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Updated: Nov 17, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Learning earth system models from observations: machine learning or data assimilation?

A J Geer1

  • 1ECMWF, Shinfield Park, Reading, RG2 9AX, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) and data assimilation (DA) can be unified using Bayesian networks. This framework allows for improved earth system models by integrating observational data with physical knowledge.

Keywords:
data assimilationearth system modellinginverse methodsmachine learningremote sensing

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Area of Science:

  • Earth System Science
  • Machine Learning
  • Data Assimilation

Background:

  • Earth sciences utilize data assimilation (DA) for weather forecasting.
  • Machine learning (ML) offers potential for direct learning from observations.
  • DA and ML share similarities as inverse methods within a Bayesian framework.

Purpose of the Study:

  • To explore the unification of DA and ML through Bayesian networks.
  • To demonstrate equivalences between DA and ML techniques.
  • To provide a practical framework for integrating physical knowledge and observational data in earth system models.

Main Methods:

  • Utilizing Bayesian networks as a unifying framework.
  • Drawing parallels between four-dimensional variational (4D-Var) DA and recurrent neural networks (RNNs).
  • Employing approximate methods for solving Bayesian networks, drawing from DA and ML.

Main Results:

  • Established equivalences between DA and ML within the Bayesian network framework.
  • Highlighted the benefits of DA in handling real-world observational uncertainties for ML.
  • Showcased how DA can incorporate ML for improved model learning.

Conclusions:

  • Bayesian networks offer a practical framework for unifying DA and ML.
  • This unification can enhance earth system models by better leveraging observational data.
  • The integrated approach addresses the challenge of improving physical models through data-driven methods.