Cloud Fractions Estimated from Shipboard Whole-Sky Camera and Ceilometer Observations between East Asia and Antarctica

Kuji, Makoto; Murasaki, Atsumi; Hori, Masahiro; Shiobara, Masataka

doi:10.2151/jmsj.2018-025

Cited by 11 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One is a sky camera system consisting of a single-lens reflex digital camera (D7000, NIKON). This is the same system used by Letu et al (2014) and Kuji et al (2018), and details can be found in their paper. The other system is a Skyview Fig.…”

Section: Resultsmentioning

confidence: 99%

Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products

Nakajima

Ishida

Nagao

et al. 2019

Prog Earth Planet Sci

Self Cite

View full text Add to dashboard Cite

This paper discusses the cloud/clear discrimination algorithm (CLAUDIA) and the cloud microphysical properties algorithm (CAPCOM), which are used by the Second-generation GLobal Imager (SGLI) aboard the GCOM-C satellite, launched in December 2017. Also described are the preliminary results of cloud products' validation. CLAUDIA was validated by comparing cloud fractions derived from satellite data against data from whole-sky images captured by ground-based fisheye cameras. User's accuracy and producer's accuracy were mostly high at around 90%, and the resulting overall accuracy was also high, ranging from 83 to 100% (average of all sites was 90.5%). CLAUDIA has proven to be sufficiently accurate to apply a cloud mask to measurements and meets the requirements for releasing data for SGLI cloud flag products (the minimum for a successful GCOM-C mission). CAPCOM was evaluated by comparing cloud properties obtained by SGLI products against data from MODIS collection 6 products (MOD06). This was done for both ocean and land in the low to middle latitudes (60°N-60°S) from August 22, 2018 to September 14, 2018. The comparison showed good correlation coefficients for cloud optical thickness, effective particle radius, and cloud-top temperature for water clouds: 0.88 (0.83), 0.92 (0.88), and 0.94 (0.92) for ocean (land), respectively. CAPCOM data for ice cloud optical thickness correlated well with data from MODIS products: 0.86 (ocean) and 0.82 (land).

show abstract

Section: Resultsmentioning

confidence: 99%

Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products

Nakajima

Ishida

Nagao

et al. 2019

Prog Earth Planet Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the development of hardware technologies such as charge-coupled device and digital image processing, many groundbased full-sky cloud measuring instruments have been successfully developed. Currently the most representative instruments include Whole Sky Imager (WSI;Johnson et al, 1989), Total Sky Imager(TSI; Long and Deluisi, 1998;Long et al, 2006), Whole Sky Camera(WSC; Kuji et al, 2018), All Sky Imager(ASI; Cazorla et al, 2008), All Sky Camera (ASC; Aebi, 2018), Infrared Cloud Imager(ICI; Shaw et al, 2005;Thurairajah and Shaw, 2005;Nugent et al, 2009;Nugent et al, 2013), Whole Sky Infrared Cloud Measuring System(WSIRCMS; Sun et al, 2008), Total Sky Cloud Imager(TCI; Yang et al, 2012), and All-Sky Infrared Visible Analyzer(ASIVA; Klebe et al, 2014). The above instruments provide hardware support for analyzing ground-based cloud images, making automated observation of ground-based cloud images possible.…”

Section: Introductionmentioning

confidence: 99%

Diurnal and nocturnal cloud segmentation of ASI images using enhancement fully convolutional networks

Shi¹,

Zhou²,

Qiu³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. Cloud segmentation plays a very important role in the astronomical observatory site selection. At present, few researchers segment cloud in the nocturnal All Sky Imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm which utilizes the advantages of deep learning fully convolutional networks (FCN) to segment cloud pixels from diurnal and nocturnal ASI images, named enhancement fully convolutional networks (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red-green-blue (RGB) color space to hue-saturation-intensity (HSI) color space. Secondly, the channel of the HSI color space is enhanced by the histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100000 times iterative training based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with other four algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in the cloud segmentation for diurnal and nocturnal ASI images than other four algorithms.

show abstract

CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning

Shi

Zhou

Qiu

2021

Neural Process Lett

View full text Add to dashboard Cite

Cloud Fractions Estimated from Shipboard Whole-Sky Camera and Ceilometer Observations between East Asia and Antarctica

Abstract: 13Cloud fractions were observed during research cruises onboard the research 14 vessel (R/V) Shirase between Japan and Antarctica using a whole-sky cam-

Cited by 11 publications

References 25 publications

Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products

Theoretical basis of the algorithms and early phase results of the GCOM-C (Shikisai) SGLI cloud products

Diurnal and nocturnal cloud segmentation of ASI images using enhancement fully convolutional networks

CloudU-Netv2: A Cloud Segmentation Method for Ground-Based Cloud Images Based on Deep Learning

Contact Info

Product

Resources

About