利用EOS／MODIS数据反演水云云底高度的初步研究

云底高度作为重要的云宏观物理特征参数,在云层与地表之间的能量交换中起着重要作用。传统的云底高度测量方法大多基于常规观测资料,利用星载被动遥感仪器的观测数据反演云底高度在国内尚未开展。论述了基于EOS／MODIS可见光、红外数据反演云底高度的原理、方法和可行性,并结合西北某空域的飞机探测数据进行了MODIS水云云底高度反演的对比试验。初步结果表明：利用MODIS数据反演水云的云底高度是可行的;在与3次飞机穿云记录的云高真实数据对比中,反演结果平均误差为249．4m。     

基于MODIS的海表面温度反演系统设计与实现

海表面温度(SST)是重要的海洋物理参数,对海洋研究具有重要意义.通过遥感数据反演是目前获取SST数据的主要方法之一.遥感反演数据具有反映大区域尺度的海温连续分布状况,且数据获取及时稳定等特点.本文基于IDL构建了面向EOS/MODIS数据的SST反演系统,讨论了系统的功能和结构,给出了具体的数据反演处理流程,对系统中采用的SST反演算法和云检测方法进行了详细说明.最后以我国东海海域为例,将反演结果与多年观测资料对比,表明系统反演得到的SST分布规律与多年观测资料一致,同时还利用同步观测的浮标数据和NASA MODIS SST产品对反演结果进行验证,结果显示系统反演的SST平均误差为1℃.数据精度满足海洋预报和其他科学研究的需要.     

基于HJ-1卫星数据反演长江三角洲地区气溶胶光学厚度

探索利用我国HJ-1卫星CCD数据,运用深蓝算法开展长江三角洲地区气溶胶光学厚度反演的可行性,并将结果与其他气溶胶光学厚度产品进行比较。针对HJ-1A和HJ-1B数据,反演结果分别与MODIS气溶胶光学厚度产品以及AERONET地基观测数据进行对比验证。结果表明:深蓝算法得到A星、B星的反演结果与MODIS气溶胶产品呈显著相关,但在数值上普遍高于MODIS产品;反演结果与AERONET站点数据之间的误差介于0.008~0.364之间,研究时段内站点数据缺乏,未对误差进行严格的统计分析。基于深蓝算法的HJ-1卫星数据反演结果虽然在数值上与MODIS气溶胶光学厚度产品存在系统性偏差,但在空间上能够较好地反映长江三角洲地区大气气溶胶分布状况,且具有空间分辨率高的优势。     

基于Sentinel-1雷达数据的青藏高原地区土壤水分反演研究

土壤水分是地—气能量交换和全球水循环的重要参数之一,也是水文、气象、农业等研究中的关键参数。高空间分辨率的土壤水分在探讨区域水文过程、生态环境保护及农业水资源管理等方面具有重要意义。基于Sentinel-1雷达数据发展了青藏高原地区高空间分辨率土壤水分反演算法,并获取了区域尺度空间分辨率为20 m的土壤水分。该算法首先基于地面数据、Sentinel-1雷达数据和MODIS归一化植被指数对水云模型进行了参数优化,其次利用优化后的水云模型构建了模拟数据库,利用人工神经网络算法对模拟数据进行训练,构建了基于神经网络的土壤水分反演算法。为了检验该算法,利用Sentinel-1雷达数据反演了青藏高原站点区域土壤水分值,并使用站点实测土壤水分数据对其进行了验证。结果表明：土壤水分反演值与站点实测值有良好的一致性,其相关系数为0.784—0.82,均方根误差为0.052 m³/m³—0.064 m³/m³。土壤水分反演值在时间序列上能够捕捉到土壤水分实测值的变化趋势。该研究可为青藏高原地区高空间分辨率的土壤水分监...     

