Add importer for DWD radar data products and reprojection onto a regular grid as part of #464 #483
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Many thanks for this contribution, @m-rempel! Although we have discussed within the pysteps community to limit the number of new importers, I can imagine that this importer is a useful addition to allow for the ensemble Kalman filter blending setup, testing and possibly some gallery examples. I will have a quick look to see if there is any overlap with other HDF5 importers to see if we can shorten the code a bit. @dnerini, what do you think? Regarding the addition to the reprojection, the code looks good, but at this moment pysteps does not yet support xarray (we will from version 2). Is there a way to rewrite this approach without xarray? If not, we could consider these improvements as part of the pysteps v2 release. @dnerini, what is your take on this? |
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| d[key] = value.value |
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What exception are you expecting here?
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| def _read_hdf5_cont(f, d): | ||
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| Recursively read nested dictionaries from a HDF5 file. |
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Good to add some extra docstrings here about what the input variables are and what you expect to return.
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| def _identify_info_bits(data): | ||
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Could you also add some docstrings here?
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| filename: str | ||
| Name of the file to import. | ||
| product: {'WX','RX','EX','RY','RW','AY','RS','YW','WN'} |
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Would it make sense to call this variable product_name? Then the prod variable can get a longer, more descriptive name as well.
pysteps/io/importers.py
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| Geodata for RADOLAN products. Hard-coded here, cause there is only basic | ||
| information about the projection in the header of the binary RADOLAN | ||
| files. | ||
| """ |
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Add some more docstrings here, too (input parameters and output). I have already added much more docstrings, but feel free to edit them from here.
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Almost forgot to mention it, but we will have to add some tests if we continue with this approach! :) |
Implementation of an importer for DWD radar data products and a reprojection onto a regular grid. These are necessary to test the implementation of the reduced-space EnKF combination approach (Nerini et al., 2019) and to compare the results with the already existing code base that uses DWD data.