Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations

O’Carroll, Anne; Eyre, J. R.; Saunders, Roger

doi:10.1175/2007jtecho542.1

Cited by 196 publications

(181 citation statements)

References 19 publications

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“…In regions where the SST variability is relatively low (e.g. between 5-25 • N and between 130 • E and 140 • W in the Pacific Ocean), the modelled SSTA is less than the observations by about 0.25-0.5 • C. O'Carroll et al (2008) suggested that the RMS of the measurement error of AMSR-E SST is about 0.42 • C. This indicates that in the regions where the SSTA variability is small (between 0.5-0.7 • C), the signal in the observations is likely to be dominated by measurement error because the signal-to-noise ratio is low. So the lowerthan-observed variability in the model may be an overestimate in the observed RMS, rather than an underestimate in the model.…”

Section: Sst Anomaly Comparisonsmentioning

confidence: 82%

Evaluation of a near-global eddy-resolving ocean model

et al. 2013

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Abstract. Analysis of the variability of the last 18 yr (1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) of a 32 yr run of a new near-global, eddy-resolving ocean general circulation model coupled with biogeochemistry is presented. Comparisons between modelled and observed mean sea level (MSL), mixed layer depth (MLD), sea level anomaly (SLA), sea surface temperature (SST), and chlorophyll a indicate that the model variability is realistic. We find some systematic errors in the modelled MLD, with the model generally deeper than observations, which results in errors in the chlorophyll a, owing to the strong biophysical coupling. We evaluate several other metrics in the model, including the zonally averaged seasonal cycle of SST, meridional overturning, volume transports through key straits and passages, zonally averaged temperature and salinity, and El Niño-related SST indices. We find that the modelled seasonal cycle in SST is 0.5-1.5 • C weaker than observed; volume transports of the Antarctic Circumpolar Current, the East Australian Current, and Indonesian Throughflow are in good agreement with observational estimates; and the correlation between the modelled and observed NINO SST indices exceeds 0.91. Most aspects of the model circulation are realistic. We conclude that the model output is suitable for broader analysis to better understand upper ocean dynamics and ocean variability at mid-and low latitudes. The new model is intended to underpin a future version of Australia's operational short-range ocean forecasting system.

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Section: Sst Anomaly Comparisonsmentioning

confidence: 82%

Evaluation of a near-global eddy-resolving ocean model

et al. 2013

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“…For instance, the lowest RSD of 0.180 K for the AATSR N3 retrieval is effectively the error in the in situ measurements and is comparable to the ~0.2 K drifter error found by O'Carroll et al (2008). The D3 retrieval, while it is more robust to atmospheric conditions and aerosol, does suffer from slightly elevated noise levels.…”

Section: Comparison With In Situmentioning

confidence: 53%

“…Although the above match-ups are not exactly coincident, their relatively similar global distribution and median differences allows us to adopt the approach of approach to O'Carroll et al (2008) and use multi-way statistics to estimate the uncertainty of comparing a single point in situ measurement to a 1-km AATSR pixel to be ~ 0.1 K.…”

Section: The Use Of Argo As a Reference Datasetmentioning

confidence: 99%

Multi sensor validation and error characteristics of Arctic satellite sea surface temperature observations

Høyer

Karagali

Dybkjær

et al. 2012

Remote Sensing of Environment

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ESA/ESRIN, via Galileo Galilei, Frascati, Rom, (Italy), Email: craig.donlon@esa .intThe Science Team chair, Craig Donlon, welcomed all participants and reminded the Science Team of the GHRSST aimto provide the best quality SST for short, medium and long-term applications -and on which we can measure the success of GHRSST. The GHRSST phase-1 Pilot Project is complete, the phase-2 international Regional/Global Task Sharing system is built and operating, and GHRSST is now in phase-3: operational delivery of a wealth of SST products and developing Climate Data Records.Progress was presented for the major GHRSST components, and the Space Agencies pointed out their priorities and expressed their continuing support of GHRSST. For instance:-The GHRSST Project Office, funded by ESA and now hosted at the University of Reading, continues to support the GHRSST Science Team for a 10 th year.-The Global Data Assembly Centre (GDAC) which is hosted at NASA JPL Physical Oceanography DAAC (PO.DAAC) now interfaces to 12 Regional Data Assembly Centres (RDACs), and ingests ~8000 files per day (~35 GB). The PO.DAAC and currently holds a total volume of 202 TB. A new web-portal, metadata database, data mining, subsetting and visualisation tools and a GHRSST forum have been established in this year.-The Long Term Stewardship and Reanalysis Facility (LTSRF) is hosted at NOAA NODC; its operations are in constant progress with automatic daily acquisition from the GDAC and archiving. Current archive holdings are over 28 TB . Progress with reanalysis is being made at both the individual sensor level (L2, L3) and with merged L4 products. One of the tasks for 2011/2012 is to develop the SST Climate Variable Data Processing Framework. -The GHRSST Multi-Product Ensemble system (GMPE) carried out a gradient inter-comparison and assessed the suitability of the ensemble spread as an error estimate. The GMPE median is shown to be more accurate than any individual L4 product in the initial comparison against the independent near-surface Argo data. With the SST Quality Monitor (SQUAM), one L3 and 13 L4 GHRSST products have been cross-compared and validated against in-situ data with the iQuam system. Progress at the various contributing RDACs has been achieved through GHRSST XII - Issue: Final Proceedings, EdinburghDate: September 27, 2011 GHRSST Project Office Page 10 of 310 continuing operational processing of SST data streams from multiple, complementary satellite sensors and additionally by providing new or improved data. For example: -New L3 data are now in production by ESA Medspiration and the Australian BOM. -Real-time and reprocessed SST skin from AVHRR and MTSAT is being provided by ABOM. -A new processing chain using physical retrievals accounting for atmospheric moisture and Saharan dust in geostationary products has been implemented at the OSI-SAF. -Navoceano selects the best quality data from the GDAC, and adds error estimates to be used for Navy Coupled Ocean Data Assimilation (NCODA). -EUMETSAT RDAC is now providing very high data qua...

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“…CC BY 4.0 License. O'Carroll et al (2008) compared three types of systems to measure sea-surface temperatures: two different radiometers and in situ observations from buoys. They discuss the assumption of the neglect of error correlations among the three data sets and the effect of representativeness errors.…”

Section: Summary and Discussion 20mentioning

confidence: 99%

Estimating observation and model error variances using multiple data sets

Anthes¹,

Rieckh²

2018

Preprint

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Abstract. In this paper we show how multiple data sets, including observations and models, can be combined using the "Ncornered hat method" to estimate vertical profiles of the errors of each system. Using data from 2007, we estimate the error 10 variances of radio occultation, radiosondes, ERA-Interim and GFS model data sets at four radiosonde locations in the tropics and subtropics. A key assumption is the neglect of error covariances among the different data sets, and we examine the consequences of this assumption on the resulting error estimates.

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Three-Way Error Analysis between AATSR, AMSR-E, and In Situ Sea Surface Temperature Observations

Cited by 196 publications

References 19 publications

Evaluation of a near-global eddy-resolving ocean model

Evaluation of a near-global eddy-resolving ocean model

Multi sensor validation and error characteristics of Arctic satellite sea surface temperature observations

Estimating observation and model error variances using multiple data sets

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