Tools and demonstration for the total variation method supporting Hamilton, Nicholas. Total variation of atmospheric data: covariance minimization about objective functions to detect conditions of interest. No. NREL/JA-5000-73703. National Renewable Energy Lab.(NREL), Golden, CO (United States), 2019.
THe methods suggested herein are applied to met mast data derived from a meteorological mast located at the National Renewable Energy Laboratory’s Flatirons campus. Additional data from the met mast is available at https://nwtc.nrel.gov/MetData (NREL, 2020) and the atmospheric conditions at NREL are summarized in Hamilton and Debnath (2019).
If the total variation tools here play a role in your research
nhamilto. (2020, January 30). nhamilto/total-variation: Total variation Python codes and demonstration data (Version 1.0). Zenodo. http://doi.org/10.5281/zenodo.3630875
For LaTeX users:
@misc{Total_variation,
author = {Nicholas Hamilton},
title = {{Total Variation. Version 1.0}},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/nhamilto/total-variation}
}MIT License
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This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.