scholarly journals CLOUD CLASSIFICATION FOR GROUND-BASED SKY IMAGE USING RANDOM FOREST

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 835-842
Author(s):  
X. Wan ◽  
J. Du
Keyword(s):  
Random Forest ◽  
Detection Method ◽  
Single Type ◽  
Cloud Detection ◽  
Short Term ◽  
Multiple Features ◽  
Clear Sky ◽  
Cloud Classification ◽  
Different Types ◽  
Main Factor

Abstract. The use of solar power as a renewable energy has grown rapidly over the last few decades. However, the amount of solar radiation reaching the ground vary significantly in the short term. Clouds are the main factor. In this paper, a novel cloud detection method for ground-based sky images is proposed. First, the multiple features from the sky images, including spectral, texture and colour features are combined into a feature set. Then, Random Forest with this feature set is used to classify different types of cloud and clear sky. The experimental results show that cumulus and cirrus clouds can be identified from sky images. Combined with random forest, three types of features and various feature combinations are used for cloud classification, respectively. The classification accuracy with multiple features is higher than that of single-type features and dual-type features.

scholarly journals A method for cloud detection and opacity classification based on ground based sky imagery

2012 ◽  
Vol 5 (11) ◽  
pp. 2881-2892 ◽  
Author(s):  
M. S. Ghonima ◽  
B. Urquhart ◽  
C. W. Chow ◽  
J. E. Shields ◽  
A. Cazorla ◽  
...  
Keyword(s):  
Training Image ◽  
Classification Model ◽  
Cloud Detection ◽  
New Classification ◽  
Short Term ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Set ◽  
The Difference ◽  
Solar Power Forecasting

Abstract. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

scholarly journals A method for cloud detection and opacity classification based on ground based sky imagery

2012 ◽  
Vol 5 (4) ◽  
pp. 4535-4569 ◽  
Author(s):  
M. S. Ghonima ◽  
B. Urquhart ◽  
C. W. Chow ◽  
J. E. Shields ◽  
A. Cazorla ◽  
...  
Keyword(s):  
Training Image ◽  
Classification Model ◽  
Cloud Detection ◽  
New Classification ◽  
Short Term ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Set ◽  
The Difference ◽  
Solar Power Forecasting

Abstract. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

scholarly journals Cloud detection and classification based on MAX-DOAS observations

2013 ◽  
Vol 6 (6) ◽  
pp. 10297-10360 ◽  
Author(s):  
T. Wagner ◽  
S. Beirle ◽  
S. Dörner ◽  
U. Friess ◽  
J. Remmers ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Scattered Light ◽  
Trace Gases ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Cloud Detection ◽  
Clear Sky ◽  
Cloud Classification ◽  
Good Agreement ◽  
Light Ring

Abstract. Multi-AXis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like the radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, also other quantities and viewing directions are considered, which allows sub-classifications like e.g. thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the CINDI campaign in the Netherlands in Summer 2009 and found very good agreement with sky images taken from ground.

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

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 %.

scholarly journals Impact of the One-Stream Cloud Detection Method on the Assimilation of AMSU-A Data in GRAPES

2020 ◽  
Vol 12 (22) ◽  
pp. 3842
Author(s):  
Zhengkun Qin ◽  
Zhiwen Wu ◽  
Juan Li
Keyword(s):  
Large Scale ◽  
Detection Method ◽  
Current Method ◽  
Prediction System ◽  
Cloud Detection ◽  
Weather System ◽  
Clear Sky ◽  
Microwave Humidity Sounder ◽  
The One ◽  
Microwave Data

Clouds affect the assimilation of microwave data from satellites and therefore the detection of clouds is important under both clear sky and cloudy conditions. We introduce a new cloud detection method based on the assimilation of data from the advanced microwave sounder unit A (AMSU-A) and the microwave humidity sounder (MHS) into the global and regional assimilation and prediction system (GRAPES) and use forecast experiments to evaluate its impact. The new cloud detection method can retain more observational data than the current method in GRAPES, thereby improving the assimilation of AMSU-A data. Verification of the method showed that, by improving the forecast of the lower-level air temperature and geopotential height, the new cloud detection method improved the forecast of the track of two typhoons. The forecast of a large-scale weather system in GRAPES was also improved by the new method in the later period of the forecast.

scholarly journals THE EFFECTIVENESS OF CHUNKING METHODS FOR ENHANCING SHORT-TERM MEMORY OF TEXTUAL INFORMATION

2021 ◽  
Vol 57 (9) ◽  
pp. 6313-6327
Author(s):  
Piwat Suppawittaya, Pratchayapong Yasri
Keyword(s):  
High School Students ◽  
Short Term Memory ◽  
Computational Thinking ◽  
Single Type ◽  
Short Term ◽  
School Students ◽  
Textual Information ◽  
Term Memory ◽  
Educational Implications ◽  
Different Types

