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Development of an Adjoint for a Complex Atmospheric Model, the ARPS, using TAF

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Automatic Differentiation: Applications, Theory, and Implementations

Part of the book series: Lecture Notes in Computational Science and Engineering ((LNCSE,volume 50))

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Abstract

Large-scale scientific computer models, such as operational weather predictions models, pose challenges for the applicability of AD tools. We report the application of TAF to the development of the adjoint model and tangent linear model of a complex atmospheric model, ARPS. Strategies to overcome the problems encountered during the development process are discussed. A rigorous verification procedure of the adjoint model is presented. Simple experiments are carried out for sensitivity study, and the results confirm the correctness of the generated models.

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Author information

Authors and Affiliations

  1. Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, OK, USA

    Ying Xiao, Ming Xue, William Martin & Jidong Gao

  2. School of Meteorology, University of Oklahoma, Norman, OK, USA

    Ming Xue

  3. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Ying Xiao

Authors
  1. Ying Xiao
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  2. Ming Xue
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  3. William Martin
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  4. Jidong Gao
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Editor information

Editors and Affiliations

  1. Institute for Scientific Computing, RWTH Aachen University, D-52056, Aachen, Germany

    Martin Bücker

  2. Department of Electrical and Computer Engineering, Marquette University, 1515 W. Wisconsin Avenue, 1881, 53201-1881, Milwaukee, WI, USA

    George Corliss

  3. Software and Tools for Computational Engineering, RWTH Aachen University, D-52056, Aachen, Germany

    Uwe Naumann

  4. Mathematics and Computer Science Division, Argonne National Laboratory, 9700 S Cass Ave, Argonne, IL, 60439, USA

    Paul Hovland  & Boyana Norris  & 

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© 2006 Springer

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Xiao, Y., Xue, M., Martin, W., Gao, J. (2006). Development of an Adjoint for a Complex Atmospheric Model, the ARPS, using TAF. In: Bücker, M., Corliss, G., Naumann, U., Hovland, P., Norris, B. (eds) Automatic Differentiation: Applications, Theory, and Implementations. Lecture Notes in Computational Science and Engineering, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28438-9_23

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