Day 4

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Paper title Identifying relevant large-scale predictors for sub-seasonal precipitation forecast using explainable neural networks
  1. Niclas Rieger Centre de Recerca Matemàtica
  2. Linus Magnusson European Weather Centre (ECMWF)
  3. Laura Ferranti European Weather Centre (ECMWF)
  4. Estrella Olmedo Institut of Marine Sciences Speaker
  5. Álvaro Corral Centre de Recerca Matemàtica (CRM)
  6. Antonio Turiel Institute of Marine Sciences - CSIC
Form of presentation Poster
  • C1. AI and Data Analytics
    • C1.07 ML4Earth: Machine Learning for Earth Sciences
Abstract text The last few years have seen an ever growing interest in weather predictions on sub-seasonal time scales ranging from 2 weeks to about 2 months. By forecasting aggregated weather statistics, such as weekly precipitation, it has indeed become possible to overcome the theoretical predictability limit of 2 weeks (Lorenz 1963; F. Zhang et al. 2019), bringing life to time scales which historically have been known as the “predictability desert”. The growing success at these time scales is largely due to the identification of weather and climate processes providing sub-seasonal predictability, such as the Madden-Julian Oscillation (MJO) (C. Zhang 2013) and anomaly patterns of global sea surface temperature (SST) [Woolnough 2007, Saravanan & Chang 2019], sea surface salinity (Li et al. 2016; Chen et al. 2019; Rathore et al. 2021), soil moisture (Koster et al. 2010) and snow cover (Lin and Wu 2011). Although much has been gained by these studies, a comprehensive analysis of all potential predictors and their relative relevance to forecast sub-seasonal rainfall is still missing.

At the same time, data-driven machine learning (ML) models have proved to be excellent candidates to tackle two common challenges in weather forecasting: (i) resolving the non-linear relationships inherent to the chaotic climate system and (ii) handling the steadily growing amounts of Earth observational data. Not surprisingly, a variety of studies have already displayed the potential of ML models to improve the state-of-the-art dynamical weather prediction models currently in use for sub-seasonal predictions, in particular for temperatures (Peng et al. 2020; Buchmann and DelSole 2021) , precipitation (Scheuerer et al. 2020) and the MJO (Kim et al. 2021; Silini, Barreiro, and Masoller 2021). It seems therefore inevitable that the future of sub-seasonal prediction lies in the combination of both the dynamical, process-based and the statistical, data-driven approach (Cohen et al. 2019).

In the advent of this new age of combined Neural Earth System Modeling (Irrgang et al. 2021), we want to provide insight and guidance for future studies (i) to what extent large-scale teleconnections on the sub-seasonal scale can be resolved by purely data-driven models and (ii) what the relative contributions of the individual large-scale predictors are to make a skillful forecast. To this end, we build neural networks to predict sub-seasonal precipitation based on a variety of large-scale predictors derived from oceanic, atmospheric and terrestrial sources. As a second step, we apply layer-wise relevance propagation (Bach et al. 2015) to examine the relative importance of different climate modes and processes in skillful forecasts.

Preliminary results show that the skill of our data-driven ML approach is comparable to state-of-the-art dynamical models suggesting that current operational models are able to correctly model large-scale teleconnections within the climate system. The ML model achieves highest skills over the tropical Pacific, the Maritime Continent and the Caribbean Sea (Fig. 1), in agreement with dynamical models. By investigating the relative importance of those large-scale predictors for skillful predictions, we find that the MJO and processes associated to SST anomalies like the El Niño-Southern Oscillation, the Pacific decadal oscillation and the Atlantic meridional mode all play an important role for individual regions along the tropics.

Additional material

Figure 1 | Forecast skill of the ML model as represented by the Brier skill score (BSS) calculated with respect to climatology. Red color shadings show regions where the ML model performs better, while blue color shadings indicate worse skill than climatology. The BSS was calculated as an average over the period from 2015 to 2020 using weekly forecasts totalling 310 individual samples, which were set aside before the training process as a test set.


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