Using ARM-SGP Multi-Sensor Datasets to Investigate Precipitation Characteristics and Vertical Variability

Principal Investigator(s):
Christopher Williams, University of Colorado, Boulder

Collaborator(s):
Ann M. Fridlind, NASA Goddard Institute for Space Studies (GISS)
Pierre Gentine, Columbia University
Zhiming Kuang, Harvard University
Marcus van Lier-Walqui, Columbia University & NASA GISS

In response to the convective processes research topic, the overarching science goal of this proposed research is to analyze ARM observations to understand breakup, coalescence, and evaporation processes in precipitating clouds and to evaluate and improve their representation in cloud resolving models.

This goal will be achieved using a 3-prong approach:

  • Develop instrument simulators with embedded 1-D microphysical models to define signatures of breakup, coalescence, and evaporation processes in ARM observations.
  • Retrieve raindrop size distributions and vertical air motions and identify signatures of breakup, coalescence, and evaporation processes in ARM observations.
  • Evaluate signatures of breakup, coalescence, and evaporation in cloud resolving models relative to signatures in ARM observations.

This proposed research will bridge the knowledge gap often separating observation and model communities by mapping both observational and model datasets onto a common domain. This common domain is beneficial because model output is not forced to mimic observations and vis-a-versa. Within this common domain, the signatures defining breakup, coalescence, and evaporation will be identified on liquid water content (LWC) ecomposition diagrams which map the evolution of number concentration and mean diameter relative to constant liquid water content lines.

Embedding a 1-D microphysical model into the PI’s vertical pointing radar instrument simulators will enable signatures of breakup, coalescence, and evaporation processes to be identified in radar Doppler spectra. Sensitivity tests will determine how uncertainties in radar spectra can be attributed to either microphysical process uncertainties or instrument uncertainties.

This proposed research will use precipitation datasets and radar Doppler spectra from ARM instruments deployed at SGP and the GOAmazon field campaign, including: disdrometers (2DVD & Parsivel), UAZR, KAZR, WACR, KSACR, and WSACR. Using a retrieval technique developed in previous DOE ASR funded research, this proposed research will retrieve raindrop size distributions and vertical air motions in precipitating clouds over SGP (at least 3 years starting in April 2011) and for GOAmazon (March 2014 – December 2015). Signatures of microphysical processes will be documented for different rain regimes using LWC decomposition diagrams.

Microphysical process signatures derived from model simulations participating in the MC3E Intercomparison Project will be evaluated relative to signatures obtained in ARM observations. Feedback through collaborators will result in improved microphysical parameterizations in models.