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AI-based solutions

Nologin Oceanic Weather Systems

What happens if we do not have observational or enough quality model data?

We can improve available operational oceanographic data through neural networks and machine learning applied techniques.

Algorithms based on Artificial Intelligence have been developed for supporting the monitoring of the marine environment, complementing (and in some cases even replacing) available measurement instrumentation. These techniques are also applied to improve our forecast model solutions, or to improve fields (such as the winds) used as forcing in the ocean model runs, resulting in forecast skill improvements.

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Artificial Intelligence applied by NOW to operational oceanography

Algorithms allowing point-source data inferences (both from a nearby model gridpoint and an in-situ mooring buoy)

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Algorithms for gap filling to avoid missing values in observational timeseries, enhancing quality of observational products and improving monitoring capabilities

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Local coastal prediction need running high-resolution models with execution times around the hour; trained convolutional neural networks can deliver analogous local predictions in minutes

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