Blar i Geophysical Institute på forfatter "Schevenhoven, Francine Janneke"

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    • An efficient training scheme for supermodels 

      Schevenhoven, Francine Janneke; Selten, Frank (Peer reviewed; Journal article, 2017-06-28)
      Weather and climate models have improved steadily over time as witnessed by objective skill scores, although significant model errors remain. Given these imperfect models, predictions might be improved by combining them ...

    • Improving weather and climate predictions by training of supermodels 

      Schevenhoven, Francine Janneke; Selten, Frank; Carrassi, Alberto; Keenlyside, Noel (Peer reviewed; Journal article, 2019-11-28)
      Recent studies demonstrate that weather and climate predictions potentially improve by dynamically combining different models into a so-called “supermodel”. Here, we focus on the weighted supermodel – the supermodel's time ...

    • Simulating climate with a synchronization-based supermodel 

      Selten, Frank M; Schevenhoven, Francine Janneke; Duane, Gregory (Peer reviewed; Journal article, 2017-12)
      The SPEEDO global climate model (an atmosphere model coupled to a land and an ocean/sea-ice model with about 250.000 degrees of freedom) is used to investigate the merits of a new multi-model ensemble approach to the climate ...

    • Supermodeling Improving Predictions with an Ensemble of Interacting Models 

      Schevenhoven, Francine Janneke; Keenlyside, Noel Sebastian; Counillon, Francois Stephane; Gupta, Alok Kumar; Koseki, Shunya; Shen, Mao-Lin (Journal article; Peer reviewed, 2023)
      The modeling of weather and climate has been a success story. The skill of forecasts continues to improve and model biases continue to decrease. Combining the output of multiple models has further improved forecast skill ...

    • Training a supermodel with noisy and sparse observations: A case study with CPT and the synch rule on SPEEDO-v.1 

      Schevenhoven, Francine Janneke; Carrassi, Alberto (Journal article; Peer reviewed, 2022)
      As an alternative to using the standard multi-model ensemble (MME) approach to combine the output of different models to improve prediction skill, models can also be combined dynamically to form a so-called supermodel. The ...