云相态是气候模式中的重要参数, 也是遥感反演过程中进行云滴有效半径、 云水含量等微物理参数反演的重要前提。 在研究了云层有效发射率光谱对云相态敏感性的基础上, 提出了基于云层有效发射率光谱的云相态表达特征, 包括800～900 cm-1区域的有效发射率斜率、 900～1 000 cm-1区域的有效发射率斜率、 上述两个区域的有效发射率斜率之差、 862.1与989.8 cm-1的有效发射率之比、 862.1与989.8 cm-1的有效发射率之差、 1 900.1与2 029.3 cm-1的有效发射率之比、 远红外窗区有效发射率平均值与900 cm-1有效发射率之比等7个特征。 建立了利用支持向量机进行云相态判别的方法, 开展了模拟数据验证试验, 并利用遗传算法优化了支持向量机的径向基核函数参数和惩罚因子。 将该方法用于处理ARM计划中SGP站点的AERI仪器获得的数据, 得到的云相态判别结果与Shupe提出的多仪器综合判别结果进行了比较。 结果表明, 利用红外波段不同窗区的有效发射率光谱特征可以实现发射率低于0.95的云层的相态判别, 建立的基于支持向量机的云相态判别方法与Shupe方法的总体判别结果较为一致, 但有约30%的云层由于发射率较大而标记为不透明云。 基于红外高光谱发射率数据的云相态判别技术充分考虑了光谱斜率、 比值和差值等信息, 是较为稳定有效的薄云相态判别方法。

As a key factor in the climate model, cloud phase is an important prerequisite to performing cloud property retrievals from remote sensor measurements. The ability to infer cloud phase using cloud emissivity spectra is investigated by numerical simulations. It is shown that for emissivity below 0.95, several spectral features such as the slopes, the ratios and the differences of the emissivity are consistent with the variation of cloud phase in some spectral regions. Specifically, these features include the slope of the cloud emissivity between 800 and 900 cm-1, the slope of the cloud emissivity between 900 and 1 000 cm-1, the difference in the mean emissivity between above-mentioned two regions, the ratio of the emissivity at 862.1 cm-1 to the emissivity at 989.8 cm-1, the difference in the emissivity between 862.1 and 989.8 cm-1, the ratio of the emissivity at 1 900.1 cm-1 to the emissivity at 2 029.3 cm-1, the ratio of the mean emissivity for far-infrared region to the emissivity at 900 cm-1. A cloud phase classifier is proposed based on support vector machines (SVM). A series of simulations including various cloud patterns are performed. The RBF kernel function parameters and the penalty factor of SVM are selected by using the genetic algorithm. The phase determination algorithm is applied for collecting data from the AERI at the SGP site. The results from the ground-based multisensor cloud phase classifier proposed by Shupe are used to validate the phase determination algorithm. It is found the two results are consistent in general. 30% clouds are indicated as opaque due to its high emissivity. The cloud with small lidar’s depolarization is misclassified as clear sky by the Shupe method. It can be concluded that the proposed algorithm considering the spectral information (spectral slopes, ratios and differences) is efficient for cloud phase determination of thin cloud.