Ocean Ensemble Forecasting and Adaptive Sampling - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 383

Chapter 16
Ocean Ensemble Forecasting and Adaptive
Sampling

Xiaodong Hong and Craig Bishop

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.

16.1 Introduction

The impact of supplemental observations on the forecast error reduction depends
on: (a) the size of the forecast error at the location where the observation is taken,
(b) the assumptions used in the data assimilation scheme about the strength of the
correlation between errors in forecasts of the observed variable and errors in all other