Sea Surface Temperature estimation from satellite observations and in-situ measurements using multifidelity Gaussian Process regression

Prempraneerach, Pradya; Perdikaris, Paris; Karniadakis, George; Chryssostomidis, Chryssostomos

doi:10.1109/icdamt.2017.7904928

Cited by 1 publication

(1 citation statement)

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“…For the sake of brevity we present the linear formulation for two fidelity levels (Babaee et al, ; Kennedy & O'Hagan, ; Prempraneerach et al, ); however, the formulation can be extended to

n > 2

levels of fidelity in a straightforward manner (see e.g., Forrester et al, ) and to nonlinear autoregressive schemes using deep GP (see Perdikaris et al, ). To this end, we consider the low‐fidelity (L) and high‐fidelity (H) observations to be of the form

y_{L} false(x false) = u_{L} false(x false) + ϵ_{L} and y_{H} false(x false) = ρ u_{L} false(x false) + δ false(x false) + ϵ_{H},

where

x = false{x_{1}, x_{2}, \dots, x_{D} false}

represents the

D

‐dimensional input space.…”

Section: Multifidelity Modelingmentioning

confidence: 99%

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

Babaee

Bastidas

DeFilippo

et al. 2020

Earth and Space Science

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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.

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“…For the sake of brevity we present the linear formulation for two fidelity levels (Babaee et al, ; Kennedy & O'Hagan, ; Prempraneerach et al, ); however, the formulation can be extended to