Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data
Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data
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DOI：
10.5194/amt-9-973-2016
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发表时间：
2015-12
影响因子：
3.8
通讯作者：
T. Taylor;C. O’Dell;C. Frankenberg;P. Partain;H. Cronk;A. Savtchenko;R. Nelson;E. J. Rosenthal
中科院分区：
文献类型：
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作者：
T. Taylor;C. O’Dell;C. Frankenberg;P. Partain;H. Cronk;A. Savtchenko;R. Nelson;E. J. Rosenthal
抽象的。美国国家航空航天局（NASA）轨道碳观测站-2（OCO-2）使命的目标是从卫星对反射太阳光的近红外测量中反演二氧化碳（CO2）干空气的柱平均摩尔分数（XCO 2）。这些估计可能会受到云和气溶胶的影响，即，污染，在仪器的视野范围内。筛选污染最严重的探测，最大限度地减少不必要的调用计算昂贵的2级（L2）XCO 2检索算法。因此，强大的云筛选方法一直是OCO-2算法开发团队的重点。两个不同的，计算成本低廉的云筛选算法已经开发了这个应用程序。A波段预处理器（ABP）使用0.76 µm O2 A波段的测量值检索表面压力，忽略云和气溶胶的散射，这会引入光子路径长度差异，导致预期和检索的表面压力之间存在较大偏差。迭代最大后验概率（IMAP）差分光学吸收光谱（DOAS）预处理器（IDP）使用在1.61 µm（弱CO2波段）和2.06 µm（强CO2波段）处进行的观测来检索CO2和H2O柱丰度的独立估计值，同时忽略大气散射。在云和散射气溶胶的存在下，在这两个光谱区域检索的CO2和H2O柱丰度显着不同。这两种算法的结合，这是敏感的光谱中的不同功能，提供了基础的云筛选的OCO-2数据集。为了验证OCO-2云筛选方法，将Aqua平台上NASA中分辨率成像光谱仪（MODIS）的配置测量结果与两种OCO-2云筛选算法的结果进行了比较。通过调整算法阈值参数，允许处理所有OCO-2探测的20%-25%，OCO-2和MODIS云屏蔽方法之间的一致性被发现在冬季（12月）和春季（4月至5月）的四个16天的轨道重复周期的OCO-2天底地，闪光地和闪光水观测。没有发现主要的、系统的、空间或时间的依赖性，尽管季节性数据集确实存在细微差异，随着太阳天顶角的增加，以及当表面被冰雪覆盖和地形复杂时，验证就更成问题。为了进一步分析云屏蔽算法的性能，OCO-2观测的初步比较，并从云气溶胶激光雷达与正交偏振（CALIOP）搭载云气溶胶激光雷达和红外探路者卫星观测（CALIPSO）的测量。这些比较突出了OCO-2云筛选算法在识别高而薄的云方面的优势，但也表明在识别表面附近的一些云方面存在一些困难，即使光学厚度大于1。
Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O2 A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 µm (weak CO2 band) and 2.06 µm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.