O2-O2云反演算法及其在TROPOMI上的应用

利用遥感技术对大气环境污染进行监测时，云是影响痕量气体反演精度的重要因素，因此在痕量气体反演中需要对云的影响进行校正，通常使用的云参数主要是有效云量和云压。本文基于O₂-O₂ 477 nm吸收波段构建了O₂-O₂云反演算法：首先，根据有效云量和云高与连续反射率和O₂-O₂斜柱浓度之间的对应关系，结合假定的云模型利用VLIDORT辐射传输模型建立关于有效云量和云压的查找表；然后，通过差分吸收光谱技术拟合卫星载荷观测的大气层顶辐射，获得O₂-O₂斜柱浓度并计算连续反射率；最后，结合辅助数据，根据查找表进行插值反演获得有效云量和云压。通过将算法应用到OMI观测数据，将反演结果与OMCLDO2产品进行对比验证，有效云量和云压空间分布一致，相关系数R均超过0.97；并还将该算法应用于下一代大气成分监测仪器TROPOMI，与FRESCO+产品对比，有效云量和云压空间分布基本一致，当地表类型为海洋时，有效云量相关系数R大于0.97，云压相关系数R大于0.94，云压反演结果存在一定的区别；通过将O₂-O₂云反演算法和FRESCO+云压反演结果与CALIOP Cloud Layer产品进行对比，结果表明，在低云情况下，O₂-O₂云反演算法线性回归方程斜率为0.782，截距为198.0 hPa，相关系数R为0.850，算法表现优于FRESCO+。而在高云情况下，FRESCO+反演结果更接近CALIOP云压结果。在OMI和TROPOMI上的应用表明O₂-O₂云反演算法在大气云反演中具有较高的准确性和可行性，可以为大气痕量气体反演的校正提供云参数，并为中国同类型卫星载荷的云反演算法提供算法参考。     

北京地区气溶胶粒径尺度谱与PM2.5浓度转换模型研究

大气细颗粒物（PM_2.5）质量浓度是重要的空气质量指标之一。为了促进区域PM_2.5浓度监测的研究，同时拓展利用CE318太阳光度计等光学传感器反演的大气气溶胶产品的应用领域，本文首先基于北京地区2014年—2017年大气气溶胶反演的粒径尺度谱分布产品，计算表征PM_2.5的粒子体积，并结合同一时间北京地区35个空气质量站点提供的PM_2.5质量浓度参考值计算转换系数，对样本区间进行划分以构建转换模型。其次，利用各CE318站点数据所得转换系数及其相对精度，对研究区PM_2.5质量浓度估算误差空间分布，以及转换系数偏差对估值误差贡献情况进行评价。研究结果表明，由CE318站PM_2.5粒子体积与其临近空气质量站PM_2.5质量浓度联合建立的转换系数是一种与气溶胶理化属性密切相关的参数指标，可将时间和空间维度上PM_2.5体积与PM_2.5质量浓度之间的关系映射到由理化属性主导的维度上，可用于对估值模型进行细化和分类，构建分段转换函数模型，使得各分段区间内具有较高的模型拟合精度。基于转换系数的北京地区PM_2.5浓度估值的相对误差多年均值介于12.8 %—28.7 %，而转换系数相对偏差对PM_2.5质量浓度估值相对误差的影响显著，二者之间具有“r”型结构。当转换系数相对偏差介于-16.3 %至24.5 %时，该偏差的出现概率约为66.5 %，使得PM_2.5质量浓度估值误差在20 %以内，表明采用此种方法对相应站点的PM_2.5质量浓度进行估值具有相当的精度和稳定性。以上研究结果可为地面观测站稀少区域利用卫星光学遥感开展空气质量大范围监测应用提供理论前提和技术支持。     

