veyepar: Utilizing Python in a Real-Time, Quasi-Operational Meteorological Environment

Author(s):	Patrick Marsh
Location	Ballroom (CDE)
Date	jul Thu 19	Days Raw Files
Start	17:00	First Raw Start	16:59
Duration	20:00	Offset	0:00:03
End	17:20	Last Raw End	17:20
Chapters	00:00 0:11:32
Total cuts_time	19 min.

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The National Oceanic and Atmospheric Administration's (NOAA) Hazardous
Weather Testbed (HWT) is a facility jointly managed by NOAA's National
Severe Storms Laboratory (NSSL), NOAA National Weather Service's (NWS)
the Storm Prediction Center (SPC), and the NOAA NWS Oklahoma
City/Norman Weather Forecast Office (OUN) within the National Weather
Center building on the University of Oklahoma South Research Campus.
The HWT is designed to accelerate the transition of promising new
meteorological insights and technologies into advances in forecasting
and warning for hazardous weather events throughout the United States.
The HWT facilities include a combined forecast and research area
situated between the operations rooms of the SPC and OUN, and a nearby
development laboratory. The facilities support enhanced collaboration
between research scientists and operational weather forecasters on
specific topics that are of mutual interest.

The cornerstone of the HWT is the yearly Experimental Forecast Program
(EFP) and Experimental Warning Program (EWP) which take place every
spring. In each of those programs, forecasters, researchers, and
developers come together to participate in a real-time operational
forecasting or warning environment with the purpose of testing and
evaluating cutting-edge tools and methods for forecasting and warning.
In the EFP program, between 5 and 10 TB of meteorological data are
processed for evaluation over the course of a 5 week period. These
data come in a variety of sources, a variety of formats, each
requiring a different set of processing.

This talk will discuss how the data flow and data creation processes
of the EFP are accomplished in a real-time setting through the use of
Python. The utilization of Python ranges from simple shell scripting,
to speeding up algorithm development (and runtimes) with Numpy and
Cython, to creating new, open source data-visualization platforms,
such as the Skew-T and Hodograph Analysis and Research Program in
Python, or SHARPpy.

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