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

Sayer, A. M.; Poulsen, C.; Arnold, C.; Campmany, E.; Dean, S. M.; Ewen, G. B. L.; Grainger, R. G.; Lawrence, B. N.; Siddans, Richard; Thomas, G. E.; Watts, P. D.

doi:10.5194/acp-11-3913-2011

Cited by 40 publications

(35 citation statements)

References 50 publications

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“…In operation, the measured reflectances in the visible and near-infrared bands are compared to the reflectances in a pre-calculated look-up table (LUT) to derive the cloud optical thickness and the CDR simultaneously. This method has been used to derive daytime CDR from the Global Retrieval of ATSR Cloud Parameters and Evaluation (ATSR-GRAPE) (Sayer et al, 2011), the Pathfinder Atmospheres -Extended (PATMOS-x) (Walther and Heidinger, 2012), the Visible Infrared Imaging Radiometer Suite (VI-IRS) , the National Oceanic and Atmospheric Administration/Advanced Very-High-Resolution Radiometer (NOAA/AVHRR) (Nakajima and Nakajima, 1995;Kawamoto et al, 2001) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) (Baum et al, 2000;Platnick et al, 2003;Sayer et al, 2011;Walther and Heidinger, 2012). Nighttime cloud properties have been derived from reflected moonlight (0.7 and 3.75 µm) as measured by the VIIRS instrument .…”

Section: Introductionmentioning

confidence: 99%

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

et al. 2015

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Abstract. The principles of cloud droplet size retrieval via Polarization and Directionality of the Earth's Reflectance (POLDER) requires that clouds be horizontally homogeneous. The retrieval is performed by combining all measurements from an area of 150 km × 150 km to compensate for POLDER's insufficient directional sampling. Using POLDER-like data simulated with the RT3 model, we investigate the impact of cloud horizontal inhomogeneity and directional sampling on the retrieval and analyze which spatial resolution is potentially accessible from the measurements. Case studies show that the sub-grid-scale variability in droplet effective radius (CDR) can significantly reduce valid retrievals and introduce small biases to the CDR (∼ 1.5 µm) and effective variance (EV) estimates. Nevertheless, the sub-grid-scale variations in EV and cloud optical thickness (COT) only influence the EV retrievals and not the CDR estimate. In the directional sampling cases studied, the retrieval using limited observations is accurate and is largely free of random noise.Several improvements have been made to the original POLDER droplet size retrieval. For example, measurements in the primary rainbow region (137-145 • ) are used to ensure retrievals of large droplet (> 15 µm) and to reduce the uncertainties caused by cloud heterogeneity. We apply the improved method using the POLDER global L1B data from June 2008, and the new CDR results are compared with the operational CDRs. The comparison shows that the operational CDRs tend to be underestimated for large droplets because the cloudbow oscillations in the scattering angle region of 145-165 • are weak for cloud fields with CDR > 15 µm. Finally, a sub-grid-scale retrieval case demonstrates that a higher resolution, e.g., 42 km × 42 km, can be used when inverting cloud droplet size distribution parameters from POLDER measurements.

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Section: Introductionmentioning

confidence: 99%

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

et al. 2015

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“…Poor agreement between the ORAC and ORAC+stereo microphysical parameters is observed in retrievals over ice (and to a far lesser degree, multi-layer cloud systems); however this is expected due to the known behaviours and limitations of the ORAC algorithm (Sayer et al, 2011). In retrieval situations where ORAC is known to perform optimally (singlelayer clouds over ice-free water bodies in this instance) the mean differences between the algorithms are small.…”

Section: Impact On Cloud Optical Propertiesmentioning

confidence: 97%

“…The higher mean so-lution costs indicate that when retrievals settle on different phases the observed conditions are not well approximated by either forward model. Causes of this poor approximation can include: multi-layer clouds (that may have been missed by CALIOP due it saturating at three optical depths), the presence of mixed phase cloud and high aerosol loading, or regions where the auxiliary data or a priori data are not appropriately defined (Sayer et al, 2011). In this instance though, an inappropriately defined CTH a priori is likely not the cause as the high costs are also present in the ORAC retrieval where it is not applied.…”

Section: Impact On Cloud Optical Propertiesmentioning

confidence: 99%

“…For example, Sayer et al (2011) provides important caveats to the use of ORAC cloud optical depth retrievals in multi-layer cloud systems, and for cloud effective radius in the case of ice clouds. A pertinent assessment of the problematic cloud conditions for macrophysical retrievals using radiometric methods is presented in Marchand et al (2010).…”

Section: Introductionmentioning

confidence: 99%

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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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“…It provides rigorous uncertainty propagation (providing pixel-by-pixel uncertainty on retrieved quantities) and inclusion of a priori knowledge and ensures that the most is made of the information provided by the measurements, by calculating all retrieved parameters as a function of all measurements simultaneously. Surface reflectance is retrieved as a bi-hemispherical reflectance, with the directional dependence of the BRDF being constrained by a priori values provided by the MODIS MCD43B surface BRDF product over land (Sayer et al, 2011) and a comprehensive sea surface reflectance model (Sayer et al, 2010) over ocean. Incorporating the common aerosol models (Sect.…”

Section: The Oxford Ral Aerosol and Cloud Retrieval (Orac)mentioning

confidence: 99%

Aerosol retrieval experiments in the ESA Aerosol_cci project

et al. 2013

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Within the ESA Climate Change Initiative (CCI) project Aerosol cci (2010-2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust)

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

Cited by 40 publications

References 50 publications

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument

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

Aerosol retrieval experiments in the ESA Aerosol_cci project

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