scholarly journals Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers

2019 ◽  
Author(s):  
Nikos Benas ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
Piet Stammes
Keyword(s):  
Size Distribution ◽  
Optical Thickness ◽  
Droplet Size Distribution ◽  
Effective Radius ◽  
High Temporal Resolution ◽  
Climate Data ◽  
Cloud Properties ◽  
Data Record ◽  
Stratocumulus Clouds ◽  
Cloud Optical Thickness

Abstract. Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the detection of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals were analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis was based on the use of SEVIRI reflectances and products from Meteosat-8 and -10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals over different underlying surfaces (ocean/land) and using different spectral combinations of visible and shortwave-infrared channels were also performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud bow and glory related irregularities to the width of the assumed droplet size distribution was analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For a case with continental clouds a broader size distribution (effective variance around 0.15) was obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

scholarly journals Sensitivity of liquid cloud optical thickness and effective radius retrievals to cloud bow and glory conditions using two SEVIRI imagers

2019 ◽  
Vol 12 (5) ◽  
pp. 2863-2879 ◽  
Author(s):  
Nikos Benas ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
Piet Stammes
Keyword(s):  
Size Distribution ◽  
Optical Thickness ◽  
Droplet Size Distribution ◽  
Effective Radius ◽  
High Temporal Resolution ◽  
Climate Data ◽  
Cloud Properties ◽  
Data Record ◽  
Stratocumulus Clouds ◽  
Cloud Optical Thickness

Abstract. Retrievals of cloud properties from geostationary satellite sensors offer extensive spatial and temporal coverage and resolution. The high temporal resolution allows the observation of diurnally resolved cloud properties. However, retrievals are sensitive to varying illumination and viewing geometries, including cloud glory and cloud bow conditions, which can lead to irregularities in the diurnal data record. In this study, these conditions and their effects on liquid cloud optical thickness and effective radius retrievals are analyzed using the Cloud Physical Properties (CPP) algorithm. This analysis is based on the use of Spinning Enhanced Visible and Infrared Imager (SEVIRI) reflectances and products from Meteosat-8 and Meteosat-10, which are located over the Indian and Atlantic Ocean, respectively, and cover an extensive common area under different viewing angles. Comparisons of the retrievals from two full days, over ocean and land, and using different spectral combinations of visible and shortwave-infrared channels, are performed, to assess the importance of these factors in the retrieval process. The sensitivity of the cloud-bow- and cloud-glory-related irregularities to the width of the assumed droplet size distribution is analyzed by using different values of the effective variance of the size distribution. The results suggest for marine stratocumulus clouds an effective variance of around 0.05, which implies a narrower size distribution than typically assumed in satellite-based retrievals. For the case with continental clouds a broader size distribution (effective variance around 0.15) is obtained. This highlights the importance of appropriate size distribution assumptions and provides a way to improve the quality of cloud products in future climate data record releases.

scholarly journals Cloud heterogeneity on cloud and aerosol above cloud properties retrieved from simulated total and polarized reflectances

2018 ◽  
Vol 11 (6) ◽  
pp. 3627-3643 ◽  
Author(s):  
Céline Cornet ◽  
Laurent C.-Labonnote ◽  
Fabien Waquet ◽  
Frédéric Szczap ◽  
Lucia Deaconu ◽  
...  
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Optical Thickness ◽  
Incidence Angle ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Cloud Droplet Size Distribution ◽  
Fractional Cloud ◽  
Cloud Optical Thickness ◽  
Heterogeneity Effects

