The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Sus, Oliver; Stengel, Martin; Stapelberg, Stefan; McGarragh, Gregory; Poulsen, C.; Povey, Adam C.; Schlundt, Cornelia; Thomas, G. E.; Christensen, Matthew; Proud, Simon; Jerg, Matthias; Grainger, R. G.; Hollmann, Rainer

doi:10.5194/amt-11-3373-2018

Cited by 38 publications

(36 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The retrieval system employed for cloud properties is the Community Cloud retrieval for CLimate (CC4CL), which is sum-10 marized in Stengel et al (2017) and described in detail in Sus et al (2018) and McGarragh et al (2018). However, further developments have taken place since v2, of which the key elements are listed in the following paragraphs.…”

Section: Algorithmsmentioning

confidence: 99%

“…More specifically, the CAL_LID_L2_05kmCLay-Prov product was downloaded from the ICARE Data and Service Center (http://www.icare.univ-lille1.fr). To investigate the sensitivity of passive imager retrievals to the thinnest cloud layers, the cloud optical depth profiles included in the CALIOP profiles were employed as in Karlsson and 5 Johansson (2013); Stengel et al (2013); Sus et al (2018). Following this approach different scenarios for excluding optically thin cloud layers are investigated when discussing validation of CMA, CPH and CTH below.…”

Section: Validationmentioning

confidence: 99%

“…ANNmask = artificial neural network for cloud detection, ANNphase = artificial neural network for cloud phase, SV = state vector, PP = postprocessed, PV = Pavolonis algorithm (Pavolonis and Heidinger, 2004;Pavolonis et al, 2005), OE = optimal estimation, BR = BUGSrad (radiative flux algorithm), TOA = top-of-atmosphere, BOA = bottomof-atmosphere (surface), LW = longwave, SW = shortwave. Upper part of the table (cloud properties) has been adopted from Sus et al (2018).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Stengel

Stapelberg

Sus

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. We present version 3 of the Cloud_cci AVHRR-PM dataset which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from Advanced Very High Resolution Radiometer (AVHRR) measurements recorded by the afternoon (post meridiem, PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellites (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling, shortwave and longwave broadband fluxes at the surface (bottom-of-atmosphere - BOA) and top-of-atmosphere (TOA). All fluxes were determined at AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) show a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at BOA and TOA. The Cloud_cci AVHRR-PM version 3 dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a Digital Object Identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

show abstract

Section: Algorithmsmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Stengel

Stapelberg

Sus

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…For CTH and CPH the scores are also shown as a function of (COT th ), although (COT th ) is here referring to the optical thickness into the cloud (top-down) at which the reference value was taken from the CALIOP profile (SD = Standard deviation). are described by prognostic equations for cloud condensate and cloud fraction obeying mass balance equations (Tiedtke, 1993). Clouds are defined by the horizontal coverage of the grid box by cloud and the mass mixing ratio of total cloud condensate, along with the constraint that cloud air is saturated with regard to liquid water and ice.…”

Section: Era-interim Reanalysismentioning

confidence: 99%

Authors' response to RC2 - Anonymous Referee #2

Stengel¹

2018

Preprint

Self Cite

View full text Add to dashboard Cite

An evaluation of the ERA-Interim clouds using satellite observations is presented. To facilitate such an evaluation in a proper way, a simplified satellite simulator has been developed and applied to 6-hourly ERA-Interim reanalysis data covering the period of 1982 to 2014. The simulator converts modelled cloud fields, for example those of the ERA-Interim reanalysis, to simulated cloud fields by accounting for specific characteristics of passive imaging satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which form the basis of many long-term observational datasets of cloud properties. It is attempted to keep the simulated cloud fields close to the original modelled cloud fields to allow a quality assessment of the latter based on comparisons of the simulated clouds fields with the observations.Applying the simulator to ERA-Interim data, this study firstly focuses on the spatial distribution and frequency of clouds (total cloud fraction) and on their vertical position, using cloud-top pressure to express the cloud fraction of high-level, mid-level and low-level clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic uncertainties in the satellite observations and are further stratified by accounting for the limited sensitivity of the observations to clouds with very low cloud optical thickness (COT).The comparisons show that ERA-Interim cloud fraction is generally too low nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low-level clouds, for which the comparisons only show a minor sensitivity to the cloud optical thickness thresholds applied. The amount of ERA-Interim high-level clouds, being higher than in the observations, agrees with the observations within their estimated uncertainties. Removing the optically very thin clouds (COT <0.15) from the model fields improves the agreement with the observations for highlevel cloud fraction locally (e.g. in the tropics), while for the mid-latitude regions, the best agreement for high-level cloud fraction is found when removing all clouds with COT <1.0. Comparisons of the cloud thermodynamic phase at the cloud top reveal a too high relative ice cloud frequency in ERA-Interim, being most pronounced in the higher latitudes. Indications are found that this is due to the suppression of liquid cloud occurrence for temperatures below −23 • C in ERA-Interim.The application of this simulator facilitates a more effective use of passive satellite observations of clouds in the evaluation of modelled cloudiness, for example in reanalyses.

show abstract

“…Including additional thermal channels allows for the possibility of retrieving information on cloud transparency from which it is possible to separate the cloud from the below-cloud signal. In particular, the 3.7 µm channel paired with 11 µm has been used to retrieve cloud top pressure along with a single microphysical parameter that describes the cloud radiative thickness in the infrared, typically referred to as the "effective emissivity" (Szejwach, 1982;Wu, 1987;Liou et al, 1990;Ou et al, 1995). In a similar man-ner, have shown that the relative interdependence of optical thickness and effective radius in the 3.7 and 11.0 µm combination allows these to be retrieved along with cloud top pressure, although with a significantly greater uncertainty than using solar wavelengths during the day.…”

Section: Introductionmentioning

confidence: 99%

The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract. The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

show abstract

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Cited by 38 publications

References 56 publications

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Cloud_cci AVHRR-PM dataset version 3: 35 year climatology of global cloud and radiation properties

Authors' response to RC2 - Anonymous Referee #2

The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach

Contact Info

Product

Resources

About