Global atmospheric data assimilation with multi-modal masked autoencoders

TL;DR

EarthNet is a multi-modal autoencoder model that efficiently performs global atmospheric data assimilation from satellite observations, producing high-quality reanalysis data faster than traditional physics-based methods.

Contribution

The paper introduces EarthNet, a novel masked autoencoder framework for satellite-based atmospheric data assimilation, enabling rapid and accurate global weather reanalysis.

Findings

EarthNet produces a 0.16° global reanalysis dataset of temperature and humidity.

It outperforms MERRA-2 and ERA5 reanalyses by 10-60% in the middle troposphere to lower stratosphere.

Reanalysis data from EarthNet is statistically comparable to MiRS observations.

Abstract

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 equivalent to the Microwave integrated Retrieval System (MiRS) observations at nearly every level of the atmosphere. Our results indicate significant promise in using EarthNet for high-frequency data assimilation and global weather forecasting.