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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 22, issue 5
Nonlin. Processes Geophys., 22, 601–611, 2015
https://doi.org/10.5194/npg-22-601-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Nonlin. Processes Geophys., 22, 601–611, 2015
https://doi.org/10.5194/npg-22-601-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 09 Oct 2015

Research article | 09 Oct 2015

A framework for variational data assimilation with superparameterization

I. Grooms and Y. Lee
Related subject area  
Subject: Predictability, Data Assimilation | Topic: Climate, Atmosphere, Ocean, Hydrology, Cryosphere, Biosphere
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Cited articles  
Abramov, R. V.: Suppression of chaos at slow variables by rapidly mixing fast dynamics through linear energy-preserving coupling, Commun. Math. Sci., 10, 595–624, 2012.
Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001.
Gaspari, G. and Cohn, S.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteorol. Soc., 125, 723–757, 1999.
Grabowski, W.: An improved framework for superparameterization, J. Atmos. Sci., 61, 1940–1952, 2004.
Grabowski, W. and Smolarkiewicz, P.: CRCP: a Cloud Resolving Convection Parameterization for modeling the tropical convecting atmosphere, Physica D, 133, 171–178, 1999.
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Superparameterization is a multiscale computational method that significantly improves the representation of cloud processes in global atmosphere and climate models. We present a framework for assimilating observational data into superparameterized models to initialize them for forecasts. The framework is demonstrated in the context of a new system of ordinary differential equations that constitutes perhaps the simplest model of superparameterization.
Superparameterization is a multiscale computational method that significantly improves the...
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