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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