从2020年的FY-3D MWRI数据反演得到全球的海表温度,并用时空匹配的iQuam浮标数据和GMI海表温度产品对反演结果进行精度验证,误差分别为0.04±1.13 K和−0.07±1.18 K。另外,在相同的条件下,用传统多波段算法反演得到的海表温度的误差分别为0.30±1.17 K和0.10±1.17 K。新算法的理论精度和实际反演精度均优于传统的多波段算法,说明本文发展的耦合微波海表发射率的多通道反演新算法是有效和精确的。

基于FY-33D/MERSI-Ⅱ和Terra/MODIS数据的森林火点提取算法——以我国西南地区为例

中国西南地区森林覆盖率高,土地利用类型多样,人类活动频繁,森林火灾风险较高,防火任务十分艰巨。遥感技术具有覆盖范围广和周期性监测等优势,被广泛应用于森林火灾监测。气象卫星搭载了对火灾敏感的红外谱段传感器且时间分辨率较高,已成为森林火灾监测的重要遥感数据源。物理阈值法是遥感数据火点提取中比较成熟的算法,但其精度受所选阈值的影响较大,导致不同区域的火点提取精度存在差异。以中国西南地区的重庆市为例,提出了一种采用FY-3D/MERSI-Ⅱ和Terra/MODIS极轨卫星数据作为数据源进行森林火点监测的算法。首先,通过分析前一年MOD14监测产品的历史数据,确定该地区的初定火点阈值;随后,消除云层、水体和太阳耀斑的干扰;最后,采用上下文背景阈值法进行火点提取。对2022年8月重庆市山火的分析表明,该算法的火点提取平均准确率超过了90%。研究结果表明,根据区域特点调整阈值可以提高该地区火点监测的准确率。

Test and Calibration for MWHTS Payload onboard Chinese FY-3D Satellite

The Microwave Humidity and Temperature Sounder(MWHTS)is the main payload of FengYun 3D(FY-3D),designed for atmospheric humidity and temperature sounding,and also for monitoring severe weather systems such as typhoons and rainstorms which will be launched in 2016.Before the launch of MWHTS,a series of experiments will be conducted in normal environment and a thermal/vacuum(T/V)chamber to determine radiometric characteristics of each channel,which are of very importance before the launch.In this paper,design and component description,as well as technical specifications and test results for RF and IF,will be provided.Then T/V calibration results,such as bandwidth correction,nonlinear error,calibration accuracy and sensitivity for all channels.

基于机器学习模型FY⁃3D MWRI海面风速反演

风云三号D星(FY-3D)微波成像仪(MWRI)L1级亮温数据可用于全球海面风速反演,本文讨论了在晴空区和云区使用多元线性统计回归模型和机器学习模型反演海面风速的情况,在晴空区将4 d测试集分别放入多元线性统计回归模型,采用随机森林(RF),支持向量回归(SVR),卷积神经网络(CNN)和Stacking融合(SF)模型对海面风速进行反演,最优的均方根误差(RMSE)分别为1.56、1.31、1.24、1.29和1.27 m/s;在云区2 d测试集上的最优RMSE分别为2.12、1.98、1.87、1.89和1.89 m/s。为了进一步验证晴空区海面风速反演的可靠性,选取美国国家浮标数据中心(NDBC)实测的浮标风速对海面反演风速进行验证,CNN反演风速与NDBC实测风速的RMSE为0.74 m/s,决定系数(R^(2))为0.80;SF反演风速与NDBC实测风速的RMSE为0.85 m/s,R^(2)为0.74。结果证实了通过机器学习模型能够很好地完成FY-3D MWRI亮温反演全球海面风速的任务。

