CLARA-A2: The second edition of the CM SAF cloud and
radiation data record from 34 years of global AVHRR data

Karlsson, Karl‐Göran; Anttila, Kati; Trentmann, Jörg; Stengel, Martin; Meirink, Jan Fokke; Devasthale, Abhay; Hanschmann, Timo; Kothe, Steffen; Jääskeläinen, Emmihenna; Sedlar, Joseph; Benas, Nikos; Zadelhoff, Gerd-Jan van; Schlundt, Cornelia; Stein, Diana L.; Finkensieper, Stephan; Håkansson, Nina; Hollmann, Rainer

doi:10.5194/acp-2016-935

Cited by 50 publications

(80 citation statements)

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“…The agreement is generally quite good for BSRN sites, with metrics that are comparable to MODIS-only albedo estimates [31,56] and generally better than AVHRR-only estimates [14,32]. The observed biases (MBD and MABD) are less than 0.05 at all BSRN sites, with no systematic tendencies for either over-or underestimation.…”

Section: Results From Demonstrator Dataset Evaluation Against Bsrn Anmentioning

confidence: 49%

“…We have also compared the SCM black-sky albedo retrievals against reprojected standard MODIS albedo data from the MCD43D V006 dataset [31] as well as the AVHRR-based CLARA-A2-SAL dataset [14,32]. The SCM retrievals are typically somewhat larger than either MCD43 or CLARA-A2, the mean difference to MCD43 being approximately 0.03 to 0.04, and 0.04 to 0.05 against CLARA-A2.…”

Section: Discussionmentioning

confidence: 99%

“…The inclusion of MABD allows for the examination of the consistency of over-or underestimations; RMSD is a suitable metric for studying the scatter in the estimates relative to in situ. In addition to the in situ data evaluation, we compare the joint MODIS-AVHRR retrievals to the current state-of-the-art MODIS (MCD43D V006) [31] and AVHRR (CLARA-A2) [32] albedo datasets. We choose the high-resolution (30arc second) MCD43D dataset as input to obtain a high MODIS BRDF spatial resolution at the input stage; first, all MCD43D data not in the highest BRDF status quality classes (0 or 1) are discarded.…”

Section: Evaluation and Comparison Methods And Data Sourcesmentioning

confidence: 99%

“…To prove the value of our concept, we have evaluated the obtained albedo over several Baseline Surface Radiation Network (BSRN) tower-based surface albedo measurement sites [29]. We also compared the obtained albedo estimates against established single-sensor datasets such as the MCD43 V006 [12,30,31] for MODIS and CLARA-A2-SAL [32] for AVHRR.…”

Section: Introductionmentioning

confidence: 99%

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A Multisensor Approach to Global Retrievals of Land Surface Albedo

et al. 2018

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Satellite-based retrievals offer the most cost-effective way to comprehensively map the surface albedo of the Earth, a key variable for understanding the dynamics of radiative energy interactions in the atmosphere-surface system. Surface albedo retrievals have commonly been designed separately for each different spaceborne optical imager. Here, we introduce a novel type of processing framework that combines the data from two polar-orbiting optical imager families, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The goal of the paper is to demonstrate that multisensor albedo retrievals can provide a significant reduction in the sampling time required for a robust and comprehensive surface albedo retrieval, without a major degradation in retrieval accuracy, as compared to state-of-the-art single-sensor retrievals. We evaluated the multisensor retrievals against reference in situ albedo measurements and compare them with existing datasets. The results show that global land surface albedo retrievals with a sampling period of 10 days can offer near-complete spatial coverage, with a retrieval bias mostly comparable to existing single sensor datasets, except for bright surfaces (deserts and snow) where the retrieval framework shows degraded performance because of atmospheric correction design compromises. A level difference is found between the single sensor datasets and the demonstrator developed here, pointing towards a need for further work in the atmospheric correction, particularly over bright surfaces, and inter-sensor radiance homogenization. The introduced framework is expandable to include other sensors in the future.

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Section: Results From Demonstrator Dataset Evaluation Against Bsrn Anmentioning

confidence: 49%

Section: Discussionmentioning

confidence: 99%

Section: Evaluation and Comparison Methods And Data Sourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Multisensor Approach to Global Retrievals of Land Surface Albedo

et al. 2018

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“…Up until now, models have been employed for sea-ice albedo retrieval using instruments such as AVHRR [24] for shortwave only and at low resolution (25 km) on weekly time-steps. Sea-ice packs many kilometres in size move at up to 15 km/day, so any method such as time-composting smears out each individual albedo record, rendering it unfit for retrieval of sea-ice albedo.…”

Section: Sea-ice Albedomentioning

confidence: 99%

Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

et al. 2018

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Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union's Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO 2 ), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications.

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An intercomparison and validation of satellite‐based surface radiative energy flux estimates over the Arctic

et al. 2017

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Accurate determination of radiative energy fluxes over the Arctic is of crucial importance for understanding atmosphere‐surface interactions, melt and refreezing cycles of the snow and ice cover, and the role of the Arctic in the global energy budget. Satellite‐based estimates can provide comprehensive spatiotemporal coverage, but the accuracy and comparability of the existing data sets must be ascertained to facilitate their use. Here we compare radiative flux estimates from Clouds and the Earth's Radiant Energy System (CERES) Synoptic 1‐degree (SYN1deg)/Energy Balanced and Filled, Global Energy and Water Cycle Experiment (GEWEX) surface energy budget, and our own experimental FluxNet / Satellite Application Facility on Climate Monitoring cLoud, Albedo and RAdiation (CLARA) data against in situ observations over Arctic sea ice and the Greenland Ice Sheet during summer of 2007. In general, CERES SYN1deg flux estimates agree best with in situ measurements, although with two particular limitations: (1) over sea ice the upwelling shortwave flux in CERES SYN1deg appears to be underestimated because of an underestimated surface albedo and (2) the CERES SYN1deg upwelling longwave flux over sea ice saturates during midsummer. The Advanced Very High Resolution Radiometer‐based GEWEX and FluxNet‐CLARA flux estimates generally show a larger range in retrieval errors relative to CERES, with contrasting tendencies relative to each other. The largest source of retrieval error in the FluxNet‐CLARA downwelling shortwave flux is shown to be an overestimated cloud optical thickness. The results illustrate that satellite‐based flux estimates over the Arctic are not yet homogeneous and that further efforts are necessary to investigate the differences in the surface and cloud properties which lead to disagreements in flux retrievals.

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CLARA-A2: The second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data

Cited by 50 publications

References 0 publications

A Multisensor Approach to Global Retrievals of Land Surface Albedo

A Multisensor Approach to Global Retrievals of Land Surface Albedo

Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications

An intercomparison and validation of satellite‐based surface radiative energy flux estimates over the Arctic

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