Cloud movement prediction using GPS information

Cloud movement prediction is the process of forecasting the direction and speed of movement of clouds in the atmosphere. By reflecting sunlight back into space and trapping heat within the atmosphere, clouds are an essential part of the Earth's climate system and play a significant role in the...

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主要作者: Xu, JingYi
其他作者: Lee Yee Hui
格式: Final Year Project
語言:English
出版: Nanyang Technological University 2023
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在線閱讀:https://hdl.handle.net/10356/166905
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spelling sg-ntu-dr.10356-1669052023-07-07T16:01:19Z Cloud movement prediction using GPS information Xu, JingYi Lee Yee Hui School of Electrical and Electronic Engineering EYHLee@ntu.edu.sg Engineering::Electrical and electronic engineering Cloud movement prediction is the process of forecasting the direction and speed of movement of clouds in the atmosphere. By reflecting sunlight back into space and trapping heat within the atmosphere, clouds are an essential part of the Earth's climate system and play a significant role in the energy balance of the planet. To predict cloud movements in the sky, ground-based cameras are used progressively nowadays to capture the image of cloud movements. The image of cloud is categorised into 4 categories: • Category 1 – Glare • Category 2 – Dark Cloud • Category 3 – Clear Sky • Category 4 – Cloud Overcast While cloud classification is typically done manually by looking through images one at a time, the accuracy will be affected due to different people's perceptions of the various cloud types, and it is extremely time consuming. By automating this process, this research aims to improve cloud classification. Different methods in improving the accuracy of cloud image classification are discussed and explored in this report. Bachelor of Engineering (Electrical and Electronic Engineering) 2023-05-16T02:30:08Z 2023-05-16T02:30:08Z 2023 Final Year Project (FYP) Xu, J. (2023). Cloud movement prediction using GPS information. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166905 https://hdl.handle.net/10356/166905 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Xu, JingYi
Cloud movement prediction using GPS information
description Cloud movement prediction is the process of forecasting the direction and speed of movement of clouds in the atmosphere. By reflecting sunlight back into space and trapping heat within the atmosphere, clouds are an essential part of the Earth's climate system and play a significant role in the energy balance of the planet. To predict cloud movements in the sky, ground-based cameras are used progressively nowadays to capture the image of cloud movements. The image of cloud is categorised into 4 categories: • Category 1 – Glare • Category 2 – Dark Cloud • Category 3 – Clear Sky • Category 4 – Cloud Overcast While cloud classification is typically done manually by looking through images one at a time, the accuracy will be affected due to different people's perceptions of the various cloud types, and it is extremely time consuming. By automating this process, this research aims to improve cloud classification. Different methods in improving the accuracy of cloud image classification are discussed and explored in this report.
author2 Lee Yee Hui
author_facet Lee Yee Hui
Xu, JingYi
format Final Year Project
author Xu, JingYi
author_sort Xu, JingYi
title Cloud movement prediction using GPS information
title_short Cloud movement prediction using GPS information
title_full Cloud movement prediction using GPS information
title_fullStr Cloud movement prediction using GPS information
title_full_unstemmed Cloud movement prediction using GPS information
title_sort cloud movement prediction using gps information
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/166905
_version_ 1772828765061120000