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Featured researches published by Feiyu Lu.


Journal of Climate | 2014

Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method*

Yun Liu; Zhengyu Liu; Shaoqing Zhang; X. Rong; Robert L. Jacob; Shu Wu; Feiyu Lu

AbstractEnsemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.


Journal of Climate | 2014

Ensemble-Based Parameter Estimation in a Coupled General Circulation Model

Yun Liu; Zhengyu Liu; Shaoqing Zhang; Robert L. Jacob; Feiyu Lu; X. Rong; Shu Wu

AbstractParameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of ...


Monthly Weather Review | 2015

Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study*

Feiyu Lu; Zhengyu Liu; Shaoqing Zhang; Yun Liu

AbstractThis paper studies a new leading averaged coupled covariance (LACC) method for the strongly coupled data assimilation (SCDA). The SCDA not only uses the coupled model to generate the forecast and assimilate observations into multiple model components like the weakly coupled version (WCDA), but also applies a cross update using the coupled covariance between variables from different model components. The cross update could potentially improve the balance and quality of the analysis, but its implementation has remained a great challenge in practice because of different time scales between model components. In a typical extratropical coupled system, the ocean–atmosphere correlation shows a strong asymmetry with the maximum correlation occurring when the atmosphere leads the ocean by about the decorrelation time of the atmosphere. The LACC method utilizes such asymmetric structure by using the leading forecasts and observations of the fast atmospheric variable for cross update, therefore, increasing t...


Monthly Weather Review | 2015

Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part II: CGCM Experiments*

Feiyu Lu; Zhengyu Liu; Shaoqing Zhang; Yun Liu; Robert L. Jacob

AbstractThis paper uses a fully coupled general circulation model (CGCM) to study the leading averaged coupled covariance (LACC) method in a strongly coupled data assimilation (SCDA) system. The previous study in a simple coupled climate model has shown that, by calculating the coupled covariance using the leading averaged atmospheric states, the LACC method enhances the signal-to-noise ratio and improves the analysis quality of the slow model component compared to both the traditional weakly coupled data assimilation without cross-component adjustments (WCDA) and the regular SCDA using the simultaneous coupled covariance (SimCC).Here in Part II, the LACC method is tested with a CGCM in a perfect-model framework. By adding the observational adjustments from the low-level atmosphere temperature to the sea surface temperature (SST), the SCDA using LACC significantly reduces the SST error compared to WCDA over the globe; it also improves from the SCDA using SimCC, which performs better than the WCDA only in ...


Journal of Climate | 2018

Local and Remote Responses of Atmospheric and Oceanic Heat Transports to Climate Forcing: Compensation versus Collaboration

Zhengyu Liu; Chengfei He; Feiyu Lu

AbstractWe present a theoretical study on local and remote responses of atmosphere and ocean meridional heat transports (AHT and OHT, respectively) to climate forcing in a coupled energy balance mo...


Climate Dynamics | 2017

Understanding the control of extratropical atmospheric variability on ENSO using a coupled data assimilation approach

Feiyu Lu; Zhengyu Liu; Yun Liu; Shaoqing Zhang; Robert L. Jacob


Physica A-statistical Mechanics and Its Applications | 2012

Universal scaling behaviors of meteorological variables’ volatility and relations with original records

Feiyu Lu; Naiming Yuan; Zuntao Fu; Jiangyu Mao


Journal of Climate | 2018

Assessing Extratropical Influence on Observed El Niño–Southern Oscillation Events Using Regional Coupled Data Assimilation

Feiyu Lu; Zhengyu Liu


Journal of Geophysical Research | 2017

A Systematic Comparison of Particle Filter and EnKF in Assimilating Time‐Averaged Observations

Huaran Liu; Zhengyu Liu; Feiyu Lu


Journal of Geophysical Research | 2017

A Systematic Comparison of Particle Filter and EnKF in Assimilating Time-Averaged Observations: PDA: PF-SIR. Versus EnKF

Huaran Liu; Zhengyu Liu; Feiyu Lu

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Robert L. Jacob

Argonne National Laboratory

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Yun Liu

Chinese Academy of Sciences

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Huaran Liu

University of Wisconsin-Madison

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Shu Wu

University of Wisconsin-Madison

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S. Zhang

Ocean University of China

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Chengfei He

Nanjing University of Information Science and Technology

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Jiangyu Mao

Chinese Academy of Sciences

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