CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis

• URL: http://arxiv.org/abs/2010.15158v1
• Date: Wed, 28 Oct 2020 18:13:19 GMT
• Title: CNN Profiler on Polar Coordinate Images for Tropical Cyclone Structure Analysis
• Authors: Boyo Chen, Buo-Fu Chen, Chun-Min Hsiao
• Abstract summary: This study applies CNN on satellite images to create the entire TC structure profiles. We provide valuable labels for training in our newly released benchmark dataset.
• Score: 11.387235721659378
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional neural networks (CNN) have achieved great success in analyzing tropical cyclones (TC) with satellite images in several tasks, such as TC intensity estimation. In contrast, TC structure, which is conventionally described by a few parameters estimated subjectively by meteorology specialists, is still hard to be profiled objectively and routinely. This study applies CNN on satellite images to create the entire TC structure profiles, covering all the structural parameters. By utilizing the meteorological domain knowledge to construct TC wind profiles based on historical structure parameters, we provide valuable labels for training in our newly released benchmark dataset. With such a dataset, we hope to attract more attention to this crucial issue among data scientists. Meanwhile, a baseline is established with a specialized convolutional model operating on polar-coordinates. We discovered that it is more feasible and physically reasonable to extract structural information on polar-coordinates, instead of Cartesian coordinates, according to a TC's rotational and spiral natures. Experimental results on the released benchmark dataset verified the robustness of the proposed model and demonstrated the potential for applying deep learning techniques for this barely developed yet important topic.

Related papers

• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones [0.907599024697789]
  This paper presents the official release of the longest typhoon satellite image dataset for 40+ years. It is aimed at benchmarking machine learning models for long-term-temporal data. The dataset is publicly available at http://agora.nii.ac.jp/digital-typhoon/.
  arXiv  Detail & Related papers  (2023-11-05T14:22:13Z)
• Revisiting Link Prediction: A Data Perspective [61.52668130971441]
  Link prediction, a fundamental task on graphs, has proven indispensable in various applications, e.g., friend recommendation, protein analysis, and drug interaction prediction. Evidence in existing literature underscores the absence of a universally best algorithm suitable for all datasets. We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
  arXiv  Detail & Related papers  (2023-10-01T21:09:59Z)
• TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones [4.650202794545353]
  This paper introduces a topologically robust physics-informed tracking framework (TROPHY) for Tropical Cyclone tracking. During preprocessing, we propose a physics-informed feature selection strategy to filter 90% of critical points that are short-lived and have low stability. We apply TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate a number of TC tracks.
  arXiv  Detail & Related papers  (2023-07-28T00:44:55Z)
• An Ensemble Machine Learning Approach for Tropical Cyclone Detection Using ERA5 Reanalysis Data [0.0]
  Tropical Cyclones (TCs) are counted among the most destructive phenomena that can be found in nature. Traditionally, TCs have been identified in large climate datasets through the use of deterministic tracking schemes. This study presents a Machine Learning ensemble approach for locating TC center coordinates.
  arXiv  Detail & Related papers  (2023-06-07T22:26:50Z)
• Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting [52.47493322446537]
  We develop a adaptive, interpretable and scalable forecasting framework, which seeks to individually model each component of the spatial-temporal patterns. SCNN works with a pre-defined generative process of MTS, which arithmetically characterizes the latent structure of the spatial-temporal patterns. Extensive experiments are conducted to demonstrate that SCNN can achieve superior performance over state-of-the-art models on three real-world datasets.
  arXiv  Detail & Related papers  (2023-05-22T13:39:44Z)
• Search to Pass Messages for Temporal Knowledge Graph Completion [97.40256786473516]
  We propose to use neural architecture search (NAS) to design data-specific message passing architecture for temporal knowledge graphs (TKGs) completion. In particular, we develop a generalized framework to explore topological and temporal information in TKGs. We adopt a search algorithm, which trains a supernet structure by sampling single path for efficient search with less cost.
  arXiv  Detail & Related papers  (2022-10-30T04:05:06Z)
• Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast [13.123672819646336]
  We propose a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-temporal features efficiently. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TC tracks prediction results.
  arXiv  Detail & Related papers  (2022-02-27T10:45:18Z)
• 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)
• DeepSatData: Building large scale datasets of satellite images for training machine learning models [77.17638664503215]
  This report presents design considerations for automatically generating satellite imagery datasets for training machine learning models. We discuss issues faced from the point of view of deep neural network training and evaluation.
  arXiv  Detail & Related papers  (2021-04-28T15:13:12Z)
• Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset [1.125851164829582]
  We explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Three DCNN models, including SegNet, U-Net, and LinkNet, are implemented next.
  arXiv  Detail & Related papers  (2020-03-30T17:32:49Z)

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.