sounderpy 3.1.0
Vertical Profile Data Retrieval and Analysis Tool For Python
Requires Python: >=3.1
Keywords: meteorology, science, data-analysis, weather, forecasting

<div align="center">
<img src="https://github.com/kylejgillett/sounderpy/assets/100786530/2e9477c9-e36a-4163-accb-fe46780058dd" width="250">

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# SounderPy: An atmospheric sounding visualization and analysis tool for Python
LATEST VERSION: v3.1.0 |  RELEASED: Oct, 2025  |  COPYRIGHT Kyle J Gillett, 2023-2025

SounderPy is an open-source Python package for retrieving, processing, and visualizing atmospheric sounding data.

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[![DOI](https://joss.theoj.org/papers/10.21105/joss.08087/status.svg)](https://doi.org/10.21105/joss.08087)

<div align="center">
   
#### [VISIT SOUNDERPY DOCUMENTATION](https://kylejgillett.github.io/sounderpy/) | [CHECK OUT EXAMPLES & TUTORIALS](https://kylejgillett.github.io/sounderpy/examplescripts.html) 

### [VISIT THE OPERATIONAL SOUNDERPY SOUNDING ANALYSIS SITE](https://sounderpysoundings.anvil.app/)
</div>


## What is SounderPy:

**ABSTRACT:** SounderPy is a simple, open-source Python package for retrieving and plotting 
vertical profile (sounding) data. Built for simplicity and reliability for all uses and users, this 
project’s goal is to provide a uniform method for sounding analysis across multiple data types. 
Severe weather analysis and forecasting requires a sound comprehension of thermodynamic and 
kinematic properties of the environment. SounderPy makes this possible with robust access to 
data and custom visualizations. The tool creates complex yet effective sounding and hodograph 
plots with high readability which are designed specifically for severe weather analysis and 
forecasting. SounderPy is capable of retrieving and plotting model forecast data, observed 
radiosonde data, Aircraft Communications Addressing and Reporting System (ACARS) 
observation data, and model reanalysis data. All of this functionality can be completed in three
simple lines of code or less, making SounderPy an accessible tool for both Python experts and 
novices. A number of scientific Python libraries build the base of SounderPy’s efficient and 
durable functionality, such as NumPy, Matplotlib, xarray, Metpy, and SHARPpy. SounderPy is 
available through GitHub and PyPi and is distributed under an MIT license. 

SounderPy has been used by several institutions. For example, this tool has been implemented by the Des Moines, Columbia, and Grand Rapids National Weather Service Offices, the State University of New York at Albany, Mississippi State University, the University of North Dakota, and others. Many students at various universities have used SounderPy in projects, posters, and papers, such as students at The University of Oklahoma, Ohio State University, Central Michigan University, Iowa State University, & Rizal Technological University.



## CITING SOUNDERPY

To cite this package, please cite [SounderPy's JOSS article](https://doi.org/10.21105/joss.08087) in AMS format (below) or in BibTeX (see citation file).

- Gillett, K. J., 2025: SounderPy: An atmospheric sounding visualization and analysis tool for Python. J. Open Source Software, 10 (112), 8087, https://doi.org/10.21105/joss.08087

[![DOI](https://joss.theoj.org/papers/10.21105/joss.08087/status.svg)](https://doi.org/10.21105/joss.08087)
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## Why SounderPy?
- Sometimes data is tough to find, and often times it’s even tougher to get it in the format you like. SounderPy gets you this data!
- The code needed for loading and parsing meteorological data, especially from models, can be large and messy. SounderPy keeps it hidden away in a PyPi package – just import and call sounderPy functions to keep your code clean!
- SounderPy functions are designed to be simple and quick making for reliable use in research, forecast/analysis operations, and simply for fun!



