Reconciling Ground-Based and Space-Based Estimates of the Frequency of...

Protat, A., S. A. Young, S. A. McFarlane, T. L'Ecuyer, G. G. Mace, J. M. Comstock, C. N. Long, E. Berry, and J. Delano (2014), Reconciling Ground-Based and Space-Based Estimates of the Frequency of Occurrence and Radiative Effect of Clouds around Darwin, Australia, J. Appl. Meteor. Climat., 53, 456-478, doi:10.1175/JAMC-D-13-072.1.

The objective of this paper is to investigate whether estimates of the cloud frequency of occurrence and associated cloud radiative forcing as derived from ground-based and satellite active remote sensing and radiative transfer calculations can be reconciled over a well-instrumented active remote sensing site located in Darwin, Australia, despite the very different viewing geometry and instrument characteristics. It is found that the ground-based radar–lidar combination at Darwin does not detect most of the cirrus clouds above 10 km (because of limited lidar detection capability and signal obscuration by low-level clouds) and that the CloudSat radar–Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) combination underreports the hydrometeor frequency of occurrence below 2-km height because of instrument limitations at these heights. The radiative impact associated with these differences in cloud frequency of occurrence is large on the surface downwelling shortwave fluxes (ground and satellite) and the top-of-atmosphere upwelling shortwave and longwave fluxes (ground). Good agreement is found for other radiative fluxes. Large differences in radiative heating rate as derived from ground and satellite radar–lidar instruments and radiative transfer calculations are also found above 10 km (up to 0.35 K day21 for the shortwave and 0.8 K day21 for the longwave). Given that the ground-based and satellite estimates of cloud frequency of occurrence and radiative impact cannot be fully reconciled over Darwin, caution should be exercised when evaluating the representation of clouds and cloud–radiation interactions in large-scale models, and limitations of each set of instrumentation should be considered when interpreting model–observation differences.

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