Journal
TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY
Volume 58, Issue 3, Pages 293-306Publisher
BLACKWELL PUBLISHING
DOI: 10.1111/j.1600-0870.2006.00178.x
Keywords
-
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available