PyDDA: A Pythonic Direct Data Assimilation framework for wind retrievals
| Main Authors: | Robert Jackson, Scott Collis, Timothy Lang, Corey Potvin, Todd Munson |
|---|---|
| Other Authors: | Zach Sherman |
| Format: | info software Journal |
| Bahasa: | eng |
| Terbitan: |
, 2020
|
| Subjects: | |
| Online Access: |
https://zenodo.org/record/3942686 |
Daftar Isi:
- This software is designed to retrieve wind kinematics (u,v,w) in precipitation storm systems from one or more Doppler weather radars using three dimensional data assimilation. Other constraints, including background fields (eg reanalysis) can be added. This package is a rewrite of the Potvin et al. (2012) and Shapiro et al (2009) wind retrieval techniques into a purely Pythonic package for easier integration with Py-ART and Python. This allows for easy installation using pip and anaconda. This new package also uses a faster minimization technique, L-BFGS-B, which provides a factor of 2 to 5 speedup versus using the predecessor code, NASA-Multidop, as well as a more elegant syntax as well as support for an arbitrary number of radars. The code is also threadsafe and has been tested using HPC tools such as Dask on large (100+ core) clusters.