8毫米云雷达与探空观测确定云底和云顶高度的对比分析

利用地基Ka波段云雷达和无线电探空仪数据进行云边界识别和对比分析研究。结果表明,8毫米波云雷达探测的云底高度比无线电探空仪观测偏低约300m,大多数情况下二者识别的云底高度接近;而判定的云顶高度偏差较大。雷达与探空云底高度判别偏差较大时,在云下常存在大气"干层",此时雷达探测更为灵敏;探空仪水平漂移及其湿度传感器的探测误差随高度增加是造成两者偏差的主要原因。通过计算和对比雷达反射率的时空变化率,给出了云雷达确定云底和云顶高度的一个可信度判据。     

地基微波辐射计的亮温观测与模拟数据的一致性分析和云检测

为了利用辐射计观测数据"晴空样本"判断辐射计各通道的工作状态,并找出各通道的订正关系、进一步研究"云天样本",针对在广州市从化区进行观测试验的一台微波辐射计的亮温观测数据(TBM)和相应地点的NCEP大气层结资料,通过辐射传输方程计算得到模拟数据(TBC)并进行全样本一致性对比,根据多通道亮温差值阈值法识别出"晴空样本"与"云天样本"(以及少量歧义数据)。对"晴空"样本进行回归分析,判断该辐射计的工作状态、建立TBM-TBC订正关系,并应用于"全样本"观测数据进行线性订正。根据辐射计云底高度数据与订正后的亮温差值的时间序列对比,证明了多通道亮温差值阈值法识别晴空效果较好。经过订正后的"晴空样本"可直接用于后续的大气温湿度反演流程,而识别出的"云天样本"(含雨天)为后续云参数反演及亮温数据的云影响订正并进入下一步的大气温湿度反演流程打下了基础。     

利用风云卫星MERSI数据反演叶面积指数的研究

风云三号A星上搭载的中分辨率成像光谱仪(Medium Resolution Imaging Spectrometer)MERSI从2008年5月底开始对地球观测,其中5个波段250m分辨率的数据包含了丰富的植被信息,在全球同类传感器数据中独具特色,在其基础上反演的陆表植被数据产品目前还不多见。利用2013年生长季在河北固城观测获取的冬小麦光谱数据,结合MERSI 250m数据计算的NDVI值,建立二者NDVI之间的线性转换模型Y=1.1458 X+0.1916;同时利用地物光谱NDVI与实测叶面积指数构建了NDVI-LAI指数模型Y=0.0899e4.459 X;然后,利用MERSI 250m数据反演出华北太行山前平原区冬小麦的叶面积指数,经与大田观测的叶面积指数以及同期MODIS的叶面积指数产品对比验证,结果表明:反演的MERSI-LAI与实际观测叶面积指数接近且具有很好的线性关系,其空间分布与MODIS的叶面积指数相近,但MODIS-LAI数值明显偏小。     

基于静止气象卫星数据的地表温度遥感估算

基于分裂窗算法和地表温度日周期变化模型,探讨了利用多时相热红外遥感数据反演地表温度的方法。首先,利用分裂窗算法及地表温度日周期变化形式,推导了多时相遥感数据反演地表温度的方法。其次,利用辐射传输模型(MODTRAN),以2006年夏季在禹城观测的3 d地表温度、气温及大气水汽数据做为输入参数、变化观测角及比辐射率,模拟了一日多个时刻与风云二号(F-2D)波谱响应函数一致的亮温数据,基于此,模拟数据库对所提算法进行了检验。最后,利用2010年9月30日FY-2D多时相热红外数据对新疆区域地表温度进行了反演,并与相应时刻的MODIS地表温度产品进行了比较。结果表明：利用模拟遥感数据反演地表温度,模拟值与估算值的相关系数达0.9,均方根误差在1.5 K以内;利用在轨FY-2D热红外数据反演得到的地表温度与MODIS温度产品趋势基本一致,两者的相关性达到了0.5,均方根误差为4.4 K。需要说明的是,此方法仅满足于晴朗无云的条件。     

