A total sky cloud detection method using real clear sky background

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

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A total sky cloud detection method using real clear sky background

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-13073-2015 ◽

2015 ◽

Vol 8 (12) ◽

pp. 13073-13098 ◽

Cited By ~ 1

Author(s):

J. Yang ◽

Q. Min ◽

W. Lu ◽

Y. Ma ◽

W. Yao ◽

...

Keyword(s):

Detection Algorithm ◽

Brightness Distribution ◽

Detection Methods ◽

Cloud Detection ◽

Image Brightness ◽

Clear Sky ◽

Sensitivity Tests ◽

Thin Cloud ◽

Distribution Sensitivity ◽

Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show, as long as the positions of the sun in the two images are the same, the cloud detection results are satisfactory. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

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An automated cloud detection method based on the green channel of total-sky visible images

Atmospheric Measurement Techniques ◽

10.5194/amt-8-4671-2015 ◽

2015 ◽

Vol 8 (11) ◽

pp. 4671-4679 ◽

Cited By ~ 17

Author(s):

J. Yang ◽

Q. Min ◽

W. Lu ◽

W. Yao ◽

Y. Ma ◽

...

Keyword(s):

Detection Algorithm ◽

Mie Scattering ◽

Brightness Distribution ◽

Adaptive Threshold ◽

Cloud Detection ◽

Blue Band ◽

Green Channel ◽

The Past ◽

Visible Images ◽

Cloud Mask

Abstract. Obtaining an accurate cloud-cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total-sky images. By analyzing the imaging principle of cameras, the green channel has been selected to replace the 2-D red-to-blue band for detecting cloud pixels from partly cloudy total-sky images in this study. The brightness distribution in a total-sky image is usually nonuniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, an adaptive threshold, and binarization. Five experimental cases show that the GBSAT algorithm produces more accurate retrieval results for all these test total-sky images.

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A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Atmospheric Measurement Techniques ◽

10.5194/amt-8-553-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 553-566 ◽

Cited By ~ 5

Author(s):

M.-H. Ahn ◽

D. Han ◽

H. Y. Won ◽

V. Morris

Keyword(s):

Water Vapor Pressure ◽

Microwave Radiometer ◽

Surface Air Temperature ◽

Detection Algorithm ◽

Probability Of Detection ◽

Cloud Base ◽

Cloud Detection ◽

Clear Sky ◽

Spectral Test ◽

Free Data

Abstract. For better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two-step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear-sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of clear-sky conditions. It is designated as cloud-free data only when both the spectral and temporal tests confirm cloud-free data. Overall, most of the thick and uniform clouds are successfully detected by the spectral test, while the broken and fast-varying clouds are detected by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for six months, from January to June 2013. The overall proportion of correctness is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of discrepancies occur when the new algorithm detects clouds while the ceilometer does not, resulting in different values of the probability of detection with different cloud-base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

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Advancing Maritime Transparent Cirrus Detection Using the Advanced Baseline Imager “Cirrus” Band

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0130.1 ◽

2021 ◽

Author(s):

Theodore M. McHardy ◽

James R. Campbell ◽

David A. Peterson ◽

Simone Lolli ◽

Richard L. Bankert ◽

...

Keyword(s):

Precipitable Water ◽

Detection Algorithm ◽

Machine Learning Techniques ◽

Orthogonal Polarization ◽

Cirrus Cloud ◽

Cloud Detection ◽

Cloud Optical Depth ◽

Clear Sky ◽

Learning Techniques ◽

Microphysical Property

AbstractWe describe a quantitative evaluation of maritime transparent cirrus cloud detection, which is based on Geostationary Operational Environmental Satellite – 16 (GOES-16) and developed with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) profiling. The detection algorithm is developed using one month of collocated GOES-16 Advanced Baseline Imager (ABI) Channel 4 (1.378 μm) radiance and CALIOP 0.532 μm column-integrated cloud optical depth (COD). First, the relationships between the clear-sky 1.378 μm radiance, viewing/solar geometry, and precipitable water vapor (PWV) are characterized. Using machine learning techniques, it is shown that the total atmospheric pathlength, proxied by airmass factor (AMF), is a suitable replacement for viewing zenith and solar zenith angles alone, and that PWV is not a significant problem over ocean. Detection thresholds are computed using the Ch. 4 radiance as a function of AMF. The algorithm detects nearly 50% of sub-visual cirrus (COD < 0.03), 80% of transparent cirrus (0.03 < COD < 0.3), and 90% of opaque cirrus (COD > 0.3). Using a conservative radiance threshold results in 84% of cloudy pixels being correctly identified and 4% of clear-sky pixels being misidentified as cirrus. A semi-quantitative COD retrieval is developed for GOES ABI based on the observed relationship between CALIOP COD and 1.378 μm radiance. This study lays the groundwork for a more complex, operational GOES transparent cirrus detection algorithm. Future expansion includes an over-land algorithm, a more robust COD retrieval that is suitable for assimilation purposes, and downstream GOES products such as cirrus cloud microphysical property retrieval based on ABI infrared channels.

