A Multifidelity Framework and Uncertainty Quantification for Sea Surface Temperature in the Massachusetts and Cape Cod Bays

Abstract

Abstract We present a multifidelity framework to analyze and hindcast predictions of sea surface temperature (SST) in the Massachusetts and Cape Cod Bays, which is a critical area for its ecological significance, sustaining fisheries and the blue economy of the region. Currently, there is a lack of accurate and continuous SST prediction for this region due to the high cost of collecting the samples (e.g., cost of buoys, maintenance, severe weather). In this work, we use SST data from satellite images and in situ measurements collected by the Massachusetts Water Resources Authority to develop multifidelity forecasting models. This multifidelity framework is based on autoregressive Gaussian process schemes that systematically exploit all correlations between data from multiple heterogeneous spatiotemporal sources with various degrees of fidelity. This enables us to obtain implicitly their functional relationships and, at the same time, quantify the uncertainty of the data‐driven predictions. Specifically, in the current work, we develop and validate progressively more complex models, including temporal, spatial, and spatiotemporal multifidelity hindcast predictions of SST in the Massachusetts and Cape Cod Bays. Together with these predictions, we present for the first time uncertainty maps for the region.

Keywords

• machine learning
• periodic kernels
• Gaussian processes
• satellite data