Machine Learning For Nowcasting

Machine Learning for Nowcasting is a computational methodology that employs machine learning algorithms to predict hydro-meteorological variables at short lead times by leveraging real-time observational and sensor data to improve the accuracy and timeliness of hydrologic forecasts.

54

Publications

9.8K

Citations

204

Authors

115

Institutions

Publications per year

2017–2026

43

Authors

204

  1. UG
    Urs Germann

    Federal Office of Meteorology and Climatology MeteoSwiss, Switzerland

    3 publications · 120 citations

    3

    h-index

  2. JL
    Jussi Leinonen

    Jet Propulsion Laboratory, United States

    2 publications · 56 citations

    2

    h-index

  3. ZC
    Zhourong Chen

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

    2 publications · 7.4K citations

    2

    h-index

  4. TV
    Tuğrul Vehbi

    Reserve Bank of New Zealand, New Zealand

    2 publications · 136 citations

    2

    h-index

  5. JR
    John R. Mecikalski

    University of Alabama in Huntsville, United States

    2 publications · 56 citations

    2

    h-index

1–5 of 204

Venues

  1. Journal of Hydrology

    3 publications · 248 citations

    3

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 7.7K citations

    3

    h-index

  3. Weather and Forecasting

    3 publications · 146 citations

    3

    h-index

  4. Atmosphere

    2 publications · 39 citations

    2

    h-index

  5. SSRN Electronic Journal

    Repository · 2 publications · 28 citations

    2

    h-index

1–5