Controlling Weather Field Synthesis Using Variational Autoencoders
- URL: http://arxiv.org/abs/2108.00048v1
- Date: Fri, 30 Jul 2021 19:17:30 GMT
- Title: Controlling Weather Field Synthesis Using Variational Autoencoders
- Authors: Dario Augusto Borges Oliveira, Jorge Guevara Diaz, Bianca Zadrozny,
Campbell Watson
- Abstract summary: This paper investigates how mapping climate data to a known distribution might help explore such biases.
We experimented using a monsoon-affected precipitation dataset from southwest In-dia, which should give a roughly stable pattern ofrainy days.
We reportcompelling results showing that mapping complexweather data to a known distribution implementsan efficient control for weather field synthesis to-wards more (or less) extreme scenarios.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: One of the consequences of climate change is anobserved increase in the
frequency of extreme cli-mate events. That poses a challenge for
weatherforecast and generation algorithms, which learnfrom historical data but
should embed an often un-certain bias to create correct scenarios. This
paperinvestigates how mapping climate data to a knowndistribution using
variational autoencoders mighthelp explore such biases and control the
synthesisof weather fields towards more extreme climatescenarios. We
experimented using a monsoon-affected precipitation dataset from southwest
In-dia, which should give a roughly stable pattern ofrainy days and ease our
investigation. We reportcompelling results showing that mapping complexweather
data to a known distribution implementsan efficient control for weather field
synthesis to-wards more (or less) extreme scenarios.
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