Articles | Volume 24, issue 2
Research article
06 Mar 2017
Research article |  | 06 Mar 2017

Insights on the role of accurate state estimation in coupled model parameter estimation by a conceptual climate model study

Xiaolin Yu, Shaoqing Zhang, Xiaopei Lin, and Mingkui Li

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Cited articles

Anderson, J.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001.
Anderson, J.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003.
Annan, J. D., Lunt, D. J., Hargreaves, J. C., and Valdes, P. J.: Parameter estimation in an atmospheric GCM using the Ensemble Kalman Filter, Nonlin. Processes Geophys., 12, 363–371,, 2005.
Barth, A., Canter, M., Schaeybroeck, B. V., Vannitsem, S., Massonnet, F., Zunz, V., Mathiot, P., Alvera-Azcarate, A., and Beckers, J.: Assimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean, Ocean Modell., 93, 22–39, 2015.
Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteorol. Soc., 131.613, 3323–3344, 2005.
Short summary
Parameter estimation (PE) with a global coupled data assimilation (CDA) system can improve the runs, but the improvement remains in a limited range. We have to come back to simple models to sort out the sources of noises. Incomplete observations and the chaotic nature of the atmosphere have much stronger influences on the PE through the state estimation (SE) process. Here, we propose the guidelines of how to enhance the signal-to-noise ratio under partial SE status.