Workflow 3) Reproject all images from swath coordinates to a common lat/lon grid

[lauracrews]

Goal: Regrid from pesky swath coordinates to something more useful

What should our method do?

• Interpolate the regularly spaced (375 m or 750 m horizontal resolution) but swath-specific coordinates to regularly spaced standard coordinates
• In other words, we need to create a common lat/lon grid where all pixels are a constant distance apart. We don't want to create a common lat/lon grid where all pixels are on e.g. 1° longitude spacing (which would not be a constant distance)

How do we do it?

• We need to experiment with the resampling options in Satpy

What questions or issues remain?

• Is there a sample file that can be experimented with even as we continue working on steps 1 and 2 (load and create images)?

[lauracrews]

Update, 10 August (Thursday) morning:

Laura will work on this task this morning. Dalton was having some luck with

scn = Scene(filenames=glob('VNPnc'), reader='viirs_l1b') local_scn = scn.resample()

which seemed to be putting all files in scn to a common grid.

Remaining questions

• Can Level 1b and Level 2 data be remapped together by resample? The initial creation of scn used the reader 'viirs_l1b' which seems specific to Level 1b data, so it may not be possible to mix data levels in scn

By running

from satpy import available_readers available_readers()

I find that there's also the reader 'viirs_l2_cloud_mask_nc' which seems like it would be required for reading the L2 cloud masks

[abkfenris]

For working with multiple passes, it helps to have a shared grid to resample to. It looks like by default Scene.resample() will most likely try to maximize each source, but it's possible to pass a grid for it to resample to.

• https://satpy.readthedocs.io/en/stable/api/satpy.scene.html#satpy.scene.Scene.resample
• https://pyresample.readthedocs.io/en/stable/api/pyresample.html#pyresample.geometry.GridDefinition