干旱/半干旱区MODIS地表温度反演与验证研究

劈窗算法是目前热红外遥感反演地表温度最常用的方法,根据Coll提出的劈窗算法建立基于MODIS适用干旱/半干旱区地表温度反演算法,并用同期的LP DAAC发布的MODIS地表温度产品和相应的53个气象站点的实际观测数据进行验证。通过分析,模型的反演精度与MODIS地表温度产品的反演精度相当,与气象观测数据相一致,反演精度较好,能够较精确地反演干旱/半干旱地区地表温度的时空变化特征。     

基于分组分裂窗算法的MTSAT-1R地表温度反演

以黑河流域上游和中游为研究区,针对MTSAT-1R卫星数据,运用MODTRAN 4.0及晴空状态下的TIGR大气廓线数据,发展了根据地表比辐射率、大气水汽含量、传感器观测角度分组模拟的分裂窗算法,进行地表温度反演。分析了传感器噪声、地表比辐射率和大气水汽含量3个参数对该算法的影响,并结合模拟数据、地面观测数据及MODIS地表温度产品,对反演结果进行分析评价。结果表明:当传感器垂直观测或大气水汽含量小于2.5g/cm2时,反演精度在1K以内;反演结果与地面观测数据对比差异较小,在阿柔站RMSE为3.7 K(日)/1.4 K(夜),在盈科站RMSE为2.4K(日)/2.0K(夜);与MODIS地表温度产品比较,空间分布呈现出一致性。总之,分组分裂窗算法能较好地用于MTSAT-1R卫星数据进行地表温度反演。     

Intercomparisons of cloud-top and cloud-base heights from ground-based Lidar,CloudSat and CALIPSO measurements

This study presents results of the intercomparison of cloud-top height (CTH) and cloud-bottom height (CBH) obtained from a space-borne active sensor Cloud Profiling Radar (CPR), the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), the space-borne passive sensor Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based Lidar measurements. Three selected cases (one daytime and two night-time cases) involving various cloud conditions such as semi-transparent thin cirrus, opaque thick tropospheric clouds and multi-layered clouds are studied, with special attention to CBH. The space-based CALIOP provides reliable heights of thin high-altitude cirrus clouds containing small ice particles, but the 94 GHz CPR has low sensitivity to these clouds. The CTHs retrieved from the CPR and CALIOP for thick tropospheric clouds are in good agreement with each other. Discrepancies between the CPR and the CALIOP values of the CBH for thick opaque clouds arise from strong Lidar signal attenuations. In cloud-overlap conditions (i.e. multi-layered clouds are present), the CALIOP has difficulties in determining the cloud vertical structure (CVS) for thick clouds underlying thin cirrus clouds due to signal attenuations, whereas the CPR detects the CTH and CBH of both the cloud layers. This fact is also confirmed by the comparison of seasonal variations of occurrences of CBH and CTH retrieved from 1 year measurements. The CBHs derived from the CPR and ground-based Lidar are generally in good agreement with each other. Especially, comparison of CBH between the ground-based Lidar and CPR retrieved from June 2006 to October 2008 shows an excellent linear relationship (coefficient of determination, R ² ～ 0.996).     

Inter-comparison of INSAT-3D atmospheric motion vectors height with cloud-base height from a Ceilometer

ABSTRACT

The atmospheric motion vectors (AMV) are derived by tracking cloud and moisture features in the subsequent images of geostationary as well as polar satellites. The heights of the AMVs are nothing but the height of cloud tracers used during the retrieval process for tracking. This height is derived using different complex techniques. In this study, a detailed comparison has been performed with the use of ground-based cloud-base height (CBH) measurements from ceilometer CL31, installed at Ahmedabad (23.03°N, 72.54°E), India and height assigned to AMVs which are retrieved from INSAT-3D satellite images. Six months CBH measurement over Ahmedabad from ceilometer CL31 has been used to inter-compare the co-located AMV heights. Although both ground-based and satellite-based techniques have their own limitations, however, it is found from this study that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds and height-assignments technique of AMVs retrieved from INSAT-3D satellite provides all high-, mid- and low-levels cloud information over this region. As an example, it is found that AMVs height of INSAT-3D is about 867.92, 750.00 and 465.09 hPa on 26 May 2014, 7 July 2014 and 29 October 2014, respectively, which matches very closely with ceilometer-measured CBH of about 873.15, 769.16 and 507.44 hPa, respectively. However, in case multi-level clouds present on rainy days, CBH measurements from ceilometer are differing from INSAT-3D AMV cloud tracer heights.     

