Simple Doppler Wind Lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model

Details Simple Doppler Wind Lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model Simple Doppler Wind Lidar adaptive observation experiments with 3D-Var and an ensemble Kalman filter in a global primitive equations model

Oct 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007georl..3419808l&link_type=abstract

Geophysical Research Letters, Volume 34, Issue 19, CiteID L19808

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Computational Geophysics: Data Analysis: Algorithms And Implementation, Computational Geophysics: Instruments And Techniques, Computational Geophysics: General Or Miscellaneous

Scientific paper

Through simple Observing System Simulation Experiments, we compare several adaptive observation strategies designed to subsample Doppler Wind Lidar (DWL) observations along satellite tracks, and examine the effectiveness of two data assimilation schemes, 3D-Var and the Local Ensemble Transform Kalman Filter (LETKF). With respect to sampling strategies, our results show that the LETKF-based ensemble spread method is superior to the other strategies tested, namely, use of a uniform distribution, the climatological spread strategy, or use of a random distribution, and is close to the ideal result obtained assuming that the true forecast error is known. With 10% DWL observations from the ensemble spread strategy, both 3D-Var and LETKF attain about 90% of the impact that 100% DWL wind profile coverage would provide. However, when the adaptive DWL observations coverage is reduced to 2%, 3D-Var becomes less effective than the LETKF assimilation scheme.

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