Deep Learning For Nowcasting

Deep Learning for Nowcasting is a computational framework that employs deep neural networks to model and predict hydro-meteorological processes at fine temporal and spatial scales, distinguishing itself from traditional nowcasting approaches by automatically extracting spatiotemporal patterns from high-dimensional observational and sensor data.

122

Publications

12.7K

Citations

385

Authors

145

Institutions

Publications per year

2017–2026

118

Authors

385

  1. XL
    Xutao Li

    Harbin Institute of Technology, China

    6 publications · 442 citations

    7

    h-index

  2. YY
    Yunming Ye

    Harbin Institute of Technology, China

    5 publications · 403 citations

    6

    h-index

  3. CL
    Chuyao Luo

    Harbin Institute of Technology, China

    4 publications · 251 citations

    5

    h-index

  4. JL
    Jussi Leinonen

    Jet Propulsion Laboratory, United States

    3 publications · 103 citations

    4

    h-index

  5. XS
    Xingjian Shi

    Hong Kong University of Science and Technology, Hong Kong SAR China

    4 publications · 8.1K citations

    4

    h-index

1–5 of 385

Venues

  1. arXiv (Cornell University)

    Repository · 10 publications · 8.4K citations

    11

    h-index

  2. Remote Sensing

    11 publications · 333 citations

    11

    h-index

  3. 10

    h-index

  4. Atmosphere

    4 publications · 210 citations

    7

    h-index

  5. 6

    h-index

1–5