PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals
Abstract
This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.
- Authors:
-
[1];
- Argonne National Lab. (ANL), Argonne, IL (United States)
- NASA Marshall Space Flight Center (MSFC), Huntsville, AL (United States)
- National Oceanic and Atmospheric Administration (NOAA), Normal, OK (United States); Univ. of Oklahoma, Norman, OK (United States)
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Atmospheric Radiation Measurement (ARM) Data Center
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1721586
- Grant/Contract Number:
- AC05-76RL01830
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Open Research Software
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 8; Journal ID: ISSN 2049-9647
- Publisher:
- Software Sustainability Institute
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; winds; radar; weather; high resolution; doppler
Citation Formats
Jackson, Robert, Collis, Scott, Lang, Timothy, Potvin, Corey, and Munson, Todd. PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals. United States: N. p., 2020.
Web. doi:10.5334/jors.264.
Jackson, Robert, Collis, Scott, Lang, Timothy, Potvin, Corey, & Munson, Todd. PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals. United States. https://doi.org/10.5334/jors.264
Jackson, Robert, Collis, Scott, Lang, Timothy, Potvin, Corey, and Munson, Todd. Wed .
"PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals". United States. https://doi.org/10.5334/jors.264. https://www.osti.gov/servlets/purl/1721586.
@article{osti_1721586,
title = {PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals},
author = {Jackson, Robert and Collis, Scott and Lang, Timothy and Potvin, Corey and Munson, Todd},
abstractNote = {This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.},
doi = {10.5334/jors.264},
journal = {Journal of Open Research Software},
number = 8,
volume = 8,
place = {United States},
year = {Wed Oct 07 00:00:00 EDT 2020},
month = {Wed Oct 07 00:00:00 EDT 2020}
}
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