Construction of a climate data record of sea surface temperature from passive microwave measurements

Emy Alerskans*, Jacob L. Høyer, Chelle L. Gentemann, Leif Toudal Pedersen, Pia Nielsen-Englyst, Craig Donlon

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002–October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed.
Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of −0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.
Original languageEnglish
Article number111485
JournalRemote Sensing of Environment
Volume236
Number of pages16
ISSN0034-4257
DOIs
Publication statusPublished - 2019

Keywords

  • Remote sensing
  • Microwave
  • AMSR-E
  • AMSR2
  • Sea surface temperature
  • Climate data record

Cite this

@article{4c5d565072104ad796dc020085b90f4d,
title = "Construction of a climate data record of sea surface temperature from passive microwave measurements",
abstract = "A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002–October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed.Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of −0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.",
keywords = "Remote sensing, Microwave, AMSR-E, AMSR2, Sea surface temperature, Climate data record",
author = "Emy Alerskans and H{\o}yer, {Jacob L.} and Gentemann, {Chelle L.} and Pedersen, {Leif Toudal} and Pia Nielsen-Englyst and Craig Donlon",
year = "2019",
doi = "10.1016/j.rse.2019.111485",
language = "English",
volume = "236",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier",

}

Construction of a climate data record of sea surface temperature from passive microwave measurements. / Alerskans, Emy; Høyer, Jacob L.; Gentemann, Chelle L.; Pedersen, Leif Toudal; Nielsen-Englyst, Pia; Donlon, Craig.

In: Remote Sensing of Environment, Vol. 236, 111485, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Construction of a climate data record of sea surface temperature from passive microwave measurements

AU - Alerskans, Emy

AU - Høyer, Jacob L.

AU - Gentemann, Chelle L.

AU - Pedersen, Leif Toudal

AU - Nielsen-Englyst, Pia

AU - Donlon, Craig

PY - 2019

Y1 - 2019

N2 - A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002–October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed.Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of −0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.

AB - A statistical regression-based microwave sea surface temperature (SST) retrieval algorithm has been developed within the European Space Agency Climate Change Initiative (ESA-CCI) SST project. The retrieval algorithm was used to generate a climate data record (CDR) of passive microwave (PMW) SST from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and its follow-on instrument AMSR2 for the period June 2002–October 2017. Multisensor Matchup Datasets (MMDs), which includes satellite orbital data collocated with in situ and auxiliary data, was used to derive consistent algorithms for AMSR-E and AMSR2. The retrieval algorithms consist of wind speed (WS) retrievals and SST retrievals, with corresponding uncertainty retrievals. The WS retrieval consists of a two-step regression model, where the second step is a localized algorithm, trained to perform well over restricted WS intervals. A two-step multiple linear regression retrieval with localized algorithms is used to retrieve SST. The first-stage algorithm is trained to perform well over restricted latitude intervals for ascending and descending orbit, respectively, whereas the second-stage retrieval uses localized SST and WS algorithms. Furthermore, a new and effective method for detecting and screening for Radio Frequency Interference (RFI) was developed.Validation of the PMW SSTs against drifter in situ SSTs shows an overall bias of −0.02 K for quality level (QL) 4 and 5 AMSR-E retrievals with a standard deviation of 0.46 K. Validation results for QL 4 and 5 AMSR2 retrievals against drifter in situ SSTs give a bias of 0.002 K and a standard deviation of 0.45 K. The corresponding mean modelled SST uncertainties, including in situ and sampling uncertainties, are estimated to 0.45 K for QL 4 and 5 AMSR-E retrievals and 0.44 K for QL 4 and 5 AMSR2 retrievals. Validation against near-surface temperature measurements from Argo floats yielded comparable results, confirming the drifting-buoy validation. The validation results demonstrate a consistent PMW SST CDR with accurate SST observations and reliable uncertainty estimates.

KW - Remote sensing

KW - Microwave

KW - AMSR-E

KW - AMSR2

KW - Sea surface temperature

KW - Climate data record

U2 - 10.1016/j.rse.2019.111485

DO - 10.1016/j.rse.2019.111485

M3 - Journal article

VL - 236

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 111485

ER -