closes issue #53 by creating convert file

[msteckle]

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🛠 Issue reference

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Added GIMMS LAI dataset from Cao 2023 per the issue #53

🧪 Testing

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• I inspected the outputted NetCDF and checked for obvious errors
• I visualized the outputted NetCDF at a timestamp to check for obvious visual errors
• I compared my convert script to recent existing ones
• I attempted to create/encode the NetCDF according to CF Compliance guidelines

🧪 (Optional) Preview

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🏎 (Optional) Quality Checklist

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• There are no unused libraries imported into the code
• There are no erroneous console logs, debuggers, or leftover testing code
• There are no hard-coded paths to your local machine in the script
• Useful headings describe sections of your code
• Variable names are generalizable and easy to read

📏 (Optional) CF Compliance In-Depth Checklist

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Dimensions

• Dimensions include time with attributes/encoding:
  • axis attribute is T
  • units attribute/encoding is days since YYYY-MM-DD HH:MM:SS
  • long_name attribute is time
  • calendar encoding is noleap
  • bounds encoding is time_bounds
• Dimensions include lon with attributes:
  • axis attribute is X
  • units attribute is degrees_east
  • long_name attribute is longitude
• Dimensions include lat with attributes:
  • axis attribute is Y
  • units attribute is degrees_north
  • long_name attribute is latitude
• Dimensions include nv, which is an array of length 2 that contains the start date and end date bounding the dataset

Data Variables and their Attributes

• The variable(s) for model comparison are present
  • the variables are linked to the time,lat, and lon dimensions
  • long_name attribute is specified
  • units attribute is specified
  • (If necessary) ancillary_variables attribute is specified if an uncertainty value is provided
  • (Optional) Float32 data type
  • (Optional) No-data values masked as NaN
• If applicable, a data uncertainty variable is present (e.g., standard_deviation or standard_error)
  • the variable is linked to the time, lat, and lon dimensions
  • long_name attribute is specified (e.g., cSoil standard_deviation)
  • units attribute is specified; it is unitless, so it should be 1
• A time_bounds variable is present
  • the variable is linked to the time and nv dimensions
  • long_name attribute is specified as time_bounds
  • units is encoded as days since YYYY-MM-DD HH:MM:SS

Global Attributes

• 'title'
• 'institution'
• 'source'
• 'history'
• 'references'
  • @reference-type{ref-name, author={Last, First and}, title={}, journal={}, year={}, volume={}, pages={num--num}, doi={no-https}}
• 'comment'
• 'Conventions'

[nocollier]

Can you try apply the scale factor you mentioned in chat? I am looking at:

https://www.ilamb.org/CMIP5v6/historical/EcosystemandCarbonCycle/LeafAreaIndex/AVHRR/AVHRR.html

and noticing that the ILAMB range plots 0-6 which would match perfectly. Seems likely they intend that.

[nocollier]

Here is my comment so I can submit the review 🤦🏻

[msteckle]

Can you try apply the scale factor you mentioned in chat? I am looking at:

https://www.ilamb.org/CMIP5v6/historical/EcosystemandCarbonCycle/LeafAreaIndex/AVHRR/AVHRR.html

and noticing that the ILAMB range plots 0-6 which would match perfectly. Seems likely they intend that.

Yes! Tada!

[msteckle]

Added conversion factor

[nocollier]

Thank you! It strikes me that it would be good to create images like this in the data creation process. It let's you know how this new dataset relates to the other in our collection. I have code to do this but it should be rewritten and use our new database ideas instead. Just food for thought.