ARL Releases Model and Observation Evaluation Toolkit Version 2.0 (MONET v2.0)
On September 4, 2018, ARL released MONET v2.0, an open source project and Python package that aims to create a common platform to analyze atmospheric composition data for weather and air quality models. First developed by ARL scientists in 2016, MONET’s original purpose was to evaluate the Community Multiscale Air Quality Model (CMAQ) for NOAA’s National Air Quality Forecast Capability (NAQFC) modeling system. Used for both implementation of the CMAQ 5.0.2 model upgrade and NAQFC emission upgrades, MONET calculated statistics and visualizations, showing model bias comparisons versus the U.S. Environmental Protection Agency’s AirNow stations in near real-time for variables such as ozone and PM2.5 (particulate matter smaller than 2.5 microns).
In developing v2.0, MONET’s architects focused on a modularized approach to data analysis with a goal to provide high-level tools to retrieve, read and combine datasets in order to speed scientific research. The resulting update features the ability to pair model output to observational data in space and time, and leverages the pandas and XArray Python packages for analyzing and visualizing data. Furthermore, the process introduces a convenient method for evaluating model output, organizing data in a manner that results in easy searchability and groupings by meta-data found within the observational datasets. Common statistical metrics, plotting routines and more are included in the package, which is modularized to permit various additional observational datasets and models. In addition, MONET’s interaction with multidimensional observations and model output was re-engineered to use a XArray Accessor, augmenting the functionality of an existing library and expanding capabilities that ultimately ensure ease of use for its clients and contributors. Further enhancements are already in work, including increased satellite and model integration, the addition of bilinear interpolation and the inclusion of additional measurements.
Features include the following:
– Xarray Accessor for both XArray.DataArray and XArray.Dataset using the .monet attribute
– Vertical interpolation using python-stratify and the .monet.stratify function
– Spatial interpolation using .monet.remap, including Nearest neighbor finder, constant latitude and longitude interpolation, and remap DataArray and entire dataset to the
– Fixes to observational datasets including AirNow, AQS, Aeronet, and ISH
– Addition of GEOS-R NESDIS NetCDF data reader
– Simplified combine tool to merge point source data with multidimensional XArray objects
– ICARTT reader using Barron Henderson’s PseudoNetCDF