A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Stubenrauch, C.; Cros, Sylvain; Guignard, Anaïs; Lamquin, Nicolas

doi:10.5194/acp-10-7197-2010

Cited by 99 publications

(111 citation statements)

References 49 publications

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“…Passive imagers do, however, have a limited sensitivity to thin cirrus clouds and algorithms utilising spectral and morphological threshold tests tend to miss a large fraction of those thin cirrus (e.g. Ackerman et al, 2008;Holz et al, 2008;Stubenrauch et al, 2010) and thus introduce a bias into the climate impact of cirrus clouds. Another well-known problem related to cloud detection from passive imagers is the difficulty to distinguish between cirrus clouds and cold surfaces in the polar regions (e.g.…”

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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Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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“…This bias increased to 2 km for the highest cloud tops and was not found to vary with cloud albedo. Other studies (Heymsfield et al, 1991;Minnis et al, 2008;Stubenrauch et al, 2010Stubenrauch et al, , 2013 show similar biases of between 0.5 and 3 km for high clouds in the tropics.…”

Section: Introductionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

“…A particular concern is that models fail to capture the observed diurnal cycle of convective cloud (Yang and Slingo, 2001;Guichard et al, 2004;Grabowski et al, 2006;Stratton and Stirling, 2012). This is generally the result of convection initiating shortly after sunrise, which develops too rapidly, quickly reaching the tropopause and producing precipitation (Guichard et al, 2004;Stratton and Stirling, 2012). Studies by Guichard et al (2004), Grabowski et al (2006), and Sato et al (2009) show that in some cases cloud-resolving models (CRMs), which explicitly resolve convection, are capable of correctly predicting the amplitude and phase of the diurnal cycle in convection.…”

Section: Introductionmentioning

confidence: 99%

“…A large number of observational studies using data from rain gauges (Wallace, 1975;Gray and Jacobsen, 1977), surface weather reports (Dai, 2001), and both polar-orbiting (Nesbitt and Zipser, 2003;Yang et al, 2008;Stratton and Stirling, 2012) and geostationary (Meisner and Arkin, 1987;Janowiak et al, 1994;Chen and Houze, 1997;Yang and Slingo, 2001;Schröder et al, 2009) satellites have attempted to quantify the diurnal cycle of cloud over land. These studies found an early-afternoon maximum in precipitation, followed by a minimum in cloud top temperature approximately 3 h later.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the diurnal cycle in cloud top temperature from SEVIRI

et al. 2017

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Abstract. The variability of convective cloud spans a wide range of temporal and spatial scales and is of fundamental importance for global weather and climate systems. Datasets from geostationary satellite instruments such as the Spinning Enhanced Visible and Infrared Imager (SEVIRI) provide high-time-resolution observations across a large area. In this study we use data from SEVIRI to quantify the diurnal cycle of cloud top temperature within the instrument's field of view and discuss these results in relation to retrieval biases.We evaluate SEVIRI cloud top temperatures from the new CLAAS-2 (CLoud property dAtAset using SEVIRI, Edition 2) dataset against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data. Results show a mean bias of +0.44 K with a standard deviation of 11.7 K, which is in agreement with previous validation studies. Analysis of the spatio-temporal distribution of these errors shows that absolute retrieval biases vary from less than 5 K over the southeast Atlantic Ocean up to 30 K over central Africa at night. Night-and daytime retrieval biases can also differ by up to 30 K in some areas, potentially contributing to biases in the estimated amplitude of the diurnal cycle. This illustrates the importance of considering spatial and diurnal variations in retrieval errors when using the CLAAS-2 dataset.Keeping these biases in mind, we quantify the seasonal, diurnal, and spatial variation of cloud top temperature across SEVIRI's field of view using the CLAAS-2 dataset. By comparing the mean diurnal cycle of cloud top temperature with the retrieval bias, we find that diurnal variations in the retrieval bias can be small but are often of the same order of magnitude as the amplitude of the observed diurnal cycle, indicating that in some regions the diurnal cycle apparent in the observations may be significantly impacted by diurnal variability in the accuracy of the retrieval.We show that the CLAAS-2 dataset can measure the diurnal cycle of cloud tops accurately in regions of stratiform cloud such as the southeast Atlantic Ocean and Europe, where cloud top temperature retrieval biases are small and exhibit limited spatial and temporal variability. Quantifying the diurnal cycle over the tropics and regions of desert is more difficult, as retrieval biases are larger and display significant diurnal variability. CLAAS-2 cloud top temperature data are found to be of limited skill in measuring the diurnal cycle accurately over desert regions. In tropical regions such as central Africa, the diurnal cycle can be described by the CLAAS-2 data to some extent, although retrieval biases appear to reduce the amplitude of the real diurnal cycle of cloud top temperatures. This is the first study to relate the diurnal variations in SE-VIRI retrieval bias to observed diurnal cycles in cloud top temperature. Our results may be of interest to those in the observation and modelling communities when using cloud top properties data from SEVIRI, particularly for studies considering the diurnal cycle of convection.

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One-dimensional variational (1D-Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data

Ishimoto¹,

Okamoto²,

Okamoto

et al. 2014

J. Geophys. Res. Atmos.

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A one-dimensional variational analysis (1D-Var retrieval) of tropospheric humidity was conducted using hyper-spectral radiance data from the Atmospheric Infrared Sounder (AIRS). For the vertical range of the atmosphere between 200 and 600 hPa, the same high-resolution retrieval of humidity profiles as for clear-sky conditions was possible over low clouds if the cloud height was lower than 800 hPa. Global analyses from a global data assimilation system were used for initial profiles, and clear conditions over 800 hPa height were determined from AIRS radiance data. Results of analyses for 50 days of global radiosonde matchup data from 21 December 2008 to 8 February 2009 revealed that our 1D-Var calculations derived humidity profiles were closer to the sonde profiles than those of a global analysis at a height over 600 hPa. Furthermore, the results of 1D-Var retrieval often represented high and supersaturated relative humidity around the supposed ice clouds. The altitudes of the high humidity region agreed with the height of ice clouds that had been detected by CloudSat/CALIPSO. As well as possibly improving the humidity profiles in a numerical model by data assimilation, it is expected that these humidity analyses using AIRS radiance data will provide additional information for the study of ice clouds in the middle to upper troposphere.

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A 6-year global cloud climatology from the Atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat

Cited by 99 publications

References 49 publications

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Evaluating the diurnal cycle in cloud top temperature from SEVIRI

One-dimensional variational (1D-Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data

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