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Self-Attentive Ensemble Transformer: Representing Ensemble Interactions in Neural Networks for Earth System Models
- Publication Year :
- 2021
-
Abstract
- Ensemble data from Earth system models has to be calibrated and post-processed. I propose a novel member-by-member post-processing approach with neural networks. I bridge ideas from ensemble data assimilation with self-attention, resulting into the self-attentive ensemble transformer. Here, interactions between ensemble members are represented as additive and dynamic self-attentive part. As proof-of-concept, I regress global ECMWF ensemble forecasts to 2-metre-temperature fields from the ERA5 reanalysis. I demonstrate that the ensemble transformer can calibrate the ensemble spread and extract additional information from the ensemble. As it is a member-by-member approach, the ensemble transformer directly outputs multivariate and spatially-coherent ensemble members. Therefore, self-attention and the transformer technique can be a missing piece for a non-parametric post-processing of ensemble data with neural networks.<br />Comment: 7 Pages, 4 Figures, Accepted at the ICML 2021 workshop "Tackling Climate Change with Machine Learning", Code to the paper: https://github.com/tobifinn/ensemble_transformer
- Subjects :
- Computer Science - Machine Learning
Physics - Atmospheric and Oceanic Physics
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2106.13924
- Document Type :
- Working Paper