基于FY-3D/MERSI-Ⅱ红外波段夏季北极云检测模型研究

基于我国风云极轨气象卫星FY-3D/MERSI-Ⅱ(FengYun-3D/Medium Resolution Spectral Imager-Ⅱ)红外通道数据,结合星载激光雷达主动探测数据,开展北极地区夏季云检测模型研究。采用概率密度函数分析方法,并引入损失率,对相关阈值进行优化,提出适用于北极夏季的红外云检测方案,构建了基于置信度结果的云检测模型。精度检验结果表明,所构建的云检测模型的检测结果与时空匹配的星载主动探测结果具有较高的一致性,个例统计结果显示置信度高于0.8,云像元检测一致性100%。当置信度低于0.2时,存在10.15%云像元误判为晴空像元,误判云像元多为云顶高度在4~6 km之间的单层云,可能是卷云引起的误判。

FY-3D/MWRI L1B亮温LST反演与降尺度研究

基于2020年2月1日FY-3C VIRR LST和FY-3D MWRI L1B亮温数据,以18°-54°N,73°-135°E的区域为样例区,利用统计回归模型和层次贝叶斯融合模型,分别进行FY-3D MWRI L1B LST反演和降尺度研究,构建了基于FY-3D单频率水平和垂直极化亮温的LST二元线性回归反演模型及基于FY-3D反演LST和FY-3C VIRR LST的层次贝叶斯融合降尺度模型,并以MYD11A1 day LST为参考数据进行了验证。结果表明:反演统计模型,对于FY-3D降轨数据,平均偏差-1.28 K,误差标准差8.85 K,均方根误差8.85 K,对于FY-3D升轨数据,平均偏差-0.81 K,误差标准差6.74 K,均方根误差6.78 K;层次贝叶斯融合降尺度模型,对于FY-3D降轨数据,平均偏差0.50 K,误差标准差5.45 K,均方根误差5.41 K,对于FY-3D升轨数据,平均偏差0.25 K,误差标准差5.54 K,均方根误差5.54 K,精度满足需求,可以为被动微波LST反演与降尺度提供思路。

Integrating Highly Spatial Satellite Image for 3D Buildings Modelling Using Geospatial Algorithms and Architecture Environment

The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.

移动新架构 3D新视界 东芝Satellite A660

随《阿凡达》的一炮走红，现在的好莱坞大片普遍都要打上3D的噱头增加卖点。可现实影院中大部分3D影片的效果都不尽如人意，特别是在色彩与对比度上明显不如普通影片好，打击了众多影迷的积极性。如果是影院的3D效果不好，你有没有想过自己在家组建3D影院呢。不用太复杂，东芝最新推出的SatelliteA660（以下简称A660）笔记本电脑不仅能让你在家独享3D大片的精彩，而且还能体验逼真的3D游戏。

基于FY-3D MERSIⅡ数据的辽宁省作物生长季日平均气温估算方法比较

作为反映气候特征的重要指标之一,日平均气温在农业气象灾害监测和气候变化研究等领域承担着至关重要的作用。与传统日平均气温的监测和估算方式相比,遥感技术具有全方位、宏观、动态等不可比拟的绝对优势,能够准确地描述日平均气温的空间异质性。为提高农业服务质量,保证农业健康、可持续发展,探索作物生长季日平均气温遥感反演方法,提高农业气象灾害监测精确度,以FY-3D MERSIⅡ遥感数据为基础,提取研究区日间地表温度(LST_(day))、夜间地表温度(LST_(night))、归一化植被指数(NDVI),同时还考虑了高程(DEM)、坡度(Slope)两个变量,结合气象站日平均气温数据,分别构建多元线性回归和随机森林日平均气温遥感反演模型,开展辽宁省2021年作物生长季(5—9月)日平均气温遥感监测的应用研究。结果表明:(1)基于多元线性回归模型反演的日平均气温均方根误差(RMSE)为1.71℃,平均绝对误差(MAE)为1.45℃;基于随机森林反演误差RMSE为1.17℃,MAE为0.96℃;整体上,随机森林的日平均气温反演结果更好,适用性更强。(2)实验当天和前1 d的降水总量对日平均气温的估算结果具有很大影响,降水量随时间的变化曲线与日平均气温的反演误差散点分布情况基本一致,呈现降水总量越大,日平均气温的反演误差越大的趋势,日平均气温反演结果受大气水汽含量的影响很大。(3)对输入的气温影响因子的重要性进行动态的统计分析,发现LST_(day)和DEM是日平均气温反演时两个最重要的变量,且LST_(day)对日平均气温反演的影响最为重要,但是随着作物的生长,DEM的重要性也越来越凸显。

超强内核3D笔记本 东芝Satellite A665—3DV

当前，i系列笔记本电脑风行．来自东芝的这款Satellite A665—3DV配置了Core i7-740QM四核的处理器．搭载了NVIDIA GeForce GTS350M 1GB显存显卡．采用4GBDDR3内存和D640GB硬盘．带蓝光康宝光驱．无处不彰显出该笔记本电脑的强劲配置。

更新的东芝 更新的3D——东芝3D笔记本电脑Satellite A660

当消费者对笔记本电脑的要求越来越高的时候，性能，尺寸，操控都是共有的诉求，但是特征化的需求才是市场上最为引人瞩目的焦点。自从美国立体大片《阿凡达》上映以来．3D立体影像则成为了市场的新宠．东芝最新推出3D笔记本的Satellite A660搭载了nVIDIA的立体幻镜系统．将3D立体影像从电影院搬回了家中．

FY-3D卫星MWHS-2辐射率资料直接同化对台风“米娜”预报的影响

文章基于天气研究和预报(weather research and forecasting,WRF)模式中的FY-3D卫星微波湿度计Ⅱ(micro-wave humidity sounder 2,MWHS-2)辐射率资料的直接同化模块,采用三维变分(three dimensional variation,3DVar)方法在晴空条件下同化MWHS-2辐射率资料,考察MWHS-2辐射率资料同化对台风“米娜”(2019)预报的影响。文中设计了4组试验,第一组试验不同化任何资料,第二组试验同化了单独的全球通信系统(global telecommunications system,GTS)常规资料,第三组试验联合同化了GTS常规资料和MWHS-2辐射率资料,第四组试验将MWHS-2辐射率资料换成先进技术微波探测计(advanced technology microwave sounder,ATMS)辐射率资料同化。研究结果表明:偏差订正后各通道观测和背景场差值的均值趋于0,同化后分析场相对观测的标准差与均方根误差较背景场显著减小,同化过程是有效的。与仅同化GTS常规资料和同化ATMS资料的试验相比,同化晴空MWHS-2辐射率资料后的增量场在台风中心附近有负的高度增量和正的温度增量,从动力与热力上有助于台风的维持。在确定性预报最后的12h,同化晴空MWHS-2辐射率资料的试验能够改进500h Pa环流形势的模拟,加强西南方向引导气流的强度,从而最终减小台风路径预报的误差。

融合GNSS和随机森林算法的FY-3D PWV高精度校正模型

FY-3D可降水量(precipitable water vapor,PWV)是我国新一代极轨气象卫星提供的新型遥感水汽产品。针对降水、云、地表反射等因素影响导致的FY-3D PWV产品精度不高的问题,提出了一种融合GNSS和随机森林算法的FY-3D PWV高精度校正模型。该模型使用随机森林算法作为核心结构,充分考虑FY-3D PWV产品的多类型影响因素,以FY-3D像元的PWV值、时间、位置、高程、地表类型以及观测姿态等特征参数为输入变量,以高精度GNSS PWV为目标变量,利用美国西部地区2020年的实测数据进行了模型训练和测试。实验结果表明,相比于初始产品和线性校正模型,该模型分别使FY-3D PWV产品的精度提高了40.68%和36.07%,说明了其在FY-3D水汽产品校正中的优越性和稳定性。