Clouds are one of the major source of uncertainties in predicting future climate changes. Thus, clouds are a high priority weather and climate research topic. Although instrument simulators are widely used for model evaluations with remote sensing measurements, reliable cloud properties retrieved from advanced retrieval algorithms are crucial not only for model evaluations but also for advancing our understanding of physical processes, which is necessary to improve model simulations.
In this talk, I introduce two retrieval algorithm developments and highlight their applications.
1) Combining CloudSat cloud profiling radar and CALIPSO lidar measurements for global ice cloud microphysics retrieval (CloudSat 2C-ICE product). It has been widely used to study global cloud climatology, estimate snowfall rate, evaluate climate model simulations, and understand anvil cloud productivity in tropical deep convective systems.
2) Utilizing the three Doppler moments of ground-based millimeter cloud radar (MMCR) to retrieve ice cloud microphysics and cloud-scale dynamics at the Department of Energy (DOE) Atmospheric Radiation Measurements (ARM) sites. The retrieval dataset has been used to develop a new ice particle sedimentation rate parameterization, study cloud scale microphysical processes, and evaluate cloud resolved model simulations effectively with ice water content partitions for bulk ice categories.