MODIS Collection 6 Shortwave-Derived Cloud Phase Discrimination Algorithm and comparisons with CALIOP and POLDER

Wednesday, 17 December 2014
Benjamin Marchant1,2, Steven E Platnick1, Thomas Arnold1,3, Kerry Meyer1,2 and Jérôme Riedi4, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Universities Space Research Association Greenbelt, Greenbelt, MD, United States, (3)Science Systems and Applications, Inc., Lanham, MD, United States, (4)Laboratoire d'Optique Atmosphérique (Lille), Villeneuve, France
Cloud thermodynamic phase (ice or liquid) discrimination is an important first step for cloud retrievals from passive sensors such as MODIS (Moderate-Resolution Imaging Spectroradiometer). Because ice and liquid phase clouds have very different scattering and absorbing properties, an incorrect cloud phase decision can lead to substantial uncertainties in the cloud optical and microphysical property products such as cloud optical thickness or effective particle radius. Furthermore, it is well-established that ice and liquid clouds have different impacts on the Earth's energy budget and hydrological cycle, thus accurately monitoring the spatial and temporal distribution of these clouds is of continued importance.

For MODIS Collection 6 (C6), the shortwave-derived cloud thermodynamic phase algorithm used by the optical and microphysical property retrievals has been completely rewritten to improve the phase discrimination skill for a variety of cloudy scenes (e.g., thin/thick clouds, over ocean/land/desert/snow/ice surface, etc). To evaluate the performance of the C6 cloud phase algorithm, extensive granule-level and global comparisons have been conducted against the heritage C5 algorithm, CALIOP, and POLDER. A wholesale improvement is seen for C6 compared to C5. We will present an overview of the MODIS C6 cloud phase algorithm updates and their impacts on cloud retrieval statistics, as well as ongoing efforts to continue algorithm improvement.