Evaluation of physical microphysical property retrieval algorithms during the 2020 IMPACTS field campaign

This study evaluates two mass–dimensional relationships [Brown and Francis (BF95); Heymsfield (H14)] and two lidar–radar microphysical retrieval algorithms [ CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE); VarPy (a variational method derived from the satellite lidar–radar data community)] to estimate aircraft-retrieved volume extinction coefficient ( σ ), ice water content (IWC), and effective radius ( r e ) during the 2020 IMPACTS deployment.

BF95 and H14 have a close 1:1 correlation ( R 2 = 0.98) with in situ observations of σ . However, only BF95 displays a linear, consistent, and almost temperature-independent low bias for IWC and r e , which likely arises from the environmental conditions used to determine each. Unlike the field-campaign-derived BF95 and H14 relationships, VarPy and 2C-ICE directly ingest the aircraft-based lidar and radar data to simulate σ , IWC, and r e .