Supporting Data for "Air Quality Estimation and Forecasting via Data Fusion with Uncertainty Quantification: Theoretical Framework and Preliminary Results"
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Description
Output data needed to generate reuslts and figures presented in the paper "Air Quality Estimation and Forecasting via Data Fusion with Uncertainty Quantification: Theoretical Framework and Preliminary Results".
Figures.py is a python code which generates the figures presented in the manuscript. This code takes input from data saved in NetCDF files with the naming format "Crossvalidation_Results_AA_MMMYYYY.nc", where "AA" is the application area (SF for San Francisco; NY for New York City) and "MMMYYY" is the month and year of the application period.
All data and codes should be downloaded to a local directory. This directory needs to be specified within the python code as the s_folder
variable.
Files
Files
(20.9 MB)
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md5:dc2e7f9f19abbf4562779ef8a89824ed
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md5:12eafee3d11c6e1db273281b7b1cf8f2
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md5:09a5347c0786d0065692dee07df4d801
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md5:2d8343dfabd364544ac8b7edbf0a23e5
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md5:8d3d662f700e326bf3e46f959e8b6024
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md5:b21c56effb090d1b697349a8a5ab12fd
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Additional details
Funding
- National Aeronautics and Space Administration
- Supporting Local Government Public Health and Air Quality Decision-Making with a Sub-City Scale Air Quality Forecasting System from Data Fusion of Models, Satellite, In Situ Measurements, and Low-Cost Sensors 80NSSC22K1473
Software
- Programming language
- Python
- Development Status
- Unsupported