<p>The cloud systems of the North Atlantic trades (NAT) have been a topic of curiosity due to significant uncertainty in their physical characteristics, physical process understanding across various spatial and temporal scales, and their impact on the regional climate system. Initial research has provided the causal link for cloud systems having distinct organizational aspects (formerly described as Sugar, Gravel, Fish, and Flower) with the net radiative flux over the region. Questions have been raised about how the changing climate will influence the frequency of occurrence of these cloud regimes and how the net radiative impact will change the regional climate system.</p><p>&#160;</p><p>However, cloud systems represent a continuous spectrum where not-so-visually distinct systems also occur. Existing clustering mechanisms sort organizations into separate classes. Yet, in reality, the organization often does not align with those pre-defined classes but transitions amongst them or simply occurs in a continuous spectrum. Using two complementary neural networks in self-supervision (without human interference), we investigate the representation learning of cloud systems both in a continuous space describing a cloud system spectrum and in a discreet space aiming to identify distinct cloud systems. 50,000 GOES-ABI cloud optical depth NAT images from 2017 &#8211; 2021 covering the EUREC<sup>4</sup>A study area are randomly cropped to 256 x 256 pixels and sorted/labeled by the machine.</p><p>&#160;</p><p>We study the climatological representation of EUREC<sup>4</sup>A&#8217;s cloud patterns in the continuous embedding space. We follow the Maria S. Merian ship track inside the feature space matching the ship-based atmospheric remote sensing and ERA5 profiles with the K-nearest satellite images. This analysis examines the consistency of the environmental conditions for cloud systems identified as close to each other in the continuous feature space. We investigate the relationship between the net cloud radiative effect and the radiative flux characteristics in the continuous space, finding a strong functional relationship with the cloud system&#8217;s pattern and distributions.</p><p>&#160;</p><p>In the discrete space, we aim to identify the optimal number of classes that could represent the continuous space. We also aim to understand if these discrete classes correspond to the categories identified in the physical and visual space. Moreover, to better understand the decision of the neural network for a particular cloud pattern, we visualize the network&#8217;s focus in the activation space. We find that different self-attention heads of the neural network learn to focus on different semantics of the cloud system distribution.</p><p>&#160;</p><p>Finally, we found that different regularizations applied during the training of the network directly impact the representation learning of the cloud system, and we show how to use such regularizations to improve the understanding of cloud systems.</p>
<p>With ever-increasing resolution, geostationary satellites are able to reveal the complex structure and organization of clouds. How cloud systems organize is important for the local climate and strongly connects to the Earth's response to warming through cloud system feedback.</p><p>Motivated by recent developments in computer vision for pattern analysis of uncurated images, our work aims to understand the organization of cloud systems based on high-resolution cloud optical depth images. We are exploiting the self-learning capability of a deep neural network to classify satellite images into different subgroups based on the distribution pattern of the cloud systems.</p><p>Unlike most studies, our neural network is trained over the central European domain, which is characterized by strong land surface type and topography variations. The satellite data is post-processed and retrieved at a higher spatio-temporal resolution (2 km, 5 min), enhanced by 66% compared to the current standard, equivalent to the future Meteosat third-generation satellite, which will be launched soon.</p><p>We show how recent advances in deep learning networks are used to understand clouds' physical properties in temporal and spatial scales. In a purely data-driven approach, we avoid the noise and bias obtained from human labeling, and with proper scalable techniques, it takes 0.86 ms and 2.13 ms to label an image at two different spatial configurations. We demonstrate explainable artificial intelligence (XAI), which helps gain trust for the neural network's performance.</p><p>To generalize the results, a thorough quantified evaluation is done on two spatial domains and two-pixel configurations (128x128, 64x64). We examine the uncertainty associated with distinct machine-detected cloud-pattern categories. For this, the learned features of the satellite images are extracted from the trained neural network and fed to an independent hierarchical - agglomerative algorithm. Therefore the work also explores the uncertainties associated with the automatic machine-detected patterns and how they vary with different cloud classification types.</p>
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