Comparisons of IASI-A and AATSR measurements of top-of-atmosphere radiance
over an extended period

Bali, Manik; Mittaz, Jonathan P. D.; Maturi, Eileen; Goldberg, Mitchell D.

doi:10.5194/amt-9-3325-2016

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The temporal stability of AATSR and its good radiometric calibration, especially for the 10.8 µm channel, have been verified against the high spectral resolution interferometer IASI on MetOp-A, which is the reference used by GSICS [20,21]. Similarly, ATSR-2's good radiometric calibration consistency to the level of 0.1 K has been verified by comparing with the high spectral resolution spectrometer AIRS [21].…”

Section: Introductionmentioning

confidence: 73%

“…The pre-launch calibration of the ATSRs demonstrated that the radiometric noise was below 0.05 K (at a reference of 270 K) in all thermal IR channels and was stable throughout the lifetimes of ATSR-2 and AATSR, and although variable for ATSR-1, the total drift of the mission was only 0.1 K [19]. The temporal stability of AATSR and its good radiometric calibration, especially for the 10.8 µm channel, have been verified against the high spectral resolution interferometer IASI on MetOp-A, which is the reference used by GSICS [20,21]. Similarly, ATSR-2's good radiometric calibration consistency to the level of 0.1 K has been verified by comparing with the high spectral resolution spectrometer AIRS [21].…”

Section: Introductionmentioning

confidence: 81%

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Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)

Tsamalis

Saunders

2018

Remote Sensing

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Sea Surface Temperature (SST) observations from space have been made by the Along Track Scanning Radiometers (ATSRs) providing 20 years (August 1991-April 2012) of high quality data. As part of the ESA Climate Change Initiative (CCI) project, SSTs have been retrieved from the ATSRs. Here, the quality of CCI SST (Phase 1) from ATSRs is validated against drifting buoys. Only CCI ATSR SSTs (Version 1.1) are considered, to facilitate the comparison with the precursor dataset ATSR Reprocessing for Climate (ARC). The CCI retrievals compared with drifting buoys have a median difference slightly larger than 0.1 K. The median SST difference is larger in the tropics (∼0.3 K) during the day, with the night time showing a spatially homogeneous pattern. ATSR-2 and AATSR show similar performance in terms of Robust Standard Deviation (RSD) being 0.2-0.3 K during night and about 0.1 K higher during day. On the other hand, ATSR-1 shows increasing RSD with time from 0.3 K to over 0.6 K. Triple collocation analysis has been applied for the first time on TMI/ATSR-2 observations and for daytime conditions when the wind speed is greater than 10 m/s. Both day and night results indicate that since 2004, the random uncertainty of drifting buoys and CCI AATSR is rather stable at about 0.22 K. Before 2004, drifting buoys have larger values (∼0.3 K), while ATSR-2 shows slightly lower values (∼0.2 K). The random uncertainty for AMSR-E is about 0.47 K, also rather stable with time, while as expected, the TMI has higher values of ∼0.55 K. It is shown for the first time that the AMSR-E random uncertainty changes with latitude, being ∼0.3 K in the tropics and about double this value at mid-latitudes. The SST uncertainties provided with the CCI data are slightly overestimated above 0.45 K and underestimated below 0.3 K during the day. The uncertainty model does not capture correctly the periods with instrument problems after the ATSR-1 3.7 µm channel failed and the gyro failure of ERS-2. During the night, the uncertainties are slightly underestimated. The CCI SSTs (Phase 1) do not yet match the quality of the ARC dataset when comparing to drifting buoys. The value of the ARC median bias is closer to zero than for CCI, while the RSD is about 0.05 K lower for ARC. ARC also shows a more homogeneous geographical distribution of median bias and RSD, although the differences between the two datasets are small. The observed discrepancies between CCI and ARC during the period of ATSR-1 are unexplained given that both datasets use the same retrieval method.

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Section: Introductionmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 81%

Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)

Tsamalis

Saunders

2018

Remote Sensing

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Experimental Research on the Temperature Characterization of a Miniature Phase Change Cells in the Blackbody for the On-Orbit Radiometric Calibration of Thermal Infrared Sensors

Hao

Sima

Liu

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)

et al. 2018

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Abstract. We present a multisensor matchup system (MMS) that allows systematic detection of satellite-based sensor-tosensor matchups and the extraction of local subsets of satellite data around matchup locations. The software system implements a generic matchup-detection approach and is currently being used for validation and sensor harmonization purposes. An overview of the flexible and highly configurable software architecture and the target processing environments is given. We discuss improvements implemented with respect to heritage systems, and present some performance comparisons. A detailed description of the intersection algorithm is given, which allows a fast matchup detection in geometry and time.

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Comparisons of IASI-A and AATSR measurements of top-of-atmosphere radiance over an extended period

Abstract: Abstract. This study examines the trustworthiness

Cited by 3 publications

References 15 publications

Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)

Quality Assessment of Sea Surface Temperature from ATSRs of the Climate Change Initiative (Phase 1)

Experimental Research on the Temperature Characterization of a Miniature Phase Change Cells in the Blackbody for the On-Orbit Radiometric Calibration of Thermal Infrared Sensors

High-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)

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