Retrieval of outgoing longwave radiation from COMS narrowband infrared imagery

Hourly outgoing longwave radiation(OLR) from the geostationary satellite Communication Oceanography Meteorological Satellite(COMS) has been retrieved since June 2010. The COMS OLR retrieval algorithms are based on regression analyses of radiative transfer simulations for spectral functions of COMS infrared channels. This study documents the accuracies of OLRs for future climate applications by making an intercomparison of four OLRs from one single-channel algorithm(OLR12.0using the 12.0 μm channel) and three multiple-channel algorithms(OLR10.8+12.0using the 10.8 and 12.0 μm channels; OLR6.7+10.8using the 6.7 and 10.8 μm channels; and OLR All using the 6.7, 10.8, and 12.0 μm channels). The COMS OLRs from these algorithms were validated with direct measurements of OLR from a broadband radiometer of the Clouds and Earth's Radiant Energy System(CERES) over the full COMS field of view [roughly(50°S–50°N, 70°–170°E)] during April 2011.Validation results show that the root-mean-square errors of COMS OLRs are 5–7 W m-2, which indicates good agreement with CERES OLR over the vast domain. OLR6.7+10.8and OLR All have much smaller errors(～ 6 W m-2) than OLR12.0and OLR10.8+12.0(～ 8 W m-2). Moreover, the small errors of OLR6.7+10.8and OLR All are systematic and can be readily reduced through additional mean bias correction and/or radiance calibration. These results indicate a noteworthy role of the6.7 μm water vapor absorption channel in improving the accuracy of the OLRs. The dependence of the accuracy of COMS OLRs on various surface, atmospheric, and observational conditions is also discussed.     

基于ERA5的快速辐射传输模式与FY-4A成像仪观测结果的偏差分析

2016年12月发射升空的FY-4A是中国第二代静止轨道气象卫星,该星上搭载了可提供东半球近实时高分辨率卫星观测数据的扫描辐射成像仪——AGRI（Advanced Geostationary Radiation Imager）。在其观测数据应用于大气参数反演或同化前,数据偏差的定量化分析是一个必要环节。采用快速辐射传输模式RTTOV（Radiative Transfer for the TIROS Operational Vertical Sounder）,基于欧洲中期天气预报中心（ECMWF）第5个全球再分析数据产品（ERA5）对AGRI的7个红外通道（通道08—14）进行了模拟,并利用MODIS云检测产品对模拟结果进行了晴空筛选,以期得到一些对AGRI的定量应用有价值的偏差分析结果。观测-模拟（O-B）的偏差分析结果显示:海洋和陆地上,通道10（7.1 μm）存在明显大于其他红外通道的系统性偏差,这很可能来源于ERA5在对流层中层对水汽的高估。通道08为近红外短波通道,地表反射作用影响强烈,陆地上存在较大的平均偏差,而海洋上平均偏差小于0.4 K。通道14在ERA5近地层气温偏高及定标偏差的影响下,海洋存在接近1 K的平均偏差;陆地上存在2 K左右的平均偏差。其余各红外通道在海洋和陆地上的平均偏差分别在0.6和1.3 K以下。偏差影响因子分析结果显示:地表海拔高度、观测天顶角对偏差也存在一定程度的影响;海洋上偏差分布存在季节变化可能来源于再分析资料中海表温度估算的季节性误差。      

