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Paper: PyDDA: A New Pythonic Multiple Doppler Retrieval Package #474
ocefpaf left a comment
My final review is to approved the paper. All my comments are minor and/or clarification questions/suggestions.
The paper describes the development a python package for solving a problem in a domain within the scope of the SciPy conference. All the code present could be easily verified (I did not run all the examples though).
I did not verified the final PDF for length due to laptop limitations at the moment (travelling with a Windows machine). My review was based only on the
A few board, major comments:
I'll put more detailed comments in the .tex directly.
I mostly have minor comments about the paper (from the PDF version I retrieved initially), which would be a good submission for SciPy.
I am going to place the general response to the 3 reviewers here, with responses to their more specific comments in the relevant comment sections in the thread above.
The figures have been regenerated to be more readable in response to all of the reviewers’ comments. In particular, the contours in the plots have been switched to hatches. In addition, we now use @CameronHomeyer’s colormap that visualizes radar reflectivity fields using a color-blind friendly scheme which will make the figures more accessible. This colormap is now used in Figures 2, 3, 4, 6, and 8. In addition, the sizes of the quivers and streamline arrows have been enhanced in the new versions of Figure 1 and 6, making these figures more readable. Finally, Figure 6 now uses a hatched contour instead of a transparent contour to denote the regions where updrafts are present.
In terms of trying to make this paper readable in black and white, it is no longer usual in the meteorology community to attempt to make radar images work in black and white as they are very difficult to visualize effectively without color. In essence, any colormap we use has to highlight regions of light rain, moderate rain, heavy rain, and hail all in ways that are easily distinguishable. This is very difficult to do effectively in black and white. Therefore, in order to better highlight significant features of the storm, we have decided that color figures are the best way to visualize the data. The use of color-blind friendly colormaps does help the figures appear better in black and white.
The variational problem is regularized in two different ways in order to ensure a physically realistic solution that is free of noise. One is through the use of a hard box constraint on the solution when executing the L-BFGS-B minimization technique. Throughout the domain, the wind velocities are constrained to be within -100 to 100 m/s which is a physically realistic scenario for virtually all weather that occurs on Earth. In addition, in order to reduce noise in the final solution, there are options to have a low pass filter as well as adjusting the smoothness constraint that is directly proportional to the Laplacian of the wind field. We have added a few sentences in the paper that mention these techniques.
We thank the reviewer for taking the time to review the paper and providing her valuable comments. We have embedded our responses below.
There are repetitions, on p.1., for example, "to use as it uses" or "In addition, the addition"
Sentences such as "This therefore permits the easy installation of PyDDA using pip of anaconda. Given that alone is a major hurdle to [...]", I think, read the opposite of of they actually mean.
To make this clearer we have replaced “this” with “installation” in this sentence.
p. 4, "Figure using a similar" : Figure number is missing.
We have fixed the figure number here.