Deep Ensembles to Improve Uncertainty Quantification of Statistical
Downscaling Models under Climate Change Conditions
- URL: http://arxiv.org/abs/2305.00975v1
- Date: Thu, 27 Apr 2023 19:53:18 GMT
- Title: Deep Ensembles to Improve Uncertainty Quantification of Statistical
Downscaling Models under Climate Change Conditions
- Authors: Jose Gonz\'alez-Abad, Jorge Ba\~no-Medina
- Abstract summary: We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models.
Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, deep learning has emerged as a promising tool for statistical
downscaling, the set of methods for generating high-resolution climate fields
from coarse low-resolution variables. Nevertheless, their ability to generalize
to climate change conditions remains questionable, mainly due to the
stationarity assumption. We propose deep ensembles as a simple method to
improve the uncertainty quantification of statistical downscaling models. By
better capturing uncertainty, statistical downscaling models allow for superior
planning against extreme weather events, a source of various negative social
and economic impacts. Since no observational future data exists, we rely on a
pseudo reality experiment to assess the suitability of deep ensembles for
quantifying the uncertainty of climate change projections. Deep ensembles allow
for a better risk assessment, highly demanded by sectoral applications to
tackle climate change.
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