Tuesday, 13 January 2004: 2:30 PM
Application of the Error Subspace Statistical Estimation (ESSE) system to real-time error forecasting, data assimilation and adaptive sampling off the Central California Coast during AOSN-II
During the August-September 2003 AOSN-II experiment, the
Error Subspace Statistical Estimation (ESSE) system was utilized in
real-time to forecast physical fields and uncertainties, assimilate
a varied set of data streams (ships, gliders, aircraft), and provide
suggestions for adaptive sampling and guide dynamical investigations.
ESSE is a physical-biogeochemical-acoustical estimation scheme which
aims to capture, forecast and reduce the dominant uncertainties,
i.e the error subspace. It is currently based on a singular value
decomposition of the minimum error variance update and on an adaptive
ensemble scheme for the forecast of the largest errors. Each ensemble
member was a sample path of the stochastic primitive equation model of
the Harvard Ocean Prediction System (HOPS). The initial conditions were
set in accord with posterior error estimates. The stochastic forcings
represented various model errors. The data assimilation and adaptive
sampling schemes were consistent.
Over the period 4 August to 3 September, 10 sets of ESSE error forecasts,
data assimilation products, adaptive sampling recommendations
were issued in real-time, using a total of 4323 ensemble members.
Operational products and dynamical interpretations were posted on the web.
Scientific and technical results will be presented, including the:
dynamical findings in Monterey Bay and the California Current System;
convergence of the ensemble; ensemble mean and most probable forecast (mpf);
forecast skill estimates; variances, covariances and singular vectors of
the ensemble; data assimilation and the modeling of data and model errors;
adaptive sampling, either subjective based on error forecasts or
quantitative based on nonlinear field and data forecasts.