Developing High-Resolution Constrained Variational Analysis of Vertical Velocity and Advective Tendencies within the Range of ARM Scanning Radars at the SGP

Principal Investigator(s):
Minghua Zhang, The Research Foundation for SUNY Stony Brook University

The objective of this proposal is to develop an algorithm and a demonstration dataset of three-dimensional constrained variational analysis of high-resolution atmospheric fields of winds, vertical velocity, temperature, water vapor, total hydrometer, and their advective tendencies within the coverage of the ARM scanning cloud radars at the SGP. The algorithm will synthesize multiple ARM measurements to variationally constrain the 3-D atmospheric fields to obey conservations of mass, total water and energy. The dataset will be developed by using, as the first guess, the recently available High-Resolution Rapid Refresh (HRRR) analysis from NCEP and the Experimental Real-Time Convection-Allowing Ensemble Prediction System (EPS) analysis from NCAR. The demonstration dataset will be developed for a 2.5-month period at 4x4 km resolution for a 100x100 km domain centered at the ARM Central Facility at the SGP. Quality control will be implemented and data uncertainties will be quantified. The algorithm will be delivered to ARM and the wider community.

The proposed data product is intended to be used as forcing data for process models of clouds, aerosol, and precipitation, as well as forcing data for Large-Eddy Simulations, Cloud Resolving and Single-Column Models. It can be also used to characterize the dynamics and thermodynamics of three-dimensional cloud systems to complement measurements from ARM scanning radars. The proposed research will lead to improved climate models by enabling more use of ARM data for improved understanding of clouds and their parameterizations in these models.