My efforts to contribute to the open source project, "Climate Modeling using Machine Learning" at this repository: https://github.com/monocongo/climate_learn
License: BSD 3-Clause "New" or "Revised" License
Shell 0.62%Python 39.32%Jupyter Notebook 60.06%
climate_learn's Introduction
Climate Modeling using Machine Learning
Research into using a machine learning (ML) approach for climate modeling
Using the results of the NCAR Community Atmosphere Model (CAM) as a basis we attempt to create a ML model that matches CAM results in order to demonstrate suitability.
Goals
Develop a ML-based climate model using Keras/TensorFlow and/or fastai which accurately predicts the results produced by CAM, using CAM inputs/outputs for training and evaluation, in order to approximately duplicate the capabilities of that model.
Refine the ML-based model for observational inputs/outputs, for use in later comparisons of model results vs. observed climatologies.
Initial Use Cases
Develop a model that learns and can predict the results of the Held-Suarez test case. This test case computes time tendency forcing values for temperature (PTTEND), zonal wind (PUTEND), and meridional wind (PVTEND). Inputs to the original equation used for this computation include temperature (T), zonal wind (U), meridional wind (V), and surface pressure (PS). The inputs and outputs of a series of runs of the NCAR CAM will be used to train and test the ML model as it's being developed.