Ice Cloud Optical Thickness, Effective Radius, And Ice Water Path Inferred From Fused MISR and MODIS Measurements Based on a Pixel‐Level Optimal Ice Particle Roughness Model. Issue 22 (17th November 2019)

• Record Type: Journal Article
• Authors: Wang, Yi; Yang, Ping; Hioki, Souichiro; King, Michael D.; Baum, Bryan A.; Di Girolamo, Larry; Fu, Dongwei
• Is Part Of: Journal of geophysical research. Volume 124:Issue 22(2019)
• Page Start: 12126
• Page End: 12140
• Publication Date: 2019-11-17
• Subjects: ice clouds -- MISR -- MODIS -- optical thickness -- effective radius -- ice particle model; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2019JD030457
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: The Multi‐angle Imaging SpectroRadiometer (MISR) provides measurements over a wider scattering‐angle range for a given location than a cross‐track scanning sensor such as the MODerate resolution Imaging Spectroradiometer (MODIS). Based on a full year (2013) of fused MISR‐MODIS datasets, we develop a variable surface roughness model for ice particles with the goal of identifying the optimal degree of roughness in the ice model for a given pixel containing single‐layer ice clouds. For the MISR‐MODIS observations over oceans, severe roughness values are often selected for a pixel when optical thickness (τ) and particle effective radius ( R eff ) are large in conjunction with larger cloud heterogeneity index ( H σ ) or a warmer cloud top temperature. Furthermore, τ, R eff, and ice water path are retrieved with the optimal model and compared to operational MODIS Collection 6 (MC6) products that assume a constant roughness. In general, the retrievals based on the present optimal model lead to greater consistency with MISR measurements, and result in larger median τ by 10.1% and smaller median R eff by 6.5% but almost identical ice water path in comparison with the MC6 counterparts. The higher average τ value is caused by a slightly larger number of large τ cases, but the smaller average R eff value is due to the shifting of the retrieved R eff value toward smaller values by approximately 2–4 μm in comparison to the MC6 distribution over all seasons. Both τ retrievals have similar regional and monthly variations, but a larger annual cycle of R eff is associated with the optimal model. Key Points: An optimal surface roughness model for ice crystals is developed based on a fused MISR‐MODIS dataset The optimal values of the degree of ice crystal surface roughness vary with cloud regimes Cloud properties retrieved based on the optimal model differ from the relevant MODIS C6 cloud property products