Published: 16 June 2017| Version 2 | DOI: 10.17632/p33b4zr4xc.2
Barry Baker


Initial applications of the Model and ObservatioN Evaluation Tool (MONET) v1.0. MONET was developed to evaluate the Community Multiscale Air Quality Model (CMAQ) for the NOAA National Air Quality Forecast Capability (NAQFC) modeling system. MONET is designed to be a modularized Python package for 1) pairing model output to observational data in space and time, 2) leveraging the pandas Python package for easy searching and grouping, and 3) analyzing and visualizing data. A convenient method for evaluating model output is introduced through this process. Data processed by MONET is easily searchable and can be grouped using meta-data found within the observational datasets. Included in the package are common statistical metrics (e.g. bias, correlation, and skill scores), plotting routines such as scatter plots, timeseries, spatial plots, and more. MONET is well modularized and effortlessly able to add further observational datasets and different models.



Earth Sciences, Software, Atmospheric Chemistry, Atmospheric Aerosols, Visualization, Atmospheric Gas