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I have the task of producing a cloudiness forecasts from GFS model. I found a way to get GFS data and manipulate it with metpy, but there are multiple data about clouds, for example: Pressure_convective_cloud_bottom Total_cloud_cover_convective_cloud Pressure_high_cloud_bottom_6_Hour_Average etc

How can I produce a meaningful forecast from them? Any tips to point me in the right direction?

Thank you

DopplerShift
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tevch
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1 Answers1

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GFS output includes a value for the total cloud cover over the depth of the entire atmosphere. When accessing this data from a THREDDS server, it's called Total_cloud_cover_entire_atmosphere.

DopplerShift
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  • Thank you. I do get some data. Any hints on where to start processing it? Like reading % at each layer at each lat-lon? – tevch Aug 16 '22 at 03:09
  • This is where I'd rely on Python and numpy/xarray to calculate perhaps the maximum along the vertical axis. Other than that, though, that's really a scientific application question and would depend (to me) on how those original values are calculated. – DopplerShift Aug 16 '22 at 16:41