A Machine Learning Augmented Data Assimilation Method for High-Resolution Observations

Lucas J. Howard*, Aneesh Subramanian, Ibrahim Hoteit

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The accuracy of initial conditions is an important driver of the forecast skill of numerical weather prediction models. Increases in the quantity of available measurements, particularly high-resolution remote sensing observational data products from satellites, are valuable inputs for improving those initial condition estimates. However, the traditional data assimilation methods for integrating observations into forecast models are computationally expensive. This makes incorporating dense observations into operational forecast systems challenging, and it is often prohibitively time-consuming. Additionally, high-resolution observations often have correlated observation errors which are difficult to estimate and create problems for assimilation systems. As a result, large quantities of data are discarded and not used for state initialization. Using the Lorenz-96 system for testing, we demonstrate that a simple machine learning method can be trained to assimilate high-resolution data. Using it to do so improves both initial conditions and forecast accuracy. Compared to using the Ensemble Kalman Filter with high-resolution observations ignored, our augmented method has an average root-mean-squared error reduced by 37%. Ensemble forecasts using initial conditions generated by the augmented method are more accurate and reliable at up to 10 days of forecast lead time.

Original languageEnglish (US)
Article numbere2023MS003774
JournalJournal of Advances in Modeling Earth Systems
Issue number1
StatePublished - Jan 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.


  • convolutional neural network
  • data assimilation
  • ensemble Kalman filter
  • Lorenz-96
  • machine learning

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • General Earth and Planetary Sciences


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