Retrieval of aerosol optical properties over the Beijing area using POLDER/PARASOL satellite polarization measurements

Aerosol optical properties over Beijing and Xianghe under several typical weather conditions (clear sky, light haze, heavy pollution and dust storm) are derived from POLDER (POLarization and Directionality of the Earths Reflectances)/PARASOL (Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) multi-directional, multi-spectral polarized signals using a more reliable retrieval algorithm as proposed in this paper. The results are compared with those of the operational retrieval algorithm of POLDER/PARASOL group and the ground-based AERONET (AErosol RObotic NETwork)/PHOTONS (PHOtometrie pour le Traitement Operational de Normalisation Satellitaire) measurements. It is shown that the aerosol optical parameters derived from the improved algorithm agree well with AERONET/PHOTONS measurement. The retrieval accuracies of aerosol optical thickness (AOT) and effective radius are 0.06 and 0.05 mu m respectively, which are close to or better than the required accuracies (0.04 for AOT and 0.1 mu m for effective radius) for estimating aerosol direct forcing.     

利用多角度POLDER偏振资料实现陆地上空大气气溶胶光学厚度和地表反照率的同时反演 I. 理论与模拟

陆地上空标量辐射对地表反射率和大气气溶胶散射都具有很强的敏感性,而偏振反射只对大气气溶胶敏感,对地表不敏感。根据这个原理并结合POLDER(POLarization and Directionality of Earth Reflectance)资料的特点,作者提出综合利用标量辐射和偏振反射信息来实现陆地上空大气气溶胶和地表反照率的同时反演。首先,利用多角度偏振辐射观测提取大气气溶胶光学参数,再利用标量辐射测量对偏振反演结果作进一步筛选和订正,同时获得地表反射率。数值模拟试验结果证明,仅利用偏振信息只能获取大气气溶胶信息,而且其结果误差较大,特别是对于散射作用较强的短波长通道如670 nm误差更大,但经过标量辐射订正后的结果得到明显改善,气溶胶光学厚度和地表反射率与真实值之间相关系数都达到0.99以上。为提高查找表的计算效率,提出并建立了反演方案所需要的半参数化数值表,利用内插方法寻求气溶胶光学厚度和地表反射率的数值解的反演方法。     

GMS多通道资料综合判别可降水云

利用１９９５年７～１０月影响山东的１２次降水天气过程３７０个时次的ＧＭＳ－５卫星云图,以及对应时次全省９５个雨量观测站自记降水资料,统计分析了静止气象卫星各通道资料与降水概率的关系。探讨了静止气象卫星云图综合识别可降水云的方法。     

利用MODIS资料反演兰州地区气溶胶光学厚度

借助6S模式对MODIS的蓝光(0.46μm)、红光(0.66μm)和中红外(2.1μm)通道进行了行星反照率对地表反射率和气溶胶光学厚度的敏感性试验, 并对蓝光和红光通道的路径辐射对行星反照率的贡献做了数值试验; 计算了MODIS的蓝光、红光和中红外通道在兰州市及其周围地区的地表反射率, 检验了Kaufman给出的三个通道地表反射率之间的关系。检验结果表明, 在兰州周围地区蓝光通道与中红外通道地表反射率之间的关系与Kaufman给出的关系比较符合, 对于兰州周围大范围区域都是适用的。利用此关系通过6S模式进一步反演了兰州城市及其附近地区 1×104 km2 范围内的气溶胶光学厚度, 反演结果较为合理。     

Identification of Aerosol Types and Their Optical Properties in the North China Plain Based on Long-Term AERONET Data

Characterization of aerosols is required to reduce uncertainties in satellite retrievals of global aerosols and for modeling the effects of these aerosols on climate.Aerosols in the North China Plain(NCP) are complex,which provides a good opportunity to study key aerosol optical properties for various aerosol types.A cluster analysis of key optical properties obtained from Aerosol Robotic Network(AERONET) data in Beijing and Xianghe during 2001-11 was performed to identify dominant aerosol types and their associated optical properties.Five dominant aerosol types were identified.The results show that the urban/industrial aerosol of moderate absorption was dominant in the region and that this type varied little with season.Urban/industrial aerosol of weak absorption was the next most common type and mainly occurs in summer,followed by that strong aerosols occurring mainly in winter.All were predominantly fine mode particles.Mineral dust(MD) and polluted dust(PD) occurred mainly in spring,followed by winter,and their absorption decreased with wavelength.In addition,aerosol dynamics and optical parameters such as refractive index and asymmetry factor were examined.Results show that the size of coarse mode particles decreased with AOD indicating the domination of external mixing between aerosols.     

