Cloud Top Pressure Retrieval Using Polarized and Oxygen A-band Measurements from GF5 and PARASOL Satellites

Cloud top pressure(CTP)is one of the critical cloud properties that significantly affects the radiative effect of clouds.Multi-angle polarized sensors can employ polarized bands(490 nm)or O_(2)A-bands(763 and 765 nm)to retrieve the CTP.However,the CTP retrieved by the two methods shows inconsistent results in certain cases,and large uncertainties in low and thin cloud retrievals,which may lead to challenges in subsequent applications.This study proposes a synergistic algorithm that considers both O_(2)A-bands and polarized bands using a random forest(RF)model.LiDAR CTP data are used as the true values and the polarized and non-polarized measurements are concatenated to train the RF model to determine CTP.Additionally,through analysis,we proposed that the polarized signal becomes saturated as the cloud optical thickness(COT)increases,necessitating a particular treatment for cases where COT<10 to improve the algorithm's stability.The synergistic method was then applied to the directional polarized camera(DPC)and Polarized and Directionality of the Earth’s Reflectance(POLDER)measurements for evaluation,and the resulting retrieval accuracy of the POLDER-based measurements(RMSEPOLDER=205.176 hPa,RMSEDPC=171.141 hPa,R^(2)POLDER=0.636,R^(2)DPC=0.663,respectively)were higher than that of the MODIS and POLDER Rayleigh pressure measurements.The synergistic algorithm also showed good performance with the application of DPC data.This algorithm is expected to provide data support for atmosphere-related fields as an atmospheric remote sensing algorithm within the Cloud Application for Remote Sensing,Atmospheric Radiation,and Updating Energy(CARE)platform.

2000-2021年青藏高原地区地表净辐射的时空变化

基于CERES卫星提供的2000年3月至2022年2月SYN1 deg–Level 3辐射数据,结合Theil-Sen Median趋势分析、Mann-Kendall检验以及EOF分析等方法,分析了22年间青藏高原地表净辐射的时空变化规律。研究发现,在空间分布特征上,青藏高原地表净辐射总体呈现南高北低的分布特征,地表净辐射变化趋势具有高度一致性,但在青藏高原南部地表净辐射变化量级远高于青藏高原北部;在时间演化特征上,地表净辐射呈现准正弦振动,年周期变化明显,特别是2016-2017年出现突变,下降约5.52 W·m^(–2),同期云覆盖度年平均值提高约18.75%。

Spatiotemporal Variations of Microwave Land Surface Emissivity(MLSE)over China Derived from Four-Year Recalibrated Fengyun 3B MWRI Data

Microwave Land Surface Emissivity(MLSE)over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures(Tbs)from Fengyun-3B Microwave Radiation Imager(FY-3B MWRI),combined with cloud properties derived from Himawari-8 Advanced Himawari Imager(AHI)observations.The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer.The MLSEs at horizontal polarizations at 10.65,18.7,and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren,or sparsely vegetated,area.The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau,which is highly consistent with MLSEs derived from AMSR-E.It demonstrates that the measurements of FY-3B MWRI Tbs,including its calibration and validation,are reliable,and the retrieval algorithm developed in this study works well.Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation,rainfall,and snow cover.In tropical and southern forest regions,the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall.In the boreal area,snow causes a significant decrease of MLSE at 36.5 GHz in winter.Meanwhile,the MLSE at lower frequencies experiences less suppression.In the desert region in Xinjiang,increases of MLSEs at all frequencies are observed with increasing snow cover.

基于卫星遥感的甲醛和乙二醛监测与应用综述

甲醛(HCHO)和乙二醛(CHOCHO)是大气中大多数VOCs(volatile organic compounds)的氧化中间产物,可作为快速检测VOC的化学指示,对空气质量监测有重要的意义。基于卫星遥感手段能获得全球范围、长时间序列的HCHO和CHOCHO数据集,对研究空气质量变化起到积极的作用。但HCHO和CHOCHO光学吸收特征弱,易受干扰成分的影响,导致卫星遥感反演结果存在较大的不确定性。本文综述可用于HCHO和CHOCHO监测的传感器发展现状、HCHO与CHOCHO遥感反演算法进展及产品现状,以及基于卫星观测的HCHO和CHOCHO产品集的应用。重点介绍国内学者针对国际载荷的甲醛和乙二醛产品所做的优化和改进,以及针对国产载荷-大气痕量气体差分吸收光谱仪(environmental trace gases Monitoring Instrument,EMI)的HCHO反演算法研发的突破,阐述其对提升国产载荷应用潜力的重要意义。最后,本文讨论并总结了HCHO和CHOCHO卫星遥感反演及应用研究需要进一步关注的问题,指出降低HCHO和CHOCHO产品不确定性对应用研究的重要作用,为临近空间创新研发,建立低轨卫星环保数据产业链提供技术支撑。

