The project aims to build the scientific foundations of statistical sampling for oceanographic applications by formulating novel algorithmic methods in statistics and blending it with ocean model predictions, to embed and test on autonomous vehicles. By sampling, we refer to the design of experiments in spatio-temporal domains, enabling autonomous platforms to decide on an optimal strategy of where and when to gather data, in a cost-effective manner. Renowned oceanographer Walter Munk called the 20th century, the century of undersampling, something particularly relevant in ocean-facing Norway with its complex fjord systems intermixed with coastal skerries. To improve the state of sampling modern tools and methods, including the use of autonomous platforms, oceanographic models and satellite remote sensing at various spatio-temporal scales are critical. However, without adequate understanding of the theoretical underpinnings of how, when and where to sample, these tools and methods are insufficient in our vast and harsh oceans. The focus of this proposal is in designing, implementing and testing algorithms for efficient spatio-temporal sampling of the coastal oceans, with a broader impact to commingling methods in spatial and computational statistics, with oceanography, with novel methods in automatic control including artificial intelligence for adaptive sampling. Deliverables include testing of the new algorithms in field experiments in Norwegian waters with existing robotic assets.
Project leader: Jo Eidsvik
Institution: Institutt for matematiske fag