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.ipynb_checkpoints
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Plot Clustering Results.ipynb
README.md
TRMM precipitation clustering.ipynb
Testing netCDF.ipynb
clusterEvents.py
eScienceProposal.docx

README.md

Precipitation incubator at eScience winter 2018-2019

  • TRMM data and all three researchers have been granted ftp server access.

How to scan these data

  • TRMM launched on 27 Nov 1997
    • TRMM is in LEO with 35 degrees inclination, 92.5 minutes / orbit. Example data below is from orbit 6294; Jan 1 1999
  • 20 or so regions... some overlap; 17 years x 12 months per year x 30 days per month x 130 files per day x 70kb per file ~ 1.1TB
  • interp_data folder only
    • XYZ is a region code, e.g. EPO/ = East Pacific Ocean (so 20 or so of these)
    • Let yyyy be the year and mm be the month running from 1998 01 to 2014 08 so '2003/' and '04/' for example
    • The URL directory format is then 'http://trmm.atmos.washington.edu/' + TLA + 'interp_data/' + yyyy + mm
  • Files cover short time intervals
    • TPR7_uw1_06294.19990101.002101_EPO.nc4 is our example filename; notice orbit, date, time and region

Why copy the data? What is S3?

S3 is AWS object storage. It does not behave like a UNIX file system in that one can't do random access into the bytes of a file. No matter; treating the files as hermetic objects will work and S3 access is reasonably fast; and we can work with it pretty much as though it is a file system using Python, specifically pandas and/or xarray. We do want to make sure the S3 bucket and the Jupyter Hub are in the same region.

I keep asking Shiv if there is some way of treating the UW Atmos TRMM ftp server like a virtual file system and he keeps patiently explaining NO so that's why I am suggesting working from a cloud copy. We can share the results with the atmos folks in case they are interested in our approach to data access.

Shiv has the copy process running with only one login per directory; so it seems to be reasonably efficient and quick "per region-month". He can comment on wall clock time. If we take that to be 3 minutes then we have a total copy time of 3/60 * 20 * 17 * 12 = 220 hours or ten days. That's rather a lot of time but at least there is no cost associated to uploading data to AWS in this way.

@Shiv this would be a good time to confirm / fix my numbers.

code example by Shiv

Note that by default username and password are identical per atmos policy.

import requests
auth_data= ('username','password')
url = "http://trmm.atmos.washington.edu/EPO/interp_data/1999/01/TPR7_uw1_06294.19990101.002101_EPO.nc4"
r = requests.get(url, auth = auth_data,stream = True)
filename = 'test.nc4'
if r.status_code == 200:
    with open(filename, 'wb') as f:
        f.write(r.content)