Remote sensing of cloud thermodynamic phase from the A-Train : Intercomparison of passive and active sensors retrievals for the establishment of a reference joint dataset

The A-Train observations provide an unprecedented opportunity for the production of high quality dataset describing cloud cover properties. We illustrate in this study that the 5 years of coincident POLDER (Polarization and Directionality of the Earth Reflectance), MODIS (MODerate Resolution Imaging Spectroradiometer) and CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) observations enable in particular the establishment of a reference dataset for the description of cloud top thermodynamic phase at global scale.

We will present the results of extensive comparisons between POLDER and MODIS cloud top phase products and discuss those in view of cloud vertical structure and optical properties derived simultaneously from collocated CALIOP active observations. These results allow to identify and quantify potential biases present in the 3 considered dataset. Among those, we will discuss the impact of thin cirrus, aerosols, snow/ice surfaces, multilayer and fractional cloud cover on global statistics of cloud phase derived from POLDER and MODIS passive measurements. It will also be shown that the different methods used to derive phase information present systematic differences correlated to observation geometries. Based on theses analysis we define criteria for the selection of high confidence cloud phase retrievals which in turn serve for the establishment of a reference cloud phase product.

This high confidence joint product has been derived for the 5 years of POLDER/Parasol and MODIS/Aqua coincident observational period. We will briefly illustrate how it can be used as a benchmark for the evaluation of other cloud climatologies, for the assessment of cloud phase representation in models and the development of better cloud phase parametrization in GCMs.