Data-driven s2s forecasting with s2spy & lilio

Reliable S2S forecasts remain a huge scientific challenge. Only for specific ‘windows of predictability’, skillful forecasts are possible, in an otherwise largely unpredictable future. Due to a number of successes in S2S forecasting, the interest in machine learning (ML) is growing fast. However, we argue there is a need for more standardization, consensus on best practices, higher efficiency, and higher reproducibility. Typical S2S ML use-cases, such as (1) pure statistical forecasting based on observations, (2) transfer learning, and (3) post-processing of dynamical model ensembles, require a large coding and preprocessing effort. Such experiments are not trivial to set up, and without sufficient experience and expertise there is a large risk of improper cross-validation and/or improper and non-standard verification. 

Driven by the need for a reliable tool to integrate expert knowledge and artificial intelligence, we are developing two python packages to tackle the scientific challenge of (sub) seasonal (S2S) forecasting. s2spy is a high level python package designed to handle and optimize the entire data-driven forecasting workflow. Lilio is an advanced calendar system for resampling timeseries into training and target data for machine learning. Our aim is to make ML workflows more transparent and easier to build, and to facilitate standardization and collaboration across the S2S community. This also contributes to higher reproducibility and works towards a wider acceptance of standards and best practices. We will present our vision and the capabilities of our package, show-casing that we can build a model from raw climate data up to verification in only a few lines of code.