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Variational Quality Control of non-gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments

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Advances in Atmospheric Sciences 2021年第9期38卷 1510-1524页

作者： Jie HE Xulin MA Xuyang GE Juanjuan LIU Wei CHENG Man-Yau CHAN Ziniu XIAO Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Beijing Institute of Applied Meteorology Beijing 100029China Department of Meteorology and Atmospheric Science and Center for Advanced Data Assimilation and Predictability TechniquesThe Pennsylvania State UniversityUniversity ParkPA 16801USA

The existence of outliers can seriously influence the analysis of variational data *** control allows us to effectively eliminate or absorb these outliers to produce better analysis *** particular,variational quality control(VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation *** this study,governing equations are derived for two VarQC algorithms that utilize different contaminated gaussian distributions(CGDs): gaussian plus flat distribution and Huber norm *** such,these VarQC algorithms can handle outliers that have non-gaussian ***,these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System(GRAPES) model-level three-dimensional variational data assimilation(m3 DVAR) *** using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the gaussian plus flat ***,real observation experiments show that the distribution of observation analysis weights conform well with theory,indicating that the application of VarQC is effective in the GRAPES m3 DVAR *** case study and longperiod data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field(geopotential height and temperature).Compared to the control experiment,VarQC experiments have noticeably better posterior mass ***,the VarQC method using the Huber distribution is superior to the VarQC method using the gaussian plus flat distribution,especially at the middle and lower levels.

关键词： variational quality control non-gaussian distribution innovation outlier data assimilation

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Variational quality control of non-gaussian innovations and its parametric optimizations for the GRAPES m3DVAR system

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Frontiers of Earth Science 2023年第2期17卷 620-631页

作者： Jie HE Yang SHI Boyang ZHOU Qiuping WANG Xulin MA Guangzhou Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510640China Guangdong Meteorological Observatory Guangzhou 510640China Qingdao Air Traffic Management Station of Civil Aviation of China Qingdao 266108China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Key Laboratory of Meteorological DisasterNanjing University of Information Science and TechnologyNanjing 210044China

The magnitude and distribution of observation innovations,which have an important impact on the analyzed accuracy,are critical variables in data *** quality control(VarQC)based on the contaminated gaussian distribution(CGD)of observation innovations is now widely used in data assimilation,owing to the more reasonable representation of the probability density function of innovations that can sufficiently absorb observations by assigning different weights ***,the inaccurate parameters prevent VarQC from showing the advantages it should have in the GRAPES(Global/Regional Assimilation and PrEdiction System)m3DVAR ***,the parameter optimization methods are considerable critical studies to improve *** this paper,we describe two probable CGDs to include the non-gaussian distribution of actual observation errors,gaussian plus flat distribution and Huber norm *** potential optimization methods of the parameters are introduced in detail for different *** different parameter configurations,the optimization analysis shows that the gaussian plus flat distribution and the Huber norm distribution are more consistent with the long-tail distribution of actual innovations compared to the gaussian *** VarQC’s cost and gradient functions with Huber norm distribution are more reasonable,while the VarQC’s cost function with gaussian plus flat distribution may converge on different minimums due to its nonconcave *** weight functions of two VarQCs gradually decrease with the increase of innovation but show different shapes,and the VarQC with Huber norm distribution shows more elasticity to assimilate the observations with a high contamination ***,we reveal a general derivation relationship between the CGDs and VarQCs.A novel schematic interpretation that classifies the assimilated data into three categories in VarQC is *** are conducive to the development of a new Var

关键词： data assimilation variational quality control contaminated gaussian distribution non-gaussian distribution innovation

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Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway non-gaussian Modeling

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Chinese Journal of Chemical Engineering 2008年第1期16卷 48-51页

作者： Changkyoo Yoo Minhan Kim Sunjin Hwang Yongmin Jo Jongmin Oh College of Environmental and Applied Chemistry Green Energy Center Kyung Hee University Gyeonggi-Do 446-701 Korea

A new on-line predictive monitoring and prediction model for periodic biological processes is proposed using the multiway non-gaussian modeling. The basic idea of this approach is to use multiway non-gaussian modeling to extract some dominant key components from daily normal operation data in a periodic process, and subsequently combining these components with predictive statistical process monitoring techniques. The proposed predictive monitoring method has been applied to fault detection and diagnosis in the biological wastewater-treatment process, which is based on strong diurnal characteristics. The results show the power and advantages of the proposed predictive monitoring of a continuous process using the multiway predictive monitoring concept, which is thus able to give very useful conceptual results for a daily monitoring process and also enables a more rapid detection of the process fault than other traditional monitoring methods.

关键词： inferential sensing multiway modeling non-gaussian distribution online predictive monitoring process supervision wastewater treatment process

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Robust SLAM localization method based on improved variational Bayesian filtering

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Journal of Southeast University(English Edition) 2022年第4期38卷 340-349页

作者： Zhai Hongqi Wang Lihui Cai Tijing Meng Qian School of Instrument Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology of Ministry of Education Southeast UniversityNanjing 210096China

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is ***,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering ***,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted ***,the system updates are iterated multiple times to further gradually correct the state estimation ***,to observe features at different depths,a feature measurement model containing depth parameters is *** results show that when the measurement noise does not obey the gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

关键词： underwater navigation and positioning non-gaussian distribution time-varying noise variational Bayesian method simultaneous localization and mapping(SLAM)

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Adaptive Linear Filtering Design with Minimum Symbol Error Probability Criterion

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International Journal of Automation and computing 2006年第3期3卷 291-303页

作者： Sheng Chen School of Electronics and Computer Science University of Southampton Highfield Southampton SO17 1B J U.K.

Adaptive digital filtering has traditionally been developed based on the minimum mean square error （MMSE） criterion and has found ever-increasing applications in communications. This paper presents an alternative adaptive filtering design based on the minimum symbol error rate （MSER） criterion for communication applications. It is shown that the MSER filtering is smarter, as it exploits the non-gaussian distribution of filter output effectively. Consequently, it provides significant performance gain in terms of smaller symbol error over the MMSE approach. Adopting Parzen window or kernel density estimation for a probability density function, a block-data gradient adaptive MSER algorithm is derived. A stochastic gradient adaptive MSER algorithm, referred to as the least symbol error rate, is further developed for sample-by-sample adaptive implementation of the MSER filtering. Two applications, involving single-user channel equalization and beamforming assisted receiver, are included to demonstrate the effectiveness and generality of the proposed adaptive MSER filtering approach.

关键词： Adaptive filtering mean square error probability density function non-gaussian distribution Parzen window estimate symbol error rate stochastic gradient algorithm.

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gaussian mixture model clustering with completed likelihood minimum message length criterion

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Journal of Southeast University(English Edition) 2013年第1期29卷 43-47页

作者：曾洪卢伟宋爱国东南大学仪器科学与工程学院南京210096 南京农业大学工学院南京210031

An improved gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

关键词： gaussian mixture model non-gaussian distribution model selection expectation-maximization algorithm completed likelihood minimum message length criterion

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