An Overview of MODIS Radiometric Calibration and Characterization

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key instruments for NASA’s Earth Observing System (EOS), currently operating on both the Terra and Aqua satellites. The MODIS is a major advance over the previous generation of sensors in terms of its spectral, spatial, and temporal resolutions. It has 36 spectral bands: 20 reflective solar bands (RSB) with center wavelengths from 0.41 to 2.1 μm and 16 thermal emissive bands (TEB) with center wavelengths from 3.7 to 14.4 μm, making observations at three spatial resolutions: 250 m (bands 1–2), 500 m (bands 3–7), and 1km (bands 8-36). MODIS is a cross-track scanning radiometer with a wide field-of-view, providing a complete global coverage of the Earth in less than 2 days. Both Terra and Aqua MODIS went through extensive pre-launch calibration and characterization at various levels. In orbit, the calibration and characterization tasks are performed using its on-board calibrators (OBCs) that include a solar diffuser (SD) and a solar diffuser stability monitor (SDSM), a v-grooved flat panel blackbody (BB), and a spectro-radiometric calibration assembly (SRCA). In this paper, we present an overview of MODIS calibration and characterization activities, methodologies, and lessons learned from pre-launch characterization and in-orbit operation. Key issues discussed in this paper include in-orbit efforts of monitoring the noise characteristics of the detectors, tracking the solar diffuser and optics degradations, and updating the sensor’s response versus scan angle. The experiences and lessons learned through MODIS have played and will continue to play major roles in the design and characterization of future sensors.     

在NCEPGDAS中同化MSG和GOES资料

首次将MSG-2（Meteosat Second Generation-2）卫星上的旋转增强可见光及红外成像仪（Spin—ning Enhanced Visibleand Infrared Imager,SEVIRI）的观测资料同化到美国国家环境预报中心（Na—tional Centers for Environmental Prediction,NCEP）全球资料同化系统（globaldataassimilationsys—tern,GDAS）中。对当前的地球静止业务环境卫星（Geostationary Operational Environmental Satel—lite,GOES）成像仪资料的同化问题也进行了进一步探讨。利用CRTM（The Community Radiative Transfer Model）模式,对SEVIRI辐射率观测资料进行了模拟。为了对红外辐射率资料进行模拟,CRTM模式中的几个关键部分得到改进,例如：动态更新地面发射率资料以及采用了快速精确的气体吸收模块。为了改进对SEVIRI和GOES成像仪辐射率资料的模拟效果,采用了GSICS（The Global Space—Based Inter—Calibration System）标定订正。初步研究结果表明,包含对SEVIRI辐射率资料的水汽通道（6．25和7．35μm）和二氧化碳通道（13．40μm）的同化对GFS（Global Forecast System）6d预报具有显著的正影响;而对其他5个SEVIRI红外窗口通道资料的同化则减小了这种正影响。通过应用GSICS标定算法,订正了SEVIRI和GOES-12成像仪观测资料的偏差,提高了对GFS预报的影响。此外,还需作进一步研究来提高对SEVIRI红外窗口通道辐射率资料同化的有效性。     

Improved fog detection from satellite in the presence of clouds

This study analyzes radiative effect of the higher clouds over the fog layer and presents the improvement of fog detection over the Korean peninsula, utilizing satellite data of the Multi-functional Transport SATellite (MTSAT)-1R and the MODerate resolution Imaging Spectroradiometer (MODIS) and the Look-Up Table (LUT) based on Radiative Transfer Model (RTM) simulations. Fog detection utilizing the satellite data from visible (0.68 µm) and infrared (3.75 µm and 10.8 µm) channels has been evaluated in comparison with ground-based observations over 52 meteorological stations in the Korean Peninsula from March 2006 to February 2007. The threshold values for fog sensing have been derived from the difference (i.e., T_3.7–11) in brightness temperature between 3.75 µm (T_3.7) and 10.8 µm (T₁₁) during day and night, and also from the reflectivity at 0.68 µm (R_0.68) in the daytime. In the twilight, however, the difference between the temperature values at 10.8 µm and their maximum within previous 15 days (i.e., T_11max-11) are used instead, because the 3.75 µm channel is inaccurate for the fog detection at dawn/dusk. The sensitivity of the T_3.7–11 values with respect to the clouds is investigated based on the cloud variables such as its height, optical thickness, and amount. The values of T_3.7–11 are the most sensitive to cloud height, followed by cloud optical thickness and effective radius, while R0.68 is insensitive to cloud height. The sensitivity is examined with various conditions of cloud phases and day/night. Sixteen cases among eighteen fog occurrences, which have been unable to be sensed by using only the conventional threshold values, are successfully detected with the additional LUT corrections, indicating a significant improvement. The method of fog detection in this study can be useful to the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Data Processing System (CMDPS) by reducing the cloud effect on fog sensing.     

