KLI

A robust surrogate data assimilation approach to real-time forecasting using polynomial chaos expansion

Metadata Downloads
Abstract
Data assimilation plays an essential role in real-time forecasting but demands repetitive model evaluations given ensembles. To address this computational challenge, a novel, robust and efficient approach to surrogate data assimilation is presented. It replaces the internal processes of the ensemble Kalman filter (EnKF) with polynomial chaos expansion (PCE) theory. Eight types of surrogate filters, which can be characterized according to their different surrogate structures, building systems, and assimilating targets, are proposed and validated. To compensate for the potential shortcomings of the existing sequential experimental design (SED), an advanced optimization scheme, named sequential experimental design-polynomial degree (SED-PD), is also advised. Its dual optimization system resolves the issue of SED by which the value of the polynomial degree had to be selected ad-hoc or by trial and error; its multiple stopping criteria ensure convergence even when an accuracy metric does not monotonically decrease over iterations. A comprehensive investigation into how to configure a surrogate filter indicates that the new partial (replacing part of original filters) and invariant (valid for entire time periods) approaches are preferred in terms of accuracy and efficiency, which helps directly reduce the number of dimensions and bridge the gap between hindcasting and real-time forecasting. Of the eight filters, the Dual Invariant Partial filter performs best, with equivalent accuracy to Dual EnKF and about 500 times greater computational efficiency. Ultimately, this proposed surrogate filter will be a promising alternative tool for performing computationally-intensive data assimilation in high-dimensional problems.
Author(s)
쩐 옥 빈김종호
Issued Date
2021
Type
Article
Keyword
AnalysisData assimilationEnsemble Kalman filterGreen technologyPolynomial chaos expansionReal-time forecastingSequential experimental design – polynomial degreeSurrogate filterUsage
DOI
10.1016/j.jhydrol.2021.126367
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9192
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_gale_infotracacademiconefile_A665246935&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20robust%20surrogate%20data%20assimilation%20approach%20to%20real-time%20forecasting%20using%20polynomial%20chaos%20expansion&offset=0&pcAvailability=true
Publisher
JOURNAL OF HYDROLOGY
Location
네덜란드
Language
영어
ISSN
0022-1694
Citation Volume
598
Citation Number
1
Citation Start Page
126367
Citation End Page
126367
Appears in Collections:
Engineering > Civil and Environmental Engineering
Authorize & License
  • Authorize공개
Files in This Item:
  • There are no files associated with this item.

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.