Aerosol Data Assimilation Using Data from Fengyun-3A and MODIS:Application to a Dust Storm over East Asia in 2011

Aerosol optical depth(AOD) is the most basic parameter that describes the optical properties of atmospheric aerosols,and it can be used to indicate aerosol content. In this study, we assimilated AOD data from the Fengyun-3 A(FY-3 A) and MODIS meteorological satellite using the Gridpoint Statistical Interpolation three-dimensional variational data assimilation system. Experiments were conducted for a dust storm over East Asia in April 2011. Each 0600 UTC analysis initialized a24-h Weather Research and Forecasting with Chemistry model forecast. The results generally showed that the assimilation of satellite AOD observational data can significantly improve model aerosol mass prediction skills. The AOD distribution of the analysis field was closer to the observations of the satellite after assimilation of satellite AOD data. In addition, the analysis resulting from the experiment assimilating both FY-3 A/MERSI(Medium-resolution Spectral Imager) AOD data and MODIS AOD data had closer agreement with the ground-based values than the individual assimilation of the two datasets for the dust storm over East Asia. These results suggest that the Chinese FY-3 A satellite aerosol products can be effectively applied to numerical models and dust weather analysis.     

支持向量数据描述在西北暴雨预报中的应用试验

传统机器学习中通常隐含假设所研究问题是类别平衡的, 气象预报中预测灾害天气时就不满足这个假设, 这时往往需要预测重要而稀少的正类 (少数类)。传统机器学习以精度最大化为目标, 在遇到不平衡类别问题时, 容易训练出把所有实例都分为反类 (多数类) 的平庸的分类器。支持向量数据描述是从支持向量机 (SVM) 发展而来的基于核的机器学习方法, 只使用一类样本就可以工作, 适合于不平衡类别。以铜川暴雨预测作为试验对象, 对SVM和支持向量数据描述 (SVDD) 进行了对比试验。试验结果表明对于这个不平衡类别问题SVDD具有优势。     

Study on Probability Distributions of Multi-Timescale Aerosol Optical Depth Using AERONET Data

The probability distribution analysis is per-formed for multi-timescale aerosol optical depth (AOD) using AErosol RObotic NETwork (AERONET) level 2.0 data.The maximum likelihood estimation is employed to determine the best-fit probability density function (PDF),and the statement that the fitting Weibull distribution will be light-tailed is proved true for these AOD samples.The best-fit PDF results for multi-site data show that the PDF of AOD samples with longer timescale in most sites tends to be stably represented by lognormal distribution,while Weibull distribution is a better fit for AOD samples with short timescales.The reason for this difference is ana-lyzed through tail characteristics of the two distributions,and an indicator for the selection between Weibull and lognormal distributions is suggested and validated.The result of this research is helpful for determining the most accurate AOD statistics for a given site and a given time-scale and for validating the retrieved AOD through its PDF.     

利用多通道MODIS遥感资料反演沙尘气溶胶尺度分布的个例试验

为了利用水面进行大气气溶胶粒子尺度分布的反演,需要水体光谱反射率资料;本文利用水库上空MODIS观测的表观反射率资料来订正出实际的水体光谱反射率。假设在干旱的春季水库水面反射率变化不大,用订正的水库水面光谱反射率平均值进行大气气溶胶尺度分布的反演。从两天个例研究来看,利用多通道MODIS资料可以反演出大气气溶胶粒子的尺度变化。结合天气分析2002年4月11日水库上空气溶胶光学厚度增加、粒子变粗,是由于上游区域的沙尘在高空强西北气流引导下到达本地区。     

基于支持向量机的遥感大雾判识

提出了一种基于支持向量机的卫星遥感数据大雾判识方法:首先通过对风云1D卫星大雾区域的各通道辐射值出现频次进行概率统计,利用其阈值来粗判识大雾;然后在粗判识的基础上通过支持向量机的方法进行大雾细判识;最后利用腐蚀和膨胀的图像处理技术对判识后的图像进行优化处理。在对我国2006年9-12月的65条监测到大雾的风云1D轨道的探测数据进行分析之后,发现大雾判识结果与专家标记吻合。检验结果表明,利用1、2、4、6、7、10通道组合进行粗判识的结果最好,5交叉正确率为89.9849%,TS评分为74.04%。利用上述方法对个例的分析检验表明,基于支持向量机的遥感大雾判识方法是切实可行的。     