高分五号大气痕量气体差分吸收光谱仪观测数据的火山喷发SO2总量反演

火山喷发产生的高浓度SO₂气体及其远距离输送会对全球气候变化和航空飞行安全产生重要影响。卫星遥感技术以大面积连续观测、高时空分辨率等优势成为大气SO₂监测的重要手段之一。作为中国第一颗紫外可见光波段的高光谱载荷,高分五号卫星大气痕量气体差分吸收光谱仪（GF-5 EMI）通过探测地球大气或表面反射、散射的紫外辐射来解析SO₂总量的分布和变化。本文首先基于大气辐射传输模型SCIATRAN,选择中低纬地区地表类型均一的海洋区域像元,模拟了典型大气条件下的晴空天顶反照率,用以评价EMI载荷天顶观测光谱的精度。其次,基于TROPOMI L1 Radiance辐亮度数据和DOAS反演原理,经477 nm O₄云筛选、光谱定标、慢变剔除、斜柱转垂直柱等步骤后,获得同一火山喷发区域的SO₂总量反演结果并与TROPOMI官方offline L2 SO₂产品进行对比分析。最后,利用GF-5 EMI UV-2通道观测数据,采用DOAS算法反演获得GF-5 EMI大气SO₂总量,并将反演结果与国际同类载荷S5P/TROPOMI SO₂总量结果进行比较分析,评判GF-5 EMI在全球火山活动SO₂变化监测方面的能力。结果显示,300—400 nm波段范围内,海洋区域采样点EMI观测光谱值低于SCIATRAN模拟光谱值,EMI与TROPOMI观测光谱呈现出相似的系统性偏差。基于高空间高光谱分辨率的TROPOMI L1 Radiance辐亮度数据、315—327 nm、325—335 nm和360—390 nm共3个波段窗口、以及上述DOAS反演原理获得的SO₂结果与TROPOMI官方发布的offline L2 SO₂产品结果相关性较高,两者的相关系数可达到0.97—0.99,相对偏差在3%—9%。GF-5 EMI能够获取火山喷发SO₂排放的时空分布特征,并与国际同类载荷TROPOMI反演结果具有较高的空间一致性,能够满足全球火山喷发监测、预警及其气候影响研究的应用需求。     

珠江三角洲地区SO2浓度卫星遥感长时间序列监测

利用卫星OMI(Ozone Monitoring Instrument)数据获得的长时间序列SO₂浓度,分析广州亚运会从申办到成功举办期间珠江三角洲地区近地面SO₂浓度的变化过程,以及亚运会举办期间珠江三角洲SO₂浓度分布状况。结果表明OMI能够反映近地表的SO₂浓度变化趋势,广州亚运会从申办到成功举办期间珠江三角洲地区近地面SO₂浓度表现出明显的季节变化,且亚运会期间该地区近地面SO₂浓度比以往年同期要低,说明珠江三角洲地区联动的空气质量保障措施明显降低了该地区近地面的SO₂浓度。     

基于卫星遥感的近地面PM2.5浓度反演进展

细颗粒物PM_2.5（Fine Particulate Matter）是影响空气质量和公共健康的关键因素之一。高时空分辨率的PM_2.5数据是公共健康风险评估和流行病学研究的基本需求。相较于地面站点,卫星遥感技术具有连续观测、宽覆盖和低成本的优势,基于卫星气溶胶光学厚度AOD（Aerosol Optical Depth）反演PM_2.5质量浓度的方法已成为热点。本研究概述了卫星AOD产品反演PM_2.5浓度的原理,介绍了用于PM_2.5反演的主要卫星AOD产品及其反演精度;总结了现有的PM_2.5估算方法及其优缺点,指出目前PM_2.5反演研究存在的问题;提出未来PM_2.5反演方向主要集中在高时空分辨率的PM_2.5浓度重建、基于激光雷达数据的三维PM_2.5浓度反演及PM_2.5化学组分反演等方向。比例因子法、物理机理模型和统计模型这3种方法都能在不同时期不同程度地准确估算PM_2.5浓度,代表了那个时期较为前沿的研究热点,但比例因子法和物理机理模型因其自身的局限性而应用较少,而统计模型因其独特的时间或时空异质性的可描述性和强大的非线性描述能力的优势而被广泛应用并不断改进。目前PM_2.5反演研究存在的问题主要有3种：（1）卫星AOD的非随机缺失问题造成估算的PM_2.5数据缺失;（2）反演模型的精度问题;（3）PM_2.5的化学成分估算问题。基于此,本文为了准确揭示近地面PM_2.5的时空变化趋势,提高基于卫星AOD产品的近地面PM_2.5反演研究的准确性,提出了几点未来的研究方向：首先,新型的高空间（如风云四号、高分五号）、高时间分辨率（Himawari-8/9）卫星AOD产品在PM_2.5的精细化估算研究上具有很大优势,这对于高时空分辨率的PM_2.5浓度重建具有重要意义;其次,随着大气探测技术的发展,星载、机载及地基激光雷达都能够获取垂直分布信息,搭载在无人机上的颗粒物传感器可实现PM_2.5垂直方向上的监测,将其与光学遥感卫星数据及地面监测数据结合,可实现三维的PM_2.5浓度反演;最后,PM_2.5化学组分信息对于分析污染成因、暴露特征等尤其重要,其时空变化趋势研究是一个重要的发展方向,然而,地面PM_2.5组分观测站网仍不完善,如何克服卫星遥感估算中对地面站网的依赖,实现PM_2.5化学成分的高精度反演需要进一步研究。通过本研究,有助于进一步了解不同PM_2.5估算方法的原理机制及其优缺点,为基于卫星AOD产品反演近地面PM_2.5浓度的新的发展方向提供启示,提升近地面PM_2.5浓度反演的精度及时空分辨率。     

