Purpose of Duplicate Time Entries

Kevin Nelson

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May 25, 2022, 3:51:18 PM5/25/22

to IBTrACS Q&A

Hello,

I was curious what the purpose was for duplicating time entries in IBTrACS v04r00 in the since 1980 netCDF file.

As an example, the entry for Melor in 2009 has time entries (since 11-17-1858) that are the same. Those data points have different longitude and latitude values between them. Is the purpose of those values to include more location data? And if so, why is the time not updated to reflect the additional observation instead of just duplicating the value.

The duplicated time values cause interpolation routines (interpolating track data to the time of a point data) to fail when they try to decide between which points to use. Please let me know when you get a chance. Thank you for your help!

- Kevin Nelson, Texas A&M University - Corpus Christi

Carl Schreck - NOAA Affiliate

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May 26, 2022, 11:19:55 AM5/26/22

to Kevin Nelson, IBTrACS Q&A

Thanks for the query. Can you let us know which times you are seeing repeated? I do not see them here:

http://ibtracs.unca.edu/index.php?name=v04r00-2009272N07164

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Carl J. Schreck III, PhD (he/him)

Research Scholar

North Carolina State University

North Carolina Institute for Climate Studies (NCICS)

Cooperative Institute Satellite Earth System Studies (CISESS)

151 Patton Ave. Asheville, NC 28801

carl.s...@noaa.gov

office: 828-257-3140

mobile: 828-484-1702

ncics.org/mjo

Kevin Nelson

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May 26, 2022, 11:35:43 AM5/26/22

to IBTrACS Q&A

Hi Carl,

Thank you for getting back to me so quickly.

The duplicates I'm seeing are in the raw netCDF data from the v4 release. They seem to fall in patterns/groups of three and before moving to a new time. The following is output from IDL after the data is read in using a custom function:

ibtracs = read_ibtracs_v4()
% Compiled module: READ_IBTRACS_V4.
% Loaded DLM: NCDF.
% Compiled module: JULDAY.
IDL> ii = WHERE(ibtracs.season EQ 2009 AND ibtracs.storm_name EQ 'MELOR', cnt)
IDL> cnt
1
IDL> print, ibtracs.storm_time[*,ii]
2455103.5 2455103.5 2455103.8 2455104.0 2455104.0
2455104.0 2455104.2 2455104.5 2455104.5 2455104.5
2455104.8 2455105.0 2455105.0 2455105.0 2455105.2
2455105.5 2455105.5 2455105.5 2455105.8 2455106.0
2455106.0 2455106.0 2455106.2 2455106.5 2455106.5
2455106.5 2455106.8 2455107.0 2455107.0 2455107.0
2455107.2 2455107.5 2455107.5 2455107.5 2455107.8
2455108.0 2455108.0 2455108.0 2455108.2 2455108.5
2455108.5 2455108.5 2455108.8 2455109.0 2455109.0
2455109.0 2455109.2 2455109.5 2455109.5 2455109.5
2455109.8 2455110.0 2455110.0 2455110.0 2455110.2
2455110.5 2455110.5 2455110.5 2455110.8 2455111.0
2455111.0 2455111.0 2455111.2 2455111.5 2455111.5
2455111.5 2455111.8 2455112.0 2455112.0 2455112.0
2455112.2 2455112.5 2455112.5 2455112.5 2455112.8
2455112.8 2455112.8 2455113.0 2455113.0 2455113.0
2455113.2 2455113.5 2455113.5 2455113.5 2455113.8
2455114.0 2455114.0 2455114.0 2455114.2 2455114.5
2455114.5 2455114.5 2455114.8 2455115.0 2455115.0
2455115.0 2455115.2 2455115.5 2455115.5 2455115.5

The times for the storm all have the Julian time of 1858-11-17 added to them as part of the reader function. I have attached the reader function for your reference if needed.

Ultimately, with the duplicates removed, there is little change to the track overall, but the duplicates cause problems when trying to select a single time or interpolating the location/other data to the time of another data point. My solution for now is to just use only the unique time values via the IDL UNIQ function. Let me know what you find out/suggest. Thanks again!

- Kevin Nelson, PhD Candidate, Texas A&M University - Corpus Christi

read_ibtracs_v4.pro

Carl Schreck - NOAA Affiliate

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May 26, 2022, 12:57:27 PM5/26/22

to Kevin Nelson, IBTrACS Q&A

It appears IDL is doing something strange with rounding. Below is what I get in python with a similar script. As you can see, the data are mostly 3-hourly, so every 0.125. IDL is doing some weird rounding. It also appears IDL is adding a 24 to the front of each number.

