MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results

Menzel, W. Paul; Frey, R.; Zhang, Hong; Wylie, Donald P.; Moeller, Christopher C.; Holz, Robert E.; Maddux, B. C.; Baum, Bryan A.; Strabala, Kathleen I.; Gumley, Liam E.

doi:10.1175/2007jamc1705.1

Cited by 294 publications

(297 citation statements)

References 27 publications

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“…Thus, there seems to be different behaviour of high-level clouds and lowlevel clouds. The low-level boundary layer cloud problem is the same as reported previously for MODIS cloud top products (Menzel et al, 2008). For CLARA-A1/PPS it can be explained as a problem with the reference atmospheric temperature profile taken from NWP analyses (here, ERA-Interim).…”

Section: Cloud Top Height Resultsmentioning

confidence: 74%

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Karlsson

Johansson

2013

Atmos. Meas. Tech.

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A method for detailed evaluation of a new satellite-derived global 28 yr cloud and radiation climatology (Climate Monitoring SAF Clouds, Albedo and Radiation from AVHRR data, named CLARA-A1) from polar-orbiting NOAA and Metop satellites is presented. The method combines 1 km and 5 km resolution cloud datasets from the CALIPSO-CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation – Cloud-Aerosol Lidar with Orthogonal Polarization) cloud lidar for estimating cloud detection limitations and the accuracy of cloud top height estimations.

Cloud detection is shown to work efficiently for clouds with optical thicknesses above 0.30 except for at twilight conditions when this value increases to 0.45. Some misclassifications of cloud-free surfaces during daytime were revealed for semi-arid land areas in the sub-tropical and tropical regions leading to up to 20% overestimated cloud amounts. In addition, a substantial fraction (at least 20–30%) of all clouds remains undetected in the polar regions during the polar winter season due to the lack of or an inverted temperature contrast between Earth surfaces and clouds.

Subsequent cloud top height evaluation took into account the derived information about the cloud detection limits. It was shown that this has fundamental importance for the achieved results. An overall bias of −274 m was achieved compared to a bias of −2762 m when no measures were taken to compensate for cloud detection limitations. Despite this improvement it was concluded that high-level clouds still suffer from substantial height underestimations, while the opposite is true for low-level (boundary layer) clouds.

The validation method and the specifically collected satellite dataset with optimal matching in time and space are suggested for a wider use in the future for evaluation of other cloud retrieval methods based on passive satellite imagery

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Section: Cloud Top Height Resultsmentioning

confidence: 74%

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Karlsson

Johansson

2013

Atmos. Meas. Tech.

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“…Menzel et al, 2008), where the determination of cloud top height requires the knowledge of the appropriate vertical temperature profile from NWP (numerical weather prediction) models, while CiPS only requires the surface skin temperature from a NWP along with the SEVIRI brightness temperatures and auxiliary data. Figure 11 shows again two density scatter plots, now with IOT CALIOP on the horizontal axes and IOT CiPS (Fig.…”

Section: Cirrus Propertiesmentioning

confidence: 99%

“…It can be retrieved from passive satellite imagers during both day and night using e.g. radiance ratioing (also referred to as CO 2 absorption, CO 2 slicing and split window technique) (Smith et al, 1970;Smith and Platt, 1978;Menzel et al, 1983;Eyre and Menzel, 1989;Zhang and Menzel, 2002;Menzel et al, 2008), radiance fitting (e.g. Szejwach, 1982;Nieman et al, 1993;Schmetz et al, 1993) and optimal estimation (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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“…Among satellite raw data, the IR window region (8μm to 14μm) is the most familiar "raw radiance" data widely used in the meteorological community. IR Tb emitted from the Earth's surface or from a cloud are used to estimate cloud top temperature [Inoue, 1987;Rossow and Lacis, 1990;King et al, 1992;Menzel et al, 2008] or land surface skin temperature [Price, 1984;Wan and Dozier, 1996;Mao et al, 2005] and for evaluation and assimilation of clouds [Chaboureau et al, 2002;Vukicevic et al, 2006;Zupanski et al, 2011b]. IR Tb is available for both daytime and nighttime, while the visible channel is generally available only for daytime.…”

Section: Modeling Systemsmentioning

confidence: 99%

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

Matsui

Santanello

Shi

et al. 2014

J. Geophys. Res. Atmos.

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Earth System modeling has become more complex, and its evaluation using satellite data has also become more difficult due to model and data diversity. Therefore, the fundamental methodology of using satellite direct measurements with instrumental simulators should be addressed especially for modeling community members lacking a solid background of radiative transfer and scattering theory. This manuscript introduces principles of multisatellite, multisensor radiance-based evaluation methods for a fully coupled regional Earth System model: NASA-Unified Weather Research and Forecasting (NU-WRF) model. We use a NU-WRF case study simulation over West Africa as an example of evaluating aerosol-cloud-precipitation-land processes with various satellite observations. NU-WRF-simulated geophysical parameters are converted to the satellite-observable raw radiance and backscatter under nearly consistent physics assumptions via the multisensor satellite simulator, the Goddard Satellite Data Simulator Unit. We present varied examples of simple yet robust methods that characterize forecast errors and model physics biases through the spatial and statistical interpretation of various satellite raw signals: infrared brightness temperature (Tb) for surface skin temperature and cloud top temperature, microwave Tb for precipitation ice and surface flooding, and radar and lidar backscatter for aerosol-cloud profiling simultaneously. Because raw satellite signals integrate many sources of geophysical information, we demonstrate user-defined thresholds and a simple statistical process to facilitate evaluations, including the infrared-microwave-based cloud types and lidar/radar-based profile classifications.

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MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results

Cited by 294 publications

References 27 publications

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

On the optimal method for evaluating cloud products from passive satellite imagery using CALIPSO-CALIOP data: example investigating the CM SAF CLARA-A1 dataset

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Introducing multisensor satellite radiance-based evaluation for regional Earth System modeling

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