Assessing VIIRS cloud base height products with data collected at the Department of Energy Atmospheric Radiation Measurement sites

A system-level analysis has been completed to assess the accuracy of cloud base height (CBH) products of lower-altitude water clouds created from Visible Infrared Imaging Radiometer Suite (VIIRS) data collected by the National Aeronautics and Space Administration (NASA) Suomi National Polar-orbiting Partnership (S-NPP) and the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS). While the analysis focused on the CBH products, it necessarily included an assessment of other key, upstream VIIRS cloud products, including VIIRS cloud top height (CTH), VIIRS cloud optical thickness (COT), and VIIRS cloud effective particle size (EPS). The COT and EPS products are used to derive a geometric cloud thickness, which is then subtracted from the CTH product to generate the CBH product. Thus, a system-level analysis is critical to establishing the detailed algorithm error budget needed to identify the major sources of errors in the CBH product and help focus future research efforts to improve this important cloud product. As a consequence, ground-based data collected at the US Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites became most useful for conducting this system-level analysis. Therefore, match-up data sets were created between VIIRS cloud products and DOE ARM site data sets from June 2013 to October 2015, and while the primary focus was on the highest-quality data at the Lamont, OK ARM site, data were analysed from a total of four ARM sites. The initial results showed the errors in the VIIRS CBH products, compared to the ARM site data sets, to be large and highly variable; however, errors in VIIRS COT and the derived VIIRS cloud geometric thickness were much smaller. Consequently, the VIIRS CTH products were replaced with the ARM CTH products, which substantially reduced the variability and errors in the VIIRS CBH products. It is concluded that the performance of the VIIRS CBH products is most strongly correlated with errors in the VIIRS CTH products, while errors in COT and geometric cloud thicknesses are acceptable. Thus, future research is needed to reduce the errors in the VIIRS CTH products in order to ensure the VIIRS CBH products are suitable for civilian and military aerodrome operations.     

Synergetic use of POLDER and MODIS for multilayered cloud identification

The purpose of this study is to investigate the possibility of identifying overlapping clouds that contain thin cirrus overlying a lower-level water cloud by synergetic use of POLDER-3 (Polarization and Directionality of the Earth Reflectance) and MODIS (MODerate resolution Imaging Spectroradiometer) data. When thin cirrus clouds overlap the liquid cloud layer, the liquid information may be obtained by POLDER observations and the presence of the cirrus may be inferred from the MODIS CO₂-slicing technique. An initial comparison of the POLDER cloud phase and the MODIS cloud-top pressure for one scene over East Asia also shows that a large portion of clouds declared as liquid water clouds by POLDER-3 correspond to the lower cloud-top pressures derived from MODIS. As a result, an overlapped cloud identification method is proposed under the assumption that the multilayered cloud would be present if the POLDER cloud phase is liquid water and the MODIS cloud-top pressure is less than 500 hPa. For the studied scene, the comparison of the multilayered cloud identification results with CloudSat and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations illustrates that the proposed method could detect multilayered clouds when the upper cirrus has a visible optical thickness of less than 2.0. Then the identification results are compared with the MODIS Cloud_Multi_Layer_Flag. It is indicated that the consistency between the multilayered clouds from the proposed synergy and MODIS-operational algorithm increases gradually from over 40% to nearly 100% with the increase of the confidence level of the MODIS multilayered clouds from the lowest to the highest. Further analysis suggests that the majority of multilayered clouds falsely classified as single-layered clouds by the proposed method may correspond to relatively thick cirrus covering lower-level water clouds. Additionally, an index by using the multilayered cloud detection differences from the two methods is proposed to provide some information on the optical thickness of the cirrus covering lower-level water cloud. Finally, quantitative comparisons are extended to four other scenes at different locations by using active measurements. The results also show that the mean visible optical thickness of the high-level clouds of the multilayered clouds detected by both methods (1.57) is remarkably less than that by only MODIS-operational method (2.84), which means that the differences between the results from the two methods are mainly caused by the different sensitivities to the visible optical thickness of the high-level cloud and could be used to indicate the range of the visible optical thickness of the cirrus clouds covering the lower-level water clouds.     

