Evaluation of Physical Microphysical Property Retrieval Algorithms during the 2020 IMPACTS Field Campaign

Abstract The NASA Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign provides high-quality, high-altitude aircraft lidar (532 nm), radar (W band), and in-cloud microphysical aircraft data taken during wintertime storm events impacting the United States. 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 . For all three microphysical parameters, VarPy and 2C-ICE retrieval errors became notably more pronounced around the dendritic growth zone (from −15° to −10°C) and near freezing (≥−5°C), which suggests that both algorithms experience difficulty addressing riming and aggregation processes and with larger particles (dendrites and plates) due in part to their simplified ice particle assumptions. However, the mean-melt diameter ice-particle assumption did yield more accurate IWC estimates, which led to slightly better overall results for VarPy.