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Sensitivity of HF radar-derived surface current self-organizing maps to various processing procedures and mesoscale wind forcing

Content Provider	Springer Nature Link
Author	Kalinić, Hrvoje Tudor, Martina žagar, Nedjeljka Vilibić, Ivica Jesenko, Blaž Mihavić, Hrvoje Cosoli, Simone
Copyright Year	2015
Abstract	We performed a number of sensitivity experiments by applying a mapping technique, self-organizing maps (SOM) method, to the surface current data measured by high-frequency (HF) radars in the northern Adriatic and surface winds modelled by two state-of-the-art mesoscale meteorological models, the Aladin (Aire Limitée Adaptation Dynamique Développement InterNational) and the Weather and Research Forecasting models. Surface current data used for the SOM training were collected during a period in which radar coverage was the highest: between February and November 2008. Different pre-processing techniques, such as removal of tides and low-pass filtering, were applied to the data in order to test the sensitivity of characteristic patterns and the connectivity between different SOM solutions. Topographic error did not exceed 15 %, indicating the applicability of the SOM method to the data. The largest difference has been obtained when comparing SOM patterns originating from unprocessed and low-pass filtered data. Introduction of modelled winds in joint SOM analyses stabilized the solutions, while sensitivity to wind forcing coming from the two different meteorological models was found to be small. Such a low sensitivity is considered to be favourable for creation of an operational ocean forecasting system based on neural networks, HF radar measurements and numerical weather prediction mesoscale models.
Ending Page	131
Page Count	17
Starting Page	115
File Format	PDF
ISSN	14200597
e-ISSN	15731499
Journal	Computational Geosciences
Issue Number	1
Volume Number	20
Language	English
Publisher	Springer International Publishing
Publisher Date	2015-12-03
Publisher Place	Cham
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Learning and adaptive systems Earth Sciences Hydrogeology Self-organizing maps Ocean surface currents Geotechnical Engineering & Applied Earth Sciences Mathematical Modeling and Industrial Mathematics Mesoscale meteorological models Adriatic Sea Soil Science & Conservation
Content Type	Text
Resource Type	Article
Subject	Computers in Earth Sciences Computational Theory and Mathematics Computer Science Applications Computational Mathematics

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