firewxpy 1.6.5

Weather Analysis and Forecasting For Fire Weather

FireWxPy

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Thank you for checking out FireWxPy! An open-source user friendly Python package to create visualizations of data specific to fire weather and fire weather forecasting. There are also graphics in FireWxPy that can be used in the meteorological field universally as well.

This package makes it easy for meteorologists to create analysis & forecast graphics specific to their needs.

Copyright (C) Meteorologist Eric J. Drewitz 2025

Inspiration

This package is largely inspired by the MetPy package which was developed and is currently being maintained by Unidata (please see citation below in the citations section).

FireWxPy Documentation

Table Of Contents

This is the landing page for all of the firewxpy documentation. The links below will direct you to the documentation for each firewxpy module.

To visit the firewxpy tutorials page where you can see examples in jupyter lab - click here

For video tutorials/demostrations checkout the FireWxPy Tutorial Series on the South Ops YouTube Channel - click here

FireWxPy Graphics Classes And Functions

1. rtma
2. spc
3. nws_temperature_forecast
4. nws_relative_humidity_forecast
5. nws_critical_firewx_forecast
6. model_dynamics
7. model_temperature
8. model_relative_humidity
9. model_critical_firewx_conditions
10. model_precipitation
11. time_cross_sections
12. two_point_cross_sections
13. gridded_obs
14. scatter_obs
15. metar_obs
16. plot_observed_sounding
17. plot_observed_sounding_custom_date_time
18. plot_forecast_soundings
19. sawti
20. plot_daily_solar_information
21. fuels charts
22. ensemble_8_day_mean_eofs

Additional Resources For Users Who Download The Data Outside Of The Function And Pass It In

This is recommended for users generating a lot of graphics with the same dataset (i.e. a lot of RTMA graphics etc.)

This method reduces the amount of data requests on the servers hosting the data

1. RTMA (Data Access)
2. NDFD_GRIDS (Data Access)
3. obs (Data Access)
4. model_data (Data Access)
5. FEMS (Data Access)
6. plot_creation_time
7. get_metar_mask

Regional Abbreviations for Model Data

If the user wants to analyze model data in the U.S. the regional abbreviations consist of the following:

• CONUS & South Canada & North Mexico
• CONUS
• NA
• 2-letter state abbreviation
• 4-letter Geographic Area Coordination Center abbreviation

If the user wants to analyze model data for an area outside of the U.S:

One important thing to note is only global models (GFS, GEFS etc.) are available here

International Regional Selections:

• Canada
• Mexico & Central America
• Caribbean
• South America
• Western Europe & Iceland
• Central & Eastern Europe
• West Africa
• Saharan Africa
• Sub Saharan Africa
• Middle East
• Asia
• East Asia
• Southeast Asia
• India
• Australia New Zealand
• Guyana

Author

Eric J. Drewitz

USDA/USFS Predictive Services Meteorologist

Southern California Geographic Area Coordination Center

Citing FireWxPy

click here to find information on how to cite FireWxPy.

Citations

MetPy: May, R. M., Goebbert, K. H., Thielen, J. E., Leeman, J. R., Camron, M. D., Bruick, Z., Bruning, E. C., Manser, R. P., Arms, S. C., and Marsh, P. T., 2022: MetPy: A Meteorological Python Library for Data Analysis and Visualization. Bull. Amer. Meteor. Soc., 103, E2273-E2284, https://doi.org/10.1175/BAMS-D-21-0125.1.

xarray: Hoyer, S., Hamman, J. (In revision). Xarray: N-D labeled arrays and datasets in Python. Journal of Open Research Software.

pygrib: Jeff Whitaker, daryl herzmann, Eric Engle, Josef Kemetmüller, Hugo van Kemenade, Martin Zackrisson, Jos de Kloe, Hrobjartur Thorsteinsson, Ryan May, Benjamin R. J. Schwedler, OKAMURA Kazuhide, ME-Mark-O, Mike Romberg, Ryan Grout, Tim Hopper, asellappenIBM, Hiroaki Itoh, Magnus Hagdorn, & Filipe. (2021). jswhit/pygrib: version 2.1.4 release (v2.1.4rel). Zenodo. https://doi.org/10.5281/zenodo.5514317

siphon: May, R. M., Arms, S. C., Leeman, J. R., and Chastang, J., 2017: Siphon: A collection of Python Utilities for Accessing Remote Atmospheric and Oceanic Datasets. Unidata, Accessed 30 September 2017. [Available online at https://github.com/Unidata/siphon.] doi:10.5065/D6CN72NW.

cartopy: Phil Elson, Elliott Sales de Andrade, Greg Lucas, Ryan May, Richard Hattersley, Ed Campbell, Andrew Dawson, Bill Little, Stephane Raynaud, scmc72, Alan D. Snow, Ruth Comer, Kevin Donkers, Byron Blay, Peter Killick, Nat Wilson, Patrick Peglar, lgolston, lbdreyer, … Chris Havlin. (2023). SciTools/cartopy: v0.22.0 (v0.22.0). Zenodo. https://doi.org/10.5281/zenodo.8216315

SAWTI: Rolinski, T., S. B. Capps, R. G. Fovell, Y. Cao, B. J. D’Agostino, and S. Vanderburg, 2016: The Santa Ana Wildfire Threat Index: Methodology and Operational Implementation. Wea. Forecasting, 31, 1881–1897, https://doi.org/10.1175/WAF-D-15-0141.1.

NumPy: Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link).

PySolar: Stafford, B. et. al, PySolar (2007), [https://pysolar.readthedocs.io/en/latest/#contributors]

Pandas: Pandas: McKinney, W., & others. (2010). Data structures for statistical computing in python. In Proceedings of the 9th Python in Science Conference (Vol. 445, pp. 51–56).

xeofs: xeofs: Rieger, N. & Levang, S. J. (2024). xeofs: Comprehensive EOF analysis in Python with xarray. Journal of Open Source Software, 9(93), 6060. DOI: https://doi.org/10.21105/joss.06060

geopandas: Kelsey Jordahl, Joris Van den Bossche, Martin Fleischmann, Jacob Wasserman, James McBride, Jeffrey Gerard, … François Leblanc. (2020, July 15). geopandas/geopandas: v0.8.1 (Version v0.8.1). Zenodo. http://doi.org/10.5281/zenodo.3946761