Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview

Shields, Janet E.; Karr, Monette E.; Johnson, Richard W.; Burden, Art R.

doi:10.1364/ao.52.001605

Cited by 83 publications

(62 citation statements)

References 23 publications

(33 reference statements)

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“…Silicon-based image sensors used in visible wavelength cameras are also sensitive up to 1.1 µm in the near infrared. The sixteen bit (or higher) versions of these, with a set of selectable bandpass and neutral density filters, can be used for enhanced day and nighttime cloud detection (Shields et al, 2013). Longwave infrared (LWIR) imaging in the 8-13 µm range measures cloud brightness temperatures which can be used to segment different cloud layers, estimate cloud heights, and potentially determine optical depth.…”

Section: Imaging System Considerations For Solar Forecastingmentioning

confidence: 99%

“…Digital sky photography began in the 1980s with the development of personal computers, and one of the leading groups developing imaging systems for atmospheric observation was the Atmospheric Optics Group at the Scripps Institute of Oceanography's (SIO's) Marine Physical Laboratory (Johnson et al, 1988(Johnson et al, , 1989. Their well known Whole Sky Imager (WSI) is still to this day one of the highest quality, if not the highest quality, sky imaging systems ever developed (Shields et al, 1998b(Shields et al, , 2013. It was developed primarily for US military applications in the 1980s and early 1990s.…”

Section: Existing Sky Imaging Hardwarementioning

confidence: 99%

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Development of a sky imaging system for short-term solar power forecasting

et al. 2015

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Abstract.To facilitate the development of solar power forecasting algorithms based on ground-based visible wavelength remote sensing, we have developed a high dynamic range (HDR) camera system capable of providing hemispherical sky imagery from the circumsolar region to the horizon at a high spatial, temporal, and radiometric resolution. The University of California, San Diego Sky Imager (USI) captures multispectral, 16 bit, HDR images as fast as every 1.3 s. This article discusses the system design and operation in detail, provides a characterization of the system dark response and photoresponse linearity, and presents a method to evaluate noise in high dynamic range imagery. The system is shown to have a radiometrically linear response to within 5 % in a designated operating region of the sensor. Noise for HDR imagery is shown to be very close to the fundamental shot noise limit. The complication of directly imaging the sun and the impact on solar power forecasting is also discussed. The USI has performed reliably in a hot, dry environment, a tropical coastal location, several temperate coastal locations, and in the great plains of the United States.

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Section: Imaging System Considerations For Solar Forecastingmentioning

confidence: 99%

Section: Existing Sky Imaging Hardwarementioning

confidence: 99%

Development of a sky imaging system for short-term solar power forecasting

et al. 2015

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“…One trend is to develop the ground-based sky imagers such as whole-sky imager (WSI) [4], total-sky imager (TSI) [5], infrared cloud imager (ICI) [6], all-sky imager (ASI) [7,8], whole-sky infrared cloud measuring system (WSIRCMS) [9], and day/night whole sky imagers (D/N WSIs) [10]. Benefiting from these devices, a number of ground-based cloud images are available for developing automatic classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep multimodal fusion for ground-based cloud classification in weather station networks

Liu

Mei

2018

J Wireless Com Network

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Most existing methods only utilize the visual sensors for ground-based cloud classification, which neglects other important characteristics of cloud. In this paper, we utilize the multimodal information collected from weather station networks for ground-based cloud classification and propose a novel method named deep multimodal fusion (DMF). In order to learn the visual features, we train a convolutional neural network (CNN) model to obtain the sum convolutional map (SCM) by using a pooling operation across all the feature maps in deep layers. Afterwards, we employ a weighted strategy to integrate the visual features with multimodal features. We validate the effectiveness of the proposed DMF on the multimodal ground-based cloud (MGC) dataset, and the experimental results demonstrate the proposed DMF achieves better results than the state-of-the-art methods.

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“…The Total Sky Imager Model TSI-880 (Yankee Environmental Systems, Inc.) based on an optical camera system is a potentially suitable system which is, however, limited to daytime sky conditions. Available Day-and-Night Imagers such as the Day/Night Whole Sky Imager of the University of California [5] and the infrared cloud imager [6] are not yet commercially available. An IR system commercially available is the NubiScope (IMK/Sattler-SES) [7].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal High-Resolution Cloud Mapping with a Ground-Based IR Scanner

Brede

Thies

Bendix

et al. 2017

Advances in Meteorology

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The high spatiotemporal variability of clouds requires automated monitoring systems. This study presents a retrieval algorithm that evaluates observations of a hemispherically scanning thermal infrared radiometer, the NubiScope, to produce georeferenced, spatially explicit cloud maps. The algorithm uses atmospheric temperature and moisture profiles and an atmospheric radiative transfer code to differentiate between cloudy and cloudless measurements. In case of a cloud, it estimates its position by using the temperature profile and viewing geometry. The proposed algorithm was tested with 25 cloud maps generated by the Fmask algorithm from Landsat 7 images. The overall cloud detection rate was ranging from 0.607 for zenith angles of 0 to 10 ∘ to 0.298 for 50-60 ∘ on a pixel basis. The overall detection of cloudless pixels was 0.987 for zenith angles of 30-40 ∘ and much more stable over the whole range of zenith angles compared to cloud detection. This proves the algorithm's capability in detecting clouds, but even better cloudless areas. Cloud-base height was best estimated up to a height of 4000 m compared to ceilometer base heights but showed large deviation above that level. This study shows the potential of the NubiScope system to produce high spatial and temporal resolution cloud maps. Future development is needed for a more accurate determination of cloud height with thermal infrared measurements.

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Day/night whole sky imagers for 24-h cloud and sky assessment: history and overview

Cited by 83 publications

References 23 publications

Development of a sky imaging system for short-term solar power forecasting

Development of a sky imaging system for short-term solar power forecasting

Deep multimodal fusion for ground-based cloud classification in weather station networks

Spatiotemporal High-Resolution Cloud Mapping with a Ground-Based IR Scanner

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