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Lu Li
,
Yongjiu Dai
,
Wei Shangguan
,
Zhongwang Wei
,
Nan Wei
, and
Qingliang Li

, avoiding describing complex physical processes. With traditional ML models (e.g., random forest), some studies have achieved satisfactory results on SM prediction (refer to a review study, Ali et al. 2015 ). Compared to traditional ML models, deep learning (DL) models significantly increase the ability to process big data, resulting in better performance ( LeCun et al. 2015 ). Recurrent neural network (RNN) is good at time series prediction among DL models. However, RNN could not learn long-term time

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Zongsheng Zheng
,
Chenyu Hu
,
Zhaorong Liu
,
Jianbo Hao
,
Qian Hou
, and
Xiaoyi Jiang

about satellite cloud images. A subfield of machine learning—deep learning (DL), as an extremely generalized approach—has been applied to various domains, such as pattern recognition, computer vision, artificial intelligence, and so on ( Xiao et al. 2010 ; Hinton et al. 2012 ; LeCun et al. 2015 ). As one of most popular deep learning models, convolutional neural networks (CNNs) have been demonstrated as a promising tool for image classification due to its locally connected layers and feasibility

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Joaquin Cuomo
and
V. Chandrasekar

learning models for weather nowcasting using radar echo data and compared them with existing models, particularly against two operational models. A common issue with deep learning approaches is that they tend to underestimate the intensity of reflectivity. To address this, we proposed the Composite model, which shows promising results on enhancing the predictions at higher reflectivity values. Although this ensemble can improve any base model, we believe that the primary focus should be put on

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Kanghui Zhou
,
Yongguang Zheng
,
Wansheng Dong
, and
Tingbo Wang

a good ability to combine the multisource data, and over 99% of the 550 observed initiation of MCS events were detected within 50 km. Deep learning (DL) is a subset of machine learning algorithms that uses multilayer artificial neural networks to deliver state-of-the-art accuracy in many tasks ( Bengio 2009 ; Schmidhuber 2015 ). Similar to traditional machine learning algorithms like artificial neural networks and SVM, DL networks can model complex nonlinear systems. Moreover, these networks

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Tao Song
,
Ying Li
,
Fan Meng
,
Pengfei Xie
, and
Danya Xu

observatories, and ground stations, the amount of ocean and air data continues to accumulate. Combining deep learning models with big data environments provides us with a new opportunity to predict the track of tropical cyclones. In recent years, data-driven deep learning algorithms have been successfully applied in image processing, natural language processing, target detection, and other fields. Deep learning technology has strong nonlinear fitting capabilities, which have shown great advantages in

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Na-Yeon Shin
,
Yoo-Geun Ham
,
Jeong-Hwan Kim
,
Minsu Cho
, and
Jong-Seong Kug

( Hsieh and Tang 1998 ), which recently developed into various deep learning algorithms ( Reichstein et al. 2019 ). In this regard, deep learning has exhibited superior performance in detecting weather features such as hurricanes, clouds, and weather fronts ( Liu et al. 2016 ; Racah et al. 2016 ; Xie et al. 2016 ; Biard and Kunkel 2019 ; Prabhat et al. 2021 ). It has been also applied to weather forecasts and climate variability predictions ( Shi et al. 2015 ; Ham et al. 2019 ; Ise and Oba 2019

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Baixin Li
,
Huan Tang
,
Dongfang Ma
, and
Jianmin Lin

mesoscale eddies is reflected in the gridded ADT and UVG data, as shown in section 2 , these two kinds of data have different traits in different eddy regions. Finally, both the spatial and temporal characteristics of the data should be considered, so as to achieve optimal detection. To address these challenges, we propose a dual-attention mechanism deep-learning network. The network has a U-shaped encoding–decoding structure and a skip connection that connects the encoding and decoding parts with

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Daniel Galea
,
Hsi-Yen Ma
,
Wen-Ying Wu
, and
Daigo Kobayashi

, making them cumbersome. However, in recent years, machine learning (ML) and deep learning (DL) methods have made significant progress in various fields, including computer vision ( Ho et al. 2020 ; Liu et al. 2022 ), as well as in the physical sciences, such as the atmospheric sciences ( Pritchard et al. 2022 ; Kurth et al. 2023 ). Some machine learning–based methods for tracking ARs, detailed below, have already been developed but still have relatively low performance. a. AR detection using

Open access
Jing-Yi Zhuo
and
Zhe-Min Tan

research? To achieve this, novel paradigms such as deep learning could offer a promising approach. The rapid advances in deep learning have substantially impacted many scientific fields, including climate science. Deep learning can be used to reconstruct missing climate information (e.g., Kadow et al. 2020 ), recognize driving climate patterns or extreme events (e.g., Racah et al. 2017 ; Barnes et al. 2020 ), climate attribution (e.g., Callaghan et al. 2021 ) and seasonal-to-decadal climate

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Ryan Lagerquist
,
Amy McGovern
,
Cameron R. Homeyer
,
David John Gagne II
, and
Travis Smith

( Cintineo et al. 2018 ). Convolutional neural networks (CNN) are specially designed to learn from spatial grids and often contain many layers, which qualifies them as a deep-learning method (section 1.1.4 of Chollet 2018 ). In traditional ML, spatial grids must be transformed into scalar features, which become the direct inputs to the model. Examples are principal components, spatial statistics (such as means and standard deviations), and raw gridpoint values (where each value in the grid is treated as

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