于氧气A带的高光谱卫星气溶胶层高优化反演

针对气溶胶被动卫星遥感中由于气溶胶模型的不确定性导致的反演误差,引入了一种基于贝叶斯理论的新型气溶胶层高反演算法,并应用于哨兵5先导(Sentinel-5P)卫星的TROPOMI(TROPOspheric Monitoring Instrument)载荷。该算法基于不同候选气溶胶模型的模型证据(气溶胶模型的条件概率密度)确定符合当前观测数据条件的气溶胶模型,并通过两种模型选择方案分别得到估算最大值解和估算平均值解作为反演结果。以TROPOMI观测到的一次真实野火事件为例,反演结果和官方产品具有很好的空间一致性,且明显降低了低估现象,证明在气溶胶先验知识缺乏的背景下该算法能够高效选择合适的气溶胶模型,为今后高光谱卫星气溶胶层高反演的业务化数据处理提供了一种新的解决方案。

New Progress and Challenges in Cloud–Aerosol–Radiation–Precipitation Interactions:Preface for a Special Issue※

Clouds play essential roles in the Earth’s radiative energy balance and global hydrological cycle.Aerosols,the particles suspended in the air,can change cloud properties by interacting with radiation or serving as cloud condensation nuclei.However,the variations in cloud properties subjected to aerosol context,and their impacts on radiation and precipitation,are related to many complicating factors such as land types,meteorological conditions,cloud types,aerosol properties,and their co-varied relationships.Complication of cloud-aerosol-radiation-precipitation interactions makes representation of clouds one of the largest uncertainties in climate models for future climate prediction.It has also become a prevailing topic in atmospheric sciences over the past several decades.

Advances in Atmospheric Radiation:Theories,Models,and Their Applications.PartⅠ:Atmospheric Gas Absorption and Particle Scattering

Atmospheric radiation is a major branch of atmospheric physics that encompasses the fundamental theories of atmospheric absorption,particle scattering(aerosols and clouds),and radiative transfer.Specifically,the simulations of atmospheric gaseous absorption and scattering properties of particles are the essential components of atmospheric radiative transfer models.Atmospheric radiation has important applications in weather,climate,data assimilation,remote sensing,and atmospheric detection studies.In PartⅠ,a comprehensive review of the progress in the field of gas absorption and particle scattering research over the past 30 years with a particular emphasis on the contributions from Chinese scientists is presented.The review of gas absorption includes the construction of absorption databases,the impact of different atmospheric absorption algorithms on radiative calculations,and their applications in weather and climate models and remote sensing.The review on particle scattering starts with the theoretical and computational methods and subsequently explores the optical modeling of aerosols and clouds in remote sensing and atmospheric models.Additionally,the paper discusses potential future research directions in this field.

Advances in Atmospheric Radiation:Theories,Models,and Their Applications.PartⅡ:Radiative Transfer Models and Related Applications

The subject of“atmospheric radiation”includes not only fundamental theories on atmospheric gaseous absorption and the scattering and radiative transfer of particles(molecules,cloud,and aerosols),but also their applications in weather,climate,and atmospheric remote sensing,and is an essential part of the atmospheric sciences.This review includes two parts(Part I and PartⅡ);following the first part on gaseous absorption and particle scattering,this part(PartⅡ)reports the progress that has been made in radiative transfer theories,models,and their common applications,focusing particularly on the contributions from Chinese researchers.The recent achievements on radiative transfer models and methods developed for weather and climate studies and for atmospheric remote sensing are firstly reviewed.Then,the associated applications,such as surface radiation estimation,satellite remote sensing algorithms,radiative parameterization for climate models,and radiative-forcing related climate change studies are summarized,which further reveals the importance of radiative transfer theories and models.