Abstract. Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo, effective radius and variance of the cloud droplet size distribution and aerosol parameters above cloud are analyzed. Three different clouds that have the same mean optical thicknesses were generated: the first with a flat top, the second with a bumpy top and the last with a fractional cloud cover. At small scale (50 m), for oblique solar incidence, the illumination effects lead to higher total but also polarized reflectances. The polarized reflectances even reach values that cannot be predicted by the 1-D homogeneous cloud assumption. At the POLDER scale (7 km × 7 km), the angular signature is modified by a combination of the plane–parallel bias and the shadowing and illumination effects. In order to quantify effects of cloud heterogeneity on operational products, we ran the POLDER operational algorithms on the simulated reflectances to retrieve the cloud optical thickness and albedo. Results show that the cloud optical thickness is greatly affected: biases can reach up to −70, −50 or +40 % for backward, nadir and forward viewing directions, respectively. Concerning the albedo of the cloudy scenes, the errors are smaller, between −4.7 % for solar incidence angle of 20∘ and up to about +8 % for solar incidence angle of 60∘. We also tested the heterogeneity effects on new algorithms that allow retrieving cloud droplet size distribution and cloud top pressures and also aerosol above clouds. Contrary to the bi-spectral method, the retrieved cloud droplet size parameters are not significantly affected by the cloud heterogeneity, which proves to be a great advantage of using polarized measurements. However, the cloud top pressure obtained from molecular scattering in the forward direction can be biased up to about 60 hPa (around 550 m). Concerning the aerosol optical thickness (AOT) above cloud, the results are different depending on the available angular information. Above the fractional cloud, when only side scattering angles between 100 and 130∘ are available, the AOT is underestimated because of the plane–parallel bias. However, for solar zenith angle of 60∘ it is overestimated because the polarized reflectances are increased in forward directions.

scholarly journals Detailed characterisation of AVHRR global cloud detection performance of the CM SAF CLARA-A2 climate data record based on CALIPSO-CALIOP cloud information

2017 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Nina Håkansson
Keyword(s):  
Optical Thickness ◽  
Detection Performance ◽  
Polar Regions ◽  
Cloud Detection ◽  
Climate Data ◽  
Data Record ◽  
Cloud Optical Thickness ◽  
Land Surfaces ◽  
Cloud Mask ◽  
Climate Data Record

Abstract. The cloud detection performance of the cloud mask being used in the CM SAF cloud, albedo and surface radiation dataset from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated in detail using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. Validation results, including their global distribution, have been calculated from collocations of AVHRR and CALIOP measurements over a ten-year period (2006–2015). The sensitivity of the results to the cloud optical thicknesses of CALIOP-observed clouds were studied leading to the conclusion that the global cloud detection sensitivity (defined as the minimum cloud optical thickness for which 50 % of clouds could be detected) was estimated to 0.225. After applying this optical thickness threshold to the CALIOP cloud mask, results were found to be basically unbiased over most of the globe except over the polar regions where a considerably underestimation of cloudiness could be seen during the polar winter. The probability of detecting clouds in the polar winter could be as low as 50 % over the highest and coldest portions of Greenland and Antarctica, showing that also a large fraction of optically thick clouds remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud’s geographical position. Best results were achieved over oceanic surfaces at mid-to-high latitudes were at least 50 % of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 over the geographically highest parts of Greenland and Antarctica. The validation method is suggested to be applied also to other satellite-based CDRs and validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterisation of various cloud CDRs from passive imagery.

scholarly journals Cloud heterogeneity effects on cloud and aerosol above cloud properties retrieved from simulated total and polarized reflectances

2017 ◽  
Author(s):  
Céline Cornet ◽  
Laurent C-Labonnote ◽  
Frédéric Szczap ◽  
Lucia Deaconu ◽  
Fabien Waquet ◽  
...  
Keyword(s):  
Size Distribution ◽  
Droplet Size ◽  
Optical Thickness ◽  
Incidence Angle ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Cloud Droplet Size Distribution ◽  
Fractional Cloud ◽  
Cloud Optical Thickness ◽  
Heterogeneity Effects

