Worldwide Weather Forecasting by Deep Learning

La prevision meteorologique a ete et demeure une tâche ardue ayant ete approchee sous plusieurs angles au fil des annees. Puisque les modeles proeminents recents sont souvent des modeles d’appentissage machine, l’importance de la disponibilite, de la quantite et de la qualite des donnees meteorologiques augmente. De plus, la revue des proeminents modeles d’apprentissage profond appliques a la prediction de series chronologiques meteorologiques suggere que leur principale limite est la formulation et la structure des donnees qui leur sont fournies en entree, ce qui restreint la portee et la complexite des problemes qu’ils tentent de resoudre. A cet effet, cette recherche fournit une solution, l’algorithme d’interpolation geospatiale SkNNI (interpolation des k plus proches voisins spherique), pour transformer et structurer les donnees geospatiales disparates de maniere a les rendre utiles pour entrainer des modeles predictifs. SkNNI se demarque des algorithmes d’interpolation geospatiale communs, principalement de par sa forte robustesse aux donnees d’observation bruitees ainsi que sa consideration accrue des voisinages d’interpolation. De surcroit, a travers la conception, l’entrainement et l’evaluation de l’architecture de reseau de neurones profond DeltaNet, cette recherche demontre la faisabilite et le potentiel de la prediction meteorologique multidimensionnelle mondiale par apprentissage profond. Cette approche fait usage de SkNNI pour pretraiter les donnees meteorologiques en les transformant en cartes geospatiales a multiples canaux meteorologiques qui sont organisees et utilisees en tant qu’elements de series chronologiques. Ce faisant, le recours a de telles cartes geospatiales ouvre de nouveaux horizons quant a la definition et a la resolution de problemes de previsions geospatiales (p. ex. meteorologiques) plus complexes. ----------ABSTRACT: Weather forecasting has been and still is a challenging task which has been approached from many angles throughout the years. Since recent state-of-the-art models are often machine learning ones, the importance of weather data availability, quantity and quality rises. Also, the review of prominent deep learning models for weather time series forecasting suggests their main limitation is the formulation and structure of their input data, which restrains the scope and complexity of the problems they attempt to solve. As such, this work provides a solution, the spherical k-nearest neighbors interpolation (SkNNI) algorithm, to transform and structure scattered geospatial data in a way that makes it useful for predictive model training. SkNNI shines when compared to other common geospatial interpolation methods, mainly because of its high robustness to noisy observation data and acute interpolation neighborhood awareness. Furthermore, through the design, training and evaluation of the DeltaNet deep neural network architecture, this work demonstrates the feasibility and potential of multidimensional worldwide weather forecasting by deep learning. This approach leverages SkNNI to preprocess weather data into multi-channel geospatial weather frames, which are then organized and used as time series elements. Thus, working with such geospatial frames opens new avenues to define and solve more complex geospatial (e.g. weather) forecasting problems.