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Simple Doppler Wind Lidar adaptive observation experiments with 3D‐Var and an ensemble Kalman filter in a global primitive equations model
Author(s) -
Liu Junjie,
Kalnay Eugenia
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2007gl030707
Subject(s) - data assimilation , ensemble kalman filter , kalman filter , computer science , doppler effect , wind speed , meteorology , algorithm , extended kalman filter , artificial intelligence , geography , physics , astronomy
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.