Abstract. Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, cloud albedo, effective radius and variance of the cloud droplet size distribution and aerosol above cloud optical thickness are analyzed. Three different clouds having the same mean optical thicknesses were generated: the first one with a flat top, the second one with a bumpy top and the last one with a fractional cloud cover. At small scale (50 m), for oblique solar incidence, the illumination effects lead to higher total but also polarized reflectances. The polarized reflectances even reach values that cannot be predicted by the 1D homogeneous cloud assumption. At the POLDER scale (7 km × 7 km), the angular signature is modified by a combination of the plane-parallel bias and the shadowing and illumination effects. In order to quantify effects of cloud heterogeneity on operational products, we ran the POLDER operational algorithms on the simulated reflectances to retrieve the cloud optical thickness and albedo. Results show that the cloud optical thickness is greatly affected: biases can reach up to −70 %, −50 % or +40 % for backward, nadir and forward viewing directions respectively. Concerning the cloud albedo, the errors are smaller, between −4.7 % for solar incidence angle of 20° and up to about 8 % for solar incidence angle of 60°. We also tested the heterogeneity effects on new algorithms that allow retrieving cloud droplet size distribution and cloud top pressures and also aerosol above clouds. Contrarily to the bi-spectral method, the retrieved cloud droplet size parameters are not significantly affected by the cloud heterogeneity, which proves to be a great advantage of using polarized measurements. However the cloud top pressure obtained from molecular scattering in the forward direction can be biased up to 120 hPa (around 1 km). Concerning the aerosol optical thickness (AOT) above cloud, the results are different depending on the available angular information. Above the fractional cloud, when only side scattering angles are available, the AOT can be underestimated because of the plane-parallel bias. For solar zenith angle of 60°, on contrary, it is overestimated because the polarized reflectances are increased in forward directions.

scholarly journals Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record

2018 ◽  
Vol 11 (1) ◽  
pp. 633-649 ◽  
Author(s):  
Karl-Göran Karlsson ◽  
Nina Håkansson
Keyword(s):  
Optical Thickness ◽  
Detection Sensitivity ◽  
Polar Regions ◽  
Cloud Detection ◽  
Climate Data ◽  
Data Record ◽  
Cloud Optical Thickness ◽  
Land Surfaces ◽  
Climate Data Record

Abstract. The sensitivity in detecting thin clouds of the cloud screening method being used in the CM SAF cloud, albedo and surface radiation data set from AVHRR data (CLARA-A2) cloud climate data record (CDR) has been evaluated using cloud information from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the CALIPSO satellite. The sensitivity, including its global variation, has been studied based on collocations of Advanced Very High Resolution Radiometer (AVHRR) and CALIOP measurements over a 10-year period (2006–2015). The cloud detection sensitivity has been defined as the minimum cloud optical thickness for which 50 % of clouds could be detected, with the global average sensitivity estimated to be 0.225. After using this value to reduce the CALIOP cloud mask (i.e. clouds with optical thickness below this threshold were interpreted as cloud-free cases), cloudiness results were found to be basically unbiased over most of the globe except over the polar regions where a considerable underestimation of cloudiness could be seen during the polar winter. The overall probability of detecting clouds in the polar winter could be as low as 50 % over the highest and coldest parts of Greenland and Antarctica, showing that a large fraction of optically thick clouds also remains undetected here. The study included an in-depth analysis of the probability of detecting a cloud as a function of the vertically integrated cloud optical thickness as well as of the cloud's geographical position. Best results were achieved over oceanic surfaces at mid- to high latitudes where at least 50 % of all clouds with an optical thickness down to a value of 0.075 were detected. Corresponding cloud detection sensitivities over land surfaces outside of the polar regions were generally larger than 0.2 with maximum values of approximately 0.5 over the Sahara and the Arabian Peninsula. For polar land surfaces the values were close to 1 or higher with maximum values of 4.5 for the parts with the highest altitudes over Greenland and Antarctica. It is suggested to quantify the detection performance of other CDRs in terms of a sensitivity threshold of cloud optical thickness, which can be estimated using active lidar observations. Validation results are proposed to be used in Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulation Package (COSP) simulators for cloud detection characterization of various cloud CDRs from passive imagery.

scholarly journals Remote sensing of water cloud droplet size distributions using the backscatter glory: a case study

