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ARM-DOE / pyart

The Python-ARM Radar Toolkit. A data model driven interactive toolkit for working with weather radar data.
https://arm-doe.github.io/pyart/
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Support for additional weighting functions in grid mapping (including range- and time-dependence) #953

Open jthielen opened 4 years ago

jthielen commented 4 years ago

In the grid mapping routines (grid_from_radars, map_gates_to_grid, and map_to_grid), would there be support for expanding the weighting_function options that are available to use, either by additional built-in options or refactoring to support custom functions?

My particular use-case is re-implementing the GridRad procedure in map_gates_to_grid. It uses nearest neighbor interpolation in the horizontal and a vertical depth cutoff combined with a range and time offset Gaussian weight. While I'm working on a vendored-and-modified implementation for my own needs (see initial work here: https://github.com/jthielen/OpenMosaic/blob/560bc90f8cada41a1817f2261c8002f13cc16eae/src/openmosaic/gridding/_gate_to_grid_map.pyx), I was wondering if such an addition would be welcome upstream in Py-ART, or at least made easier to do through custom weighting function support.

If built-in additions are welcome, I'd imagine some version of the GridRad procedure and/or some combination of 4DDG procedures of Langston et al. (2007) would be particularly useful to include.

zssherman commented 4 years ago

I would say having an option for custom weights or more built in weights would be great. @scollis Your thoughts?

scollis commented 4 years ago

100% yes

From: Zach Sherman notifications@github.com Reply-To: ARM-DOE/pyart reply@reply.github.com Date: Tuesday, August 4, 2020 at 12:47 PM To: ARM-DOE/pyart pyart@noreply.github.com Cc: Scott scollis.acrf@gmail.com, Mention mention@noreply.github.com Subject: Re: [ARM-DOE/pyart] Support for additional weighting functions in grid mapping (including range- and time-dependence) (#953)

I would say having an option for custom weights or more built in weights would be great. @scollis Your thoughts?

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