Efficient POD-Kriging Surrogate Models for Rainfall Forecasting

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.