中国区域MODIS陆上气溶胶光学厚度产品检验

以我国MODIS共享网站积累的MODIS L1B数据和美国威斯康辛大学提供的IMAPP软件包气溶胶产品软件为基础, 经过产品运行本地化改进处理, 在国家卫星气象中心建立了气溶胶产品业务化生成和发布机制。为支持气溶胶遥感产品算法改进以及潜在用户对产品的合理应用, 给出对国家卫星气象中心运行的MODIS气溶胶遥感产品质量检验分析结果。利用2005年1月— 2007年5月AERONET地基气溶胶监测网的L2.0级气溶胶光学厚度产品作为真值, 用它匹配MODIS陆上气溶胶光学厚度产品开展检验。检验结果表明:以卫星过境前后30min地基观测时间平均值匹配地基站点位置10 km半径范围内的卫星反演结果空间平均值开展检验, 总体样本的气溶胶光学厚度均方根误差约为0.25;满足产品误差要求 (±0.05±0.20τ) 的样本占总样本数的44%; 气溶胶光学厚度反演结果精度具有季节和地域差异, 干季(秋、冬、春)的气溶胶光学厚度误差较小, 而雨季气溶胶光学厚度误差较大, 云是雨季气溶胶光学厚度反演结果误差较大的主要影响因素。     

Fog detection using geostationary satellite data: Temporally continuous algorithm

A fog detection algorithm that uses geostationary satellite data has been developed and tested. This algorithm focuses on continuous fog detection since temporal discontinuities, especially at dawn and dusk, are a major problem with current fog detection algorithms that use satellite imagery data. This is because the spectral radiance at 3.7 μm contains overlapping emissive and reflectance components. In order to determine the radiance at 3.7 μm under fog conditions, radiative transfer model simulations were performed. The results showed that the radiance at 3.7 μm obviously varies with the solar zenith angle, and the brightness temperature differences between 3.7 μm and 10.8 μm are completely dissimilar between day and night (positive and varying with the angle during the daytime, but negative and constant at night). In this algorithm, a dynamic threshold is used as a function of the solar zenith angle. Moreover, additional criteria such as infrared, split-window channels, and a water vapor channel are used to remove high-level clouds. Also, the visible reflectance (0.67 μm) channel is used in the daytime algorithm because visible channel images are very practical for confirming a fog area with the high reflectivity and the smooth texture. The clear-sky visible reflectance for the previous 15 days was also employed to eliminate the surface effect that appeared during dawn and dusk. As the results, fog areas were estimated continuously, allowing the lifecycle of the fog system, from its development to decline, to appear obviously in the resulting images. Moreover, the estimated fog areas matched well with surface observations, except in a high latitude region that was covered by thin cirrus clouds.     

A New Application of Unsupervised Learning to Nighttime Sea Fog Detection

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the 3.7 μm and 10.8 μm channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation–maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.     

MODIS数据云相态反演在一次暴雪过程中的应用

该文选取新疆阿克苏地区2006年11月22—24日的EOS/MODIS卫星遥感资料,同时选取相关气象站的观测资料,应用三光谱云相态反演法,对阿克苏地区暴雪云团的变化过程进行了研究,通过三光谱亮温差点聚图对云相态变化的分析,清晰地展示出该天气系统的发生、发展到最终暴雪形成过程中的物理机制。反演结果与实际气象观测情况相一致;表明三光谱云相态反演法在理论和实际应用方面具有一定优势。     

