Highers—The AVHRR-based higher spatial resolution sea surface temperature data set intended for studying the ocean south of Japan

1996 ◽  
Vol 52 (4) ◽  
pp. 441-455 ◽  
Author(s):  
Futoki Sakaida ◽  
Hiroshi Kawamura
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Spatial Resolution ◽  
Temperature Data ◽  
Sea Surface ◽  
Data Set ◽  
South Of Japan ◽  
Surface Temperature Data

scholarly journals Updating a Crucial Source of Sea Surface Temperature Data

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Terri Cook
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Systematic Errors ◽  
Temperature Data ◽  
The State ◽  
Climate System ◽  
Sea Surface ◽  
Data Set ◽  
Earth’S Climate ◽  
Surface Temperature Data

A new version of a major sea surface temperature data set reduces systematic errors in measurements of one of the most important indicators of the state of Earth’s climate system.

scholarly journals DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

2020 ◽  
Vol 13 (3) ◽  
pp. 1609-1622 ◽  
Author(s):  
Alexander Barth ◽  
Aida Alvera-Azcárate ◽  
Matjaz Licer ◽  
Jean-Marie Beckers
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Error Variance ◽  
Temperature Data ◽  
Satellite Observations ◽  
Sea Surface ◽  
Function Decomposition ◽  
Surface Temperature Data

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

scholarly journals Can sparse proxy data constrain the strength of the Atlantic meridional overturning circulation?

2014 ◽  
Vol 7 (1) ◽  
pp. 419-432 ◽  
Author(s):  
T. Kurahashi-Nakamura ◽  
M. Losch ◽  
A. Paul
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Atlantic Meridional Overturning Circulation ◽  
Temperature Data ◽  
Meridional Overturning Circulation ◽  
Sea Surface ◽  
Proxy Data ◽  
Overturning Circulation ◽  
Meridional Overturning ◽  
Surface Temperature Data

Abstract. In a feasibility study, the potential of proxy data for the temperature and salinity during the Last Glacial Maximum (LGM, about 19 000 to 23 000 years before present) in constraining the strength of the Atlantic meridional overturning circulation (AMOC) with a general ocean circulation model was explored. The proxy data were simulated by drawing data from four different model simulations at the ocean sediment core locations of the Multiproxy Approach for the Reconstruction of the Glacial Ocean surface (MARGO) project, and perturbing these data with realistic noise estimates. The results suggest that our method has the potential to provide estimates of the past strength of the AMOC even from sparse data, but in general, paleo-sea-surface temperature data without additional prior knowledge about the ocean state during the LGM is not adequate to constrain the model. On the one hand, additional data in the deep-ocean and salinity data are shown to be highly important in estimating the LGM circulation. On the other hand, increasing the amount of surface data alone does not appear to be enough for better estimates. Finally, better initial guesses to start the state estimation procedure would greatly improve the performance of the method. Indeed, with a sufficiently good first guess, just the sea-surface temperature data from the MARGO project promise to be sufficient for reliable estimates of the strength of the AMOC.

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