SOUNDERPY DOCUMENTATION

LATEST VERSION: v2.0.4 | RELEASED: August 6, 2023 | COPYRIGHT Kyle J Gillett, 2023

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This script is used to access vertical profile data for calculations or plotting of a vertical profile (sounding).

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What is SounderPy:

• SounderPy is a Python package used to access vertical profile data for calculations or plotting of a vertical profile (sounding). SounderPy's main use is for getting the data, but some basic plotting tools are included.

SounderPy is currently capable of accessing and processing data from:

• ECMWF CDS ERA5 reanalysis [1940-present] *note: you must set up an account through the CDS to unlock ERA5 data. (see: https://cds.climate.copernicus.eu/api-how-to)
• UNIDATA THREDDS TDS RAP reanalysis [2005-present]
• UNIDATA THREDDS TDS RUC reanalysis [2005-2020]
• The University of Wyoming RAOB archive [1973-present, depending on station]
• Iowa State University's RAOB archive [1945-present, depending on station]
• The IGRAv2 Observed profile archive [1905-present, depending on station]
• Iowa State University's BUFKIT archive [2011-present, depending on station & model]
• Penn State University's BUFKIT archive [most-recent run, depending on station & model]
• UNIDATA THREDDS TDS RAP-most-recent-analysis [now, most recent analysis only]

Why SounderPy?

• Sometimes data is tough to find, and often times is even tougher to get it in the format you like. SounderPy gets you this data!
• The code needed for loading and parsing vertical 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!

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How to use SounderPy:

1. Make sure your environment has the required dependencies:

   • cdsapi>=0.6.1
   • matplotlib>=3.3.0, <=3.7.1
   • metpy>=1.5.1
   • netcdf4>=1.6.4
   • numpy>=1.20.0
   • pandas>=1.2.0
   • siphon>=0.9
   • scipy>= 1.10.1
   • xarray>=0.18.0

2. pip install sounderpy
   

   Find it at https://pypi.org/project/sounderpy/

3. import sounderpy as spy
   

4. year  = '2011'
   month = '04'
   day   = '27'
   hour  = '22'
   latlon = [33.19, -87.46]
   method = 'rap'
   

5. raw_data = spy.get_model_data(method, latlon, year, month, day, hour)
   

6. clean_data = spy.parse_data(raw_data)
   

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and boom! Now you have a callable dictionary of vertical profile data including...

1. Temperature
2. Dewpoint
3. Relative Humidity
4. Pressure
5. Height
6. Height AGL
7. Vertical Velocity
8. U-component Wind
9. V-component Wind

You can make a quick plot of the data using built-in MetPy plotting functions!, just call... spy.metpy_sounding(clean_data)