基于高光谱数据的气象卫星成像仪红外通道模拟仿真

采用基于光谱匹配的计算方法开展红外通道模拟仿真,该方法利用高光谱大气探测仪的实际观测数据来模拟气象卫星成像仪红外通道观测目标亮温.利用高光谱大气探测仪(IASI)的实际观测数据,进行了2010年2月14日12:57(UTC)、2010年2月15日00:57(UTC)、2010年5月2日12:57(UTC)MTSAT-1R卫星成像仪(JAMI)红外通道的模拟仿真.结果显示:JAMI 4个红外通道的模拟亮温与观测亮温偏差平均值的绝对值都小于1K,模拟亮温与观测亮温的相关系数都大于0.93,表明这种基于高光谱数据的方法可用于气象卫星成像仪红外通道的模拟仿真.进而利用IASI的观测数据进行了 2010年11月6个时刻FY-2E卫星成像仪(VISSR)红外通道的模拟仿真,并将通道模拟数据与GSICS定标的观测数据以及与业务定标的观测数据相比,结果表明:2010年11月期间,FY-2E卫星成像仪IR2、IR3、IR4通道的GSICS定标比业务定标有明显的改进.     

近红外CO2高光谱探测仪通道选择

近红外波段 (1.6 μm) 遥感可探测大气CO₂含量信息,应用于碳循环研究中。宽波段、高分辨率不但对仪器研制是一个挑战,而且巨大的数据量对观测的正演、反演也是一个挑战性课题。该文应用自由度及信息量分析法,对近红外高光谱波段中探测通道进行CO₂信息量分析,选择前20~100个高信息量的CO₂探测通道,并进行了反演模拟测试。结果表明:前20个高信息量通道占所有通道总信息量的76.4%,仅用所选的前20个通道进行反演,与所有通道参加反演的结果相比,误差增加0.3×10-6;通道数增至60时,信息量增加,通道数再增加,信息量则增加不显著;CO₂反演误差存在相似的关系。在高CO₂信息量分布上,弱吸收性质的1.6 μm波段和强吸收性质的2.06 μm波段表现出不同特点。     

A Cloud Detection Scheme for the Chinese Carbon Dioxide Observation Satellite(TANSAT)

Cloud detection is an essential preprocessing step for retrieving carbon dioxide from satellite observations of reflected sunlight.During the pre-launch study of the Chinese Carbon Dioxide Observation Satellite(TANSAT),a cloud-screening scheme was presented for the Cloud and Aerosol Polarization Imager(CAPI),which only performs measurements in five channels located in the visible to near-infrared regions of the spectrum.The scheme for CAPI,based on previous cloudscreening algorithms,defines a method to regroup individual threshold tests for each pixel in a scene according to the derived clear confidence level.This scheme is proven to be more effective for sensors with few channels.The work relies upon the radiance data from the Visible and Infrared Radiometer(VIRR)onboard the Chinese Feng Yun-3A Polar-orbiting Meteorological Satellite(FY-3A),which uses four wavebands similar to that of CAPI and can serve as a proxy for its measurements.The scheme has been applied to a number of the VIRR scenes over four target areas(desert,snow,ocean,forest)for all seasons.To assess the screening results,comparisons against the cloud-screening product from MODIS are made.The evaluation suggests that the proposed scheme inherits the advantages of schemes described in previous publications and shows improved cloud-screening results.A seasonal analysis reveals that this scheme provides better performance during warmer seasons,except for observations over oceans,where results are much better in colder seasons.     

A case study of cirrus layers with variable 3.74 µm reflection properties in the first FIRE experiment, 2 November 1986

