eva3dm

An R-package focusing on EVAluation of 3D weather and air quality Models.

The following workflow is recommended:

1. Pre-processing of observations:

• Download of observations, METAR can be downloaded using the R-package riem or via the Iowa State University site, Air Quality data for 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.

This package includes:

Model Post-processing functions:

✔ 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;

✔ 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;

Data pre-processing functions:

✔ mda8(), ma8h(), hourly(), and daily() 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.

Model evaluation functions:

✔ 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().

Visualization and Utility functions:

✔ 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;