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| 1 | Comparison and combination of EAKF and SIR-PF in the Bayesian filter framework显示文摘Bayesian estimation theory provides a general approach for the state estimate of linear or nonlinear and Gaussian or non-Gaussian systems. In this study, we first explore two Bayesian-based methods: ensemble adjustment Kalman filter(EAKF) and sequential importance resampling particle filter(SIR-PF), using a well-known nonlinear and non-Gaussian model(Lorenz '63 model). The EAKF, which is a deterministic scheme of the ensemble Kalman filter(En KF), performs better than the classical(stochastic) En KF in a general framework. Comparison between the SIR-PF and the EAKF reveals that the former outperforms the latter if ensemble size is so large that can avoid the filter degeneracy, and vice versa. The impact of the probability density functions and effective ensemble sizes on assimilation performances are also explored. On the basis of comparisons between the SIR-PF and the EAKF, a mixture filter, called ensemble adjustment Kalman particle filter(EAKPF), is proposed to combine their both merits. Similar to the ensemble Kalman particle filter, which combines the stochastic En KF and SIR-PF analysis schemes with a tuning parameter, the new mixture filter essentially provides a continuous interpolation between the EAKF and SIR-PF. The same Lorenz '63 model is used as a testbed, showing that the EAKPF is able to overcome filter degeneracy while maintaining the non-Gaussian nature, and performs better than the EAKF given limited ensemble size. | SHEN Zheqi ZHANG Xiangming TANG Youmin | 2016 | Acta Oceanologica Sinica2016，35，3： | 3 |
| 2 | A variational successive corrections approach for the sea ice concentration analysis显示文摘The sea ice concentration observation from satellite remote sensing includes the spatial multi-scale information.However,traditional data assimilation methods cannot better extract the valuable information due to the complicated variability of the sea ice concentration in the marginal ice zone.A successive corrections analysis using variational optimization method,called spatial multi-scale recursive filter(SMRF),has been designed in this paper to extract multi-scale information resolved by sea ice observations.It is a combination of successive correction methods(SCM)and minimization algorithms,in which various observational scales,from longer to shorter wavelengths,can be extracted successively.As a variational objective analysis scheme,it gains the advantage over the conventional approaches that analyze all scales resolved by observations at one time,and also,the specification of parameters is more convenient.Results of single-observation experiment demonstrate that the SMRF scheme possesses a good ability in propagating observational signals.Further,it shows a superior performance in extracting multi-scale information in a two-dimensional sea ice concentration(SIC)experiment with the real observations from Special Sensor Microwave/Imager SIC(SSMI). | Xuefeng Zhang Lu Yang Hongli Fu Dong Li Zheqi Shen Lianxin Zhang Xuhui Hu | 2020 | Acta Oceanologica Sinica2020，39，9： | 1 |
| 3 | A two-stage inflation method in parameter estimation to compensate for constant parameter evolution in Community Earth System Model显示文摘Parameter estimation is defined as the process to adjust or optimize the model parameter using observations.A long-term problem in ensemble-based parameter estimation methods is that the parameters are assumed to be constant during model integration.This assumption will cause underestimation of parameter ensemble spread,such that the parameter ensemble tends to collapse before an optimal solution is found.In this work,a two-stage inflation method is developed for parameter estimation,which can address the collapse of parameter ensemble due to the constant evolution of parameters.In the first stage,adaptive inflation is applied to the augmented states,in which the global scalar parameter is transformed to fields with spatial dependence.In the second stage,extra multiplicative inflation is used to inflate the scalar parameter ensemble to compensate for constant parameter evolution,where the inflation factor is determined according to the spread growth ratio of model states.The observation system simulation experiment with Community Earth System Model(CESM)shows that the second stage of the inflation scheme plays a crucial role in successful parameter estimation.With proper multiplicative inflation factors,the parameter estimation can effectively reduce the parameter biases,providing more accurate analyses. | Zheqi Shen Youmin Tang | 2022 | Acta Oceanologica Sinica2022，41，2： | 1 |
| 4 | Federatedmutual learning:a collaborative machine learning method for heterogeneous data,models,and objectives显示文摘Federated learning(FL)is a novel technique in deep learning that enables clients to collaboratively train a shared model while retaining their decentralized data.However,researchers working on FL face several unique challenges,especially in the context of heterogeneity.Heterogeneity in data distributions,computational capabilities,and scenarios among clients necessitates the development of customized models and objectives in FL.Unfortunately,existing works such as FedAvg may not effectively accommodate the specific needs of each client.To address the challenges arising from heterogeneity in FL,we provide an overview of the heterogeneities in data,model,and objective(DMO).Furthermore,we propose a novel framework called federated mutual learning(FML),which enables each client to train a personalized model that accounts for the data heterogeneity(DH).A“meme model”serves as an intermediary between the personalized and global models to address model heterogeneity(MH).We introduce a knowledge distillation technique called deep mutual learning(DML)to transfer knowledge between these two models on local data.To overcome objective heterogeneity(OH),we design a shared global model that includes only certain parts,and the personalized model is task-specific and enhanced through mutual learning with the meme model.We evaluate the performance of FML in addressing DMO heterogeneities through experiments and compare it with other commonly used FL methods in similar scenarios.The results demonstrate that FML outperforms other methods and effectively addresses the DMO challenges encountered in the FL setting. | Tao SHEN Jie ZHANG Xinkang JIA Fengda ZHANG Zheqi LV Kun KUANG Chao WU Fei WU | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，10： | 0 |
| 5 | A new ensemble-based targeted observational method and its application in the TPOS 2020显示文摘Ensemble Kalman filter-based targeted observation is one of the best methods for determining the optimal observational array for oceanic buoy deployment.This study proposes a new algorithm suitable for a‘cross-region and cross-variable’approach by introducing a projection operator into the optimization process.A targeted observational analysis was conducted for El Niño–Southern Oscillation(ENSO)events in the tropical western Pacific for the Tropical Pacific Observation System(TPOS)2020.The prediction target was at the Niño 3.4 region and the first 10 optimal observational sites detected reduced initial uncertainties by 70%,with the best observational array located where the Rossby wave signal dominates.At the vertical level,the most significant contribution was derived from observations near the thermocline.This study provides insights into understanding ENSO-related variability and offers a practical approach to designing an optimal mooring array.It serves as a scientific guidance for designing a TPOS observation network. | Weixun Rao Youmin Tang Yanling Wu Zheqi Shen Xiangzhou Song Xiaojing Li Tao Lian Dake Chen Feng Zhou | 2023 | National Science Review2023，10，11： | 0 |
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