Summary Remotely sensed scanning radiometer and lidar data on cirrus clouds were obtained during the cirrus FIRE IFO experiment in November 1986 from the ER-2 aircraft plat-form.Data were examined particularly on 2 November for an area in the vicinity of Wausau, Wisconsin where unusual effects were noticed in bispectral histograms from various channels in the scanner data.After calibration of the data in spectral channels of both the Scan Cloud Radiometer (SCR) and Multichannel Cloud Radiometer (MCR) instruments, including direct comparison between compatible channels in the two instruments, it was found that the 0.856 µm SCR channel gave good data, whereas the 0.665 µm and 0.74 µm SCR channels gave large offsets, when compared with the MCR 0.754 µm data. The latter channel was found to compare well in a second comparison with coincident AVHRR channel satellite data. Similarly, the SCR 11.17 µm data gave consistent results and the SCR 3.74 µm data were carefully calibrated.Bispectral histograms formed between 0.856 µm, 3.74 µm and 11.17 µm SCR channel data indicated that some coherent layers of cirrus clouds were giving enhanced solar reflectance at 3.74 µm, indicative of small (~ <25 µm radius) particles, whereas other neighbouring layers gave little reflectance.A comparison of 0.856 µm reflections with 11.17 µm absorption optical depth indicated that the small particles where probably ice crystals. A comparison of 3.74 µm solar albedo and 11.17 µm absorption optical depths of these layers with theoretical calculations for ice spheres indicated a mode radius of about 8 µm for the cloud particle size distribution. An estimate from similar recent calculations on hexagonal ice crystals indicated that the retrieved effective radius would be increased to 25 µm. The difference between the two retrieved radii was a measure of the uncertainty in the retrievals, considering also differences in the assumed size distributions.Qualitative comparison with ER-2 lidar data gave a tentative identification of the reflecting layers.The results demonstrate the power of the 3.74 µm channel for identification of small-particle layers in cirrus.With 9 Figures     

Verification and Correction of Cloud Base and Top Height Retrievals from Ka–band Cloud Radar in Boseong,Korea

In this study,cloud base height(CBH) and cloud top height(CTH) observed by the Ka-band(33.44 GHz) cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013(September-November) were verified and corrected.For comparative verification,CBH and CTH were obtained using a ceilometer(CL51) and the Communication,Ocean and Meteorological Satellite(COMS).During rainfall,the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops,and this difference increased with rainfall intensity.During dry periods,however,the CBH and CTH observed by the cloud radar,ceilometer,and COMS were similar.Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS.In cases of rainfall or missing cloud radar data,the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data.These corrected cloud data were used to classify cloud types,which revealed that low clouds occurred most frequently.     

利用MODIS卷云通道对长江中游一次大雾过程的监测

利用MODIS卫星通道的红外特征,对2009年1月8—11日长江中游地区的一次大雾过程进行了分析。结果表明,由于云、雾、地表所处高度不同,位于水汽的强吸收带的MODIS卷云通道(1.38μm),在辐射传输过程中对水汽吸收的程度有一定的差异,从而导致MODIS接收到这些目标物的反射率差异较明显。其中雾与地表在1.38μm波段的反射率近似相等,而与云则有明显的差别。另外,通过31波段与20波段辐射亮温差的光谱廓线分析,发现云雾的亮温差要明显小于地表。     

利用AVHRR数据反演陆地气溶胶光学厚度

开发AVHRR可见光通道反演陆地气溶胶光学厚度 (AOD) 的算法对于研究长时间序列AOD的变化有重要意义。AVHRR由于缺少2.1 μm通道而不能采用MODIS的暗背景算法,该文利用背景合成算法进行陆地AOD反演。背景合成算法是指假设一段时间内地表反射率变化不大且会出现相对清洁大气, 采用最小值合成即可得到地表反射率,再通过辐射传输模式6S制作的查算表查算得到AOD的反演结果。将此算法应用到2009年AVHRR中国部分陆地区域 (15°~45°N,75°~135°E) 得到AOD的时空分布,将反演结果与同期Aqua/MODIS的MOD04 AOD产品进行对比分析表明,华北和华东地区的反演效果较好,西北地区结果较差。以长江三角洲地区为例可知,AVHRR AOD产品与MODIS AOD产品以及AERONET观测的AOD相比相关系数基本在0.6以上,从时间变化规律来看,AVHRR AOD和MODIS AOD产品年变化趋势具有很好的一致性。该文为建立长时间序列AVHRR AOD数据集提供了一个较为可行的方法。     

