학술논문

Modeling Mountain Snowpack Dynamics with CGANS: A Validation Study
Document Type
Conference
Source
IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium Geoscience and Remote Sensing Symposium, IGARSS 2020 - 2020 IEEE International. :3995-3998 Sep, 2020
Subject
Aerospace
Computing and Processing
Geoscience
Photonics and Electrooptics
Signal Processing and Analysis
Data models
Training
Time series analysis
Predictive models
Numerical models
Generative adversarial networks
Snow
Language
ISSN
2153-7003
Abstract
In our previous work [1] we show the effectiveness of learning based schemes to emulate numerical simulations. Specifically, we explore the use of GANs to model Snow Water Equivalent (SWE). In this work, we present further analysis of these results, with a model now trained on 30 years of SWE data compared to 10 years for the previous. First, we study the model outputs with station data and find the outputs to closely match the station data. Second, we study the uncertainty of the cGAN based results. We study the correlation between noise and the outputs generated. We hope these contributions can help the community leverage the power of these deep learning models for future works.