The NASA A-Train satellites provide a unique combination of active and passive measurements that will advance our understanding of clouds globally. We propose to study global ice cloud and arctic mixed-phase cloud microphysical properties by combining CloudSat radar, CALIPSO lidar and MODIS measurements.
Because of differences in detection sensitivity and cloud attenuation, CloudSat radar and CALIPSO lidar measurements cover different parts of ice clouds although there will be some overlaps between the two instruments. In order to provide more complete ice cloud properties, we propose to generate ice cloud microphysical property profiles by combining the radar and lidar measurements, which will be more physically consistent than would be obtained by simply combining separate CloudSat and CALIPSO ice cloud products. Different approaches will be implemented for ice clouds with partially overlapped and non-overlapped lidar and radar measurements to generate the global ice cloud product with detailed error characterization. This product can be used to better understand ice cloud microphysical properties and related cloud processes globally, and to validate and improve ice cloud representation in GCMs.
We also propose to develop an algorithm to retrieve microphysical properties of arctic mixed-phase clouds by combining radar, lidar and MODIS measurements from the A-train. Several recent global coupled atmospheric oceanic model simulations suggest that the Arctic is one of the most sensitive areas to climate change with large uncertainties due in part to our poor understanding of arctic cloud systems. Applying the proposed mixed-phase cloud algorithm to the A-Train data will provide cloud microphysical properties on different scales to better characterize mixed-phase cloud systems which occur frequently in the arctic. The microphysical properties retrieved using our algorithm, combined with surface-based retrievals and atmospheric thermodynamic and dynamic data, will be used to study arctic mixed-phase cloud processes and to improve their representation in GCMs.
Currently, I am a member of the CloudSat standard data product algorithm development team. I am responsible for two CloudSat standard data products: radar-only and combined radar and lidar cloud scenario classifications with responsibilities including: algorithm development and refinement, operational code development and maintenance, and product validation. Therefore, in addition to the proposed research described above, I will carry on my responsibilities on these two standard products.
I would like to serve as a member of the CloudSat and CALIPSO mission science teams.
