Model and ObservatioN Evaluation Toolkit (MONET)

MONET is an open source project and Python package that aims to create a common platform for atmospheric composition data analysis for weather and air quality models.

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. 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.

Our goals is to provide easy tools to retrieve, read and combine datasets in order to speed scientific research. Currently, MONET is able to process several models and observations related to air composition and meteorology.

Please cite our work.

What’s New

MONET v2.2.0 has been released. MONET has re-engineered the way it deals with multidimensional observations or model output by using an xarray accessor giving MONET a flexible and intuitive way of expanding xarray for multidimensional geospatial information commonly used in meteorology, climate and air quality all while making it easier on the user to use MONET and add to it.

MONET also underwent a major restructure with v2.2.0. All I/O functions have been moved to a sister repository:

Features include:

  • Xarray Accessor for both xarray.DataArray and xarray.Dataset using the .monet attribute
  • Pandas Accessor for pandas.DataFrame using the .monet attribute
  • vertical interpolation using python-stratify using the .monet.stratify function
  • spatial interpolation using .monet.remap including:
    • Nearest neighbor finder
    • Constant latitude interpolation
    • Constant longitude interpolation
    • remap DataArray to current grid using pyresample nearest neighbor or xesmf
    • remap entire dataset to current grid using pyresample nearest neighbor or xesmf
    • find nearest i,j or lat,lon
    • interpolate to constant latitude or longitude
  • Simplified combine tool to combine point source data with multidimensional xarray objects


Baker, Barry; Pan, Li. 2017. “Overview of the Model and Observation Evaluation Toolkit (MONET) Version 1.0 for Evaluating Atmospheric Transport Models.” Atmosphere 8, no. 11: 210