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High-Throughput Total Internal Reflection Fluorescence and Direct Stochastic Optical Reconstruction Microscopy Using a Photonic Chip

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Quantizing reconstruction losses for improving weather data synthesis.

Daniela Szwarcman¹, Jorge Guevara², Maysa M G Macedo²

¹IBM Research, Rio de Janeiro, Brazil.

Scientific Reports

|February 9, 2024

Summary

This summary is machine-generated.

Synthesizing extreme weather events is crucial for climate change resilience. This study enhances variational autoencoders (VAEs) by incorporating histogram awareness into reconstruction loss, significantly improving the generation of rare climate scenarios.

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Area of Science:

Climate Science
Artificial Intelligence
Machine Learning

Background:

Accurate climate change modeling requires synthesizing extreme weather events.
Generative models struggle to produce high-quality, under-represented climate samples.
Variational Autoencoders (VAEs) are powerful for data synthesis but need improvement for rare events.

Purpose of the Study:

To improve the synthesis of extreme weather fields using variational autoencoders (VAEs).
To address the challenge of generating under-represented samples in climate data.
To enhance the performance of VAEs for climate risk and resilience modeling.

Main Methods:

Investigated quantizing reconstruction losses for VAEs.
Proposed histogram-based penalties to the reconstruction loss.
Applied methods to precipitation weather fields for evaluating extreme precipitation synthesis.

Main Results:

The proposed histogram-aware reconstruction loss significantly improved VAE performance.
Enhanced generation of under-represented extreme weather samples, particularly extreme precipitation.
Substantial improvement in VAEs' ability to synthesize extreme weather events.

Conclusions:

Histogram awareness in reconstruction loss is an effective method for improving VAEs in climate modeling.
This approach enhances the synthesis of extreme climate scenarios, vital for risk assessment.
The findings contribute to more robust climate resilience models.