## What kind of data?:

| **DATA**                        | **FUNCTION**       | **TYPE**          | **TIME RANGE** |
|---------------------------------|--------------------|-------------------|----------------|
| ECMWF CDS ERA5 reanalysis*      | get_model_data()   | Reanalysis        | 1940-present   |
| UNIDATA THREDDS TDS RAP         | get_model_data()   | Reanalysis        | 2005-present   |
| UNIDATA THREDDS TDS RUC         | get_model_data()   | Reanalysis        | 2005-2020      |
| UNIDATA THREDDS NCEP-FNL        | get_model_data()   | Reanalysis        | 2005-2020      |
| ISU's BUFKIT archive            | get_bufkit_data()  | Model Forecast    | 2011-present   |
| PSU's BUFKIT feed               | get_bufkit_data()  | Model Forecast    | Most recent runs |
| UNIDATA THREDDS TDS RAP         | get_model_data()   | Model Analysis    | Most recent run |
| OU ACARS Archive                | acars_data()       | Observations      | 2019-06/2024   |
| The Unv. of WY RAOB Archive     | get_obs_data()     | Observations      | 1973-present   |
| IGRAv2 Observation archive      | get_obs_data()     | Observations      | 1905-present   |

-------

## Installation

1. #### Install the SounderPy software:

   - SounderPy is available on PyPi and can be installed via ``pip``:

     ```
      pip install sounderpy
      ```
   - or via ``conda forge`` by first setting up the ``conda-forge`` channel:

     ```
      conda config --add channels conda-forge
      conda config --set channel_priority strict
     ```
     then
     ```
     conda install sounderpy
     ```
     or
     ```
     mamba install sounderpy
     ```
   
3. #### Import SounderPy into your Python project:
   ```py
   import sounderpy as spy
   ```
   
5. #### Lets declare a few simple variables we can use to get data:
   ```py
   year  = '2014' 
   month = '06'
   day   = '16'
   hour  = '18'
   station = 'OAX'
   ```
7. #### Get some data!
   ```py
   # this will get us 18z observations on June 16th, 2014 from OAX (Omaha, Neb)
   clean_data = spy.get_obs_data(station, year, month, day, hour)
   ```
   and boom! Now you have a callable dictionary of vertical profile reanalysis data including... 

   + Temperature
   + Dewpoint
   + Pressure
   + Height 
   + U-component Wind 
   + V-component Wind
   + *(model data comes with omega (vertical velocity)!)*
   
### SounderPy can also plot profile data on unique sounding and hodograph figures!
   ```py 
   spy.build_sounding(clean_data, color_blind=True)
   ```
   
   ```py 
   spy.build_hodograph(clean_data, dark_mode=True)
   ```
   <div align="center">
   <img src="https://kylejgillett.github.io/sounderpy/_images/example-sounding_light.png" width="600">
   
   <img src="https://kylejgillett.github.io/sounderpy/_images/example-hodograph_dark.png" width="600">
   </div>

### To learn more about what you can do with SounderPy, [check out the documentation](https://kylejgillett.github.io/sounderpy/)
------

## AUTHORS AND CONTRIBUTORS
### **AUTHOR: Kyle J Gillett, University of North Dakota** 
##### *CONTRIBUTOR: Scott Thomas, NWS Grand Rapids | VWP Hodograph, Buoy-sites listing*
##### *CONTRIBUTOR: Amelia R H Urquhart, University of Oklahoma | ecape-parcels library*
##### *CONTRIBUTOR: Daryl Herzmann, Iowa State University | SounderPy Feedstock for conda-forge*
##### *CONTRIBUTOR: Ryan Vandersmith | Stepwise CAPE/CIN plot*

------



## REFERENCES
- 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.
  
- Hoyer, S. & Hamman, J., (2017). xarray: N-D labeled Arrays and Datasets in Python. Journal of Open Research Software. 5(1), p.10. DOI: https://doi.org/10.5334/jors.148
  
- J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.

- Ryan M. May, Sean C. Arms, Patrick Marsh, Eric Bruning, John R. Leeman, Kevin Goebbert, Jonathan E. Thielen, Zachary S Bruick, and M. Drew. Camron. Metpy: a Python package for meteorological data. 2023. URL: Unidata/MetPy, doi:10.5065/D6WW7G29.

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

- Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, and SciPy 1.0 Contributors. (2020) SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261-272.

- Marsh, P., Halbert, K., Blumberg, G., Supinie, T., Esmaili, R., Szkodzinski, J., "SHARPpy: Sounding/Hodograph Analysis and Research Program in Python." GitHub. Available at: https://github.com/sharppy/SHARPpy.