PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals. Issue 1 (7th October 2020)

• Record Type: Journal Article
• Authors: Jackson, Robert; Collis, Scott; Lang, Timothy; Potvin, Corey; Munson, Todd
• Is Part Of: Journal of open research software. Volume 8:Issue 1(2020)
• Publication Date: 2020-10-07
• Subjects: winds -- radar -- weather -- high resolution -- doppler; Computer software -- Reusability -- Periodicals; Open source software -- Periodicals; 005
• DOI: 10.5334/jors.264
• Languages: English
• ISSNs: 2049-9647

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 athttps://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.