Cloud detection and classification with the use of whole-sky ground-based images

Kazantzidis, Andreas; Tzoumanikas, Panagiotis; Bais, A. F.; Fotopoulos, S.; Economou, George

doi:10.1016/j.atmosres.2012.05.005

Cited by 190 publications

(154 citation statements)

References 16 publications

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“…They also proposed to use this kind of studies to determine sites for solar panels to improve solar resource assessment models. Kazantzidis et al (2012) compared an automatic estimation of the cloud coverage and classification derived from a simple whole sky imaging system to synoptic data. According to their results, 83 % (broken cloudiness) and 94 % (overcast cloudiness) of the analyzed images agreed within ± 1 and ± 2 oktas, respectively, compared to the weather observations.…”

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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“…In this work we are working with colored images which have Red (R), Green (G) and Blue (B) channels. Different approaches can be found in the literature to application of thresholding to colored images, for example: finding the threshold on all three channels independently [11], or transforming the cartesian RGB space into the cylindrical version Hue (H) Saturation (S) and Luminance (HSL) space and applying thresholds on the saturation channel [12]. Other approaches ( [13] and references therein) have dropped the green channel and apply thresholds on a R-B or R/B derived channel.…”

Section: Thresholding With a Principal Component Analysismentioning

confidence: 99%

All Sky Camera, LIDAR and Electric Field Meter: Auxiliary instruments for the ASTRI SST-2M prototype

Leto

Sánchez

Bellassai

et al. 2015

EPJ Web of Conferences

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Abstract. ASTRI SST-2M is the end-to-end prototype telescope of the Italian National Institute of Astrophysics, INAF, designed to investigate the 10-100 TeV band in the framework of the Cherenkov Telescope Array, CTA. The ASTRI SST-2M telescope has been installed in Italy in September 2014, at the INAF observing station located at Serra La Nave on Mount Etna. The telescope is foreseen to be completed and fully operative in spring 2015 including auxiliary instrumentation needed to support both operations and data analysis. In this contribution we present the current status of a sub-set of the auxiliary instruments that are being used at the Serra La Nave site, namely an All Sky Camera, an Electric Field Meter and a Raman Lidar devoted, together with further instrumentation, to the monitoring of the atmospheric and environmental conditions. The data analysis techniques under development for these instruments could be applied at the CTA sites, where similar auxiliary instrumentation will be installed.

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“…Clouds play a crucial role in the earth's radiation budget and global climate change owing to interfacing with sun powered and earthly radiation [7]. Of all the ten different clouds classified by International Satellite Cloud Climatology Project (ISCCP), low level stratus and stratocumulus are the most vital in the earth's radiation budget since they are optically thick and have a large global coverage.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Time Series Convective Cloud Profile From Doppler Weather Radar Max (Z) Product Using a Novel Image Processing Technique

2017

IJAERD

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Cloud detection and classification with the use of whole-sky ground-based images

Cited by 190 publications

References 16 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

All Sky Camera, LIDAR and Electric Field Meter: Auxiliary instruments for the ASTRI SST-2M prototype

Extraction of Time Series Convective Cloud Profile From Doppler Weather Radar Max (Z) Product Using a Novel Image Processing Technique

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