Publication Date:
2021-11-08
Description:
The prediction of the drift of floating objects is an important task, with applications
such as marine transport, pollutant dispersion, and search-and-rescue activities. But
forecasting surface drift is also very challenging, because it depends in a complex
way on various interacting factors such as the wind, the ocean surface current,
and the wave field. Furthermore, although each of the cited factors can be fore-
casted by deterministic models, the latter all suffer from limitations, resulting in
imperfect predictions. In the present study, we try and predict the drift of buoys
launched during the DART06 (Dynamics of the Adriatic sea in Real-Time 2006)
and MREA07 (Maritime Rapid Environmental Assessment 2007) sea trials, using
the so-called hyper-ensemble technique: different models are combined in order to
minimize departure from independent observations during a training period; the ob-
tained combination is then used in forecasting mode. We review and try out different
hyper-ensemble techniques, going from simple ensemble mean to techniques based
on data assimilation, which dynamically update the model’s weights in the combi-
nation when new observations become available. We show that the latter methods
alleviate the need of fixing the training length a priori, as older information is au-
tomatically discarded, and hence they lead to better results. Moreover, they allow
to determine a characteristic time during which the model weights are more or less
stable, which allows to predict how long the obtained combination will be valid in
forecasting mode.
Description:
Published
Description:
149–167
Description:
4A. Oceanografia e clima
Description:
JCR Journal
Description:
open
Keywords:
super-ensemble, surface drift forecast
;
03. Hydrosphere::03.01. General::03.01.05. Operational oceanography
Repository Name:
Istituto Nazionale di Geofisica e Vulcanologia (INGV)
Type:
article
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