firewxpy 2.1.1

A Python package that makes various types of real-time weather graphics with an emphasis on fire weather. These graphics are designed for automation in an operational setting.

FireWxPy

Anaconda Downloads:

PIP Downloads:

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 2024-2026

Table of Contents

1. Documentation
2. Jupyter Lab Tutorials
3. Installation Instructions
4. Citations

Documentation

Observational Data

1. Soundings
2. Vertical Profiles

Real Time Mesoscale Analysis (RTMA)

1. RTMA CONUS
2. RTMA Comparison CONUS
3. RTMA Alaska
4. RTMA Comparison Alaska
5. RTMA Hawaii
6. RTMA Comparison Hawaii

Jupyter Lab Tutorials

Observational Data

1. Observed Soundings (Current and Archived)
2. Vertical Profiles (Current and Archived)

Real Time Mesoscale Analysis

1. RTMA CONUS
2. RTMA Comparison CONUS
3. RTMA OCONUS
4. RTMA Comparison OCONUS

Installation Instructions

How To Install

Copy and paste either command into your terminal or anaconda prompt:

Install via Anaconda

conda install firewxpy

Install via pip

pip install firewxpy

How To Update To The Latest Version

Copy and paste either command into your terminal or anaconda prompt:

Update via Anaconda

This is for users who initially installed FireWxPy through Anaconda

conda update firewxpy

Update via pip

This is for users who initially installed FireWxPy through pip

pip install --upgrade firewxpy

FireWxPy < 2.0 is Depreciated

Click Here for the legacy FireWxPy < 2.0 Documentation

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

WxData: Eric J. Drewitz. (2026). edrewitz/WxData: WxData 2.0.2 (WxData2.0.2). Zenodo. https://doi.org/10.5281/zenodo.17727621

shapeography: Eric J. Drewitz. (2026). edrewitz/shapeography: Shapeography 1.2 Released (shapeography1.2). Zenodo. https://doi.org/10.5281/zenodo.19141532

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