Performance Evaluation of the Chesapeake Bay Data Assimilation and Forecasting System

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Wednesday, 7 January 2015
Kayo Ide, University of Maryland, College Park, MD; and M. J. Hoffman and J. Cipriani

As a first step toward a reanalysis and improved forecast system for the Chesapeake Bay, the local ensemble transform Kalman filter (LETKF) has been interfaced with a Regional Ocean Modeling System (ROMS) implementation on the Chesapeake Bay (ChesROMS). Like many estuary circulation systems, forcing uncertainty is a dominant factor in the error growth in addition to model errors most importantly over-mixing that leads to reduced stratification. To account for forcing errors, a carefully designed forcing ensemble is used to drive the ensemble states. The ChesROMS-LETKF greatly reduces the analysis and subsequent forecast errors in the temperature, salinity, and current fields. Most of the improvement in temperature and currents comes from satellite sea surface temperature (SST), while in situ salinity profiles provide improvement to salinity. Corrections permeate through all vertical levels and some correction to stratification is seen in the analysis.