Search Results

HIDRA-D: deep-learning model for dense sea level forecasting using sparse altimetry and tide gauge data.

• Authors : Rus, Marko^1,2 (AUTHOR); Ličer, Matjaž^2,3 (AUTHOR) ; Kristan, Matej¹ (AUTHOR)

• Source: Geoscientific Model Development. 2026, Vol. 19 Issue 5, p2177-2195. 19p.

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HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures

• Authors : M. Rus; H. Mihanović; M. Ličer

Subjects: QE1-996.5; Geology

• Source: Geoscientific Model Development, Vol 18, Pp 605-620 (2025)

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SICNet season V1.0: a transformer-based deep learning model for seasonal Arctic sea ice prediction by incorporating sea ice thickness data

• Authors : Y. Ren; X. Li; Y. Wang

Subjects: QE1-996.5; Geology

• Source: Geoscientific Model Development, Vol 18, Pp 2665-2678 (2025)

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Forecasting tropical cyclone tracks in the northwestern Pacific based on a deep-learning model

• Authors : L. Wang; B. Wan; S. Zhou

Subjects: QE1-996.5; 13. Climate action; Geology

• Source: eISSNGeoscientific Model Development, Vol 16, Pp 2167-2179 (2023)

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Deep learning model based on multi-scale feature fusion for precipitation nowcasting

• Authors : J. Tan; Q. Huang; S. Chen

Subjects: Geology; QE1-996.5

• Source: Geoscientific Model Development, Vol 17, Pp 53-69 (2024)

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A comparative analysis for a deep learning model (hyDL-CO v1.0) and Kalman filter to predict CO concentrations in China

• Authors : Weichao Han; Tai-Long He; Zhaojun Tang

Subjects: QE1-996.5; 13. Climate action; Geology

• Source: Geoscientific Model Development, Vol 15, Pp 4225-4237 (2022)

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Sub3DNet1.0: a deep-learning model for regional-scale 3D subsurface structure mapping

• Authors : Z. Jiang; D. Mallants; L. Gao

Subjects: QE1-996.5; 0202 electrical engineering, electronic engineering, information engineering; Geology

• Source: Geoscientific Model Development, Vol 14, Pp 3421-3435 (2021)Geoscientific Model Development, vol. 14, no. 6, pp. 3421-3435

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MSDM v1.0: A machine learning model for precipitation nowcasting over eastern China using multisource data

• Authors : Dawei Li; Yudi Liu; Chaohui Chen

Subjects: QE1-996.5; 13. Climate action; 0207 environmental engineering

• Source: Geoscientific Model Development, Vol 14, Pp 4019-4034 (2021)

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EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – Part 1: Development of deep learning model

• Authors : J. F. Quinting; C. M. Grams

Subjects: Geology; QE1-996.5

• Source: Geoscientific Model Development, Vol 15, Pp 715-730 (2022)

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Identifying lightning processes in ERA5 soundings with deep learning.

• Authors : Ehrensperger, Gregor^1,2 (AUTHOR) ; Simon, Thorsten² (AUTHOR); Mayr, Georg J.² (AUTHOR)

• Source: Geoscientific Model Development. 2025, Vol. 18 Issue 4, p1141-1153. 13p.

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