Earthdata Forum

I'm using the merged S3 cyan data "L3b_DAY_CYAN.nc" for my project. The dataset has "BinList" and "BinIndex" as variables. How do I convert bin_num to latitude & longitude locations? Thanks!

Here is the white paper:

https://oceancolor.gsfc.nasa.gov/docs/format/Ocean_Level-3_Binned_Data_Products.pdf

If you are using SeaDAS look at the l3bindump utility
Tommy

Thank you Tommy for your prompt reply.
I read through the white paper but still couldn't understand how bins are tranlated into lat/lon locations.
Altheratively I found this link: https://oceancolor.gsfc.nasa.gov/docs/format/l3bins/
from another post: viewtopic.php?t=2207#p7783 from 2021. However the link no longer works.

I found a python code translation in another post: https://clouds.eos.ubc.ca/~phil/courses/eosc582/html/find_bins.html
that looks promising. But I'm quite sure how to define "numrows" as an input of the Bin_calc object.

Finally I tried to use l3bindump in SeaDAS, but keep getting the follow error:
execution exception: java.io.IOException: l3bindump failed with exit code 139.
Check log for more details.
/Users/marcokam/SeaDAS/ocssw/bin/ocssw_runner: line 23: 1745 Segmentation fault: 11 $*

Please see screenshot attached.
I'm using macOS Big Sur 11.7.9, SeaDAS version 8.3.0.
Thank you!

One thing I notice is that your product is wrong.

"CI_Cyan" should be "CI_cyano" but I wouldn't expect that to cause a Seg Fault.

I'll leave this for one of our SeaDAS experts to debug.

Tommy

The URL in the post you found was moved with the reorganized website. It now lives here:https://oceancolor.gsfc.nasa.gov/resources/docs/format/l3bins/

The sample code on that page will show you how to get the geolocation for a given bin, however, it doesn't show how to retrieve the geophysical values from the stored bin sums and sums of squares. For that see the appendix in https://oceancolor.gsfc.nasa.gov/images/resources/seawifs/PreLVol32.pdf

You may want to just use the l3mapgen program (part of the SeaDAS distribution) to get a raster image that does the math and outputs a geolocated product.

Sean

Thanks Sean! This is extremely helpful.

wongsydoris wrote: ↑Tue Aug 29, 2023 9:47 pm America/New_York [...]
I read through the white paper but still couldn't understand how bins are tranlated [SIC] into lat/lon locations.
[...]

It can be a bit tricky to reproduce the integer number of bins for a given latitude band. In the distant past I encountered differences moving to Intel (i386?) from SGI IRIX 64, and later with IDL, Matlab, Octave, R and Python on Intel where some builtin sequence generators used different algorithms. My solution was to use l3bindump with a level-3 binned PAR climatology (PAR is computed where there are clouds, so I never encounter valid data for bins that don't have a climatological PAR value) to get bin numbers and lat/lon locations.

Thanks for your comment gnwiii. My SeaDAS is still not working properly so I can't use l3bindump. ButI actually found a python code that seem to work for me. I modified it a bit so it runs faster. Sharing it here in case anyone is also looking for a way outside of SeaDAS.

================================
def binnum2latlon(binnum, nrows):
latbin = (np.arange(0, nrows, dtype=np.float_) + 0.5) * (180.0 / nrows) - 90.0
numbin = (np.cos(latbin * np.pi / 180.0) * (2.0 * nrows) + 0.5).astype(dtype=np.int_)
basebin = np.cumsum(numbin)+1
basebin = np.insert(basebin, 0, 1)
row = nrows - 1
if binnum < 1:
binnum = 1

row = (binnum >= basebin).sum() - 1
clat = latbin[row]
clon = (360.0 * (binnum - basebin[row] + 0.5) / numbin[row]) - 180.0
return (clat,clon)
================================

Note that "nrows" depends on the resolution of the satelite image.
The L3b_DAY_CYAN data have sensor spatial resolution of 300m, so nrows = 69120 (according to the "binIndexDim" dimension in the nc file).
From this doc on l3bins: https://oceancolor.gsfc.nasa.gov/resources/docs/format/l3bins/
Data with resolution of 9km: nrows = 2160
Data with resolution of 4km: nrows = 4320

Since l3bindump does not work on my laptop, I was only able to test this code with the example Tom provided in an earlier post (i.e. bin 4568737875 translate to clat=30.141927 and clon=-89.317931).

I've observed a potential issue with your product. Specifically, it seems that "CI_Cyan" should be labeled as "CI_cyano." However, I wouldn't anticipate this to result in a Segmentation Fault. I'll leave this matter for one of our SeaDAS experts to investigate further.