Improving Millimeter Radar Attenuation Corrections in High Latitude Mixed Phase Clouds via Radio-Soundings and a Suite of Active and Passive Instruments

• Published in: Hydrological Processes
• Main Authors: Kalogeras P., Battaglia A.
• Other Authors: Kalogeras, P., Battaglia, A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: Attenuation; Cloud; Ice; Laser radar; Liquid; Microwave radiometry; Radar; north slope; Alaska
• Online Access: http://hdl.handle.net/11583/2959264; https://doi.org/10.1109/TGRS.2022.3142533

Supercooled liquid clouds are very frequent in high-latitude regions. In addition to their substantial effect to visible and infrared radiation, they affect the signal of millimeter radars by producing non-negligible attenuation. Such attenuation must be properly corrected if the information of millimeter radars is used in quantitative retrievals for inferring ice microphysical properties. This study proposes a multi-sensor scheme for refining the vertical distribution of supercooled liquid water content (SLWC) compared to state-of-the-art methods that equipartition the liquid water path measured by microwave radiometer to all pixels identified as cloudy by the radars and warmer than -40°C. Our methodology is applicable in high-latitude, mixed-phase environments, based on the synergy between radar and lidar binary cloud phase masking, microwave radiometer, and radio sounding observations. The technique is demonstrated via data collected by the U.S. DoE Atmospheric Radiation Measurement (ARM) Program climate research facility at the North Slope of Alaska (NSA) and compared with the state-of-the-art methods. Path integrated attenuation (PIA) at W- and G-Band frequencies (> 95 GHz) is then assessed. Results indicate that the different in-cloud distributions of the liquid condensate lead to round-trip PIA discrepancies of cloudy volumes that range in [2, 5] dB, at W- and G-Band frequencies. These differences far exceed those encountered when changing some of the algorithm’s arbitrary assumptions and weighting functions.