Calibration of the AATSR instrument

Smith, David L.; Delderfield, J.; Drummond, D.; Edwards, Thomas M.; Mutlow, C. T.; Read, P. D.; Toplis, GM

doi:10.1016/s0273-1177(01)00273-3

Cited by 43 publications

(21 citation statements)

References 1 publication

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“…The 1.6 µm channel is comparatively unbiased (except for scenes over sea, which show a slight low bias in AATSR) although the median ratio varies by about 0.05 over the course of the year. These relative biases are larger than the reported random error on the measurements, which are of the order of 2-3% of the signal (Smith et al, 2001(Smith et al, , 2002. The shape of the relative bias is consistent between all three channels through the course of the year.…”

Section: Consistency Between Atsr-2 and Aatsr Cloud Retrievalsmentioning

confidence: 49%

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

et al. 2011

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Abstract. The Along-Track Scanning Radiometers (ATSRs) provide a long time-series of measurements suitable for the retrieval of cloud properties. This work evaluates the freelyavailable Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) dataset (version 3) created from the ATSR-2 (1995ATSR-2 ( -2003 and Advanced ATSR (AATSR; 2002 onwards) records. Users are recommended to consider only retrievals flagged as high-quality, where there is a good consistency between the measurements and the retrieved state (corresponding to about 60% of converged retrievals over sea, and more than 80% over land). Cloud properties are found to be generally free of any significant spurious trends relating to satellite zenith angle. Estimates of the random error on retrieved cloud properties are suggested to be generally appropriate for optically-thick clouds, and up to a factor of two too small for optically-thin cases. The correspondence between ATSR-2 and AATSR cloud properties is high, but a relative calibration difference between the sensors of order 5-10% at 660 nm and 870 nm limits the potential of the current version of the dataset for trend analysis. As ATSR-2 is thought to have the better absolute calibration, the discussion focusses on this portion of the record. Cloud-top heights from GRAPE compare well to ground-based data at four sites, particularly for shallow clouds. Clouds forming in boundary-layer inversions are typically around 1 km too high Correspondence to: A. M. Sayer (andrew.sayer@nasa.gov) in GRAPE due to poorly-resolved inversions in the modelled temperature profiles used. Global cloud fields are compared to satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and a climatology of liquid water content derived from satellite microwave radiometers. In all cases the main reasons for differences are linked to differing sensitivity to, and treatment of, multi-layer cloud systems. The correlation coefficient between GRAPE and the two MODIS products considered is generally high (greater than 0.7 for most cloud properties), except for liquid and ice cloud effective radius, which also show biases between the datasets. For liquid clouds, part of the difference is linked to choice of wavelengths used in the retrieval. Total cloud cover is slightly lower in GRAPE (0.64) than the CALIOP dataset (0.66). GRAPE underestimates liquid cloud water path relative to microwave radiometers by up to 100 g m −2 near the Equator and overestimates by around 50 g m −2 in the storm tracks. Finally, potential future improvements to the algorithm are outlined.

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Section: Consistency Between Atsr-2 and Aatsr Cloud Retrievalsmentioning

confidence: 49%

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

et al. 2011

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“…Thermal channels are calibrated using two on board black bodies at known temperatures which are observed during each across-track scan of the instrument. This makes it possible to determine single channel equivalent brightness temperatures correct to ±0.05 K (Smith et al, 2001). The instrument also has an on board visible/near-infrared calibration system enabling the visible channels to be calibrated to an accuracy of better than 4 % (Smith et al, 2008), which is subsequently improved via vicarious calibration using scenes of known stable surface BRDF (certain deserts and ice caps).…”

Section: The Along Track Scanning Radiometer (Atsr)mentioning

confidence: 99%

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Poulsen

Siddans

Thomas³

et al. 2012

Atmos. Meas. Tech.

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Abstract. Clouds play an important role in balancing the Earth's radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase.The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

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“…Like clouds, aerosol layers also cannot be considered to be black bodies, so their reflectance at 3.7 µm is not zero. The magnitude of this effect is much lower than that of clouds, however, good calibration of IR channels of AATSR (better than 0.1 Kelvin according to Smith et al, 2001) makes it possible to use this effect for AOT retrieval. In order to utilize lookup table approach to make the inversion and retrieve AOT, we need to express the measured top of atmosphere radiances analytically.…”

Section: Theoretical Basis Of Aerosol Optical Thickness Retrieval In mentioning

confidence: 99%

“…4.1). Good infrared calibration of the AATSR instrument (Smith et al, 2001) provides the possibility of applying the same approach also to aerosol layers. This paper describes a new retrieval approach to determine AOT over snow.…”

Section: Introductionmentioning

confidence: 99%

Remote sensing of aerosols over snow using infrared AATSR observations

et al. 2011

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Abstract. Infrared (IR) retrievals of aerosol optical thickness (AOT) are challenging because of the low reflectance of aerosol layer at longer wavelengths. In this paper we present a closer analysis of this problem, performed with radiative transfer (RT) simulations for coarse and accumulation mode of four main aerosol components. It shows the strong angular dependence of aerosol IR reflectance at low solar elevations resulting from the significant asymmetry of aerosol phase function at these wavelengths. This results in detectable values of aerosol IR reflectance at certain non-nadir observation angles providing the advantage of multiangle remote sensing instruments for a retrieval of AOT at longer wavelengths. Such retrievals can be of importance e.g. in case of a very strong effect of the surface on the top of atmosphere (TOA) reflectance in the visible spectral range. In the current work, a new method to retrieve AOT of the coarse and accumulation mode particles over snow has been developed using the measurements of Advanced Along Track Scanning Radiometer (AATSR) on board the ENVISAT satellite. The algorithm uses AATSR channel at 3.7 µm and utilizes its dual-viewing observation technique, implying the forward view with an observation zenith angle of around 55 degrees and the nadir view. It includes cloud/snow discrimination, extraction of the atmospheric reflectance out of measured brightness temperature (BT) at 3.7 µm, and interpolation of look-up tables (LUTs) for a given aerosol reflectance. The algorithm uses LUTs, separately simulated with RT forward calculations. The resulting AOT at 500 nm is estimated from the value at 3.7 µm using a fixed Angström parameter.Correspondence to: L. G. Istomina (lora@iup.physik.uni-bremen.de)The presented method has been validated against groundbased Aerosol Robotic Network (AERONET) data for 4 high Arctic stations and shows good agreement.A case study has been performed at W-Greenland on 5 July 2008. The day before was characterized by a noticeable dust event. The retrieved AOT maps of the region show a clear increase of AOT in the Kangerlussuaq area. The area of increased AOT was detected on 5 July on the ice sheet east of Kangelussuaq, opposite to the observed north easterly wind at ground level. This position can be explained by a small scale atmospheric circulation transporting the mobilized mineral dust upslope, after its intrusion into the upper branch of the circulation.The performed study of atmospheric reflectance at 3.7 µm also shows possibilities for the detection and retrievals of cloud properties over snow surfaces.

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Calibration of the AATSR instrument

Cited by 43 publications

References 1 publication

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Remote sensing of aerosols over snow using infrared AATSR observations

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