Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder

• URL: http://arxiv.org/abs/2211.00860v1
• Date: Wed, 2 Nov 2022 04:08:32 GMT
• Title: Insight into cloud processes from unsupervised classification with a rotationally invariant autoencoder
• Authors: Takuya Kurihana, James Franke, Ian Foster, Ziwei Wang, Elisabeth Moyer
• Abstract summary: Current cloud classification schemes are based on single-pixel cloud properties and cannot consider spatial structures and textures. Recent advances in computer vision enable the grouping of different patterns of images without using human predefined labels. We describe the use of such methods to generate a new AI-driven Cloud Classification Atlas (AICCA)
• Score: 10.739352302280667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clouds play a critical role in the Earth's energy budget and their potential changes are one of the largest uncertainties in future climate projections. However, the use of satellite observations to understand cloud feedbacks in a warming climate has been hampered by the simplicity of existing cloud classification schemes, which are based on single-pixel cloud properties and cannot consider spatial structures and textures. Recent advances in computer vision enable the grouping of different patterns of images without using human predefined labels, providing a novel means of automated cloud classification. This unsupervised learning approach allows discovery of unknown climate-relevant cloud patterns, and the automated processing of large datasets. We describe here the use of such methods to generate a new AI-driven Cloud Classification Atlas (AICCA), which leverages 22 years and 800 terabytes of MODIS satellite observations over the global ocean. We use a rotationally invariant cloud clustering (RICC) method to classify those observations into 42 AI-generated cloud class labels at ~100 km spatial resolution. As a case study, we use AICCA to examine a recent finding of decreasing cloudiness in a critical part of the subtropical stratocumulus deck, and show that the change is accompanied by strong trends in cloud classes.

Related papers

• Removing cloud shadows from ground-based solar imagery [0.33748750222488655]
  We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds.
  arXiv  Detail & Related papers  (2024-07-18T10:38:24Z)
• DCS-Net: Pioneering Leakage-Free Point Cloud Pretraining Framework with Global Insights [55.051626723729896]
  We introduce a novel solution called the Differentiable Center Sampling Network (DCS-Net) It tackles the information leakage problem by incorporating both global feature reconstruction and local feature reconstruction as non-trivial proxy tasks. Experimental results demonstrate that our method enhances the expressive capacity of existing point cloud models.
  arXiv  Detail & Related papers  (2024-02-03T08:58:23Z)
• Masked Spatio-Temporal Structure Prediction for Self-supervised Learning on Point Cloud Videos [75.9251839023226]
  We propose a Masked-temporal Structure Prediction (MaST-Pre) method to capture the structure of point cloud videos without human annotations. MaST-Pre consists of two self-supervised learning tasks. First, by reconstructing masked point tubes, our method is able to capture appearance information of point cloud videos. Second, to learn motion, we propose a temporal cardinality difference prediction task that estimates the change in the number of points within a point tube.
  arXiv  Detail & Related papers  (2023-08-18T02:12:54Z)
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [83.05340155068721]
  We devise a new 3d point cloud generation framework using a divide-and-conquer approach. All patch generators are based on learnable priors, which aim to capture the information of geometry primitives. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation.
  arXiv  Detail & Related papers  (2023-07-22T11:10:39Z)
• Cloud Classification with Unsupervised Deep Learning [6.285964948191585]
  Our framework learns cloud features directly from radiance data produced by NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument. We present preliminary results showing that our method extracts physically relevant information from radiance data and produces meaningful cloud classes.
  arXiv  Detail & Related papers  (2022-09-30T16:56:58Z)
• AICCA: AI-driven Cloud Classification Atlas [0.0]
  This study reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA)
  arXiv  Detail & Related papers  (2022-09-29T21:01:31Z)
• Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Survey [104.71816962689296]
  Unsupervised point cloud representation learning has attracted increasing attention due to the constraint in large-scale point cloud labelling. This paper provides a comprehensive review of unsupervised point cloud representation learning using deep neural networks.
  arXiv  Detail & Related papers  (2022-02-28T07:46:05Z)
• Deep Learning Based Cloud Cover Parameterization for ICON [55.49957005291674]
  We train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON simulations. Globally trained NNs can reproduce sub-grid scale cloud cover of the regional simulation. We identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained data.
  arXiv  Detail & Related papers  (2021-12-21T16:10:45Z)
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [81.71904691925428]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We also design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point cloud completion. Our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
  arXiv  Detail & Related papers  (2021-08-19T17:58:56Z)
• Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder [10.660968055962325]
  We describe an automated rotation-invariant cloud clustering (RICC) method. It organizes cloud imagery within large datasets in an unsupervised fashion. Results suggest that the resultant cloud clusters capture meaningful aspects of cloud physics.
  arXiv  Detail & Related papers  (2021-03-08T16:45:14Z)
• Classification and understanding of cloud structures via satellite images with EfficientUNet [0.0]
  classification of cloud organization patterns was performed using a new scaled-up version of Convolutional Neural Network (CNN) Dice coefficient has been used for the final evaluation metric, which gave the score of 66.26% and 66.02% for public and private (test set) leaderboard on Kaggle competition respectively.
  arXiv  Detail & Related papers  (2020-09-27T19:50:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.