Preprints
https://doi.org/10.5194/npg-2015-76
https://doi.org/10.5194/npg-2015-76
21 Jan 2016
 | 21 Jan 2016
Status: this preprint has been withdrawn by the authors.

Impact of Optimal Observational Time Window on Parameter Optimization and Climate Prediction: Simulation with a Simple Climate Model

A. A. Yuxin Zhao, B. B. Xiong Deng, and C. C. Shuo Yang

Abstract. Usually, an optimal time window (OTW) centred at the assimilation time to collect measured data for an assimilation cycle, can greatly improve the CDA analysis skill. Here, with a simple coupled model, we study the impact of optimal OTWs on the quality of parameter optimization and climate prediction. Results show that the optimal OTWs of valid atmosphere or ocean observations exist for the parameter being estimated and incorporating the parameter optimization will do some impact on the optimal OTWs for the state estimation. And using the optimal OTWs can enhance the predictability both of the atmosphere and ocean.

This preprint has been withdrawn.

A. A. Yuxin Zhao, B. B. Xiong Deng, and C. C. Shuo Yang

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
A. A. Yuxin Zhao, B. B. Xiong Deng, and C. C. Shuo Yang
A. A. Yuxin Zhao, B. B. Xiong Deng, and C. C. Shuo Yang

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This preprint has been withdrawn.

Short summary
An optimal time window centred at the assimilation time to collect measured data for an assimilation cycle, can improve the CDA analysis skill. We study the impact of optimal OTWs on the quality of parameter optimization and climate prediction in a simple coupled model. Results show that the optimal OTWs of valid atmosphere or ocean observations exist for the parameter being estimated and incorporating the parameter optimization will enhance the predictability both of the atmosphere and ocean.