The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations |
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| Authors: | Takemasa Miyoshi Masaru Kunii |
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| Institution: | (1) Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, MD 20742, USA |
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| Abstract: | The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and
real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale
numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in
parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using
the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies.
The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously
improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical
models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial
and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations
and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble
members improves the analyses consistently. |
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