Atmospheric Chemistry and Physics (Feb 2018)

Estimating precipitation susceptibility in warm marine clouds using multi-sensor aerosol and cloud products from A-Train satellites

  • H. Bai,
  • H. Bai,
  • C. Gong,
  • M. Wang,
  • M. Wang,
  • Z. Zhang,
  • T. L'Ecuyer

DOI
https://doi.org/10.5194/acp-18-1763-2018
Journal volume & issue
Vol. 18
pp. 1763 – 1783

Abstract

Read online

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol–cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), CloudSat, Moderate Resolution Imaging Spectroradiometer (MODIS), and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) from June 2006 to April 2011 are analyzed to estimate precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR in warm marine clouds. We find that SPOP strongly depends on atmospheric stability, with larger values under more stable environments. Our results show that precipitation susceptibility for drizzle (with a −15 dBZ rainfall threshold) is significantly different than that for rain (with a 0 dBZ rainfall threshold). Onset of drizzle is not as readily suppressed in warm clouds as rainfall while precipitation intensity susceptibility is generally smaller for rain than for drizzle. We find that SPOP derived with respect to aerosol index (AI) is about one-third of SPOP derived with respect to cloud droplet number concentration (CDNC). Overall, SPOP demonstrates relatively robust features throughout independent liquid water path (LWP) products and diverse rain products. In contrast, the behaviors of SI and SR are subject to LWP or rain products used to derive them. Recommendations are further made for how to better use these metrics to quantify aerosol–cloud–precipitation interactions in observations and models.