使用SVM进行短期气候预测的应用试验

从气象角度介绍了利用SVM方法的预报原理,对西安短期气候预测做了实验,并分析预报结果,得出SVM方法在天气预报领域有一定的使用空间的结论。     

利用MODIS资料遥感水体上空气溶胶粒子尺度的数值试验

利用卫星探测辐射的光谱依赖性来反演大气气溶胶粒子尺度分布,首先要建立反演算法并检验其可行性。本文参照文献[1]对暗的海洋上空MODIS资料的处理方法,针对大陆水库上空卫星探测MODIS辐射信号,利用6S(Second Simulation of the Satellite Signal in the Solar Spectrum)辐射模式建立一个查算表(Look-up table:LUT)来反演大气气溶胶粒子尺度分布;然后通过数值试验对所建立的查算表进行检验,讨论其方法的可行性。数值试验结果表明,查算表对大陆性气溶胶尺度分布有反演能力,但拟合误差sεl值随着气溶胶光学厚度的增加而增加。     

Data selection using support vector regression

Geophysical data sets are growing at an ever-increasing rate, requiring computationally efficient data selection(thinning)methods to preserve essential information. Satellites, such as Wind Sat, provide large data sets for assessing the accuracy and computational efficiency of data selection techniques. A new data thinning technique, based on support vector regression(SVR), is developed and tested. To manage large on-line satellite data streams, observations from Wind Sat are formed into subsets by Voronoi tessellation and then each is thinned by SVR(TSVR). Three experiments are performed. The first confirms the viability of TSVR for a relatively small sample, comparing it to several commonly used data thinning methods(random selection, averaging and Barnes filtering), producing a 10% thinning rate(90% data reduction), low mean absolute errors(MAE) and large correlations with the original data. A second experiment, using a larger dataset, shows TSVR retrievals with MAE < 1 m s-1and correlations 0.98. TSVR was an order of magnitude faster than the commonly used thinning methods. A third experiment applies a two-stage pipeline to TSVR, to accommodate online data. The pipeline subsets reconstruct the wind field with the same accuracy as the second experiment, is an order of magnitude faster than the nonpipeline TSVR. Therefore, pipeline TSVR is two orders of magnitude faster than commonly used thinning methods that ingest the entire data set. This study demonstrates that TSVR pipeline thinning is an accurate and computationally efficient alternative to commonly used data selection techniques.     

基于激光雷达资料的气溶胶辐射效应研究

利用新型激光雷达气溶胶探测资料及综合数值模式,以地形复杂的兰州市及周边地区冬季典型天气形势下的大气边界层为研究对象,通过理想试验模拟研究了城市气溶胶辐射效应与大气边界层的相互作用。结果表明:夜间,低空(50~600 m)气溶胶所在气层冷却效应明显,温度降低0.13~0.18℃,600 m高度以上,气溶胶浓度较低,其冷却效应较小,温度降低不足0.1℃;白天,受气溶胶短波辐射效应影响,边界层内增温明显,增温最大值位于低层脱地逆温层顶300 m高度附近,600 m以上由于气溶胶浓度减小,加热率亦降低,增温由0.2℃减至0.1℃。此外,气溶胶的存在使得所在层的风速降低。可见,激光雷达探测资料在边界层模式中有很好的应用价值,对于研究气溶胶辐射效应的大气边界层响应有重要意义。     

基于支持向量机的雷暴大风识别方法

基于北京市观象台雷达基数据和加密自动气象站数据,利用支持向量机算法建立了雷暴大风天气的有效识别模型。首先确立了9个用于识别雷暴大风的预报因子:回波顶高、最大反射率因子、最大反射率因子所在高度、垂直积分液态水含量、垂直积分液态水含量随时间变率、垂直积分液态水含量密度、雷暴大风发生前最大反射率因子下降高度、风暴移动速度、速度谱宽。通过计算各预报因子在大风和非大风样本中的概率分布,得到对应的各项预报因子雷暴大风的隶属度,利用得到的隶属度函数对样本进行归一化处理。确立核函数和模型参数,利用支持向量机建立雷暴大风天气的提前识别和临近预警模型。通过对北京2017年7月7日飑线和2012年5月19日块状回波引起的灾害大风典型个例的识别效果检验,得到两个个例预测的命中率、误判率和临界成功指数分别为92.0%,22.1%,73.0%和99.1%,40.5%,59.2%,对于提高雷暴大风预警预报的准确率有一定帮助。     

