scholarly journals A Multiscale Sequential Data Assimilation System and Its Application to Short-term Traffic Flow Prediction

2020 ◽  
Vol 32 (11) ◽  
pp. 3893
Author(s):  
Wenzhong Shi ◽  
Runjie Wang
Keyword(s):  
Data Assimilation ◽  
Traffic Flow ◽  
Sequential Data ◽  
Short Term ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Sequential Data Assimilation

scholarly journals An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting

2020 ◽  
Vol 9 (6) ◽  
pp. 340
Author(s):  
Xiaohua Tong ◽  
Runjie Wang ◽  
Wenzhong Shi ◽  
Zhiyuan Li
Keyword(s):  
Data Assimilation ◽  
Traffic Flow ◽  
Sequential Data ◽  
L1 Norm ◽  
Flow Measurements ◽  
Norm Constraint ◽  
Filter Divergence ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Sequential Data Assimilation

Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression.

scholarly journals Development of the Nonstationary Incremental Analysis Update Algorithm for Sequential Data Assimilation System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yoo-Geun Ham ◽  
Hyo-Jong Song ◽  
Jaehee Jung ◽  
Gyu-Ho Lim
Keyword(s):  
Data Assimilation ◽  
Time Window ◽  
Sequential Data ◽  
Incremental Analysis ◽  
Lorenz Model ◽  
Time Step ◽  
Data Assimilation System ◽  
Forward Operator ◽  
Assimilation System ◽  
Sequential Data Assimilation

This study introduces a modified version of the incremental analysis updates (IAU), called the nonstationary IAU (NIAU) method, to improve the assimilation accuracy of the IAU while keeping the continuity of the analysis. Similar to the IAU, the NIAU is designed to add analysis increments at every model time step to improve the continuity in the intermittent data assimilation. However, unlike the IAU, the NIAU procedure uses time-evolved forcing using the forward operator as corrections to the model. The solution of the NIAU is superior to that of the forward IAU, of which analysis is performed at the beginning of the time window for adding the IAU forcing, in terms of the accuracy of the analysis field. It is because, in the linear systems, the NIAU solution equals that in an intermittent data assimilation method at the end of the assimilation interval. To have the filtering property in the NIAU, a forward operator to propagate the increment is reconstructed with only dominant singular vectors. An illustration of those advantages of the NIAU is given using the simple 40-variable Lorenz model.

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