Machine Learning Nowcasting

Machine Learning Nowcasting is a computational framework within radar nowcasting that employs machine learning algorithms to model and predict short-term spatiotemporal evolution of atmospheric phenomena directly from observational data, distinguishing itself from traditional numerical or extrapolation-based methods through data-driven learning of complex non-linear dynamics.

42

Publications

1.7K

Citations

157

Authors

82

Institutions

Publications per year

2017–2026

38

Authors

157

  1. UG
    Urs Germann

    Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland

    3 publications · 120 citations

    3

    h-index

  2. ZL
    Zhouyayan Li

    University of Iowa, United States

    2 publications · 36 citations

    2

    h-index

  3. JR
    John R. Mecikalski

    University of Alabama in Huntsville, United States

    2 publications · 56 citations

    2

    h-index

  4. TV
    Thomas van Florenstein Mulder

    Reserve Bank of New Zealand, New Zealand

    2 publications · 136 citations

    2

    h-index

  5. AR
    Adam Richardson

    Columbia University, United States

    2 publications · 136 citations

    2

    h-index

1–5 of 157

Venues

  1. Geoscientific model development

    3 publications · 97 citations

    3

    h-index

  2. 2

    h-index

  3. Remote Sensing

    2 publications · 75 citations

    2

    h-index

  4. Space Weather

    2 publications · 423 citations

    2

    h-index

  5. SSRN Electronic Journal

    Repository · 2 publications · 28 citations

    2

    h-index

1–5