Cart (Loading....) | Create Account
Close category search window

Water level prediction skill of an operational marine forecast using a hybrid Kalman filter and time series modeling approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sorensen, J.V.T. ; DHI Water & Environ., Denmark ; Madsen, H.

Summary form only given. The operational service the "Water Forecast" gives 5-day forecasts for the North Sea, Baltic Sea and interconnecting waters every 12 hours. Predictions of a range of physical and environmental parameters are provided. In this contribution, focus will be on water level. An ongoing development is focused on data assimilation of tidal gauge data. A cost-effective Kalman filter based procedure that uses a regularized constant Kalman gain is applied for the tidal gauge data. This approach gives an acceptable computational overhead for operational applications. The now- and forecast skill of the scheme is evaluated and compared to standard modeling results. Data assimilation improves the forecast skill, but local time series models of varying complexity often possess a longer forecast horizon at measurement points. For these error correction methods however, the problem is to extrapolate this correction spatially to increase the skill in validation points. A hybrid of the Kalman filter and local time series models is constructed by assimilating water levels predicted by the time series models. Its prediction skill is validated against the previous results.

Published in:

OCEANS 2003. Proceedings  (Volume:2 )

Date of Conference:

22-26 Sept. 2003

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.