Validation and homogenization of cloud optical depth and cloud fraction retrievals for GERB/SEVIRI scene identification using Meteosat-7 data
Validation and homogenization of cloud optical depth and cloud fraction retrievals for GERB/SEVIRI scene identification using Meteosat-7 data
复制标题
使用 Meteosat-7 数据验证和均匀化 GERB/SEVIRI 场景识别的云光学深度和云分数检索
DOI：
10.1016/j.atmosres.2004.03.010
复制
发表时间：
2004
影响因子：
5.5
通讯作者：
L. Gonzalez
中科院分区：
文献类型：
--
作者：
A. Ipe;C. Bertrand;N. Clerbaux;S. Dewitte;L. Gonzalez
对地静止地球辐射预算 (GERB) 仪器于 2002 年夏季与 Meteosat 第二代 (MSG) 卫星上的旋转增强型可见光和红外成像仪 (SEVIRI) 一起发射。该宽带辐射计的目标是借助对地静止轨道，以高时间分辨率对大气层顶部 (TOA) 辐射通量进行近乎实时的估计。为了推断这些通量，需要对测量的辐射进行辐射到通量的转换。由于我们计划使用从云和地球辐射能系统 (CERES) 实验开发的角度依赖模型 (ADM) 来进行这种转换，因此 GERB 地面部分将不得不依赖 SEVIRI 数据上的一些场景识别，这些数据尽可能接近来自 CERES 的数据，以便选择合适的 ADM。在本文中，我们简要介绍了用于检索由多个成像器像素组成的足迹上的云光学深度和云分数的方法。然后，我们使用近乎同步的 Meteosat-7 成像仪和 CERES 单卫星足迹数据对相同目标的两种特征的检索进行比较。这些目标被定义为 CERES 辐射计足迹。我们根据地表类型和云相研究两个数据集之间可能存在的差异，如果存在差异，则建议一些基于 CERES 检索的均质化 GERB 检索的策略。
The Geostationary Earth Radiation Budget (GERB) instrument was launched during the 2002 summer together with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board of the Meteosat Second Generation (MSG) satellite. This broadband radiometer will aim to deliver near real-time estimates of the top of the atmosphere (TOA) radiative fluxes at high temporal resolution thanks to the geostationary orbit. To infer these fluxes, a radiance-to-flux conversion needs to be performed on measured radiances. Since we plan to carry out such a conversion by using the angular dependency models (ADMs) developed from the Clouds and the Earth's Radiant Energy System (CERES) experiment, the GERB ground segment will have to rely on some scene identification on SEVIRI data which mimic as close as possible the one from CERES in order to select the proper ADM. In this paper, we briefly present the method we used to retrieve cloud optical depth and cloud fraction on footprints made of several imager pixels. We then compare the retrieval of both features on the same targets using nearly time-simultaneous Meteosat-7 imager and CERES Single Satellite Footprint data. The targets are defined as CERES radiometer footprints. We investigate the possible discrepancies between the two datasets according to surface type and cloud phase and, if they exist, suggest some strategies to homogenize GERB retrievals based on CERES ones.