Articles | Volume 20, issue 6
https://doi.org/10.5194/npg-20-1031-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/npg-20-1031-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
The local ensemble transform Kalman filter and the running-in-place algorithm applied to a global ocean general circulation model
S. G. Penny
Applied Mathematics and Scientific Computation, University of Maryland, College Park, Maryland, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
National Centers for Environmental Prediction (NCEP), NOAA Center for Weather and Climate Prediction, College Park, Maryland, USA
E. Kalnay
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
J. A. Carton
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
B. R. Hunt
Department of Mathematics, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
Applied Mathematics and Scientific Computation, University of Maryland, College Park, Maryland, USA
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Institute for Physical Science and Technology, University of Maryland, College Park, Maryland, USA
Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, Maryland, USA
T. Miyoshi
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
RIKEN Advanced Institute for Computational Science, Kobe, Japan
G. A. Chepurin
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
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Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmdd-8-7395-2015, https://doi.org/10.5194/gmdd-8-7395-2015, 2015
Revised manuscript not accepted
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DasPy is a ready to use open source parallel multivariate land data assimilation framework with joint state and parameter estimation using Local Ensemble Transform Kalman Filter. The Community Land Model (4.5) was integrated as model operator. The Community Microwave Emission Modelling platform, COsmic-ray Soil Moisture Interaction Code and the Two-Source Formulation were integrated as observation operators for the multivariate assimilation of soil moisture and soil temperature, respectively.
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