The Assimilation in the Unstable Subspace (AUS) was introduced by Trevisan and Uboldi
in 2004, and developed by Trevisan, Uboldi and Carrassi, to minimize the analysis and forecast
errors by exploiting the flow-dependent instabilities of the forecast-analysis cycle system, which
may be thought of as a system forced by observations. In the AUS scheme the assimilation is
obtained by confining the analysis increment in the unstable subspace of the forecast-analysis
cycle system so that it will have the same structure of the dominant instabilities of the system.
The unstable subspace is estimated by Breeding on the Data Assimilation System (BDAS). AUS-
BDAS has already been tested in realistic models and observational configurations, including a
Quasi-Geostrophicmodel and a high dimensional, primitive equation ocean model; the experiments
include both fixed and“adaptive”observations. In these contexts, the AUS-BDAS approach greatly
reduces the analysis error, with reasonable computational costs for data assimilation with respect,
for example, to a prohibitive full Extended Kalman Filter.
This is a follow-up study in which we revisit the AUS-BDAS approach in the more basic, highly
nonlinear Lorenz 1963 convective model. We run observation system simulation experiments in a
perfect model setting, and with two types of model error as well: random and systematic. In the
different configurations examined, and in a perfect model setting, AUS once again shows better
efficiency than other advanced data assimilation schemes. In the present study, we develop an
iterative scheme that leads to a significant improvement of the overall assimilation performance
with respect also to standard AUS. In particular, it boosts the efficiency of regime’s changes
tracking, with a low computational cost.
Other data assimilation schemes need estimates of ad hoc parameters, which have to be tuned
for the specific model at hand. In Numerical Weather Prediction models, tuning of parameters —
and in particular an estimate of the model error covariance matrix — may turn out to be quite
difficult. Our proposed approach, instead, may be easier to implement in operational models.
| Identifer | oai:union.ndltd.org:unibo.it/oai:amsdottorato.cib.unibo.it:984 |
| Date | 27 June 2008 |
| Creators | Pilolli, Massimo <1966> |
| Contributors | Rizzi, Rolando, Trevisan, Anna |
| Publisher | Alma Mater Studiorum - Università di Bologna |
| Source Sets | Università di Bologna |
| Language | English |
| Detected Language | English |
| Type | Doctoral Thesis, PeerReviewed |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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