Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model

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題名:	Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model
作者:	Yang,SC;Corazza,M;Carrassi,A;Kalnay,E;Miyoshi,T
貢獻者:	大氣物理研究所
關鍵詞:	NUMERICAL WEATHER PREDICTION;DATA-ASSIMILATION SYSTEM;VARIATIONAL ASSIMILATION;METEOROLOGICAL OBSERVATIONS;ADAPTIVE OBSERVATIONS;THEORETICAL ASPECTS;ANALYSIS SCHEMES;ERROR;EVOLUTION;4D-VAR
日期:	2009
上傳時間:	2010-06-29 18:36:15 (UTC+8)
出版者:	中央大學
摘要:	Local ensemble transform Kalman filter (LETKF) data assimilation, three-dimensional variational data assimilation (3DVAR), and four-dimensional variational data assimilation (4DVAR) schemes are implemented in a quasigeostrophic channel model. Their advantages and disadvantages are compared to assess their use in practical applications. LETKF and 4DVAR, which take into account the flow-dependent errors, outperform 3DVAR under a perfect model scenario. Given the same observations, LETKF produces more accurate analyses than 4DVAR with a 12-h window by effectively correcting the fast-growing errors with the flow-dependent background error covariance. Even though 4DVAR performance benefits substantially from using a longer assimilation window, LETKF is also able to achieve a satisfactory accuracy compared to the 24-h 4DVAR analyses. It is shown that the advantage of the LETKF over 3DVAR is a result of both the ensemble averaging and the information about the "errors of the day'' provided by the ensemble. The analysis corrections at the end of the 12-h assimilation window are similar for LETKF and the 12- h window 4DVAR, and they both resemble bred vectors. At the beginning of the assimilation window, LETKF analysis corrections obtained using a no-cost smoother also resemble the corresponding bred vectors, whereas the 4DVAR corrections are significantly different with much larger horizontal scales.
關聯:	MONTHLY WEATHER REVIEW
顯示於類別:	[大氣物理研究所 ] 期刊論文

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