Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model

被引:2
|
作者
Singh, Tarkeshwar [1 ]
Mittal, Rashmi [2 ]
Upadhyaya, H. C. [1 ]
机构
[1] Indian Inst Technol IIT Delhi, New Delhi, India
[2] IBM Res, New Delhi, India
关键词
Data assimilation; Ensemble Kalman filter; LMDZ5; DART; Global reanalysis; GENERAL-CIRCULATION MODEL; SENSITIVITY; PERFORMANCE; SIMULATION; SYSTEM;
D O I
10.1007/978-3-319-25138-7_26
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work describes the implementation and evaluation of an Ensemble Adjustment Kalman Filter (EAKF) with a global atmospheric zoom model (version 5) of the Laboratoire de Meteorologie Dynamique (LMDZ5, Z stands for zoom). An interface has been developed to use Data Assimilation Research Testbed (DART), a community EAKF system, with LMDZ5 model. The NCEP PREBUFR real observation data have been assimilated to evaluate the performance of newly developed LMDZ5-DART system. It has been demonstrated with the help of a numerical experiment that LMDZ5-DART system successfully assimilates real observations. A one month LMDZ5-DART analysis has been created using assimilation of NCEP PREBUFR observation data, and the assimilated fields are compared with NCEP CDAS reanalysis. Results show that LMDZ5-DART produces remarkably similar reanalysis to NCEP products. This is therefore a very encouraging result towards a long-term goal of creating a high quality analysis over the Indian subcontinent from the assimilation of local satellite products.
引用
收藏
页码:284 / 298
页数:15
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