The Model and ObservatioN Evaluation Toolkit (MONET), developed at NOAA Air Resources Laboratory is an open source python package to evaluate gridded chemical transport and weather models. MONET was originally 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 existing Python packages for easy searching and grouping; and (3) analyzing and visualizing data. This process introduces a convenient method for evaluating model output. MONET processes data that is easily searchable and that can be grouped using meta-data found within the observational datasets. Common statistical metrics (e.g., bias, correlation, and skill scores), plotting routines such as scatter plots, timeseries, spatial plots, and more are included in the package. MONET is well-modularized and can add further observational datasets and different models. The tool is accessible to everyone including university and national laboratory researchers, as well as graduate students and postdocs. The goal is to provide the tools to evaluate research, operational, and regulatory models against a variety of observations including surface, aircraft, and satellite data all within a common framework.
The sources code is available on Github© and a user guide is available on ReadtheDocs
For general information or questions, contact Barry Baker and for technical questions please post issues on our Github© page