A rapidly relocatable prediction system: operational implementation and validation

Abstract

MODAS-NRLPOM is a scalable, portable, and rapidly relocatable system for nowcasting and short-term (2-day) forecasting in support of real-time naval operations. The analyses and forecasts can be available within an hour or two of a request, making the system useful in emergency situations. The Modular Ocean Data Assimilation System (MODAS) combines remote sensed data (altimetry and sea surface temperature) with in situ measurements to produce an analysis of the ocean that can be considerably more accurate than conventional climatology. Geostrophic velocities are derived from the T and S distributions, and the barotropic transport is computed from the computed dynamic height. The MODAS nowcast field provides initial and boundary condition for NRLPOM, a version of the Princeton Ocean Model (POM) that has been implemented at the Naval Research Laboratory (NRL) for real-time naval applications. We will present the results from real-time exercises in coastal domains. The goals are: 1) to determine the network of observations necessary for accurate dynamical and acoustic prediction in coastal waters, 2) to verify the accuracy of the operational datasets available for the MODAS nowcast, and 3) to evaluate the nowcast and forecast capabilities using model-data comparisons.