A Multifidelity Framework and Uncertainty Quantification for Sea Surface Temperature in the Massachusetts and Cape Cod Bays-Reference-Cited by-同舟云学术

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

Published:2020-02 Issue:2 Volume:7 Page:
ISSN:2333-5084
Container-title:Earth and Space Science
language:en
Short-container-title:Earth and Space Science

Author:

Babaee H.¹²,Bastidas C.²,DeFilippo M.²,Chryssostomidis C.²,Karniadakis G. E.³

Affiliation:

1. Department of Mechanical Engineering and Materials Science University of Pittsburgh Pittsburg PA USA

2. MIT Sea Grant Massachusetts Institute of Technology Cambridge MA USA

3. Division of Applied Mathematics Brown University Providence RI USA

Funder

National Oceanic and Atmospheric Administration

Office of Naval Research

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Science (miscellaneous)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019EA000954

Reference36 articles.

1. Robust empirical relationships for estimating the carbonate system in the southern California current system and application to CalCOFI hydrographic cruise data (2005–2011);Alin S. R.;Journal of Geophysical Research: Oceans,2018

2. Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations

3. A multi-scale high-resolution analysis of global sea surface temperature

4. Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM)

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