Weather scenarios from AROME-EPS forecasts using autoencoder and clustering methods

The use of ensemble prediction systems (EPS) is challenging because of the huge information it provides. Forecasts from ensemble prediction systems (EPS) are often summarised by statistical quantities (ie quantiles maps). Although such mathematical representation is efficient for capturing the ensemble distribution, it lacks physical consistency, which raises issues for many applications of EPS in an operational context. In order to provide a physically-consistent synthesis of the French convection-permitting AROME-EPS forecasts, we propose to automatically draw a few scenarios that are representative of the different possible outcomes. Each scenario is a reduced set of EPS members.

To design a scenario synthesis, the procedure can be divided into two parts. A first step aims at extracting relevant features in each EPS member in order to reduce the problem dimensionality. Then, a clustering is done based on these features.

The originality of our work is to leverage the capacities of deep learning for the features extraction. For that purpose, we use a convolutional autoencodeur (CAE) to learn an optimal low-dimensional representation (also called latent space representation) of the input forecast field. In this work, the algorithm is developed to work on 1h-accumulated rainfall from AROME-EPS, with a focus on convective cases.

The CAE is trained on around 150 000 forecasts and its performance is evaluated based on the quality of the reconstructed input fields from the latent space. To examine the reconstruction quality, an object-oriented approach is used. CAE is also compared with the commonly-used principal component analysis (PCA). In a second part, different clustering methods (kmeans, HDBSCAN, …) are applied to EPS members in the latent space and evaluated using subjective and objective diagnostics.