No abstract

Forecasting the formation and development of clouds is a central element of modern weather forecasting systems. Incorrect clouds forecasts can lead to major uncertainty in the overall accuracy of weather forecasts due to their intrinsic role in the Earth's climate system. Few studies have tackled this challenging problem from a machine learning point-of-view due to a shortage of high-resolution datasets with many historical observations globally. In this paper, we present a novel satellitebased dataset called "CloudCast". It consists of 70,080 images with 10 different cloud types for multiple layers of the atmosphere annotated on a pixel level. The spatial resolution of the dataset is 928 x 1530 pixels (3x3 km per pixel) with 15-min intervals between frames for the period 2017-01-01 to 2018-12-31. All frames are centered and projected over Europe. To supplement the dataset, we conduct an evaluation study with current stateof-the-art video prediction methods such as convolutional long short-term memory networks, generative adversarial networks, and optical flow-based extrapolation methods. As the evaluation of video prediction is difficult in practice, we aim for a thorough evaluation in the spatial and temporal domain. Our benchmark models show promising results but with ample room for improvement. This is the first publicly available global-scale dataset with high-resolution cloud types on a high temporal granularity to the authors' best knowledge.

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1–15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5–6 days. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. We apply state-ofthe-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.

<p>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.</p><p>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 dataset<sub>1</sub> , 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.</p><p>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.<br>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.</p><p>1) Kniffka, Stengel, and Hollmann, “SEVIRI Cloud Mask Dataset - Edition 1.”</p>