Cloud thermodynamic phase inferred from merged POLDER and MODIS data
Cloud thermodynamic phase inferred from merged POLDER and MODIS data
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DOI：
10.5194/acp-10-11851-2010
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发表时间：
2007-10
影响因子：
6.3
通讯作者：
J. Riedi;B. Marchant;S. Platnick;B. Baum;F. Thieuleux;C. Oudard;F. Parol;J. Nicolas;P. Dubuisson
中科院分区：
文献类型：
--
作者：
J. Riedi;B. Marchant;S. Platnick;B. Baum;F. Thieuleux;C. Oudard;F. Parol;J. Nicolas;P. Dubuisson
抽象。云特性的全球空间分布和日分布是理解水文循环的关键问题，也是推动改进数值天气模型和大气环流模型的关键。卫星数据提供了深入了解全球云特性的最佳途径。特别是，云的热力学相的确定是从卫星测量推断云的光学和微物理特性的过程中的关键的第一步。重要的是，云相的推导与此确定的置信度的估计，使此信息可以包括在随后的检索（光学厚度，有效粒子半径，冰/液态水含量）。在这项研究中，我们结合联合收割机三个不同的和有据可查的方法推断云的阶段到一个单一的算法。该算法适用于由MODIS（中分辨率成像光谱仪）和POLDER 3（偏振和方向性的地球反射率）仪器获得的数据。结果表明，这种协同算法可以经常使用，以获得云相位沿着与一个指数，有助于区分模糊的阶段，从自信的阶段情况。所得到的产品提供了从置信液体到置信冰的半连续指数，而不是通常的液相、冰相、混合相（冰和液体颗粒的潜在组合）或简单的未知相云的离散分类。索引值同时提供关于相位和相关联的置信度的信息。这种方法预计将是有用的云同化和建模工作，同时提供更多的洞察力来自卫星数据的全球云的属性。
Abstract. The global spatial and diurnal distribution of cloud properties is a key issue for understanding the hydrological cycle, and critical for advancing efforts to improve numerical weather models and general circulation models. Satellite data provides the best way of gaining insight into global cloud properties. In particular, the determination of cloud thermodynamic phase is a critical first step in the process of inferring cloud optical and microphysical properties from satellite measurements. It is important that cloud phase be derived together with an estimate of the confidence of this determination, so that this information can be included with subsequent retrievals (optical thickness, effective particle radius, and ice/liquid water content). In this study, we combine three different and well documented approaches for inferring cloud phase into a single algorithm. The algorithm is applied to data obtained by the MODIS (MODerate resolution Imaging Spectroradiometer) and POLDER3 (Polarization and Directionality of the Earth Reflectance) instruments. It is shown that this synergistic algorithm can be used routinely to derive cloud phase along with an index that helps to discriminate ambiguous phase from confident phase cases. The resulting product provides a semi-continuous index ranging from confident liquid to confident ice instead of the usual discrete classification of liquid phase, ice phase, mixed phase (potential combination of ice and liquid particles), or simply unknown phase clouds. The index value provides simultaneously information on the phase and the associated confidence. This approach is expected to be useful for cloud assimilation and modeling efforts while providing more insight into the global cloud properties derived from satellite data.