ai2es/WAF_ML_Tutorial_Part1
Python code to assist in familiarizing meteorologists with machine learning
This project offers practical guidance and code examples for meteorologists looking to apply traditional machine learning techniques to weather data. It takes raw meteorological observations, like satellite and radar images from the SEVIR dataset, and demonstrates how to process them to train models for weather prediction or analysis. The primary users are meteorologists, weather forecasters, and researchers in atmospheric science who want to integrate machine learning into their operational workflows or research.
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Use this if you are a meteorologist who wants to understand and apply foundational machine learning models to real-world weather data, without needing to be a machine learning expert already.
Not ideal if you are looking for advanced deep learning techniques (these are covered in Part 2) or if you are not interested in meteorological applications.
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Jupyter Notebook
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CC0-1.0
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Last pushed
Feb 27, 2023
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