Time series inversion of spectra from ground-based radiometers
Time series inversion of spectra from ground-based radiometers
复制标题
地面辐射计光谱的时间序列反演
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
P. Eriksson
中科院分区：
文献类型：
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作者：
O. Christensen;P. Eriksson
抽象。从地面光谱仪中检索大气成分的时间序列通常需要根据聚焦的高度区域进行不同的时间平均。这可能导致一个仪器存在多个数据集，这使得仪器之间的验证和比较复杂化。本文提出了一种可能的解决方案，将时间域的最大后验概率（MAP）检索算法。增加状态向量以包括跨越时间段的测量，并且在先验不确定性矩阵中明确指定真实大气状态之间的时间相关性。这使得MAP方法能够有效地为每个海拔选择最佳的时间平滑，从而无需使用多个数据集来覆盖不同的海拔。该方法相比，传统的平均光谱使用模拟检索的水蒸气在中间层。模拟结果表明，该方法提供了一个显着的优势相比，传统的方法，延长了额外的10公里以上的灵敏度，而不会降低时间分辨率在较低的高度。该方法还测试了Onsala空间天文台（OSO）的水蒸气微波辐射计确认在模拟中发现的优点。此外，它示出了该方法如何可以及时插值数据，并提供诊断值来评估插值数据。
Abstract. Retrieving time series of atmospheric constituents from ground-based spectrometers often requires different temporal averaging depending on the altitude region in focus. This can lead to several datasets existing for one instrument, which complicates validation and comparisons between instruments. This paper puts forth a possible solution by incorporating the temporal domain into the maximum a posteriori (MAP) retrieval algorithm. The state vector is increased to include measurements spanning a time period, and the temporal correlations between the true atmospheric states are explicitly specified in the a priori uncertainty matrix. This allows the MAP method to effectively select the best temporal smoothing for each altitude, removing the need for several datasets to cover different altitudes. The method is compared to traditional averaging of spectra using a simulated retrieval of water vapour in the mesosphere. The simulations show that the method offers a significant advantage compared to the traditional method, extending the sensitivity an additional 10 km upwards without reducing the temporal resolution at lower altitudes. The method is also tested on the Onsala Space Observatory (OSO) water vapour microwave radiometer confirming the advantages found in the simulation. Additionally, it is shown how the method can interpolate data in time and provide diagnostic values to evaluate the interpolated data.