地球静止卫星和网格化站点支持下的PM2.5精细制图

许多城市建立的相对稠密的网格化监测站点,为精细化监管城市空气质量奠定了基础。本文选用徐州市网格化监测数据、地球静止卫星Himawari-8/AHI及COMS/GOCI的表观反射率和气溶胶光学厚度数据、气象和其他辅助数据,开展了徐州地区0.005°空间分辨率网格的PM_2.5浓度精细化制图研究。本文使用了极端梯度提升（XGBoost）、随机森林（RF）及时空加权回归（GTWR）等3种方法,并选用多种特征参数组合进行对比分析。综合分析模型精度和过拟合程度,结果表明XGBoost模型表现最好,其R²为0.90,RMSE为11.65 μg/m³。进一步将本文结果与国控站点、清华大学的TAP数据集和马里兰大学的CHAP数据集的对比分析,结果表明基于网格化站点的PM_2.5制图结果能更好地反映城市内部不同区域的PM_2.5浓度分布差异性,弥补因国控站点稀疏带来的缺陷,更好地服务于城市空气质量精准管控。     

GF-5 AIUS切高校正算法研究与反演验证

利用红外掩星获取温压及其大气成分廓线,需要获得精确的指向信息,针对高分五号大气环境甚高分辨率探测仪（GF-5 AIUS）一级光谱信息,本文首先分析了不同切高的光谱变化情况,在此基础上,利用查找表方法进行切高校正。在低切高段（10—20 km）,选取N₂吸收波段,模拟6—40 km高度上的透过率光谱,通过查找表校正方法对一级数据中的低层切高进行校正。在高切高段（20—100 km）,模拟6—90 km切高上的透过率光谱,利用O₃吸收波段（20—90 km）进行校正。校正算法采取全局最小均方根误差的方法进行统计实验,确定最小校正半径为15 km。最后选取O₃、HCl、N₂O共3种大气成分的反演波段,进行校正后的透过率数据对比验证与反演产品精度的验证。结果表明：在O₃、HCl、N₂O共3种成分的反演通道上,校正后的观测透过率与模拟的透过率数据一致性较好,两者最大偏差小于0.1,切高校正算法得到的结果与模拟透过率吻合度较好;同时基于Aura MLS（Microwave Limb Sounder）、ACE-FTS（Atmospheric Chemistry Experiment Fourier-Transform Spectrometer）Level 2产品进行了O₃、HCl、N₂O单廓线交叉验证。结果表明：GF-5 AIUS反演的单廓线O₃与MLS、ACE-FTS相对偏差在30 km以下小于50%,在30—60 km小于20%;单廓线HCl与MLS、ACE-FTS相对偏差整体小于20%;单廓线N₂O与MLS、ACE-FTS相对偏差30 km以下小于20%,30—60 km相对偏差较大。     

机载激光雷达数据分析与反演青海云杉林结构信息

利用机载激光雷达点云数据,计算了9种度量指标,并将其分为冠层的高度指标、结构复杂度指标和覆盖度指标。利用高度指标和结构复杂度指标,结合大量实测单木结构与年龄估测数据,从样点和区域尺度分别分析了青海云杉林冠层垂直结构分布,分析得知实验区内主要以中龄林和成熟林为主,冠层垂直分布复杂程度偏低,高度分化程度一般。通过回归分析发现首次回波覆盖度指标FCI与实测的有效植被面积指数PAI_e有良好的相关性（R²=0.66）,在此基础上基于辐射传输模型反演了实验区内PAI_e的水平分布,且用实测数据验证发现反演的PAI_e略高于实测值（R²=0.67）,绝对平均误差为0.65。分析结果很好地反映了激光雷达在森林空间结构信息提取方面的应用潜力。     

