Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Stubenrauch, C.; Rossow, William B.; Kinne, Stefan; Ackerman, S. A.; Cesana, G; Chepfer, Hélène; Girolamo, Larry Di; Getzewich, Brian; Guignard, Anaïs; Heidinger, Andrew K.; Maddux, B. C.; Menzel, W. Paul; Minnis, Patrick; Pearl, Cindy; Platnick, S.; Poulsen, C.; Riédi, J.; Sun‐Mack, Sunny; Walther, A.; Winker, D. M.; Zeng, S.; Zhao, Guangyu

doi:10.1175/bams-d-12-00117.1

Cited by 483 publications

(298 citation statements)

References 63 publications

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“…The POLDER measurements before 2009 agree well with those of other sensors (Stubenrauch et al, 2013;Zeng et al, 2011). Therefore, the POLDER L1B radiance and L2 CDR products in 2008 were used in this study.…”

Section: Polder Multi-angle Polarized Observationssupporting

confidence: 58%

“…One of the key radiative properties of liquid water clouds is the droplet size distribution, which is represented by the droplet effective radius (CDR) and the effective variance (EV) (Bréon and Doutriaux-Boucher, 2005;Bréon and Colzy, 2000;Hansen and Travis, 1974). The observation of CDR and EV not only has a significant influence on the modeling of clouds' climate feedbacks (Stubenrauch et al, 2013;Dandin et al, 1997) but is also meaningful to aerosol-cloud-precipitation interaction research (Penner et al, 2004;Shang et al, 2014). Satellite retrievals of the CDR and EV have been used to extend the detailed knowledge gained from specific cloud campaign studies to the larger spatial and temporal scales that are relevant to climate (Liu et al, 2002(Liu et al, , 2011Bréon, 2006).…”

Section: Introductionmentioning

confidence: 99%

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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: Polder Multi-angle Polarized Observationssupporting

confidence: 58%

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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“…Two flags indicating the presence of surface water and permanent ice/snow are included to gain additional information about the observed surface type. Due to the seasonal variations in the global circulation and the presence of cirrus clouds (Stubenrauch et al, 2013) the day of the year (DOY) is used. The DOY is converted to two variables, sin(2π DOY/365) and cos(2π DOY/365), in order to remove the hard transition from 31 December to 1 January.…”

Section: Auxiliary Datamentioning

confidence: 99%

“…High-level clouds cover 27-37 % of the Earth's surface (Stubenrauch et al, 2013; the exact figure depends on the satellite instrument and its sensitivity to thin and sub-visual cirrus) and consequently play an important role in the climate system by reflecting the incoming solar radiation and absorbing the outgoing thermal radiation. In this study we focus on cirrus clouds, here defined as all clouds that consist of ice crystals.…”

Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

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“…Menzel et al, 2008). This is a hint that the ANN may model a statistical, rather than physical, relationship between the input and output variables, as the CTH has an annual cycle and a clear latitude dependency (Stubenrauch et al, 2013). It might also be that the 13.4 µm brightness temperature only provides redundant information with respect to Figure 1.…”

Section: Relative Importance Of the Cips Input Datamentioning

confidence: 99%

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

2017

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Abstract. Cirrus clouds remain one of the key uncertainties in atmospheric research. To better understand the properties and physical processes of cirrus clouds, accurate largescale observations from satellites are required. Artificial neural networks (ANNs) have proved to be a useful tool for cirrus cloud remote sensing. Since physics is not modelled explicitly in ANNs, a thorough characterisation of the networks is necessary.In this paper the CiPS (Cirrus Properties from SE-VIRI) algorithm is characterised using the space-borne lidar CALIOP. CiPS is composed of a set of ANNs for the cirrus cloud detection, opacity identification and the corresponding cloud top height, ice optical thickness and ice water path retrieval from the imager SEVIRI aboard the geostationary Meteosat Second Generation satellites. First, the retrieval accuracy is characterised with respect to different land surface types. The retrieval works best over water and vegetated surfaces, whereas a surface covered by permanent snow and ice or barren reduces the cirrus detection ability and increases the retrieval errors for the ice optical thickness and ice water path if the cirrus cloud is thin (optical thickness less than approx. 0.3). Second, the retrieval accuracy is characterised with respect to the vertical arrangement of liquid, ice clouds and aerosol layers as derived from CALIOP lidar data. The CiPS retrievals show little interference from liquid water clouds and aerosol layers below an observed cirrus cloud. A liquid water cloud vertically close or adjacent to the cirrus clearly increases the average retrieval errors for the optical thickness and ice water path, respectively, only for thin cirrus clouds with an optical thickness below 0.3 or ice water path below 5.0 g m −2 . For the cloud top height retrieval, only aerosol layers affect the retrieval error, with an increased positive bias when the cirrus is at low altitudes. Third, the CiPS retrieval error is characterised with respect to the properties of the investigated cirrus cloud (ice optical thickness and cloud top height). On average CiPS can retrieve the cirrus cloud top height with a relative error around 8 % and no bias and the ice optical thickness with a relative error around 50 % and bias around ±10 % for the most common combinations of cloud top height and ice optical thickness. Similarities with physically based retrieval methods are evident, which implies that even though the retrieval methods differ in the implementation of physics in the model, the retrievals behave similarly due to physical constraints. Finally, we also show that the ANN retrievals have a low sensitivity to radiometric noise in the SEVIRI observations. For optical thickness and ice water path the relative uncertainty due to noise is less than 10 % down to sub-visual cirrus. For the cloud top height retrieval the uncertainty due to noise is around 100 m for all cloud top heights.

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Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel

Cited by 483 publications

References 63 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

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

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