Arctic Weather Satellite Sensitivity to Supercooled Liquid Water in Snowfall Conditions

Abstract

The aim of this study is to highlight the issue of missed supercooled liquid water (SLW) detection in the current radar/lidar derived products and to investigate the potential of the combined use of the EarthCARE mission and the Arctic Weather Satellite (AWS)—Microwave Radiometer (MWR) observations to fill this observational gap and to improve snowfall retrieval capabilities. The presence of SLW layers, which is typical of snowing clouds at high latitudes, represents a significant challenge for snowfall retrieval based on passive microwave (PMW) observations. The strong emission effect of SLW has the potential to mask the snowflake scattering signal in the high-frequency channels (>90 GHz) exploited for snowfall retrieval, while the detection capability of the combined radar/lidar SLW product—which is currently used as reference for the PMW-based snowfall retrieval algorithm—is limited to the cloud top due to SLW signal attenuation. In this context, EarthCARE, which is equipped with both a radar and a lidar, and the AWS-MWR, whose channels cover a range from 50 GHz to 325.15 GHz, offer a unique opportunity to improve both SLW detection and snowfall retrieval. In the current study, a case study is analyzed by comparing available PMW observations with AWS-MWR simulated signals for different scenarios of SLW layers, and an extensive comparison of the CloudSat brightness temperature (TB) product with the corresponding simulated signal is carried out. Simulated TBs are obtained from a radiative transfer model applied to cloud and precipitation profiles derived from the algorithm developed for the EarthCARE mission (CAPTIVATE). Different single scattering models are considered. This analysis highlights the missed detection of SLW layers embedded by the radar/lidar product and the sensitivity of AWS-MWR channels to SLW. Moreover, the new AWS 325.15 GHz channels are very sensitive to snowflakes in the atmosphere, and unaffected by SLW. Therefore, their combination with EarthCARE radar/lidar measurements can be exploited to both improve snowfall retrieval capabilities and to constrain snowfall microphysical properties.