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Adaptive Filtering and Nonlinear Models for Post-processing of Weather Forecasts
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Kalman filters have been used by SMHI to improve the quality of their forecasts. Until now they have used a linear underlying model to do this. In this thesis it is investigated whether the performance can be improved by the use of nonlinear models such as polynomials and neural networks. The results suggest that an improvement is hard to achieve by this approach and that it is likely not worth the effort to implement a nonlinear model.

Place, publisher, year, edition, pages
2015. , 40 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-119642ISRN: LiTH-ISY-EX--15/4876--SEOAI: oai:DiVA.org:liu-119642DiVA: diva2:825407
External cooperation
SMHI
Subject / course
Automatic Control
Supervisors
Examiners
Available from: 2015-08-21 Created: 2015-06-23 Last updated: 2015-08-21Bibliographically approved

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fulltext(1133 kB)283 downloads
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Adam, Wettring
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf