Deep Learning Based Short-Term Total Cloud Cover Forecasting

Ishara Bandara, Li Zhang, Kamlesh Mistry

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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In this research, we conduct deep learning based Total Cloud Cover (TCC) forecasting using satellite images. The proposed system employs the Otsu's method for cloud segmentation and Long Short-Term Memory (LSTM) variant models for TCC prediction. Specifically, a region-based Otsu's method is used to segment clouds from satellite images. A time-series dataset is generated using the TCC information extracted from each image in image sequences using a new feature extraction method. The generated time series data are subsequently used to train several LSTM variant models, i.e. LSTM, bi-directional LSTM and Convolutional Neural Network (CNN)-LSTM, for future TCC forecasting. Our approach achieves impressive average RMSE scores with multi-step forecasting, i.e. 0.0543 and 0.0823, with respect to both the first half of daytime and full daytime TCC forecasting on a given day, using the generated dataset.
Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN)
Place of PublicationItaly
Number of pages8
ISBN (Electronic)978-1-7281-8671-9
ISBN (Print)978-1-6654-9526-4
Publication statusE-pub ahead of print - 30 Sept 2022

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