2004 ◽  
Vol 4 (5) ◽  
pp. 1255-1263 ◽  
Author(s):  
B. Mayer ◽  
M. Schröder ◽  
R. Preusker ◽  
L. Schüller
Keyword(s):  
Remote Sensing ◽  
Size Distribution ◽  
Droplet Size ◽  
Cloud Droplet ◽  
Droplet Size Distribution ◽  
Single Scattering ◽  
Effective Radius ◽  
Radiative Properties ◽  
High Angular Resolution ◽  
Size Distributions

Abstract. Cloud single scattering properties are mainly determined by the effective radius of the droplet size distribution. There are only few exceptions where the shape of the size distribution affects the optical properties, in particular the rainbow and the glory directions of the scattering phase function. Using observations by the Compact Airborne Spectrographic Imager (CASI) in 180° backscatter geometry, we found that high angular resolution aircraft observations of the glory provide unique new information which is not available from traditional remote sensing techniques: Using only one single wavelength, 753nm, we were able to determine not only optical thickness and effective radius, but also the width of the size distribution at cloud top. Applying this novel technique to the ACE-2 CLOUDYCOLUMN experiment, we found that the size distributions were much narrower than usually assumed in radiation calculations which is in agreement with in-situ observations during this campaign. While the shape of the size distribution has only little relevance for the radiative properties of clouds, it is extremely important for understanding their formation and evolution.

scholarly journals FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations

2014 ◽  
Vol 7 (11) ◽  
pp. 3873-3890 ◽  
Author(s):  
C. K. Carbajal Henken ◽  
R. Lindstrot ◽  
R. Preusker ◽  
J. Fischer
Keyword(s):  
Optical Thickness ◽  
Single Layer ◽  
Cloud Water ◽  
Effective Radius ◽  
Cloud Property ◽  
Microphysical Properties ◽  
Microphysical Property ◽  
Resolution Imaging ◽  
Cloud Optical Thickness ◽  
Cloud Water Path

Abstract. A newly developed daytime cloud property retrieval algorithm, FAME-C (Freie Universität Berlin AATSR MERIS Cloud), is presented. Synergistic observations from the Advanced Along-Track Scanning Radiometer (AATSR) and the Medium Resolution Imaging Spectrometer (MERIS), both mounted on the polar-orbiting Environmental Satellite (Envisat), are used for cloud screening. For cloudy pixels two main steps are carried out in a sequential form. First, a cloud optical and microphysical property retrieval is performed using an AATSR near-infrared and visible channel. Cloud phase, cloud optical thickness, and effective radius are retrieved, and subsequently cloud water path is computed. Second, two cloud top height products are retrieved based on independent techniques. For cloud top temperature, measurements in the AATSR infrared channels are used, while for cloud top pressure, measurements in the MERIS oxygen-A absorption channel are used. Results from the cloud optical and microphysical property retrieval serve as input for the two cloud top height retrievals. Introduced here are the AATSR and MERIS forward models and auxiliary data needed in FAME-C. Also, the optimal estimation method, which provides uncertainty estimates of the retrieved property on a pixel basis, is presented. Within the frame of the European Space Agency (ESA) Climate Change Initiative (CCI) project, the first global cloud property retrievals have been conducted for the years 2007–2009. For this time period, verification efforts are presented, comparing, for four selected regions around the globe, FAME-C cloud optical and microphysical properties to cloud optical and microphysical properties derived from measurements of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The results show a reasonable agreement between the cloud optical and microphysical property retrievals. Biases are generally smallest for marine stratocumulus clouds: −0.28, 0.41 μm and −0.18 g m−2 for cloud optical thickness, effective radius and cloud water path, respectively. This is also true for the root-mean-square deviation. Furthermore, both cloud top height products are compared to cloud top heights derived from ground-based cloud radars located at several Atmospheric Radiation Measurement (ARM) sites. FAME-C mostly shows an underestimation of cloud top heights when compared to radar observations. The lowest bias of −0.3 km is found for AATSR cloud top heights for single-layer clouds, while the highest bias of −3.0 km is found for AATSR cloud top heights for multilayer clouds. Variability is low for MERIS cloud top heights for low-level clouds, and high for MERIS cloud top heights for mid-level and high-level single-layer clouds, as well as for both AATSR and MERIS cloud top heights for multilayer clouds.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

