Optimal estimation of sea surface temperature from AMSR-E

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

Standard

Optimal estimation of sea surface temperature from AMSR-E. / Nielsen-Englyst, Pia; Høyer, Jacob L.; Pedersen, Leif Toudal; Gentemann, Chelle L.; Alerskans, Emy; Block, Tom; Donlon, Craig.

In: Remote Sensing, Vol. 10, No. 2, 229, 2018.

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

Harvard

Nielsen-Englyst, P, Høyer, JL, Pedersen, LT, Gentemann, CL, Alerskans, E, Block, T & Donlon, C 2018, 'Optimal estimation of sea surface temperature from AMSR-E', Remote Sensing, vol. 10, no. 2, 229. https://doi.org/10.3390/rs10020229

APA

Nielsen-Englyst, P., Høyer, J. L., Pedersen, L. T., Gentemann, C. L., Alerskans, E., Block, T., & Donlon, C. (2018). Optimal estimation of sea surface temperature from AMSR-E. Remote Sensing, 10(2), [229]. https://doi.org/10.3390/rs10020229

CBE

Nielsen-Englyst P, Høyer JL, Pedersen LT, Gentemann CL, Alerskans E, Block T, Donlon C. 2018. Optimal estimation of sea surface temperature from AMSR-E. Remote Sensing. 10(2). https://doi.org/10.3390/rs10020229

MLA

Vancouver

Nielsen-Englyst P, Høyer JL, Pedersen LT, Gentemann CL, Alerskans E, Block T et al. Optimal estimation of sea surface temperature from AMSR-E. Remote Sensing. 2018;10(2). 229. https://doi.org/10.3390/rs10020229

Author

Nielsen-Englyst, Pia ; Høyer, Jacob L. ; Pedersen, Leif Toudal ; Gentemann, Chelle L. ; Alerskans, Emy ; Block, Tom ; Donlon, Craig. / Optimal estimation of sea surface temperature from AMSR-E. In: Remote Sensing. 2018 ; Vol. 10, No. 2.

Bibtex

@article{0eee95f18ab145f0beea42d08fd58f34,
title = "Optimal estimation of sea surface temperature from AMSR-E",
abstract = "The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64{\%} matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62{\%} of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.",
keywords = "Optimal estimation, Remote sensing, Sea surface temperature (SST), Microwave",
author = "Pia Nielsen-Englyst and H{\o}yer, {Jacob L.} and Pedersen, {Leif Toudal} and Gentemann, {Chelle L.} and Emy Alerskans and Tom Block and Craig Donlon",
note = "This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).",
year = "2018",
doi = "10.3390/rs10020229",
language = "English",
volume = "10",
journal = "Remote Sensing",
issn = "2072-4292",
publisher = "M D P I AG",
number = "2",

}

RIS

TY - JOUR

T1 - Optimal estimation of sea surface temperature from AMSR-E

AU - Nielsen-Englyst, Pia

AU - Høyer, Jacob L.

AU - Pedersen, Leif Toudal

AU - Gentemann, Chelle L.

AU - Alerskans, Emy

AU - Block, Tom

AU - Donlon, Craig

N1 - This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

PY - 2018

Y1 - 2018

N2 - The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.

AB - The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA's Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST.

KW - Optimal estimation

KW - Remote sensing

KW - Sea surface temperature (SST)

KW - Microwave

U2 - 10.3390/rs10020229

DO - 10.3390/rs10020229

M3 - Journal article

VL - 10

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 2

M1 - 229

ER -