Articles | Volume 15, issue 5
Development and technical paper
15 Mar 2022
Development and technical paper |  | 15 Mar 2022

DINCAE 2.0: multivariate convolutional neural network with error estimates to reconstruct sea surface temperature satellite and altimetry observations

Alexander Barth, Aida Alvera-Azcárate, Charles Troupin, and Jean-Marie Beckers

Data sets

MODIS Terra Level 3 SST Thermal IR Daily 4km Nighttime v2014.0, Ver. 2014.0 OBPG

NOAA Optimum Interpolation 1/4 Degree Daily Sea Surface Temperature (OISST) Analysis, Version 2 R. W. Reynolds, V. F. Banzon, and NOAA CDR Program

Model code and software

gher-ulg/DINCAE.jl: v2.0.0 (v2.0.0) A. Barth

Short summary
Earth-observing satellites provide routine measurement of several ocean parameters. However, these datasets have a significant amount of missing data due to the presence of clouds or other limitations of the employed sensors. This paper describes a method to infer the value of the missing satellite data based on a convolutional autoencoder (a specific type of neural network architecture). The technique also provides a reliable error estimate of the interpolated value.