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首页> 外文会议>Remote Sensing of Clouds and the Atmosphere XII; Proceedings of SPIE-The International Society for Optical Engineering; vol.6745 >Improvement of broadband radiance to flux conversion by using the synergy between active and passive remote sensing instruments
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Improvement of broadband radiance to flux conversion by using the synergy between active and passive remote sensing instruments

机译：利用有源和无源遥感仪器之间的协同作用改善宽带辐射通量转换

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摘要

The ESA EarthCARE (Earth Clouds Aerosols and Radiation Explorer) mission includes the BBR (Broad-Band Radiometer), the instrument responsible to provide measurements of broadband radiances over the along-track satellite path. The BBR footprint will be geolocated in space and time with the passive sensor, MSI (Multi-Spectral Imager), and the active sensors, ATLID (ATmospheric LIDar) and CPR (Cloud Profiler Radar) onboard the same platform. The role of the BBR was defined to provide the boundary condition for top of atmosphere flux densities. Thus, the radiance to flux conversion is the main objective for the BBR retrieval algorithms. This conversion has been so far carried out by using specific angular distribution models (ADMs). In this process, every radiance is classified in a unique scene bin of observations characterized by a similar anisotropic behaviour. Each of these scene bins is defined by a range of values distinguishable by the MSI. But the MSI can only extract vertically integrated retrievals. Therefore, in multi-layer cloud configurations, scene identification (ID) by means of the MSI retrievals will not distinguish the 3-D structure of the real scenes. Thus, these scenes will most probably be wrongly identified. But, since active sensors are present on the same satellite platform, it would be possible to use their observations to contribute to the BBR scene ID. This work shows a preliminary simulation approach to demonstrate the advantages of this methodology by applying it to multi-layer clouds. The clouds have been built with a stochastic cloud generator model, and the radiative transfer simulations have been carried out with the EarthCARE Simulator, a Monte-Carlo code capable to reproduce the observations of the different mission instruments taking into account the specific characteristics of each sensor.
机译：ESA EarthCARE（地球云气溶胶和辐射探测器）任务包括BBR（宽带辐射计），该仪器负责提供沿轨道卫星路径的宽带辐射测量。 BBR足迹将在同一平台上使用无源传感器MSI（多光谱成像仪）和有源传感器ATLID（ATmospheric LIDar）和CPR（Cloud Profiler雷达）在空间和时间上进行地理定位。定义了BBR的作用是为大气通量密度的顶部提供边界条件。因此，辐射通量转换是BBR检索算法的主要目标。到目前为止，这种转换是通过使用特定的角度分布模型（ADM）进行的。在此过程中，将每个辐射度归类到以相似的各向异性行为为特征的唯一观察场景中。这些场景容器中的每一个都由MSI可区分的一系列值定义。但是MSI只能提取垂直集成的检索。因此，在多层云配置中，借助MSI检索的场景识别（ID）将无法区分真实场景的3-D结构。因此，很可能会错误地识别这些场景。但是，由于有源传感器存在于同一卫星平台上，因此有可能使用它们的观测结果来贡献BBR场景ID。这项工作展示了一种初步的仿真方法，通过将其应用于多层云来证明该方法的优势。使用随机云生成器模型构建了云，并使用EarthCARE Simulator进行了辐射传输模拟，EarthCARE Simulator是一种Monte-Carlo代码，能够考虑到每个传感器的特定特性来再现不同任务仪器的观测结果。

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