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Name: Utilizing Python in a Real-Time, Quasi-Operational Meteorological Environment
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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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Description:	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. markdown
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