3D cloud tomography during EUREC4A: Synergy of MISR multi-angle satellite imaging and airborne remote sensing onboard HALO

Abstract:

Clouds remain one of the most uncertain factors in climate predictions. The behavior of shallow trade-wind cumuli in a changing climate is particularly uncertain. To reduce this uncertainty, an improved understanding of the macro- and microphysical properties of these clouds is necessary. Operational retrievals using spaceborne passive imaging are based on one-dimensional radiative transfer. They apply well to extended stratiform cloud layers, but break down for three-dimensional (3D) convective, vertically developed clouds. This causes a considerable gap in the observations. To close this gap, a new retrieval method tailored to the 3D morphology of convective clouds is needed. Previous studies have demonstrated a 3D tomographic cloud reconstruction (TCR) method using airborne multi-angle imaging observations to recover volumetric information about cloud optical and microphysical properties. To facilitate global coverage, this method must be advanced for satellite observations. Herein we present the first 3D TCR using satellite observations from Terra’s Multi-angle Imaging SpectroRadiometer MISR. During the EUREC⁴A campaign the High-Altitude and LOng-range research aircraft HALO performed an underpass coordinated with the Terra spacecraft. The MISR observations are used in synergy with HALO’s remote sensing observations to demonstrate the potential and limitations of the current 3D TCR method. This method opens up unprecedented opportunities for the retrieval of cloud structure and microphysical properties for convective clouds. While MISR provides a wide range of viewing angles (±70°), HALO adds observations of passive (hyperspectral, polarimetric imagers and microwave radiometer) and active (lidar and cloud radar) remote sensing with a spatial resolution of roughly a factor 10 higher compared to MISR. These new observations are greatly needed in cloud-process modeling which will help advance our understanding of the cloud-climate feedback