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The improved thin cloud detection using BRDF model based background reflectance from GOCI geostationary satellite imagery

10.5194/egusphere-egu2020-6408 ◽

2020 ◽

Author(s):

Jong-Min Yeom ◽

Hye-Won Kim ◽

Jeongho Lee ◽

Seonyoung Park ◽

Sangcherl Lee

Keyword(s):

Land Surface ◽

Ocean Color ◽

Detection Algorithm ◽

Land Application ◽

Surface Reflectance ◽

Cloud Detection ◽

Land Area ◽

Spectral Bands ◽

Thin Cloud ◽

Cloud Mask

<p>In this study, the improved algorithm of thin cloud detection for geostationary ocean color imager (GOCI) satellite was developed to classify the thin cloud area over land area. The new cloud mask approach of GOCI satellite is required to expand its ocean dedicated application to other applications such for vegetation in land or aerosol optical properties (AOPs) in atmosphere due to its attractive shortwave wavelength bands of ocean color sensors. However,&#160;when trying to apply the advantages of the ocean color bands to the land area,&#160;only visible spectral bands of GOCI make it difficult to expand the land application&#160;the other way&#160;due to its limitation of cloud detection for relatively bright land surface. Furthermore, the geostationary of GOCI satellite has highly sensitive to geometry location of sun, meaning that high effective (Bidirectional Reflectance Distribution Function) BRDF effects make it also difficult to detect cloud mask in land surface due to its&#160;anisotropically scattered surface reflectance. In this paper, cloud mask algorithm of GOCI is proposed to consider those limitations by mainly using background surface reflectance from BRDF model. Therefore,&#160;minimum difference in reflectance between TOA and land as baseline of clear atmosphere and background surface reflectance underneath cloud were estimated from BRDF model. In conclusion, our new thin cloud detection is effectively detect the thin cloud over land surface area under limited ocean color bands of GOCI.&#160;The improved thin cloud detection algorithm of GOCI will be not only useful for following on instruments such as GOCI-II of Geo-KOMPSAT-2B and Sentinel 3 Ocean and Land Color Instrument (OLCL), but also applicable for existing geostationary satellites such as Geo-KOMPSAT-2A AMI, Himawari, and GOES-R as alternative cloud masking approach.</p>

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A RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

10.5194/amt-2016-222 ◽

2016 ◽

Author(s):

Jun Yang ◽

Qilong Min ◽

Weitao Lu ◽

Ying Ma ◽

Wen Yao ◽

...

Keyword(s):

Detection Algorithm ◽

Qualitative Assessment ◽

The Sun ◽

Cloud Detection ◽

Recognition Error ◽

Atmospheric Scattering ◽

Clear Sky ◽

Different Types ◽

Rgb Images ◽

The Difference

Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed, which combined the merits of the differencing and threshold methods and named "differencing and threshold combination algorithm (DTCA)". Firstly, the channel operation was applied to transform 3-D RGB images to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels, and, when the sun is visible in the image, the true clear sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds, with an average recognition error rate of 8.7 %.

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A cloud detection algorithm using the downwelling infrared radiance measured by an infrared pyrometer of the ground-based microwave radiometer

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-9413-2014 ◽

2014 ◽

Vol 7 (9) ◽

pp. 9413-9452 ◽

Cited By ~ 1

Author(s):

M.-H. Ahn ◽

D. Han ◽

H.-Y. Won ◽

V. Morris

Keyword(s):