基于垂直订正和湿度订正方法估算川渝地区逐小时PM2.5

针对PM_2.5污染比较严重的川渝地区，本研究利用日本静止葵花卫星（Himawari-8）反演的气溶胶光学厚度（AOD）进行垂直订正和湿度订正估算川渝地区2017年—2018年09：00—16：00时的PM_2.5浓度。首先，基于气象观测的能见度（vis）数据与消光系数σa(λ)之间的关系，引入“标高（Ha）”对气溶胶光学厚度进行垂直订正；其次，根据匹配的气象和环境监测数据对每一个站点逐一拟合1—12月份的吸湿增长因子，并采用反距离加权空间插值（IDW）方法构建吸湿订正因子网格，从而进行湿度订正估算。研究结果表明，经垂直订正和湿度订正后，相比AOD与PM_2.5之间的相关性，“干”消光系数σdry与PM_2.5的相关性显著提高，相关系数（r）由0.12—0.45提高至0.32—0.69；验证09：00至16：00时卫星估算结果，相关系数（r）均大于0.7，均方根误差（RMSE）为18.59±2.26 μg/m³；提取所有观测站点进行验证，r=0.82，RMSE=18.64 μg/m³。     

利用遥感数据评价燃煤电厂空气质量

卫星观测数据可以评价燃煤电厂的空气质量等级。NO₂、SO₂ 和烟尘是燃煤电厂排放的主要污染物,本文利用卫星遥感观测的NO₂、SO₂和气溶胶光学厚度AOD(Aerosol Optical Depth)开展燃煤电厂空气质量评价。以中国华北地区为实验区,分析对比了3种污染物不同时间分辨率和空间分辨率的污染状况,确定了单因子的5级分级标准,根据燃煤电厂排放污染物的权重不同,提出了评价近地表空气质量状况的模型。本文综合考虑3种污染因子来反映电厂空气质量,有利于提高评价的准确性以及反应信息的全面性。结果表明,该模型能正确反映不同地区电厂的空气质量特点。     

G0分布下基于白化滤波的极化SAR图像CFAR检测

在已有的极化合成孔径雷达(PolSAR)图像恒虚警(CFAR)检测方法中,存在着高分辨下杂波模型适用性差的难题。为解决此问题,提出了一种G_0分布下虚警概率具有闭合解析表达形式的CFAR检测方法,并定义虚警损失率(CFAR Loss, C_L)参数用以量化评估CFAR检测方法的恒虚警保持效果。首先,在乘积模型框架下,引入了逆Gamma纹理变量假设,推导出了多视极化白化滤波(MPWF)检测量的概率密度函数(PDF)。然后,对MPWF检测量的概率密度函数积分得到了虚警概率关于CFAR检测阈值的解析表达式,并设计了相应的CFAR检测流程。最后,采用仿真数据和AIRSAR实测数据对已有方法和新方法进行了算法运行时间、检测量拟合性能及目标检测性能对比。实验结果表明,方法运行时间比已有方法缩短3至30倍,具有良好的实时性;日本玉野地区的AIRSAR实测数据结果表明G_0分布对高分辨不均匀海区具有良好的拟合性能,且新方法在G_0分布和非G_0分布海区均能有效检测出目标,鲁棒性较强,相比其他检测方法品质因数(FoM)平均高出15.78%;C_L分析结果表明新方法具有良好的恒虚警保持性能,同时指出杂波对数累积量散点距离G_0分布曲线越近,新方法的恒虚警保持效果越好。     

