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Using the NCAR Command Language (NCL) (or, you don’t have to use Python!)

Discover the power of NCAR Command Language (NCL) for scientific data processing and visualization in Earth sciences. With robust file handling, data analysis functions, and publication-quality graphics, NCL offers flexibility and outstanding support. This guide explores NCL's capabilities and quirks compared to Python, making it an excellent alternative for scientists in specialized fields.

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Using the NCAR Command Language (NCL) (or, you don’t have to use Python!)

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  1. Using the NCAR Command Language (NCL) (or, you don’t have to use Python!) Michael C. Coniglio Research Meteorologist NOAA/National Severe Storms Laboratory (NSSL) AMS Student Conference January 6, 2019

  2. Python vs. NCL • Like Python, NCL • Is an interpreted, interactive scripting language (no need for specific computer architecture to compile and use the program) • Has robust file handling (netCDF, ascii, GRIB2, etc.) • Has data analysis functions built in • Can produce publication-quality graphics • Is highly customizable • Is free and open source • Has quirks that can drive you mad at times • Slower than compiled languages (Fortran, C, etc.) for intensive computations/looping (but NCL does have an algebra that supports much-faster array operations) • Unlike Python, NCL • Was developed specifically for scientific data processing and visualization in Earth-science related fields (Python has broader applications for computational science, like Matlab). • Has a syntax very similar to Fortran-90

  3. http://www.ncl.ucar.edu • Support/development is outstanding

  4. http://www.ncl.ucar.edu Support/development is outstanding

  5. http://www.ncl.ucar.edu Examples, examples, examples!

  6. http://www.ncl.ucar.edu Examples, examples, examples!

  7. Examples, examples, examples!

  8. http://www.ncl.ucar.edu

  9. http://www.ncl.ucar.edu

  10. Mesoscale Predictability Experiment (MPEX) Trapp, R.J., D.J. Stensrud, M.C. Coniglio, R.S. Schumacher, M.E. Baldwin, S. Waugh, and D.T. Conlee, 2016: Mobile Radiosonde Deployments during the Mesoscale Predictability Experiment (MPEX): Rapid and Adaptive Sampling of Upscale Convective Feedbacks.Bull. Amer. Meteor. Soc.,97, 329–336.

  11. Mesoscale Predictability Experiment (MPEX) Trapp, R.J., D.J. Stensrud, M.C. Coniglio, R.S. Schumacher, M.E. Baldwin, S. Waugh, and D.T. Conlee, 2016: Mobile Radiosonde Deployments during the Mesoscale Predictability Experiment (MPEX): Rapid and Adaptive Sampling of Upscale Convective Feedbacks.Bull. Amer. Meteor. Soc.,97, 329–336.

  12. Excellent functions for skewT-logP diagrams

  13. Flexibility to get creative : Observed reflectivity ≥ 40 dBZ : Simulated reflectivity : Observed dryline : Observed dewpoint (°C) Tfcst - Tob (°C) model forecasts of surface dewpoint valid 22 UTC 10 May 2010 too moist too dry ## Model A Model B dBZ °C

  14. In Summary • You can be a scientist without Python! • NCL is a well-supported, flexible, earth-scientist-centric alternative • Excellent complement with Fortran (for more intensive computational needs) • http://www.ncl.ucar.edu • “Python/NCL: Free alternatives for Earth Scientists”, Nicolas Barrier, Institute on Research and Development, UMR Marbec, Marseille, France

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