Water Vapor Pressure ◽

Microwave Radiometer ◽

Surface Air Temperature ◽

Detection Algorithm ◽

Probability Of Detection ◽

Cloud Base ◽

Cloud Detection ◽

Clear Sky ◽

Spectral Test ◽

Free Data

Abstract. For a better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb) measured by an infrared radiometer installed on top of a microwave radiometer. The two step algorithm consists of a spectral test followed by a temporal test. The measured Tb is first compared with a predicted clear sky Tb obtained by an empirical formula as a function of surface air temperature and water vapor pressure. For the temporal test, the temporal variability of the measured Tb during one minute compares with a dynamic threshold value, representing the variability of the clear sky condition. It is designated as cloud free data only when both the spectral and temporal tests confirm a cloud free data. Overall, most of the thick and uniform clouds are successfully screened out by the spectral test, while the broken and fast-varying clouds are screened out by the temporal test. The algorithm is validated by comparison with the collocated ceilometer data for 6 months, from January 2013 to June 2013. The overall proportion correct is about 88.3% and the probability of detection is 90.8%, which are comparable with or better than those of previous similar approaches. Two thirds of failures occur when the new algorithm detects clouds while the ceilometer does not detect, resulting in different values of the probability of detection with different cloud base altitude, 93.8, 90.3, and 82.8% for low, mid, and high clouds, respectively. Finally, due to the characteristics of the spectral range, the new algorithm is found to be insensitive to the presence of inversion layers.

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Cloud detection for MIPAS using singular vector decomposition

Atmospheric Measurement Techniques ◽

10.5194/amt-2-533-2009 ◽

2009 ◽

Vol 2 (2) ◽

pp. 533-547 ◽

Cited By ~ 16

Author(s):

J. Hurley ◽

A. Dudhia ◽

R. G. Grainger

Keyword(s):

Singular Vector ◽

Detection Method ◽

Michelson Interferometer ◽

High Spectral Resolution ◽

Detection Methods ◽

Cloud Detection ◽

The Past ◽

Vector Decomposition ◽

Atmospheric Sounding ◽

Thin Cloud

Abstract. Satellite-borne high-spectral-resolution limb sounders, such as the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT, provide information on clouds, especially optically thin clouds, which have been difficult to observe in the past. The aim of this work is to develop, implement and test a reliable cloud detection method for infrared spectra measured by MIPAS. Current MIPAS cloud detection methods used operationally have been developed to detect cloud effective filling more than 30% of the measurement field-of-view (FOV), under geometric and optical considerations – and hence are limited to detecting fairly thick cloud, or large physical extents of thin cloud. In order to resolve thin clouds, a new detection method using Singular Vector Decomposition (SVD) is formulated and tested. This new SVD detection method has been applied to a year's worth of MIPAS data, and qualitatively appears to be more sensitive to thin cloud than the current operational method.

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An automated cloud detection method based on green channel of total sky visible images

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-4581-2015 ◽

2015 ◽

Vol 8 (5) ◽

pp. 4581-4605 ◽

Cited By ~ 3

Author(s):

J. Yang ◽

Q. Min ◽

W. Lu ◽

W. Yao ◽

Y. Ma ◽

...

Keyword(s):

Detection Algorithm ◽

Mie Scattering ◽

Brightness Distribution ◽

Adaptive Threshold ◽

Cloud Detection ◽

Visual Effects ◽

Blue Band ◽

Green Channel ◽

Visible Images ◽

Cloud Mask

Abstract. Getting an accurate cloud cover state is a challenging task. In the past, traditional two-dimensional red-to-blue band methods have been widely used for cloud detection in total sky images. By analyzing the imaging principle of cameras, green channel has been selected to replace the 2-D red-to-blue band for total sky cloud detection. The brightness distribution in a total sky image is usually non-uniform, because of forward scattering and Mie scattering of aerosols, which results in increased detection errors in the circumsolar and near-horizon regions. This paper proposes an automatic cloud detection algorithm, "green channel background subtraction adaptive threshold" (GBSAT), which incorporates channel selection, background simulation, computation of solar mask and cloud mask, subtraction, adaptive threshold, and binarization. Several experimental cases show that the GBSAT algorithm is robust for all types of test total sky images and has more complete and accurate retrievals of visual effects than those found through traditional methods.

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A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

Atmospheric Measurement Techniques ◽

10.5194/amt-13-5491-2020 ◽

2020 ◽

Vol 13 (10) ◽

pp. 5491-5511

Author(s):

Benjamin R. Scarino ◽

Kristopher Bedka ◽

Rajendra Bhatt ◽

Konstantin Khlopenkov ◽

David R. Doelling ◽

...

Keyword(s):

Weather Forecasting ◽

Deep Convection ◽

Detection Methods ◽

Cloud Detection ◽

Bidirectional Reflectance ◽

Cloud Optical Depth ◽

Convective Clouds ◽

Clear Sky ◽

Almost All ◽

Bidirectional Reflectance Distribution

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

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