Study on Quantitative Identification Sand and Dust Storm Using MODIS Data

In order to detect the scope and the intensity quanti cationally, the spectrum characteristic of sand and dust storm was analyzed in detail by using several MODIS data; bands that can distinguish sand and dust from cloud and surface were detected; two indices for determining the scope and intensity of sand and dust storm were found out, and were tested in several storms. Our study result shows: 1) The spectral characteristic of sand and dust in solar wavelengths is that the reflectance increases with the increasing of the wavelength. This is similar to the characteristic of the spectrum of soil. Also, the reflectance of large size dusts increases faster than small size dusts. 2) Small size dusts show typical characteristic of aerosol, being sensitive to blue band of 0.46 μm and insensitive to short wave infrared bands of 1.6 and 2.1 μm. 3) Large size dusts do not have aerosol characteristic, not sensitive to blue band but sensitive to short wave infrared bands. 4) Bands of 3.7 and 8.5 μm are sensitive to dusts. The difference of two bands can be used to identify sands and dusts and reflect the intensity to a certain extent. And 5) the two indices are very effective to monitor sands and dusts by testing a lot of sand and dust storm cases during the period of 2002-2005. Also, the method is simple and easy for operational use.     

2010—2012年我国西北地区沙尘个例气溶胶特征分析

利用2010—2012年间中国西北地区敦煌、民勤和塔中3个站点的CE-318太阳光度计观测资料,反演获得了气溶胶440 nm波段的大气气溶胶光学厚度（AOD）及440—870 nm波长指数（Alpha）,同时结合Moderate Resolution Imaging Spectroradiometer(MODIS)卫星L1B产品及环境颗粒物监测仪Tapered Element Oscillating Microbalance(TEOM)观测的PM10数据,挑选出2010—2012年间沙尘天气特征明显的6个日期,并对这6天的气溶胶光学特性、PM10浓度变化特征及沙尘气溶胶来源进行了分析。研究结果表明：MODIS卫星图有明显沙尘天气过境时,当天的AOD值较高,Alpha值则较低,且AOD和Alpha表现出相反的变化趋势。这表明在这3个站点沙尘气溶胶占主导,PM10浓度变化与AOD变化趋势有较好的正相关性。Hybrid Single Particle Lagrangian Integrated Trajectory（HYSPLIT）后向轨迹分析表明,气团大多起源于塔克拉玛干沙漠或干旱、半干旱区。     

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.     

黑河流域土壤质地分类数据建立及其模拟效果检验

原全球土壤质地类型分布在黑河地区的数据(下称原全球数据)不能体现该流域不同海拔高度土壤分布的非均匀性特征,为了定量表征研究区土壤的非均匀分布,在流域范围收集了53个土壤亚类剖面数据,对照土壤三角,与国际通用的STASGO 30 s土壤质地类型分类标准对应,基于黑河流域1∶100000土壤亚类分布图,建立了黑河流域30 s分辨率的土壤质地类型分布图(黑河数据)。这套数据可以描述出原全球STASGO 30 s数据不能表现出的山区和绿洲区的土壤非均匀分布实际。分别使用黑河数据和原全球数据作为下垫面土壤分布,运用耦合了陆面过程的中尺度大气模式,对黑河流域中上游区域进行了模拟,检验了两套数据对气温模拟的准确性,讨论了土壤分布变化对环境要素模拟的影响。结果表明,使用黑河土壤分布数据对上游山区的气温模拟性能好于原全球数据,中游绿洲区使用两套土壤分布数据的模拟值都偏高,大部分站点使用黑河土壤分布数据的模拟值略高于使用原全球数据的模拟值。这可能与模式中使用的全球土壤水、热参数在研究区的代表性以及中游区的人类活动加剧了土壤温、湿度的时空异质性有关。因此,黑河流域土壤水、热参数以及高分辨率的土壤温、湿度分布是提高该区域土壤—大气相互作用数值模拟性能的重要途径。环境要素变化与土壤类型分布变化呈非线性关系。气温模拟值对土壤分布变化最为敏感。     

利用卫星遥感信息解译山东冬小麦产量类型分布

利用1991～2000年NOAA气象卫星遥感资料和同期山东冬小麦产量资料，分区建立了冬小麦产量与植被指数之间的关系式，推算出每个像元点的产量，按照划分高中低产田的阈值，将该像元点确定为某一产量类型，在GIS的支持下，形成了山东省冬小麦高、中、低产田分布图，同时，对于中低产田的成因进行了初步分析。