Intercomparison of Multi-Sensor Retrievals of Cloud Microphysical Properties—Results using ARM data at Darwin, Australia

Tropical clouds play an important role in the radiative energy balance and can impact water vapor transport in the Tropical Tropopause Layer (TTL). Forecasting the future climate invariably depends on accurate prediction of clouds and their associated radiative feedbacks. Long-term datasets provide the cloud properties statistics needed to evaluate and improve climate model simulations. In this study, we present statistics of cloud microphysical properties and computed heating rates in ice clouds over a 3.5-year period at the ARM Darwin site. We will compare microphysical properties derived from several independent lidar-radar retrieval algorithms and radar reflectivity – Doppler velocity algorithms. Our goal is to understand and evaluate the impact of algorithm uncertainty on our knowledge of the radiative forcing of tropical ice clouds.