Investigating the impact of marine boundary layer dynamics on the evolution of atmospheric rivers and our ability to predict onshore precipitation
Abstract
Atmospheric rivers (ARs) are long narrow bands of enhanced water vapor transport that bring significant amounts of moisture from tropical and extratropical regions to the mid-latitudes typically resulting in beneficial rainfall. However, strong ARs (or a quick series of ARs) can result in extreme rainfall overland potentially causing widespread flooding and localized debris flows. In addition, strong ARs are associated with high wind speeds that can result in property damage. Forecasting the development and evolution ofatmospheric rivers presents a significant challenge due to the complex physical processes and interactions occurring in the lower atmosphere. High-resolution dropsonde observations have shown that the vertical structure of the MBL plays an important role in modulating dynamical processes impacting wind shear and stability within AR environments. One important potential source of forecast error in the representation of the evolution of ARs is the characterization and behavior of the marine boundary layer (MBL) in modulating AR strength. Current planetary boundary layer (PBL) formulations have been developed with limited data sets, often over land, with minimal (if any) correction for dealing with fluxes generated at the atmosphere-ocean interface.The impact MBL model error in AR environments has on the forecast skill of onshore precipitation is currently poorly understood. We hypothesize that the complex nature of the MBL plays an important role in AR onshore precipitation via atmospheric stability modulation, air-sea interactions, and horizontal water vapor flux. Specifically, the propagation of MBL model error in AR environments is believed to reduce #downstream# precipitation forecast skill, but the magnitude is unknown. Here, we propose to investigate this issue by performing ensemble-based experiments to investigate the skill of numerical models to accurately represent the dynamical structure of the MBL in AR environments. The numerical experiments will study the sensitivity of existing PBL schemes and utilize dropsonde observations for model verification. The process-based analysis of the performance of model parameterizations to represent the MBL structure and the resulting impact on onshore precipitation will help inform ensemble forecast configuration design to better sample key sources of MBL uncertaintyand guide future boundary layer model development.- Approved for Public Release -
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412752
Entities
People
- Matthew Simpson
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego