Use of data assimilation via linear low-order models for the initialization of El Nino Southern Oscillation predictions

Publication Type  Journal Article
Year of Publication  2001
Authors  Canizares, R.; Kaplan, A.; Cane, M. A.; Chen, D.; Zebiak, S. E.
Journal Title  Journal of Geophysical Research-Oceans
Volume  106
Issue  C12
Pages  30947-30959
Journal Date  Dec 15
ISBN Number  0148-0227
Accession Number  ISI:000173402400003
Key Words  surface temperature anomalies; statistical-models; optimal-growth; coupled model; enso; predictability; cycle
Abstract  

The utility of a Kalman filter (KF) for initialization of an intermediate nonlinear coupled model for El Nino - Southern Oscillation prediction is studied via an approximation of the nonlinear coupled model by a system of seasonally dependent linear models. The low-dimensional nature of such an approximation allows one to determine a sequence of "perfect" initial states that start a trajectory segment best fitting the observed data. Defining these perfect initial conditions as "true" states of the model, we compute a priori parameters of the KF and test its ability to produce an estimate of the "truth" superior to the less theoretically sound estimates. We find that in this application such a KF does not produce an estimate outperforming a pure observational projection as an initial condition for the coupled model forecast. The violation of standard KF assumptions on temporal whiteness of observational errors and system noise is identified as the reason for this failure.

Notes  

513WBTimes Cited:7Cited References Count:25

URL  <Go to ISI>://000173402400003