The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements

Stengel, Martin; Mieruch, S.; Jerg, Matthias; Karlsson, Karl‐Göran; Scheirer, Ronald; Maddux, B. C.; Meirink, Jan Fokke; Poulsen, C.; Siddans, Richard; Walther, A.; Hollmann, Rainer

doi:10.1016/j.rse.2013.10.035

Cited by 46 publications

(78 citation statements)

References 46 publications

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“…COT and REF are retrieved using the classical Nakajima and King (1990) approach, which is based on the principle that cloud reflectance is mainly dependent on COT at a non-absorbing, visible wavelength and on REF at an absorbing, near-infrared wavelength. In the CPP algorithm (Stengel et al, 2015;Roebeling et al, 2006), the doubling-adding KNMI (DAK, De Haan et al, 1987;Stammes, 2001) radiative transfer model is used to simulate visible (0.6 µm) and near-infrared (1.6/3.7 µm) TOA reflectances as a function of viewing geometry, COT, REF and CPH. These simulated reflectances are stored in a look-up table (LUT), and satellite-observed reflectances are matched to this LUT in an iterative manner, leading to the derivation of COT and REF.…”

Section: Cloud Products Derived From the CM Saf Cloud Physical Propermentioning

confidence: 99%

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

et al. 2017

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Abstract. The second edition of the satellite-derived climate data record CLARA ("The CM SAF Cloud, Albedo And Surface Radiation dataset from AVHRR data" -second edition denoted as CLARA-A2) is described. The data record covers the 34-year period from 1982 until 2015 and consists of cloud, surface albedo and surface radiation budget products derived from the AVHRR (Advanced Very High Resolution Radiometer) sensor carried by polar-orbiting, operational meteorological satellites. The data record is produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF) project as part of the operational ground segment. Its upgraded content and methodology improvements since edition 1 are described in detail, as are some major validation results. Some of the main improvements to the data record come from a major effort in cleaning and homogenizing the basic AVHRR level-1 radiance record and a systematic use of CALIPSO-CALIOP cloud information for development and validation purposes. Examples of applications studying decadal changes in Arctic summer surface albedo and cloud conditions are provided.

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Section: Cloud Products Derived From the CM Saf Cloud Physical Propermentioning

confidence: 99%

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

et al. 2017

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“…The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is commonly used to validate CTH (e.g. Holz et al, 2008;Stengel et al, 2013). CALIOP measures the backscatter from a pulsed laser beam as a function of height, which is predominately a function of the number of particles in the beam.…”

Section: Weighting Functionsmentioning

confidence: 99%

“…For example, AVHRR measurements from 1978 are still reprocessed for climate studies (Stengel et al, 2013;Heidinger et al, 2014). Figure 5 outlines the interlinking cycles of algorithm and operational development.…”

Section: Distinction Between Maturity and Uncertaintymentioning

confidence: 99%

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

Povey

Grainger

2015

Atmos. Meas. Tech.

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Abstract. This paper discusses a best-practice representation of uncertainty in satellite remote sensing data. An estimate of uncertainty is necessary to make appropriate use of the information conveyed by a measurement. Traditional error propagation quantifies the uncertainty in a measurement due to well-understood perturbations in a measurement and in auxiliary data -known, quantified "unknowns". The underconstrained nature of most satellite remote sensing observations requires the use of various approximations and assumptions that produce non-linear systematic errors that are not readily assessed -known, unquantifiable "unknowns". Additional errors result from the inability to resolve all scales of variation in the measured quantity -unknown "unknowns". The latter two categories of error are dominant in underconstrained remote sensing retrievals, and the difficulty of their quantification limits the utility of existing uncertainty estimates, degrading confidence in such data. This paper proposes the use of ensemble techniques to present multiple self-consistent realisations of a data set as a means of depicting unquantified uncertainties. These are generated using various systems (different algorithms or forward models) believed to be appropriate to the conditions observed. Benefiting from the experience of the climate modelling community, an ensemble provides a user with a more complete representation of the uncertainty as understood by the data producer and greater freedom to consider different realisations of the data.

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“…The result of retrieving all parameters by varying all channels simultaneously is a radiatively consistent set of cloud properties. The cloud retrieval has thus far been applied to ATSR-2 and AATSR, as well as SEVIRI, Advanced Very High Resolution Radiometer (AVHRR) and MODerate Resolution Imaging Spectroradiometer (MODIS) in the context of the ESA Climate Change Initiative (CCI) programme (Stengel et al, 2013).…”

Section: Optimal Estimation Cloud Retrieval Algorithmmentioning

confidence: 99%

Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

et al. 2016

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Abstract. In this paper we evaluate the impact on the cloud parameter retrievals of the ORAC (Optimal Retrieval of Aerosol and Cloud) algorithm following the inclusion of stereo-derived cloud top heights as a priori information. This is performed in a mathematically rigorous way using the ORAC optimal estimation retrieval framework, which includes the facility to use such independent a priori information. Key to the use of a priori information is a characterisation of their associated uncertainty. This paper demonstrates the improvements that are possible using this approach and also considers their impact on the microphysical cloud parameters retrieved. The Along-Track Scanning Radiometer (AATSR) instrument has two views and three thermal channels, so it is well placed to demonstrate the synergy of the two techniques. The stereo retrieval is able to improve the accuracy of the retrieved cloud top height when compared to collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), particularly in the presence of boundary layer inversions and high clouds. The impact of the stereo a priori information on the microphysical cloud properties of cloud optical thickness (COT) and effective radius (RE) was evaluated and generally found to be very small for single-layer clouds conditions over open water (mean RE differences of 2.2 (±5.9) microns and mean COD differences of 0.5 (±1.8) for single-layer ice clouds over open water at elevations of above 9 km, which are most strongly affected by the inclusion of the a priori).

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The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements

Cited by 46 publications

References 46 publications

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

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

Known and unknown unknowns: uncertainty estimation in satellite remote sensing

Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR

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