Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing

Kox, Stephan; Bugliaro, Luca; Ostler, A.

doi:10.5194/amt-7-3233-2014

Cited by 64 publications

(88 citation statements)

References 35 publications

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“…CiPS has a better performance in all aspects with respect to COCS, another algorithm that uses ANNs for retrieving the CTH and IOT from SEVIRI using CALIOP as reference (Kox et al, 2014). Significant improvements have been made for the detection of the thinner cirrus clouds and the retrieval of the corresponding IOT.…”

Section: Discussionmentioning

confidence: 99%

“…This is ca. 10 times faster than the combined CTH and IOT retrieval by COCS (Kox et al, 2014). ANN computations are highly parallelisable, meaning that the computation time can be reduced significantly by distributing the computations across multiple cores.…”

Section: Trainingmentioning

confidence: 99%

“…The COCS algorithm retrieves CTH and IOT of cirrus clouds from SEVIRI (Kox et al, 2014). It combines V2 CALIOP L2 cloud layer data, SEVIRI thermal observations and auxiliary data using an ANN to retrieve CALIOP-like cirrus properties for the full SEVIRI field of view every 15 min and 24 h per day.…”

Section: The Cocs Algorithmmentioning

confidence: 99%

“…V2 CALIOP layer data used in Kox et al (2014) had to fulfil three filtering conditions to be classified as a cirrus cloud: (1) to exclude inaccurate retrievals due to diverging extinction retrievals in opaque cloud layers, the maximum IOT was limited to 2.5. (2) To ensure that the cirrus clouds were not falsely classified layers of aerosols or liquid water clouds, the mid-layer temperature had to be 243 K or colder.…”

Section: The Cocs Algorithmmentioning

confidence: 99%

“…As an attempt to combine the advantages from a polar orbiting lidar and a geostationary imager, Kox et al (2014) present an approach for the detection and retrieval of optical thickness and top height of cirrus clouds from SE-VIRI. Their algorithm COCS (Cirrus Optical properties from CALIOP and SEVIRI) utilises an artificial neural network (ANN) trained with coincident CALIOP backscatter and SE-VIRI thermal observations in order to estimate CALIOP-like cirrus properties from SEVIRI.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Trainingmentioning

confidence: 99%

Section: The Cocs Algorithmmentioning

confidence: 99%

Section: The Cocs Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

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Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method

et al. 2016

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Retrieval of ice cloud properties using IR measurements has a distinct advantage over the visible and near‐IR techniques by providing consistent monitoring regardless of solar illumination conditions. Historically, the IR bands at 3.7, 6.7, 11.0, and 12.0 µm have been used to infer ice cloud parameters by various methods, but the reliable retrieval of ice cloud optical depth τ is limited to nonopaque cirrus with τ < 8. The Ice Cloud Optical Depth from Infrared using a Neural network (ICODIN) method is developed in this paper by training Moderate Resolution Imaging Spectroradiometer (MODIS) radiances at 3.7, 6.7, 11.0, and 12.0 µm against CloudSat‐estimated τ during the nighttime using 2 months of matched global data from 2007. An independent data set comprising observations from the same 2 months of 2008 was used to validate the ICODIN. One 4‐channel and three 3‐channel versions of the ICODIN were tested. The training and validation results show that IR channels can be used to estimate ice cloud τ up to 150 with correlations above 78% and 69% for all clouds and only opaque ice clouds, respectively. However, τ for the deepest clouds is still underestimated in many instances. The corresponding RMS differences relative to CloudSat are ~100 and ~72%. If the opaque clouds are properly identified with the IR methods, the RMS differences in the retrieved optical depths are ~62%. The 3.7 µm channel appears to be most sensitive to optical depth changes but is constrained by poor precision at low temperatures. A method for estimating total optical depth is explored for estimation of cloud water path in the future. Factors affecting the uncertainties and potential improvements are discussed. With improved techniques for discriminating between opaque and semitransparent ice clouds, the method can ultimately improve cloud property monitoring over the entire diurnal cycle.

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Stratus, Stratocumulus, and Remote Sensing

Dong,

Minnis

2023

Fast Processes in Large‐Scale Atmospheric Models

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Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing

Cited by 64 publications

References 35 publications

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Estimating nocturnal opaque ice cloud optical depth from MODIS multispectral infrared radiances using a neural network method

Stratus, Stratocumulus, and Remote Sensing

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