Intelligent pointing increases the fraction of cloud-free CO2 and CH4 observations from space

Ray Nassar, Cameron G. MacDonald, Bruce Kuwahara, Alexander Fogal, Joshua Issa, Anthony Girmenia, Safwan Khan, Chris E. Sioris
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Abstract

For most CO 2 and CH 4 satellites, only a small percentage (∼10%) of observations yield successful retrievals, with the remaining ∼90% rejected, primarily due to the effects of clouds. Discarding this large fraction of data is an inefficient strategy worth reconsidering due to the costs involved in developing, launching and operating the satellites to make these observations. However, if real-time cloud data are available together with pointing capability, cloud data can guide the instrument pointing in an “intelligent pointing” strategy for cloud avoidance. In this work, multiple intelligent pointing simulations were conducted, demonstrating the significant advantages of this approach for satellites in a highly elliptical orbit (HEO), from which nearly the whole Earth disk can be observed. Multiple factors are shown to contribute to intelligent pointing efficiency such as the size and shape (or aspect ratio) of the field of view (FOV). For the current baseline orbit and Imaging Fourier Transform Spectrometer (IFTS) observing characteristics for the proposed Arctic Observing Mission (AOM), the monthly fraction of cloud-free observations is roughly a factor of 2 (ranging from ∼1.5–2.5) more than obtained with standard pointing (in which cloud information is not used). A similar efficiency is expected in a geostationary orbit (GEO) with an IFTS, however, for a dispersive instrument in HEO or GEO, the gain is more modest. This result is primarily attributed to the ∼1:1 aspect ratio of the IFTS FOV, since it is more efficient for cloud avoidance and scanning irregularly-shaped land masses than the long and narrow slit projection of a typical dispersive spectrometer. These results have implications for the design of future CO 2 or CH 4 monitoring satellites and constellation architectures, as well as other fields of satellite earth observation in which clouds significantly impact observations.
智能指向增加了来自太空的无云CO2和CH4观测的比例
对于大多数CO 2和ch4卫星,只有一小部分(~ 10%)的观测数据能够成功地反演,其余的~ 90%被拒绝,这主要是由于云的影响。由于开发、发射和操作卫星进行这些观测所涉及的成本,放弃这一大部分数据是一种低效的策略,值得重新考虑。然而,如果实时的云数据和指向能力是可用的,云数据可以指导仪器指向一个“智能指向”策略,以避免云。在这项工作中,进行了多次智能指向模拟,证明了该方法对于高椭圆轨道(HEO)卫星的显着优势,从这个轨道上可以观察到几乎整个地球盘。研究表明,影响智能指向效率的因素有很多,比如视场的大小和形状(或长宽比)。对于拟议的北极观测任务(AOM)目前的基线轨道和成像傅立叶变换光谱仪(IFTS)观测特征,无云观测的月分数大约是标准指向(其中不使用云信息)获得的2倍(范围从~ 1.5-2.5)。在具有IFTS的地球静止轨道(GEO)上预期也有类似的效率,然而,对于高轨道或地球静止轨道上的色散仪器,增益则较为有限。这一结果主要归因于IFTS FOV的~ 1:1宽高比,因为它比典型色散光谱仪的狭长狭缝投影更有效地避开云层和扫描不规则形状的陆地。这些结果对未来二氧化碳或甲烷监测卫星和星座结构的设计,以及其他云对观测有显著影响的卫星对地观测领域具有启示意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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