An R-package focusing on EVAluation of 3D weather and air quality Models, streamlining the entire workflow from data preparation to post-processing, statistical analysis, and visualization [1].

Whether you're working with meteorological models like WRF or evaluating air quality simulations (WRF-Chem, CAMx, WACCM, etc), eva3dm provides a comprehensive toolkit to handle observational data, model outputs, and evaluation metrics in an efficient and reproducible manner. Key Features:

• Pre-processing of observational and model data
• Post-processing tools for extracting time-series, calculating derived variables (e.g., accumulated rain, humidity variables and wind variables), and preparing data for analysis
• Statistical and categorical model evaluation
• Custom plotting tools for spatial data and georeferencing evaluation results
• Integration with other packages like terra, ncdf4, qualR, riem and openair

[1] Schuch, D., (2025). “eva3dm: A R-package for model evaluation of 3D weather and air quality models.” Journal of Open Source Software, 10(108), 7797, doi:10.21105/joss.07797

Open a R session and type the following command on the terminal to install the official version from CRAN:

install.packages("eva3dm")

the last version can be installed directly from github (using the remotes package, in this example):

remotes::install_github("schuch666/eva3dm")

All the functions include examples in the documentation.

The following workflow is recommended:

1. Pre-processing of observations:

• Download of observations, time-series of meteorological variables can be obtained from the METAR (METeorological Aerodrome Report) from Automated Surface Observing System/Automated Weather Observing System (ASOS/AWOS) can be downloaded using the R-package riem, or via the Iowa State University website and METAR data from Integrated Surface Database (ISD) can be downloaded using the R-package worldmet or the NOAA Global hourly ISD website, time-series of pollutant concentrations from Brazil can be downloaded using the R-package qualR, or QUALAR and MonitorAir sites, and a range of satellite products are available at NASA giovanni website.

• QA of the observation data.

• Process observation data for evaluation.

• Process of site-list if plan to extract time-series from the model.

2. Pre-processing of model output: Extraction and pre-processing of model outputs;

3. Model Evaluation: The functions eva() (to evaluate time-series) and sat() (to evaluate against satellite products) can be used to perform statistical (more details in stat()) and categorical (more details in cate()) evaluation;

4. Visualization: try some of the visualization functions from this package or other packages.

The function template() can help to setup folders and scripts from steps 1-4 for different models, observations datasets and evaluation types.

This package includes:

✔ extract_serie() extract and save time-series from WRF outputs and input files (and compatible NetCDF files);

✔ extract_mean() extract, average (or max, min, etc) and save variables in a NetCDF file;

✔ extract_max_8h() extract, calculate maximum (or avarage, max, min) 8h average and save variables in a NetCDF file;

✔ calculate_column() Calculate the column concentration of trace gases from WRF-Chem;

✔ wrf_rast() extract variables and create SpatRaster or SpatVector from WRF files (and compatible NetCDF files) and the contrapart rast_to_netcdf() that converts rast to an array compatible to a NetCDF WRF file.

✔ select() function to select time-series (data.frame) based on time;

✔ mda8(), ma8h(), hourly(), daily(), monthly(), and yearly() process and calculate calculate time-series;

✔ rh2q(), q2rh(), that convert humidity units;

✔ uv2ws(), uv2wd(), that convert model wind components into wind speed and velocity;

✔ rain() to calculate hourly precipitation from model accumulated precipitation variables.

✔ eva() data pairing and evaluation for time-series, %IN% allows fair evaluation;

✔ sat() evaluation for satellite image, %IN% can be used for fair evaluation;

✔ stat() calculate statistical metrics (integrated in eva() and sat());

✔ cate() calculate categorical metrics (integrated in eva() and sat());

✔ write_stat() and read_stat() to write and read evaluation results for eva() and sat().

✔ ncdump() print a ncdump -h equivalent command for a NetCDF file;

✔ vars() return the name of the variables on NetCDF file;

✔ atr() read and write attributes from a Netcdf file;

✔ interp() Interpolation (project and resample);

✔ plot_rast() custom plot for terra SpatRaster objects;

✔ plot_diff() custom plot for absolute or relative difference of terra SpatRaster objects;

✔ overlay() custom plot to overlay points or plot point-data,%at% can be used to georeference the evaluation results;

✔ legend_range() custom legend, display max, min and average;

✔ template() function that create post-processing and evaluation scripts;

✔ %at% combine a table (with results from eva() or sat() for example) with a table with geographical coordinates. The goal of this function is to georeference and visualize the statistical results;

✔ %IN% filter a table (with model/observations time-series) based on a second table (using the column names). Also can be used to crop a SpatRaster based on a second SpatRaster. The goal is to be used to perform fair comparison of different evaluations (using the same number of observation).

If you like to contribute to eva3dm take a look at the contribution guidelines page and by participating in this project you agree to abide the code of conduct terms.