Python 3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:25:59)
[GCC 10.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import xarray as xr
>>>
>>> ds_decoded = xr.open_dataset(
... '/store/carl/ibtracs/v04r00/IBTrACS.since1980.v04r00.nc')
>>> ds_encoded = xr.open_dataset(
... '/store/carl/ibtracs/v04r00/IBTrACS.since1980.v04r00.nc',
... decode_cf=False)
>>> ii = (ds_decoded['season'] == 2009) & (ds_decoded['name'] == b'MELOR')
>>> print(ds_decoded['time'][ii,:].values)
[['2009-09-28T12:00:00.000039936' '2009-09-28T15:00:00.000039936'
'2009-09-28T18:00:00.000039936' '2009-09-28T21:00:00.000039936'
'2009-09-29T00:00:00.000039936' '2009-09-29T03:00:00.000039936'
'2009-09-29T06:00:00.000039936' '2009-09-29T09:00:00.000039936'
'2009-09-29T12:00:00.000039936' '2009-09-29T15:00:00.000039936'
'2009-09-29T18:00:00.000039936' '2009-09-29T21:00:00.000039936'
'2009-09-30T00:00:00.000039936' '2009-09-30T03:00:00.000039936'
'2009-09-30T06:00:00.000039936' '2009-09-30T09:00:00.000039936'
'2009-09-30T12:00:00.000039936' '2009-09-30T15:00:00.000039936'
'2009-09-30T18:00:00.000039936' '2009-09-30T21:00:00.000039936'
'2009-10-01T00:00:00.000039936' '2009-10-01T03:00:00.000039936'
'2009-10-01T06:00:00.000039936' '2009-10-01T09:00:00.000039936'
'2009-10-01T12:00:00.000039936' '2009-10-01T15:00:00.000039936'
'2009-10-01T18:00:00.000039936' '2009-10-01T21:00:00.000039936'
'2009-10-02T00:00:00.000039936' '2009-10-02T03:00:00.000039936'
'2009-10-02T06:00:00.000039936' '2009-10-02T09:00:00.000039936'
'2009-10-02T12:00:00.000039936' '2009-10-02T15:00:00.000039936'
'2009-10-02T18:00:00.000039936' '2009-10-02T21:00:00.000039936'
'2009-10-03T00:00:00.000039936' '2009-10-03T03:00:00.000039936'
'2009-10-03T06:00:00.000039936' '2009-10-03T09:00:00.000039936'
'2009-10-03T12:00:00.000039936' '2009-10-03T15:00:00.000039936'
'2009-10-03T18:00:00.000039936' '2009-10-03T21:00:00.000039936'
'2009-10-04T00:00:00.000039936' '2009-10-04T03:00:00.000039936'
'2009-10-04T06:00:00.000039936' '2009-10-04T09:00:00.000039936'
'2009-10-04T12:00:00.000039936' '2009-10-04T15:00:00.000039936'
'2009-10-04T18:00:00.000039936' '2009-10-04T21:00:00.000039936'
'2009-10-05T00:00:00.000039936' '2009-10-05T03:00:00.000039936'
'2009-10-05T06:00:00.000039936' '2009-10-05T09:00:00.000039936'
'2009-10-05T12:00:00.000039936' '2009-10-05T15:00:00.000039936'
'2009-10-05T18:00:00.000039936' '2009-10-05T21:00:00.000039936'
'2009-10-06T00:00:00.000039936' '2009-10-06T03:00:00.000039936'
'2009-10-06T06:00:00.000039936' '2009-10-06T09:00:00.000039936'
'2009-10-06T12:00:00.000039936' '2009-10-06T15:00:00.000039936'
'2009-10-06T18:00:00.000039936' '2009-10-06T21:00:00.000039936'
'2009-10-07T00:00:00.000039936' '2009-10-07T03:00:00.000039936'
'2009-10-07T06:00:00.000039936' '2009-10-07T09:00:00.000039936'
'2009-10-07T12:00:00.000039936' '2009-10-07T15:00:00.000039936'
'2009-10-07T18:00:00.000039936' '2009-10-07T19:00:00.000066560'
'2009-10-07T20:00:00.000053248' '2009-10-07T21:00:00.000039936'
'2009-10-08T00:00:00.000039936' '2009-10-08T03:00:00.000039936'
'2009-10-08T06:00:00.000039936' '2009-10-08T09:00:00.000039936'
'2009-10-08T12:00:00.000039936' '2009-10-08T15:00:00.000039936'
'2009-10-08T18:00:00.000039936' '2009-10-08T21:00:00.000039936'
'2009-10-09T00:00:00.000039936' '2009-10-09T03:00:00.000039936'
'2009-10-09T06:00:00.000039936' '2009-10-09T09:00:00.000039936'
'2009-10-09T12:00:00.000039936' '2009-10-09T15:00:00.000039936'
'2009-10-09T18:00:00.000039936' '2009-10-09T21:00:00.000039936'
'2009-10-10T00:00:00.000039936' '2009-10-10T03:00:00.000039936'
'2009-10-10T06:00:00.000039936' '2009-10-10T09:00:00.000039936'
'2009-10-10T12:00:00.000039936' '2009-10-10T15:00:00.000039936'
'2009-10-10T18:00:00.000039936' '2009-10-10T21:00:00.000039936'
'2009-10-11T00:00:00.000039936' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT'
'NaT' 'NaT']]
>>> print(ds_encoded['time'][ii,:].values)
[[ 55102.5 55102.625 55102.75 55102.875
55103. 55103.125 55103.25 55103.375
55103.5 55103.625 55103.75 55103.875
55104. 55104.125 55104.25 55104.375
55104.5 55104.625 55104.75 55104.875
55105. 55105.125 55105.25 55105.375
55105.5 55105.625 55105.75 55105.875
55106. 55106.125 55106.25 55106.375
55106.5 55106.625 55106.75 55106.875
55107. 55107.125 55107.25 55107.375
55107.5 55107.625 55107.75 55107.875
55108. 55108.125 55108.25 55108.375
55108.5 55108.625 55108.75 55108.875
55109. 55109.125 55109.25 55109.375
55109.5 55109.625 55109.75 55109.875
55110. 55110.125 55110.25 55110.375
55110.5 55110.625 55110.75 55110.875
55111. 55111.125 55111.25 55111.375
55111.5 55111.625 55111.75 55111.79166667
55111.83333333 55111.875 55112. 55112.125
55112.25 55112.375 55112.5 55112.625
55112.75 55112.875 55113. 55113.125
55113.25 55113.375 55113.5 55113.625
55113.75 55113.875 55114. 55114.125
55114.25 55114.375 55114.5 55114.625
55114.75 55114.875 55115. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000.
-9999000. -9999000. -9999000. -9999000. ]]

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