Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model

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• 关键词：Data assimilation ; Ensemble Kalman filter ; LMDZ5 ; DART ; Global reanalysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8964
• 期：1
• 页码：284-298
• 全文大小：6,235 KB
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• 作者单位：Tarkeshwar Singh (15)
  Rashmi Mittal (16)
  H. C. Upadhyaya (15)
  
  15. Indian Institute of Technology (IIT) Delhi, New Delhi, India
  16. IBM Research, New Delhi, India
  
• 丛书名：Dynamic Data-Driven Environmental Systems Science
• ISBN：978-3-319-25138-7
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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 Météorologie 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.