Cloud Mask Nowcasting over Germany Using Deep Learning

The transition to renewable energy sources such as solar energy has increased the interest in predicting cloud masks from remote sensing data. Even though deep learning methods have achieved great success on multiple meteorological tasks, only limited research has been conducted on nowcasting cloud masks based on high temporal and spatial resolution satellite data.

This study investigates forecasting cloud masks over Germany six frames into the future based on satellite images. We compare predictions between three deep learning architectures (ConvLSTM, U-Net, and MetNet) relative to two baseline models (optical flow and persistence). We train and evaluate our models using two years of the ICARE SAFNWC Cloud Mask dataset₁ , with a temporal resolution of 15 minutes per frame and a spatial resolution of 3×3 km per pixel. In our experiments we use a larger area of 256×256 pixels to predict the target area of size 128×128 pixels, leading to overall better performance compared to using an input size equal to the output size. Besides comparing different network architectures, we also investigate the effect of varying the temporal input size and output size for ConvLSTM. Finally, we examine the effect of adding more features (land/sea mask and elevation map) and changing the loss function.

In summary, we have performed a comprehensive study investigating cloud mask nowcasting using 70,000 spatially and temporally aligned data frames, examining three loss functions, six evaluation metrics, and three deep learning models.
During the presentation, we will highlight the main results from the study and present details of the model architectures, datasets, and how space and time affect the performance of the models.

1) Kniffka, Stengel, and Hollmann, “SEVIRI Cloud Mask Dataset - Edition 1.”