The effectiveness of students’ memorization of textual information was investigated in this study with 50 high school students. The information was presented to the participants in three different types: 10 distinct alphabets, 10 distinct numbers, and a combination of 5 distinct alphabets and 5 distinct numbers. This information was divided into three different chunking methods: One-Chunk where the whole information was told all at once, Two-Chunks where the information was divided into 5 and 5, and Three-Chunks where the information was delivered in 3-3-4, 4-3-3, and 3-4-3 fashions. The statistical results revealed that a single type of information (either all alphabets or all numbers) was found to be easier to recall than the combined information. Furthermore, dividing the information into two or three chunks was found to enhance human memorization more significantly. In addition, the study showed that when a combined type of information was shown, grouping the information into two chunks was more effective to enhance short-term memory than providing it in one chunk. Educational implications can be drawn from this study that in order to assist students to memorize and retain learning materials more effectively, it is essential to help classify them into 2-3 groups when being delivered. Also, learning should emphasize more on how to help students learn to take in information more effectively by themselves through the use of tree thinking, binary thinking, and computational thinking.

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

2017 ◽  
Vol 10 (3) ◽  
pp. 1191-1201 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Quantitative Evaluation ◽  
Detection Algorithm ◽  
Qualitative Assessment ◽  
The Sun ◽  
Cloud Detection ◽  
Atmospheric Scattering ◽  
Clear Sky ◽  
Different Types ◽  
The Difference ◽  
Rgb Image

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 that combined the merits of the differencing and threshold methods, named differencing and threshold combination algorithm (DTCA). Firstly, the channel operation was applied to transform 3-D RGB image 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. If 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 that the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds and performed well under both visible sun and blocked sun conditions.

scholarly journals Cloud detection and classification based on MAX-DOAS observations

2014 ◽  
Vol 7 (5) ◽  
pp. 1289-1320 ◽  
Author(s):  
T. Wagner ◽  
A. Apituley ◽  
S. Beirle ◽  
S. Dörner ◽  
U. Friess ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Scattered Light ◽  
Colour Index ◽  
Optical Absorption Spectroscopy ◽  
Measuring Instruments ◽  
Cloud Detection ◽  
Clear Sky ◽  
Cloud Classification ◽  
Good Agreement ◽  
Light Ring

Abstract. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations of aerosols and trace gases can be strongly influenced by clouds. Thus, it is important to identify clouds and characterise their properties. In this study we investigate the effects of clouds on several quantities which can be derived from MAX-DOAS observations, like radiance, the colour index (radiance ratio at two selected wavelengths), the absorption of the oxygen dimer O4 and the fraction of inelastically scattered light (Ring effect). To identify clouds, these quantities can be either compared to their corresponding clear-sky reference values, or their dependencies on time or viewing direction can be analysed. From the investigation of the temporal variability the influence of clouds can be identified even for individual measurements. Based on our investigations we developed a cloud classification scheme, which can be applied in a flexible way to MAX-DOAS or zenith DOAS observations: in its simplest version, zenith observations of the colour index are used to identify the presence of clouds (or high aerosol load). In more sophisticated versions, other quantities and viewing directions are also considered, which allows subclassifications like, e.g., thin or thick clouds, or fog. We applied our cloud classification scheme to MAX-DOAS observations during the Cabauw intercomparison campaign of Nitrogen Dioxide measuring instruments (CINDI) campaign in the Netherlands in summer 2009 and found very good agreement with sky images taken from the ground and backscatter profiles from a lidar.

Comprehensive Effects of Short-Term Rental Platforms Across Hotel Types in U.S. and International Destinations

2021 ◽  
pp. 193896552110335
Author(s):  
John W. O’Neill ◽  
Jihwan Yeon
Keyword(s):  
Service Providers ◽  
Comprehensive Analysis ◽  
Competitive Market ◽  
Substitution Effects ◽  
Short Term ◽  
Hotel Performance ◽  
The World ◽  
Different Types ◽  
Location Type ◽  
The Impact

In recent years, short-term rental platforms in the lodging sector, including Airbnb, VRBO, and HomeAway, have received extensive attention and emerged as potentially alternative suppliers of services traditionally provided by established commercial accommodation providers, that is, hotels. Short-term rentals have dramatically increased the available supply of rooms for visitors to multiple international destinations, potentially siphoning demand away from hotels to short-term rental businesses. In a competitive market, an increase in supply with constant demand would negatively influence incumbent service providers. In this article, we examine the substitution effects of short-term rental supply on hotel performance in different cities around the world. Specifically, we comprehensively investigate the substitution effects of short-term rental supply on hotel performance based on hotel class, location type, and region. Furthermore, we segment the short-term rental supply based on its types of accommodations, that is, shared rooms, private rooms, and entire homes, and both examine and quantify the differential effects of these types of short-term rentals on different types of hotels. This study offers a comprehensive analysis regarding the impact of multiple short-term rental platforms on hotel performance and offers both conceptual and practical insights regarding the nature and extent of the effects that were identified.

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