Artificial intelligence for geoscience:Progress,challenges,and perspectives

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection techniques.Traditional models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical processes.However,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world applicability.In contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical knowledge.ML techniques have shown promise in addressing Earth science-related questions.Nevertheless,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into geoscience.The integration of physics-based and data-driven methodologies into hybrid models presents an alternative paradigm.These models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data requirements.This review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and geoscience.It examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in geoscience.The paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

基于葵花-8卫星大气产品的地表下行短波辐射计算

地表下行短波辐射DSSR(Downward Surface Shortwave Radiation)的准确估算在气候变化研究和地表太阳能估算等领域具有重要作用。新一代静止气象卫星葵花-8(Himawari-8)具有高达10 min的对地观测能力,为DSSR近实时估算提供了新机遇。然而,日本宇宙航空研究开发机构(JAXA)对外公开的葵花-8辐射产品中,没有将其反演的云、气溶胶产品作为DSSR的输入参数,从而没有形成一整套的DSSR估算算法流程,缺乏产品输出的一致性。大气中的云、气溶胶是DSSR的重要影响因子,本文重点考虑云、气溶胶对太阳辐射的影响,基于大气辐射传输模式RSTAR构建了DSSR查找表,开发了DSSR的快速计算方法,进而将JAXA葵花-8二级云、气溶胶产品(光学厚度,粒子有效半径等)作为快速化计算方法的输入参量,计算得到了DSSR。通过与JAXA葵花-8二级DSSR产品(JAXA DSSR)对比,发现两者具有很好的空间一致性。为了进一步评价本文的DSSR计算精度,分别选取了陆地(Yonsei)和海洋(0n165e)的观测数据验证了2016年4、7、10和12月本文计算的DSSR和同时期的JAXA DSSR产品,验证结果显示两者的DSSR在两个观测站点均具有非常高的相关性(全天空、晴空和云天条件下的相关系数R均大于0.88)。在两个站点云天条件下的验证结果中,考虑了云相态并在冰云模型中使用了非球形冰晶粒子(六棱柱)来计算DSSR,获得了比JAXA DSSR更小的偏差。本文提出的快速化计算方法能快速准确地计算DSSR,可为计算地表辐射收支等研究提供重要数据支撑。

GF-5 DPC数据的云检测方法研究

高分五号(GF-5)号卫星所搭载的大气多角度偏振探测仪(DPC)能够对地球进行多波段,多角度和的连续观测,其数据对研究全球大气云分布及云辐射反馈作用提供新的视角。本文通法国多角度偏振载荷POLDER(POLarization and Directionality of the Earth’s Reflectances)云检测算法为参考,结合DPC多波段反射率、偏振反射率、表观压强等信息开发了一个适用于DPC的云检测算法。算法主要分为3个部分:首先是阈值方法对云像元进行检测,同时引入表观压强对不同高度的云(如卷云、层积云等)进行进一步的条件约束,然后利用865 nm波段偏振反射率对海表反射的太阳耀斑区进行识别,修正了反射率阈值识别云像元时受到的太阳耀斑干扰。为了验证算法的准确性,利用2018-10-01的MODIS的MOD06云掩码产品与本文云检测算法结果进行定性分析,从目视判读结果可以看出本文云检测结果与MOD06产品具有较高的吻合度;随后又利用2018-10-01—04的CALIPSO-VFM数据与本文云检测结果和MYDO6云掩码产品进行定量分析,分别计算了中低纬度区域(60°N—60°S)的云/晴空像元命中率和云/晴空像元错误预报率,计算结果显示算法云命中率均值相较MYD06云掩码产品高出13.501%的前提下云错误预报率仅高出3.561%,可表明该算法在全球中低纬度区域有着良好的云检测效果。本文提出的云检测算法,可为后续DPC的云参数、水汽、气溶胶等研究提供重要数据支撑。

高分五号卫星大气参数探测综述

高分五号卫星搭载了大气痕量气体差分吸收光谱仪、主要温室气体探测仪、大气多角度偏振探测仪、大气环境红外甚高分辨率探测仪、可见短波红外高光谱相机和全谱段光谱成像仪共6台载荷,对全球环境监测及中国大气气溶胶、臭氧、二氧化硫、二氧化氮、二氧化碳、甲烷和云等多个环境与气象综合监测方面有着重要的意义。为了更好的了解高分五号卫星任务的大气监测内容,本文对高分五号卫星其中的4个大气探测载荷特征和数据处理算法进展进行了总结,涉及各个载荷参数信息、算法介绍和产品初步结果等相关内容。高分五号卫星是中国专门用于大气环境监测任务的遥感卫星,随着后续卫星的发射升空,将对中国乃至全球大气环境监测提供强有力的数据支撑。

