Remote sensing of trace gases, aerosols, and clouds from measurements of up-welling radiation by GOME (Global Ozone Monitoring Experiment) aboard ERS-2 and SCIAMACHY (Scanning Imaging Absorption spectroMeter for Atmospheric CHartograhY) aboard ENVISAT und

RESINC，旨在提高我们对云不均匀性对从国美和SCIAMACHY检索微量气体、气溶胶和云的影响的理解。这些仪器的设想是，在未来二十年里，产生大气成分的全球分布，从而提高我们对各种区域和全球问题的认识，这些问题对地球大气的化学和物理很重要。目前的遥感方法通常假设在仪器的瞬时视场（IFOV）内天空无云或云的水平均匀性，而实际上，云在所有空间尺度上都是高度不均匀的。由于微量气体测量所需的高信噪比，GOME和SCIAMACHY的IFOV相对较大。因此，正确处理云的非同质性对于充分利用其潜力至关重要。该项目的主要目的是量化与这种内在不均匀性相关的不确定性，从而提高从国美和SCIAMACHY检索微量气体、气溶胶和云的准确性。一个特征良好的三维辐射传输模型将用于模拟各种典型云情景的卫星观测。这些数据将作为现有检索的测试，这些检索假定为均匀云。基于这项研究的发现，参数化将被开发、测试和应用，这使得云的不均匀性在检索中得到明确的解释。

Remote Sensing and Inhomogeneous Clouds, RESINC, aims to improve our understanding of the influence of cloud inhomogeneity on the retrieval of trace gases, aerosols, and clouds from GOME and SCIAMACHY. These instruments were conceived to yield, throughout the next two decades, global distributions of atmospheric constituents and thereby improve our knowledge of a variety of regional and global issues of importance for the chemistry and physics of the Earth´s atmosphere. Current remote sensing methods generally assume either a cloudless sky or horizontal homogeneity of the cloud within the instantaneous field of view (IFOV) of the instrument while in reality, clouds are highly inhomogeneous at all spatial scales. The IFOV of GOME and SCIAMACHY is relatively large due to the high signal-to-noise ratio required for trace gas measurements. Hence, a correct treatment of cloud inhomogeneity is essential to exploit their full potential. The main aim of this project is to quantify the uncertainty associated with this instrinsic inhomogeneity and thereby to improve the accuracy of the retrieval of trace gases, aerosols, and clouds from GOME and SCIAMACHY. A well-characterised three-dimensional radiative transfer model will be used to simulate satellite observations for a variety of typical cloud scenarios. These data will serve as a test for the existing retrievals, which assume homogeneous clouds. Based on the findings of this study, a parameterisation will be developed, tested, and applied, which enables cloud inhomogeneity to be explicitly accounted for in the retrieval.