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A multi-wavelength classification method for polar stratospheric cloud types using infrared limb spectra

机译:利用红外肢体光谱对平流层极地云类型进行多波长分类的方法

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The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument on board the ESA Envisat satellite operated from July 2002 until April 2012. The infrared limb emission measurements represent a unique dataset of daytime and night-time observations of polar stratospheric clouds (PSCs) up to both poles. Cloud detection sensitivity is comparable to space-borne lidars, and it is possible to classify different cloud types from the spectral measurements in different atmospheric windows regions. brbr Here we present a new infrared PSC classification scheme based on the combination of a well-established two-colour ratio method and multiple 2-D brightness temperature difference probability density functions. The method is a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption. The method has been tested in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions, geometries, and composition. The Bayesian classifier distinguishes between solid particles of ice and nitric acid trihydrate (NAT), as well as liquid droplets of super-cooled ternary solution (STS). brbr The classification results are compared to coincident measurements from the space-borne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument over the temporal overlap of both satellite missions (June 2006–March 2012). Both datasets show a good agreement for the specific PSC classes, although the viewing geometries and the vertical and horizontal resolution are quite different. Discrepancies are observed between the CALIOP and the MIPAS ice class. The Bayesian classifier for MIPAS identifies substantially more ice clouds in the Southern Hemisphere polar vortex than CALIOP. This disagreement is attributed in part to the difference in the sensitivity on mixed-type clouds. Ice seems to dominate the spectral behaviour in the limb infrared spectra and may cause an overestimation in ice occurrence compared to the real fraction of ice within the PSC area in the polar vortex. brbr The entire MIPAS measurement period was processed with the new classification approach. Examples like the detection of the Antarctic NAT belt during early winter, and its possible link to mountain wave events over the Antarctic Peninsula, which are observed by the Atmospheric Infrared Sounder (AIRS) instrument, highlight the importance of a climatology of 9 Southern Hemisphere and 10 Northern Hemisphere winters in total. The new dataset is valuable both for detailed process studies, and for comparisons with and improvements of the PSC parameterizations used in chemistry transport and climate models.
机译:ESA Envisat卫星上的迈克尔逊被动大气探测干涉仪(MIPAS)仪器于2002年7月至2012年4月运行。红外肢体发射测量结果代表了极地平流层云(PSC)的白天和黑夜观测的独特数据集,直至两极。云探测灵敏度可与星载激光雷达相媲美,并且可以根据不同大气窗口区域中的光谱测量结果对不同的云类型进行分类。 在这里,我们基于成熟的双色比方法和多个2-D亮度温差概率密度函数的组合,提出了一种新的红外PSC分类方案。该方法是基于贝叶斯定理的简单概率分类器,具有很强的独立性假设。该方法已与实际PSC粒度分布,几何形状和成分的辐射传递模型计算数据库一起进行了测试。贝叶斯分类器区分冰和硝酸三水合物(NAT)的固体颗粒,以及过冷三元溶液(STS)的液滴。 将分类结果与两次卫星任务在时间上重叠时使用正交极化(CALIOP)仪器的星载激光雷达Cloud-Aerosol Lidar的同时测量结果进行了比较(2006年6月至2012年3月)。这两个数据集对于特定的PSC类均显示出良好的一致性,尽管查看几何形状以及垂直和水平分辨率差异很大。在CALIOP和MIPAS冰类之间观察到差异。 MIPAS的贝叶斯分类器比CALIOP识别出南半球极涡中的冰云要多得多。这种分歧部分归因于混合型云的敏感性差异。与极涡中PSC区域内的冰的实际比例相比,冰似乎在肢体红外光谱的光谱行为中占主导地位,并且可能导致冰的发生高估。 使用新的分类方法处理了整个MIPAS测量期间。大气红外测深仪(AIRS)仪器观察到了一些例子,例如在初冬探测到南极NAT带,以及将其与南极半岛的山波事件联系起来,这些都凸显了南半球9和南半球气候学的重要性。总共有10个北半球冬季。新的数据集对于详细的过程研究以及与化学传输和气候模型中使用的PSC参数化的比较和改进都是有价值的。

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