Probabilistic global maps of the CO2 column at daily and monthly scales from sparse satellite measurements
Probabilistic global maps of the CO2 column at daily and monthly scales from sparse satellite measurements
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DOI：
10.1002/2017jd026453
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发表时间：
2017-07-27
影响因子：
4.4
通讯作者：
Crisp, David
中科院分区：
文献类型：
--
作者：
Chevallier, Frederic;Broquet, Gregoire;Crisp, David
大气中二氧化碳的柱平均干空气摩尔分数（XCO 2）是通过像轨道碳观测站（OCO-2）那样的分散卫星测量来测量的。我们表明，全球连续地图的XCO 2（对应于3级的卫星数据）在日常或粗糙的时间分辨率可以推断出这些数据与卡尔曼滤波器建立在模型的持久性。我们的应用这种方法对2年的OCO-2检索表明，过滤器提供更好的信息比气候学的XCO 2在每日和每月的尺度。假设指定的观测不确定性统计数据在XCO 2地图的每个网格单元中从客观方法（基于一致性诊断）进行调整，则由过滤器在每日和每月尺度上预测的误差相当好地代表了真实的误差统计数据，除了冬季半球高纬度的偏差和缺乏分辨率（即，太小的辨别技能）的预测误差标准偏差。由于稀疏的卫星采样，滤波器描述的XCO 2的大尺度模式似乎落后于真实的信号几周。最后，过滤器提供了有趣的见解检索的质量，无论是在随机和系统误差。
The column-average dry air-mole fraction of carbon dioxide in the atmosphere (XCO2) is measured by scattered satellite measurements like those from the Orbiting Carbon Observatory (OCO-2). We show that global continuous maps of XCO2 (corresponding to level 3 of the satellite data) at daily or coarser temporal resolution can be inferred from these data with a Kalman filter built on a model of persistence. Our application of this approach on 2 years of OCO-2 retrievals indicates that the filter provides better information than a climatology of XCO2 at both daily and monthly scales. Provided that the assigned observation uncertainty statistics are tuned in each grid cell of the XCO2 maps from an objective method (based on consistency diagnostics), the errors predicted by the filter at daily and monthly scales represent the true error statistics reasonably well, except for a bias in the high latitudes of the winter hemisphere and a lack of resolution (i.e., a too small discrimination skill) of the predicted error standard deviations. Due to the sparse satellite sampling, the broad-scale patterns of XCO2 described by the filter seem to lag behind the real signals by a few weeks. Finally, the filter offers interesting insights into the quality of the retrievals, both in terms of random and systematic errors.