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Title: Skillful statistical prediction of subseasonal temperature by training on dynamical model data
Abstract This paper derives statistical models for predicting wintertime subseasonal temperature over the western US. The statistical models are trained on two separate datasets, namely observations and dynamical model simulations, and are based on least absolute shrinkage and selection operator (lasso). Surprisingly, statistical models trained on dynamical model simulations can predict observations better than observation-trained models. One reason for this is that simulations involve orders of magnitude more data than observational datasets.  more » « less
Award ID(s):
1822221
PAR ID:
10431069
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Environmental Data Science
Volume:
2
ISSN:
2634-4602
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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