Investigation of Hydrometeor Distributions in Continental and Maritime Storm Systems: Application of Cloud-Resolving Simulations, a Polarimetric Scanning Radar Simulator and Polarimetric Radar Observations

Principal Investigator(s):
Steven Rutledge, Colorado State University
Wei-Kuo Tao, National Aeronautics and Space Administration
Brenda Dolan, Colorado State University
Toshi Matsui, University of Maryland

Takamichi Iguchi, University of Maryland

Cloud-resolving models (CRMs) are important tools to help understand cloud and precipitation processes, and their interactions with atmospheric aerosols, the solid and liquid Earth’s surface, and greenhouse gases involved in climate change. And consequently, the accuracy of CRM simulations -- in other words, robust evaluation of their simulated cloud dynamics and microphysics -- is becoming a critical step. In the last decade, the widespread emergence of polarimetric radars has started providing critical parameters for evaluating CRMs, including direct polarimetric radar observables, and retrieved geophysical parameters such as precipitation rate, hydrometeor identification (rain, hail, graupel, snow, ice crystals, etc.) as well as vertical and horizontal wind speeds. Validating microphysical fields predicted by CRMs has been a longstanding need in the community. These polarimetric-radar based datasets provide a significant opportunity to validate the performance of CRMs, and eventually improve the microphysical, dynamical and life cycle simulation of convective systems, which is one of the primary themes of the DOE ASR program.

For this, we will develop a novel synthetic computational framework for CRM evaluation, so called POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS). POLARRIS will be applied to the CRM outputs to diagnose polarimetric radar observables, allowing direct comparison between the radar-retrievals and CRM outputs through a unified physical approach.

The overall goal of this proposed project is to utilize detailed multi-sensor polarimetric radar data from both DOE ARM field campaigns and routine data collection to evaluate and improve the microphysical and dynamical fields simulated by 3-D CRM simulations using different microphysical parameterizations. Case studies from continental and maritime deep convection will be undertaken. Utilizing the improved CRM framework, we will then evaluate the sensitivity of deep convection to varying aerosol concentrations (cloud condensation nuclei and ice nuclei) that may be associated with more polluted conditions in future climate scenarios. A series of controlled experiments that consider both changes in aerosol concentrations and environmental parameters such as atmospheric instability and warm cloud depth will be undertaken. Recent studies show that the invigoration of deep convection by aerosols is not a simple process but involves couplings to atmospheric instability and warm cloud depth. Warm cloud depth is defined by the vertical distance between the freezing level and cloud base.