The Combined Influence of Initial Condition Errors in SWTs and ARs on AR Forecasts Applying the Moist Adjoint Error Energy Metric to Diagnose Cases from AR Recon
Abstract
Atmospheric river (AR) is a long and narrow corridor of strong horizontal water vapor transport, which plays a key role in transporting water vapor from the tropics and subtropics to middle latitudes and feed precipitation over the U.S. West Coast. AR Reconnaissance (AR Recon) is a research and operations partnership to support improved prediction of landfalling ARs on the U.S. West Coast. One of the core concepts of AR Recon is that the initial condition errors in the representation of essential atmospheric structures (EASs) are key to forecast error growth. These include the AR itself or its precursors, and conditions proximate to the AR that will affect its evolution. Meanwhile, errors in two or more EASs can interact in ways that amplify error growth. A key example of this in the early design of AR Recon was the relative position of the AR, the strength of the stable layer capping the strongest water vaportransport layer, and the position and characteristics of a cold-air short-wave trough (SWT). In cases like that, the SWT often approaching from the northwest could become close enough to the AR that it could induce vertical lift in the air layer at or just above the top of the AR. Where this interaction begins tapping the AR#s warm and moist air, it causes condensation and precipitation in the lower-to-mid troposphere. The latent heating from precipitation is critical to the intensification of AR and the development of relevant dynamical systems. Therefore, the timing, position, and strength of the latent heat released by the interaction between SWT and AR can strongly influence the storm#s evolution later in the forecasts. Although this mechanism was presented early in AR Recon planning, a detailed evaluation of this mechanism has not been conducted. In this proposed project, the combined influence of initialcondition errors in SWTs and ARs on AR forecasts will be investigated using a moist adjoint method. Specifically, this research aims to (1) investigatethe impacts of the interaction between SWT and AR on the development of ARs; (2) examine the combined influence of initial condition errors in SWTs and ARs on latent heating and then on AR forecasts; and (3) explore the mechanism underneath theamplified errors in AR forecasts from the interaction between SWT and AR. This proposed research could provide support to future ARRecon planning.Approved for public release.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 09, 2024
- Source ID
- N000142412754
Entities
People
- Fred Ralph
Organizations
- Office of Naval Research
- United States Navy
- University of California, San Diego