基于被动遥感卫星可见至红外通道观测的云特性遥感

云是地球辐射收支、水循环和生物化学循环的主要调节者。自1960年人类第一颗气象卫星(TIROS)的发射起,被动卫星遥感已经发展成为获取云观测资料最高效的手段之一。被动卫星具有观测范围大、时间跨度久的特点,其中利用可见至红外波段(0.40~15μm)的遥感成像仪相对高光谱成像仪、微波成像仪具有时空分辨率更高的观测优势。首先,针对极轨卫星多光谱成像仪、多角度偏振成像仪和新一代静止卫星成像仪三类载荷概述了被动卫星基于可见至红外波段的观测特点。然后,介绍了包括云检测、云相态、云顶参数、云光学和微物理参数的观测原理和应用方法。最后,通过总结和展望为被动卫星可见至红外资料的云特性遥感研究提供一些思考。

A review of the estimation of downward surface shortwave radiation based on satellite data:Methods, progress and problems

The estimation of downward surface shortwave radiation(DSSR)is important for the Earth’s energy budget and climate change studies.This review was organised from the perspectives of satellite sensors,algorithms and future trends,retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years.The shortwave radiation reaching the Earth’s surface is affected by both atmospheric and land surface parameters.In recent years,studies have given detailed considerations to the factors which affect DSSR.It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces(such as snow-covered areas)on DSSR estimation.This review classified DSSR retrieval methods into four categories:empirical,parameterisation,look-up table and machine-learning methods,and evaluated their advantages,disadvantages and accuracy.Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud,haze,water vapor and other land surface parameters such as albedo of complex terrain and bright surface,organically combine machine learning and other methods,use the new-generation geostationary satellite and polar orbit satellite data to produce highresolution DSSR products,and promote the application of radiation products in hydrological and climate models.

大气冰冻圈总量与时空结构

大气冰冻圈是大气层内所有冰体的总称,包括冰晶、雪花等,对全球辐射平衡、水循环和灾害天气的形成均具有重要影响,长期以来受到广泛关注.然而,将大气冰冻圈作为一个整体圈层的定量研究较少,相关认识基本停留在概念和理论阶段.本文基于CloudSat和CALIPSO卫星数据联合反演的DARDAR产品,估算了全球大气冰冻圈的总量、三维空间分布和季节变化特征.结果显示:大气冰冻圈总质量约为63[58.8~65.7]Gt,上界为高层冰云的云顶,下界略低于零温层.大尺度环流系统决定了大气冰冻圈的纬向带状分布特征,冰晶形成与增长的微物理机制导致大气冰体在热带上空–8°C等温层出现高值中心.大气冰体的高值区主要分布于低纬度强对流区,从垂直剖面上看,主要分布于对流层中层和低层.痕量冰区(冰水路径<15.0 g/m^(2))主要分布于副热带等高压系统控制区.在高海拔地区,由于水汽含量较低,大气冰含量整体偏低,如青藏高原冬季冰水路径仅为31.3 g/m^(2),约为全球平均值的四分之一.该项研究为进一步认识大气冰冻圈的天气气候效应奠定了基础.

An Introduction to the Chinese High-Resolution Earth Observation System:Gaofen-1~7 Civilian Satellites

The Chinese High-resolution Earth Observation System(CHEOS)program has successfully launched 7 civilian satellites since 2010.These satellites are named by Gaofen(meaning high resolution in Chinese,hereafter noted as GF).To combine the advantages of high temporal and comparably high spatial resolution,diverse sensors are deployed to each satellite.GF-1 and GF-6 carry both high-resolution cameras(2m resolution panchromatic and 8m resolution multispectral camera),providing high spatial imaging for land use monitoring;GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter,mostly monitoring marine targets;GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera,dedicated to environmental remote sensing and climate research,such as aerosol,clouds,and greenhouse gas monitoring;and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying,facilitating the accumulation of basic geographic information.This study provides an overview of GF civilian series satellites,especially their missions,sensors,and applications.