scholarly journals Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

2017 ◽  
Vol 10 (12) ◽  
pp. 4747-4759 ◽  
Author(s):  
Rintaro Okamura ◽  
Hironobu Iwabuchi ◽  
K. Sebastian Schmidt
Keyword(s):  
Deep Learning ◽  
Radiative Transfer ◽  
Optical Thickness ◽  
Cloud Droplet ◽  
Effective Radius ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Optical Remote Sensing ◽  
Transfer Effects ◽  
Cloud Properties

Abstract. Three-dimensional (3-D) radiative-transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. The challenge is that 3-D effects manifest themselves across multiple satellite pixels, which traditional single-pixel approaches cannot capture. In this study, we present two multi-pixel retrieval approaches based on deep learning, a technique that is becoming increasingly successful for complex problems in engineering and other areas. Specifically, we use deep neural networks (DNNs) to obtain multi-pixel estimates of cloud optical thickness and column-mean cloud droplet effective radius from multispectral, multi-pixel radiances. The first DNN method corrects traditional bispectral retrievals based on the plane-parallel homogeneous cloud assumption using the reflectances at the same two wavelengths. The other DNN method uses so-called convolutional layers and retrieves cloud properties directly from the reflectances at four wavelengths. The DNN methods are trained and tested on cloud fields from large-eddy simulations used as input to a 3-D radiative-transfer model to simulate upward radiances. The second DNN-based retrieval, sidestepping the bispectral retrieval step through convolutional layers, is shown to be more accurate. It reduces 3-D radiative-transfer effects that would otherwise affect the radiance values and estimates cloud properties robustly even for optically thick clouds.

scholarly journals The Cloud_cci simulator for the ESA Cloud_cci climate data record and its application to a global and a regional climate model

2018 ◽  
Author(s):  
Salomon Eliasson ◽  
Karl Göran Karlsson ◽  
Erik van Meijgaard ◽  
Jan Fokke Meirink ◽  
Martin Stengel ◽  
...  
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Cloud Fraction ◽  
Climate Data ◽  
European Continent ◽  
Cloud Properties ◽  
Data Record ◽  
The Impact ◽  
Climate Data Record

Abstract. The Cloud_cci satellite simulator has been developed to enable comparisons between the Cloud_cci Climate Data Record (CDR) and climate models. The Cloud_cci simulator is applied here to the EC-Earth Global Climate Model as well as the RACMO Regional Climate Model. We demonstrate the importance of using a satellite simulator that emulates the retrieval process underlying the CDR as opposed to taking the model output directly. The impact of not sampling the model at the local overpass time of the polar-orbiting satellites used to make the dataset was shown to be large, yielding up to 100 % error in Liquid Water Path (LWP) simulations in certain regions. The simulator removes all clouds with optical thickness smaller than 0.2 to emulate the Cloud_cci CDR's lack of sensitivity to very thin clouds. This reduces Total Cloud Fraction (TCF) globally by about 10 % for EC-Earth and by a few percent for RACMO over Europe. Globally, compared to the Cloud_cci CDR, EC-Earth is shown to be mostly in agreement on the distribution of clouds and their height, but it generally underestimates the high cloud fraction associated with tropical convection regions, and overestimates the occurrence and height of clouds over the Sahara and the Arabian sub-continent. In RACMO, TCF is higher than retrieved over the northern Atlantic Ocean, but lower than retrieved over the European continent, where in addition the Cloud Top Pressure (CTP) is underestimated. The results shown here demonstrate again that a simulator is needed to make meaningful comparisons between modelled and retrieved cloud properties. It is promising to see that for (nearly) all cloud properties the simulator improves the agreement of the model with the satellite data.

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