...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Tellus, Series A. Dynamic meteorology & oceanography >A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model
【24h】

A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model

机译：利用简单耦合模型对耦合参数同化的增强参数校正进行气候估计和预报的研究

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Uncertainties in physical parameters of coupled models are an important source of model bias and adversely impact initialisation for climate prediction. Data assimilation using error covariances derived from model dynamics to extract observational information provides a promising approach to optimise parameter values so as to reduce such bias. However, effective parameter estimation in a coupled model is usually difficult because the error covariance between a parameter and the model state tends to be noisy due to multiple sources of model uncertainties. Using a simple coupled model consisting of the 3-variable Lorenz model and a slowly varying slab ‘ocean’, this study first investigated how to enhance the signal-to-noise ratio in covariances between model states and parameters, and then designed a data assimilation scheme for enhancive parameter correction (DAEPC). In DAEPC, parameter estimation is facilitated after state estimation reaches a ‘quasi-equilibrium’ where the uncertainty of coupled model states is sufficiently constrained by observations so that the covariance between a parameter and the model state is signal dominant. The observation-updated parameters are applied to improving the next cycle of state estimation and the refined covariance of parameter and model state further improves parameter correction. Performing dynamically adaptive state and parameter estimations with speedy convergence, DAEPC provides a systematic way to estimate the whole array of coupled model parameters using observations, and produces more accurate state estimates. Forecast experiments show that the DAEPC initialisation with observation-estimated parameters greatly improves the model predictability – while valid ‘atmospheric’ forecasts are extended two times longer, the ‘oceanic’ predictability is almost tripled. The simple model results here provide some insights for improving climate estimation and prediction with a coupled general circulation model.
机译：耦合模型物理参数的不确定性是模型偏差的重要来源，会对气候预测的初始化产生不利影响。使用源自模型动力学的误差协方差来提取观测信息的数据同化为优化参数值以减少此类偏差提供了一种有前途的方法。但是，由于模型不确定性的多种来源，参数和模型状态之间的误差协方差趋于嘈杂，因此在耦合模型中进行有效的参数估计通常很困难。使用由3变量Lorenz模型和缓慢变化的平板构成的简单耦合模型，本研究首先研究了如何提高模型状态与参数之间的协方差中的信噪比，以及然后设计了一个数据同化方案，用于增强参数校正（DAEPC）。在DAEPC中，在状态估计达到＆＃x2018;准平衡＆＃x2019;之后，就可以进行参数估计。耦合模型状态的不确定性受到观察的充分约束，因此参数与模型状态之间的协方差是信号主导的。观测更新后的参数用于改善状态估计的下一个周期，并且参数和模型状态的精确协方差进一步改善了参数校正。通过快速收敛执行动态自适应状态和参数估计，DAEPC提供了一种系统的方法来使用观测值来估计耦合模型参数的整个阵列，并产生更准确的状态估计。预测实验表明，使用观测估计参数进行的DAEPC初始化极大地提高了模型的可预测性。有效期为＆＃x2018;大气＆＃x2019;预报的时间延长了两倍，即＆oceanic＆＃x2019;可预测性几乎增加了两倍。这里的简单模型结果为通过耦合的一般环流模型改善气候估计和预报提供了一些见识。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号