Local ensemble Kalman filtering in the presence of model bias
Open Access
- 1 January 2006
- journal article
- Published by Stockholm University Press in Tellus A: Dynamic Meteorology and Oceanography
- Vol. 58 (3), 293
- https://doi.org/10.1111/j.1600-0870.2006.00178.x
Abstract
We modify the local ensemble Kalman filter (LEKF) to incorporate the effect of forecast model bias. The method is based on augmentation of the atmospheric state by estimates of the model bias, and we consider different ways of modeling (i.e. parameterizing) the model bias.We evaluate the effectiveness of the proposed augmented state ensemble Kalman filter through numerical experiments incorporating various model biases into the model of Lorenz and Emanuel. Our results highlight the critical role played by the selection of a good parameterization model for representing the form of the possible bias in the forecast model. In particular, we find that forecasts can be greatly improved provided that a good model parameterizing the model bias is used to augment the state in the Kalman filter.Keywords
This publication has 14 references indexed in Scilit:
- Efficient parameter estimation for a highly chaotic systemTellus A: Dynamic Meteorology and Oceanography, 2004
- The Postprocessing Galerkin and Nonlinear Galerkin Methods---A Truncation Analysis Point of ViewSIAM Journal on Numerical Analysis, 2003
- Data Assimilation in the Presence of Forecast Bias: The GEOS Moisture AnalysisMonthly Weather Review, 2000
- A Hybrid Ensemble Kalman Filter–3D Variational Analysis SchemeMonthly Weather Review, 2000
- Data assimilation in the presence of forecast biasQuarterly Journal of the Royal Meteorological Society, 1998
- A General Weak Constraint Applicable to Operational 4DVAR Data Assimilation SystemsMonthly Weather Review, 1997
- An Introduction to Estimation Theory (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice)Journal of the Meteorological Society of Japan. Ser. II, 1997
- Initialization of the HIRLAM Model Using a Digital FilterMonthly Weather Review, 1992
- The Effect of Serially Correlated Observation and Model Error on Atmospheric Data AssimilationMonthly Weather Review, 1992
- Treatment of bias in recursive filteringIEEE Transactions on Automatic Control, 1969