The computation of cloud base height from paired whole-sky imaging cameras

We have developed a novel approach to the extraction of cloud base height (CBH) from pairs of whole-sky imagers (WSIs). The core problem is to spatially register cloud fields from widely separated WSIs; this complete, triangulation provides the CBH measurements. The wide camera separation and the self-similarity of clouds defeats standard matching algorithms when applied to static views of the sky. In response, we use optical flow methods that exploit the fact that modern WSIs provide image sequences. We will describe the algorithm, a confidence metric for its performance, a method to correct the severe projective effects of the WSI camera, and results on real data.     

Simulation of atmospheric profile retrieval from hyperspectral infrared data under cloudy conditions

In this paper, simulated space-based high spectral resolution AIRS (Atmospheric Infrared Sounder) infrared radiances with different cloud top heights and effective cloud fractions are used to demonstrate measurement sensitivity and atmospheric profile retrieval performance. Simulated cloudy retrievals of atmospheric temperature and moisture derived from the statistical eigenvector regression algorithm are analysed with different effective cloud fractions and different cloud heights. The results show that knowledge of cloud height is critical to sounding retrieval performance and the root mean square error of retrieved temperature and the mixed ratio of water vapour below the cloud top increases with effective cloud fraction. When there is 50 hPa error in the cloud height the retrieval accuracy of temperature and humidity decrease, compared with when the cloud height is known perfectly; the temperature retrieval is more sensitive to cloud height error than humidity retrieval. Collocated cloudy AIRS and the associated clear MODIS (Moderate Resolution Imaging Spectroradiometer) infrared observations within the AIRS field of view (FOV) are also used to demonstrate profile retrieval improvement below the cloud layer. It is demonstrated that using collocated clear MODIS multispectral imager data along with AIRS high spectral resolution infrared radiances can greatly improve the single FOV cloudy retrieval even under opaque cloudy conditions.     

Observational study of cloud base height and its frequency over a tropical station,Thiruvananthapuram, using a ceilometer

This study aims to investigate the characteristic features of cloud base height (CBH) over Thiruvananthapuram during different seasons. CBH data were used for the present work derived from the Vaisala Laser Ceilometer, CL31 (VLC) installed at the campus of the Centre for Earth Science Studies, Akkulam (8.29° N, 76.59° E, 15 m above sea level). The VLC was in operation from the second week of July 2006 onwards. From the study, we found that CBH shows distinct diurnal and seasonal variations during all the seasons (except on rainy days). The diurnal variation for low-level clouds was different from that for the mid-level clouds. A cloud-free layer is evident in the region between 2.5 and 4 km. This cloud-free zone is more prominent during the southwest monsoon period compared to other seasons. Moreover, the monthly variations of cloud frequency and CBH were also described in addition to the different periodicities in cloud frequency. The periodicities found in the cloud frequency were 8 days and 30 days and these are significant at the 5% level. Thermodynamic parameters from the radiosonde were also related to the cloud frequency for various seasons and they were in good agreement.     

