Advancing Understanding and Prediction of the Entrainment Zone and Convective Boundary Layer Using Dual-Polarization Radar Observations

The lowest level of the atmosphere, known as the boundary layer, is critical to understanding and predicting the weather. During the daytime when the sun heats the Earth’s surface, the resulting rising motions mix the atmosphere, and the boundary layer deepens and becomes what is known as the convective boundary layer (CBL). Information about the depth of the CBL and the entrainment zone (EZ) at the top of the CBL can provide significant insight into atmospheric processes and how they are handled in numerical models. This project will use a newly developed technique to investigate the CBL and EZ using the US national weather radar system. The resulting data will inform and influence weather model development and the project includes a significant educational component. This award will provide funding to expand the creation of radar-derived CBL and EZ data to many more sites and to analyze that data to address unknowns related to how wind shear and atmospheric stability impact entrainment in the CBL. CBL depth and EZ depth will be derived at sites across the US from data produced by the WSR-88D radar network. These radars can measure turbulent mixing where air parcels of different densities meet. By using dual-polarization differential reflectivity measurements and the quasi-vertical profile (QVP) technique, the researchers can produce a time series of the height of the top of the CBL and the depth of the EZ. This data will then be compared to profiles of the atmosphere, primarily from weather balloons, to verify the results and analyze the data to find the best atmospheric predictors of CBL and EZ properties. This data can then be compared to numerical models to inform the development of boundary layer parameterization schemes. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.