Abstract
The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3–11.3 μm; IR2, 11.5–12.5 μm and WV 6.3–7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.
Highlights
Clouds play an important role in the Earth system
This study demonstrated the capability of Artificial Neural Network (ANN) classifier by comparing it with the clustering method currently used by FY-2C operational products both at pixel level and cloud patch level
The precision of the classifiers ranks at the following order: Probabilistic Neural Network (PNN), Self-Organizing Map (SOM) > Back Propagation (BP), Modular Neural Networks (MNN), Jordan/Elman > Co-Active Neuro-Fuzzy Inference System (CANFIS), Principal Component Analysis (PCA) > Support Vector Machine (SVM)
Summary
Clouds play an important role in the Earth system. They significantly affect the heat budget by reflecting short-wave radiation [1], and absorbing and emitting long-wave radiation [2]. The net effect is a function of the cloud optical properties and the properties of the underlying surface [3]. Different types of clouds have different radiative effects on the Earth surface-atmosphere system. Accurate and automatic cloud detection and classification are useful for numerous climatic, hydrologic and atmospheric applications [4]. An accurate and cost-effective method of cloud detection and classification based on satellite images has been a great interest of many scientists [5,6]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have