太赫兹临边探测O3与HCl敏感性分析

臭氧(O_3)是平流层大气最重要的成分之一,而随着人类活动增加的影响,平流层O_3不断减少。在O_3的衰减循环中,Cl化合物是最主要的介质,而氯化氢(HCl)是活性氯的储存库,因此同时探测O_3与HCl对进一步认清平流层O_3的源与汇具有重要意义。本文基于太赫兹临边探测辐射传输软件包ARTS(Atmospheric Radiative Transfer Simulator),模拟研究太赫兹临边探测O_3与HCl的敏感因子量化大气辐射传输及其他大气成分对O_3和HCl的影响。结果表明:(1)在太赫兹波段,O_3与HCl分别在切高大于8 km与大于10 km处,具有很高的敏感性,可以探测O_3与HCl垂直廓线;(2)太赫兹临边探测对于探测目标O_3和HCl,在10 km及以下高度,对目标的浓度变化不敏感,而在中高空则很敏感;温度与水汽的变化对O_3与HCl的探测影响不大,而传感器的天线半带宽及光谱分辨率对于O_3与HCl的探测有一定的影响。     

GPS动态定位数据处理模型的比较研究 (英文)

The characteristics of three GPS kinematic data processing models, Least Squares （LS）, Kalman filtering and filtering are discussed and their advantages and disadvantages are compared. With observational data and pertinent data processing software, the applicable condition, context and effect of the three models are experimented. Results show that when the mobile platform is in uniform motion, the accuracy of the three models are almost equal; when the mobile platform is in stochastic acceleration, the accuracy of H∞ filtering model is superior to that of LS, while that of Kalman filtering is the worst.     

极区海冰密集度AMSR-E数据反演算法的试验与验证

海冰密集度是极区海冰监测的重要参数,目前分辨率最高的微波海冰密集度产品为德国Bremen大学发布的针对AMSR-E 89 GHz频段数据利用ASI算法反演的网格数据。为实现中国极区遥感产品从无到有的战略步骤,本文针对AMSR-E 89GHz频段微波数据的ASI算法,进行了插值算法、系点值和天气滤波器一系列试验。针对北极海区,着重对影响反演结果的主要参数——纯冰和纯水的亮温极化差异阈值,即系点值(P₁和P₀)进行了2009年全年的统计分析。研究表明,2009年北极纯冰和纯水的代表区域P₁和P₀年平均值分别为10.0 K和46.67 K;2 K以上的系点值差异引起的海冰密集度差别较为显著;同样的系点值差异在不同极化差异P取值范围对海冰密集度的影响也不同。通过统计确定的系点值推算并修正了海冰密集度反演公式,对2009年全年北极海冰密集度进行了反演,并与Bremen大学产品进行了比较。继而对白令海和楚科奇海12个晴空下MODIS可见光样本数据进行反演,以验证AMSR-E冰密集度反演结果,并对误差原因进行了分析。本研究反演结果与MODIS样本比对的误差略小于Bremen大学的反演产品,空间平均误差为3.84%,空间平均绝对误差10.83%。     

Accuracy of GPS-derived relative positions as a function of interstation distance and observing-session duration

 Ten days of GPS data from 1998 were processed to determine how the accuracy of a derived three-dimensional relative position vector between GPS antennas depends on the chord distance (denoted L) between these antennas and on the duration of the GPS observing session (denoted T). It was found that the dependence of accuracy on L is negligibly small when (a) using the `final' GPS satellite orbits disseminated by the International GPS Service, (b) fixing integer ambiguities, (c) estimating appropriate neutral-atmosphere-delay parameters, (d) 26 km ≤ L ≤ 300 km, and (e) 4 h ≤T ≤ 24 h. Under these same conditions, the standard error for the relative position in the north–south dimension (denoted S _n and expressed in mm) is adequately approximated by the equation S _n =k _n /T ^0.5 with k _n =9.5 ± 2.1 mm · h^0.5 and T expressed in hours. Similarly, the standard errors for the relative position in the east–west and in the up-down dimensions are adequately approximated by the equations S _e =k _e /T ^0.5 and S _u =k _u /T ^0.5, respectively, with k _e =9.9 ± 3.1 mm · h^0.5 and k _u =36.5 ± 9.1 mm · h^0.5. Received: 5 February 2001 / Accepted: 14 May 2001     

Spatially weighted supervised classification for remote sensing

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.     

