Abstract
Abstract. Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.
Highlights
Detection of multilayer clouds using passive sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) is a challenging but important remote sensing need
Since the upper cloud layer optical thickness is critical in understanding the impact of multilayer cloud scenes on MYD06 cloud optical property retrievals, cloud optical thickness from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) 5 km layer product is merged with the CLDCLASS-lidar product
Regardless of the inclusion of the PH04 test, the results shown here indicate that it is probable that MYD06 will detect a multilayer cloud if the separation distance d is greater than 1 km and the upper-layer cloud optical thickness (COT) is greater than about 1.2
Summary
Detection of multilayer clouds using passive sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) is a challenging but important remote sensing need. The MOD06 and MYD06 multilayer cloud flag previously has been evaluated by Wang et al (2016) using the 2B-CLDCLASS-LIDAR product for the years 2007– 2010 and by Desmons et al (2017), who in parallel evaluated the PARASOL-POLDER multilayer cloud detection algorithm using the 2B-GEOPROF-lidar and CALIOP 5 km cloud layer products for the years 2006–2010 These investigations, broadly defined multilayer clouds in the radar and lidar data sets and implicitly did not consider the intent of the MOD06 and MYD06 multilayer cloud detection algorithm, which is to identify scenes where a second cloud layer adversely impacts the optical property retrievals of the radiatively dominant cloud layer (the primary example being a thin ice cloud overlying an optically thicker liquid water cloud), rather than as a strict multilayer detection algorithm. In the last section, we show the impact of multilayer clouds on cloud effective radius (CER) retrievals
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