Comparative Analysis of Cloud Boundary Observed by Millimeter Wave Cloud Radar and Radiosonde

The observation data obtained by a Ka-band cloud radar and radiosondes during July to August 2013,in Inner Mongolia were used to detect the cloud boundary.The results show that the Cloud Base Height(CBH) determined by the cloud radar is about 300 m lower than that from the radiosonde,and the CBHs from two equipments are close in most cases;however,the heights of cloud top are largely different.The analysis of the case with larger CBH deviation indicates that the radar detection is more sensitive than the radiosonde because of the presence of an atmosphere "dry layer" under the cloud base;The main causes of the deviation are the horizontal drift of the radiosonde and the humidity sensor error that increases with increasing height.By calculating and comparing the variation rate of cloud reflectivity,a credibility criterion is given for cloud radar detection of the height of cloud base and cloud top.     

Retrieval of snow grain size over Greenland from MODIS

This paper presents a new automatic algorithm to derive optical snow grain size at 1 km resolution using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. The retrieval is conceptually based on an analytical asymptotic radiative transfer model which predicts spectral bidirectional snow reflectance as a function of the grain size and ice absorption. The snow grains are modeled as fractal rather than spherical particles in order to account for their irregular shape. The analytical form of solution leads to an explicit and fast retrieval algorithm. The time series analysis of derived grain size shows a good sensitivity to snow melting and snow precipitation events. Pre-processing is performed by a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which includes gridding MODIS data to 1 km resolution, water vapor retrieval, cloud masking and an atmospheric correction. MAIAC cloud mask is a new algorithm based on a time series of gridded MODIS measurements and an image-based rather than pixel-based processing. Extensive processing of MODIS TERRA data over Greenland shows a robust discrimination of clouds over bright snow and ice. Because in-situ grain size measurements over Greenland were not available at the time of this work, the validation was performed using data of Aoki et al. (Aoki, T., Hori, M., Motoyoshi, H., Tanikawa, T., Hachikubo, A., Sugiura, K., et al. (2007). ADEOS-II/GLI snow/ice products — Part II: Validation results using GLI and MODIS data. Remote Sensing of Environment, 111, 274-290) collected at Barrow (Alaska, USA), and Saroma, Abashiri and Nakashibetsu (Japan) in 2001-2005. The retrievals correlate well with measurements in the range of radii ~ 0.1-1 mm, although retrieved optical diameter may be about a factor of 1.5 lower than the physical measured diameter. As part of validation analysis for Greenland, the derived grain size from MODIS over selected sites in 2004 was compared to the microwave brightness temperature measurements of SSM/I radiometer which is sensitive to the amount of liquid water in the snowpack. The comparison showed a good qualitative agreement, with both datasets detecting two main periods of snowmelt. Additionally, MODIS grain size was compared with predictions of the snow model CROCUS driven by measurements of the automatic weather stations of the Greenland Climate Network. We found that the MODIS value is on average a factor of two smaller than CROCUS grain size. This result agrees with the direct validation analysis indicating that the snow reflectance model may need a “calibration” factor of ~ 1.5 for the retrieved grain size to match the physical snow grain size. Overall, the agreement between CROCUS and MODIS results was satisfactory, in particular before and during the first melting period in mid-June. Following detailed time series analysis of snow grain size for four permanent sites, the paper presents maps of this important parameter over the Greenland ice sheet for the March-September period of 2004.     

基于TVDI的大范围干旱区土壤水分遥感反演模型研究

温度植被干旱指数TVDI(Temperature Vegetation Dryness Index)是一种基于光学与热红外遥感通道数据进行植被覆盖区域表层土壤水分反演的方法。当研究区域较大、地表覆盖格局差异显著时,利用TVDI模型来反演陆表土壤水分,精度通常较低。对Sandholt的TVDI土壤水分反演模型进行了改进:利用云掩膜校正和多天平均温度合成来减少云的影响;同时对研究区域地形起伏、覆盖类型差异的影响进行了消除;对TVDI模型干边的模拟方法进行了改进。最后,使用铝盒采样等方法利用新疆地区观测得到的地面数据来拟合改进后的模型参数,并对2009年5月和8月的土壤水分进行了反演实验。与实测数据的比较分析表明,该模型能基本满足大区域土壤水分反演的要求,改进后的模型对新疆地区的土壤水分估算精度有较显著的提高。