Storm intensity forecasting using machine learning and RAMP distributed high computing environments
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Ramp kit storm forecast

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Authors: Sophie Giffard-Roisin, Alexandre Boucaud, Mo Yang, Balazs Kegl, Claire Monteleoni (AppStat-CDS)

The goal is to predict the hurricane evolution (24h forecast) using collected data from all past hurricanes (since 1979). New version.

Set up

  1. clone this repository
git clone
cd storm_forecast
  1. install the dependancies
conda install -y -c conda conda-env     # First install conda-env
conda env create                        # Use environment.yml to create the 'storm_forecast' env
source activate storm_forecast       # Activates the virtual env
  • without conda (best to use a virtual environment)
python -m pip install -r requirements.txt
  1. download the data
python        # quick-test data for testing ~200Mb
  1. get started with the storm_forecast_starting_kit.ipynb

New submissions

  1. create a new submission <new_sub> by building on the existing ones
cp -r submissions/starting_kit submissions/<new_sub>
  1. modify the *.py files in submissions/<new_sub> with your favorite editor

  2. test the submission with

ramp_test_submission --quick-test --submission <new_sub>
  1. if the job complete, you can submit the code in the sandbox of


BSD license : see LICENSE file


This package was created with Cookiecutter and the ramp-kits/cookiecutter-ramp-kit project template issued by the Paris-Saclay Center for Data Science.