Articles | Volume 24, issue 4
Nonlin. Processes Geophys., 24, 681–694, 2017
Nonlin. Processes Geophys., 24, 681–694, 2017

Research article 17 Nov 2017

Research article | 17 Nov 2017

Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

Yuxin Zhao et al.

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Nonlin. Processes Geophys. Discuss.,,, 2016
Preprint withdrawn
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Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Climate, atmosphere, ocean, hydrology, cryosphere, biosphere
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Cited articles

Anderson, J. L.: An ensemble adjustment Kalman Filter for data assimilation, Mon. Weather Rev., 129, 2884–2903,<2884:AEAKFF>2.0.CO;2, 2001.
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642,<0634:ALLSFF>2.0.CO;2, 2003.
Anderson, J. L.: An adaptive covariance inflation error correction algorithm for ensemble filters, Tellus A, 59, 210–224,, 2007.
Anderson, J. L.: Spatially and temporally varying adaptive covariance inflation for ensemble filter, Tellus A, 61, 72–83,, 2009.
Chen, D., Zebiak, S. E., Busalacchi, A. J., and Cane, M. A.: An improved procedure for EI Nino forecasting: implications for predictability, Science, 269, 1699–1702, 1995.
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Here with a simple coupled model that simulates typical scale interactions in the climate system, we study the optimal OTWs for the coupled media so that climate signals can be most accurately recovered by CDA. Results show that an optimal OTW determined from the de-correlation timescale provides maximal observational information that best fits the characteristic variability of the coupled medium during the data blending process.