拉曼雷达重叠函数的模拟与校准

重叠函数的校准对地基激光雷达低空大气探测的准确性至关重要。目前被广泛用于校准拉曼激光雷达重叠函数的双通道实验标定法,需要满足弹性散射通道与氮气拉曼散射通道的重叠函数近似相等的前置条件。但实际仪器光路往往会偏离理想状态,使得该前置条件无法得到满足,导致校准失败。本文使用光线追踪法模拟重叠函数,计算了各种光路失调情况下弹性散射通道与氮气拉曼散射通道的比值,并引入了一种镜头遮蔽实验来评估双通道实验标定法的前置条件是否得到满足。数值模拟结果表明,当雷达接收面的不同象限被遮蔽时,如果弹性散射通道与氮气通道的信号强度比值基本保持不变,则满足前置条件;如果弹性散射通道与氮气通道的信号强度比值有显著差异,则可判定仪器光学系统失调,需要调整直至满足上述前置条件时才能使用双通道实验标定法标定。利用该方法对南京市气象局安装的拉曼激光雷达进行了光路测评和调整,并与CCD(charge-coupled device)侧向成像激光雷达观测信号做对比,结果显示调整后的定标效果更好。     

利用FY-2C卫星数据反演云辐射特性

本文利用FY-2C静止卫星提供的可见光、中红外和热红外观测数据, 开展了水云光学厚度、粒子有效半径和云顶温度的云参数遥感探测理论和反演方法研究。基于FY-2C可见光、中红外(3.75 μm)与热红外(11 μm)通道辐射率对云光学厚度、 云滴有效半径、云顶温度辐射参数的敏感性分析, 提出三通道同时反演云的光学厚度、云滴有效半径及云顶温度的迭代方案; 通过个例分析进行了云参数反演试验, 并将结果与MODIS的云反演产品进行了对比, 最后对反演误差进行了分析。主要结论如下:(1) 个例反演得到的云参数与各通道探测数据有着较好的对应关系, 迭代计算标准偏差在允许的计算精度范围内(<0.89%), 反演结果具有合理性; (2) 通过与MODIS云反演产品的对比可以看到, 两者云光学厚度、云滴有效半径的均值和直方图分布都非常一致, 而MODIS的云顶温度比FY-2C反演值要高, 考虑到FY-2C的 11 μm通道测量的辐射值与MODIS相比偏小, 因此认为我们的反演方法与MODIS方法的精度是相当的。     

CE318型太阳光度计标定方法初探

初步探索了CE318型太阳光度计的室内和野外标定方法, 并对两种方法的标定结果进行对比。基于积分球辐射源的辐亮度标定方法, 对CE318型太阳光度计不同波段天空散射辐射通道进行标定实验; 分别采用Langley标定法和标准仪器相对标定方法, 对CE318型太阳光度计的不同波段太阳直接辐射通道进行了标定实验。实验结果显示:天空散射辐射通道的标定结果在670 nm, 870 nm和1020 nm波段与仪器出厂时给定的标定结果偏差不超过6%, 而440 nm波段处得到的标定结果要高于出厂时给定的结果, 偏离幅度约18.9%。太阳直接辐射各通道的标定结果普遍大于出厂时给定的结果, 说明滤光片老化较为严重, 需要进行更换, 以保证观测精度。由于标准仪器相对标定方法对太阳直接辐射通道的标定结果明显优于Langley法, 因此利用标准仪器相对标定方法对中国气象局太阳光度计站网仪器进行定期标定更能够保证仪器观测数据的准确性。     

中国地区MODIS反照率两种反演结果的比较

以2002-2004年中国地区Terra MODIS数据开展对照反演试验,获得大范围具有相同时空代表性的反照率全反演结果和当量反演结果,统计分析MODIS反照率两种反演结果的差异,结果表明:在MODIS 1-7波段及可见光、近红外和短波波段,黑空反照率、白空反照率的当量反演结果与全反演结果的绝对偏差均小于0.05,且黑空反照率两种反演结果的绝对偏差明显小于白空反照率两种反演结果的绝对偏差;MODIS反照率两种反演结果在红外波段的绝对偏差大于其在可见光波段的绝对偏差;夏季MODIS反照率两种反演结果的绝对偏差最大,秋季则最小.