Deep Learning Based Short-Term Total Cloud Cover Forecasting

Abstract

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.

A framework for cloud cover prediction using machine learning with data imputation

“…Further deep-learning techniques are also used to classify cloudy or clear skies using images [11]. Various deep-learning techniques are applied to these satellite images for forecasting [12]- [15].…”