EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Efficient POD-Kriging Surrogate Models for Rainfall Forecasting

Citlali Cabrera Gutiérrez1 and Jean Christophe Jouhaud2
Citlali Cabrera Gutiérrez and Jean Christophe Jouhaud
  • 1Institut de Recherche Technologique Saint Exupéry, France (
  • 2Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique

Complex models calculations can be very expensive and time consuming. A surrogate model aims at producing results which are very close to the ones obtained using a complex model, but with largely reduced calculation times. Building a surrogate model requires only a few calculations with the real model. Once the surrogate model is built, further calculations can be quickly realized.

In this study, we propose to build surrogate models by combining Proper Orthogonal Decomposition (POD) and kriging (also known as Gaussian Process Regression) for immediate forecasts. More precisely, we create surrogate models for rainfall forecasts on short deadlines. Currently rainfall forecasts in France are calculated for 15 minutes time laps using the AROME-PI model developed by M ́et ́eo-France. In this work, we show that the results obtained with our surrogate models are not only close to the ones obtained by AROME-PI, but they also have a better time resolution (1 minute) and a reduced calculation time.

How to cite: Cabrera Gutiérrez, C. and Jouhaud, J. C.: Efficient POD-Kriging Surrogate Models for Rainfall Forecasting, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21780,, 2020


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