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Global Atmospheric Data Assimilation with Multi-modal Masked Autoencoders by Vandal, Thomas J.; Duffy, Kate; McDuff, Daniel; Nachmany, Yoni; Hartshorn, Chris is a scholarly article available to read on EtoBox.

What is Global Atmospheric Data Assimilation with Multi-modal Masked Autoencoders about?

Global data assimilation enables weather forecasting at all scales and provides valuable data for studying the Earth system. However, the computational demands of physics-based algorithms used in operational systems limits the volume and diversity of observations that are assimilated. Here, we present "EarthNet", a multi-modal foundation model for data assimilation that learns to predict a global gap-filled atmospheric state solely from satellite observations. EarthNet is trained as a masked autoencoder that ingests a 12 hour sequence of observations and learns to fill missing data from other sensors. We show that EarthNet performs a form of data assimilation producing a global 0.16 degree reanalysis dataset of 3D atmospheric temperature and humidity at a fraction of the time compared to operational systems. It is shown that the resulting reanalysis dataset reproduces climatology by evaluating a 1 hour forecast background state against observations. We also show that our 3D humidity predictions outperform MERRA-2 and ERA5 reanalyses by 10% to 60% between the middle troposphere and lower stratosphere (5 to 20 km altitude) and our 3D temperature and humidity are statistically equival

Author

Vandal, Thomas J.; Duffy, Kate; McDuff, Daniel; Nachmany, Yoni; Hartshorn, Chris

Published

2024

Language

EN