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Machine learning and data assimilation with a quasigeostrophic vorticity system

This repository contains code for investigating machine learning and data assimilation with a quasigeostrophic vorticity model.

Repository structure

  • ai contains scripts for training a neural net to replace the numerical model.
  • assimilation contains scripts for running data assimilation with the numerical model and the trained model.
  • forecast contains scripts for comparing "weather forecasts" run with the numerical model and the trained model.
  • numerical_model contains FORTRAN90 code for running the numerical model. This is compiled with f2py so it can be called from Python.

Dependencies

  • python=3.6
  • numpy
  • keras
  • seaborn
  • matplotlib
  • netCDF4
  • iris

Installation

  1. Set up conda environment. Run conda activate lorenz96_machine_learning.
  2. Run source setup.sh from the root directory. This will build the numerical model and add it to the Python path.

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Evaluating neural network-based surrogates for the forecast model in an ensemble Kalman filter

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