PyDDA (Pythonic Direct Data Assimilation)

A Pythonic Multiple Doppler Radar Wind Retrieval Package

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) clusers.

The user has an option to adjust strength of data, mass continuity constraints as well as implement a low pass filter. This new version now also has an option to plot a horizontal cross section of a wind barb plot overlaid on a background field from a grid.

Angles.py is from Multidop and was written by Timothy Lang of NASA.

We recommend using Python 3.8+ or better and using anaconda or pip to install the required dependencies of PyDDA.

In addition, in order to use the capability to load HRRR data as a constraint, the cfgrib package is needed. Since this does not work on Windows, this is an optional depdenency for those who wish to use HRRR data. To install cfgrib, simply do:

pip install cfgrib

Finally, PyDDA now supports Jax and TensorFlow as optional dependencies. PyDDA can be configured to use these two packages to perform the cost function minimization. We highly encourage users to take advantage of these two new engines, as they offer advantages such as better calculation of gradients and faster convergence. In addition, GPUs and TPUs are supported by these packages that can help drastically accelerate the calculations. For Tensorflow type:

conda install -c conda-forge tensorflow=2.6 tensorflow-probability

For Jax, type:

conda install -c conda-forge jax

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Links to important documentation

1. Examples
2. Developer reference guide
3. Contributor's guide

Installation instructions

The best way to install PyDDA is by using pip. If you are using PyDDA as an end user, type the following in a bash shell:

pip install pydda

Or, if you have Anaconda, you can install using:

conda install -c conda-forge pydda

Installing from source is recommended if you want to use the latest features and want to make contributions to PyDDA. In order to install from source, in a bash shell or the Anaconda prompt if you are in Windows, type the following:

git clone https://github.com/openradar/PyDDA
cd PyDDA
python setup.py install

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Acknowledgments

Core components of the software are adopted from the Multidop package by converting the C code to Python.

The development of this software is supported by the Climate Model Development and Validation (CMDV) activity which is funded by the Office of Biological and Environmental Research in the US Department of Energy Office of Science.

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Contributing

We have a set of goals that we wish to accomplish using PyDDA, including the assimilation of data from various models in the retrieval, improved visualizations, use of radar data in antenna coordinates, and improved documentation. For more details on what contributions would be useful to acheiving these goals, see the PyDDA Roadmap.

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Further support

We are now requesting that all questions related to PyDDA that are not potential software issues to be relegated to the openradar Discourse group with a 'pydda' tag on your post. This enables the entire open radar science community to answer questions related to PyDDA so that both the maintainer and users can answer questions people may have.

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References

You must cite these papers if you use PyDDA:

Potvin, C.K., A. Shapiro, and M. Xue, 2012: Impact of a Vertical Vorticity Constraint in Variational Dual-Doppler Wind Analysis: Tests with Real and Simulated Supercell Data. J. Atmos. Oceanic Technol., 29, 32–49, https://doi.org/10.1175/JTECH-D-11-00019.1

Shapiro, A., C.K. Potvin, and J. Gao, 2009: Use of a Vertical Vorticity Equation in Variational Dual-Doppler Wind Analysis. J. Atmos. Oceanic Technol., 26, 2089–2106, https://doi.org/10.1175/2009JTECHA1256.1