Ocean Ensemble Forecasting and Adaptive Sampling

Abstract

An ocean adaptive sampling algorithm, derived from the Ensemble Transform Kalman Filter (ETKF) technique, is illustrated in this Chapter using the glider observations collected during the Autonomous Ocean Sampling Network (AOSN) II field campaign. This algorithm can rapidly obtain the prediction error covariance matrix associated with a particular deployment of the observation and quickly assess the ability of a large number of future feasible sequences of observations to reduce the forecast error variance. The uncertainty in atmospheric forcing is represented by using a time-shift technique to generate a forcing ensemble from a single deterministic atmospheric forecast. The uncertainty in the ocean initial condition is provided by using the Ensemble Transform (ET) technique, which ensures that the ocean ensemble is consistent with estimates of the analysis error variance. The ocean ensemble forecast is set up for a 72 h forecast with a 24 h update cycle for the ocean data assimilation. Results from the atmospheric forcing perturbation and ET ocean ensemble mean are examined and discussed. Measurements of the ability of the ETKF to predict 24–48 h ocean forecast error variance reductions over the Monterey Bay due to the additional glider observations are displayed and discussed using the signal variance, signal variance summary map, and signal variance summary bar charts, respectively.