Detecting anomalous CO2 flux using space borne spectroscopy

Over the time-scale, earth's atmospheric CO₂ concentration has varied and that is mostly determined by balance among the geochemical processes including burial of organic carbon in sediments, silicate rock weathering and volcanic activity. The best recorded atmospheric CO₂ variability is derived from Vostok ice core that records last four glacial/interglacial cycles. The present CO₂ concentration of earth's atmosphere has exceeded far that it was predicted from the ice core data. Other than rapid industrialization and urbanization since last century, geo-natural hazards such as volcanic activity, leakage from hydrocarbon reservoirs and spontaneous combustion of coal contribute a considerable amount of CO₂ to the atmosphere. Spontaneous combustion of coal is common occurrence in most coal producing countries and sometimes it could be in an enormous scale. Remote sensing has already proved to be a significant tool in coalfire identification and monitoring studies. However, coalfire related CO₂ quantification from remote sensing data has not endeavoured yet by scientific communities because of low spectral resolution of commercially available remote sensing data and relatively sparse CO₂ plume than other geological hazards like volcanic activity. The present research has attempted two methods to identify the CO₂ flux emitted from coalfires in a coalmining region in north China. Firstly, a band rationing method was used for column atmospheric retrieval of CO₂ and secondly atmospheric models were simulated in fast atmospheric signature code (FASCOD) to understand the local radiation transport and then the model was implemented with the inputs from hyperspectral remote sensing data. It was observed that retrieval of columnar abundance of CO₂ with the band rationing method is faster as less simulation required in FASCOD. Alternatively, the inversion model could retrieve CO₂ concentration from a (certain) source because it excludes the uncertainties in the higher altitude.     

CO2 concentration and its spatiotemporal variation in the troposphere using multi-sensor satellite data,carbon tracker,and aircraft observations

Satellite-based atmospheric CO₂ observations have provided a great opportunity to improve our understanding of the global carbon cycle. However, thermal infrared (TIR)-based satellite observations, which are useful for the investigation of vertical distribution and the transport of CO₂, have not yet been studied as much as the column amount products derived from shortwave infrared data. In this study, TIR-based satellite CO₂ products – from Atmospheric Infrared Sounder, Tropospheric Emission Spectrometer (TES), and Thermal And Near infrared Sensor for carbon Observation – and carbon tracker mole fraction data were compared with in situ Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) data for different locations. The TES CO₂ product showed the best agreement with CONTRAIL CO₂ data resulting in R² ~ 0.87 and root-mean-square error ~0.9. The vertical distribution of CO₂ derived by TES strongly depends on the geophysical characteristics of an area. Two different climate regions (i.e., southeastern Japan and southeastern Australia) were examined in terms of the vertical distribution and transport of CO₂. Results show that while vertical distribution of CO₂ around southeastern Japan was mainly controlled by horizontal and vertical winds, horizontal wind might be a major factor to control the CO₂ transport around southeastern Australia. In addition, the vertical transport of CO₂ also varies by region, which is mainly controlled by anthropogenic CO₂, and horizontal and omega winds. This study improves our understanding of vertical distribution and the transport of CO₂, both of which vary by region, using TIR-based satellite CO₂ observations and meteorological variables.     

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (K_dPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological K_dPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (b_b) coefficients) and apparent optical properties (downwelling attenuation coefficient (K_d) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the K_dPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and K_dPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.     

Impact of land-cover layout on particulate matter 2.5 in urban areas of China

ABSTRACT

Urbanization in China is closely connected with ambient particulate matter 2.5 (PM_2.5). However, the potential for altering PM_2.5 through the urban landscape characteristics is uncertain. In this study, we analyzed the urban PM_2.5 pollution situation for 2014–2016 and investigated the impact of landscape factors on urban PM_2.5 in China at the city level. All the prefecture-level cities were stratified by urban population size into small (<500,000), medium (500,000–1,000,000), and large (>1,000,000), and the other second-level administrative cities were assigned as ‘other’ cities. The multivariate regression model including both urban landscape factors and social-economic variables explained 70.0%, 32.8%, 19.2%, and 12.4% of the arithmetic mean PM_2.5 concentration (AMC-PM_2.5) for the other, small, medium, and large cities, respectively. With regard to the configuration of land cover, agricultural activity is a major contributor of PM_2.5 pollution, for which the explanatory power ranged from 7.6% (for the large cities) to 64% (for the other cities). In addition, grassland aggregation also has a limited but negative effect on urban PM_2.5 pollution, despite the negligible effect on dry deposition. Overall, these findings likely reflect the interaction between urban air quality and urbanization, and will have implications for air quality control strategies.