KSC - Observational Data Clustering Preprocessor

Jülich DATA

Abstract

Preprocessing routine for ground-based atmospheric monitoring network data. Utilizing a k-means soft constrained clustering algorithm to derive a representative sub-sampling of the availiable data into an assimilation and validation set. This work was partially performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS- LEE) and received funding from the Helmholtz Association of German Research Centres.

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