Automatic cloud detection applied to NOAA-11 /AVHRR imagery

Derrien, M.; Farki, B.; Harang, L.; Legléau, H.; Noyalet, A.; Pochic, D.; Sairouni, A.

doi:10.1016/0034-4257(93)90046-z

Cited by 139 publications

(68 citation statements)

References 14 publications

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“…With increasing availability of satellite data, scientists started deriving cloud properties (Ackerman et al, 1998;Christodoulou et al, 2003;Ebert, 1987;Gao and Wiscombe, 1994;Garand, 1988;Parikh, 1977;Porcú and Levizzani, 1992;Romano et al, 2007;Saunders and Kriebel, 1988;Schröder et al, 2002;Welch et al, 1992). The cloud detection threshold test by Derrien et al (1993) is a real-time processing scheme that is applied to the different channels of irradiances from the NOAA-11 satellite. This algorithm was further developed and adjusted to new instruments on further satellites LeGleau, 2005, 2013).…”

Section: A Werkmeister Et Al: Comparing Cloud Coverage -A Case Studymentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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Abstract. In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively.Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14 % for AVHRR and 8 ± 16 % for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22 %) than instantaneous SEVIRI cloud fraction estimates (8 ± 29 %) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94 % and false alarm rates between 21 and 29 % for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76 %. We find poor skill, however, whenever broken clouds occur (probability of detection is 32 % for AVHRR and 12 % for SEVIRI in Hannover, Germany).In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0 %) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each "cloud-contaminated" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.

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Section: A Werkmeister Et Al: Comparing Cloud Coverage -A Case Studymentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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“…Saunders and Kriebel, 1988;Derrien et al, 1993;Ackerman et al, 1998;Kriebel et al, 2003;Derrien and LeGleau, 2005;Krebs et al, 2007) by applying spectral tests on brightness temperatures and temperature differences (e.g. Inoue, 1985;Ackerman et al, 1990).…”

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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“…[3,10,11]. The signatures of clouds are similar in the two visible channels (0.6 and 0.8 μm), but the reflection of the sea surface is higher at 0.6 μm while land surfaces appear brighter at 0.8 μm [3].…”

Section: Methodsmentioning

confidence: 83%

“…Cloud boundary determination is a difficult task due to the nature of the atmosphere with the complex interaction between clouds and aerosols and due to the uncertainties of the instrumental measurements used for their detection [10,11,33].…”

Section: Discussionmentioning

confidence: 99%

“…In the literature, various methods have been proposed for the detection and the classification of clouds using visible/infrared (VIS/IR) imaging and most of them rely on a combination of threshold tests applied to different spectral channels [7][8][9][10][11][12], for example multispectral thresholding models [9,[11][12][13][14] or simple intensity threshold analysis and IHS threshold technique [15][16][17] applied to individual pixels. The other models are spatial coherence methods [18,19] or dynamic cloud clustering based on histogram analysis [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

et al. 2015

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A multilayer perceptron neural network cloud mask for Meteosat Second Generation SEVIRI (Spinning Enhanced Visible and Infrared Imager) images is introduced and evaluated. The model is trained for cloud detection on MSG SEVIRI daytime data. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the error back-propagation algorithm. The model is fed by six bands of MSG data (0.6, 0.8, 1.6, 3.9, 6.2 and 10.8 μm) with 10 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 88.96%, when trained to map two predefined values that classify cloud and clear sky. The network was further evaluated using sixty MSG images taken at different dates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the feasibility of using machine learning models of cloud detection in MSG SEVIRI imagery.

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Automatic cloud detection applied to NOAA-11 /AVHRR imagery

Cited by 139 publications

References 14 publications

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking

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