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|Sep 26, 2019 at 7:39 p.m.
|Sep 27, 2019 at 10:09 p.m.
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So much metadata. So little time.
IN the world of met data, gridded datasets at continental or regional scales which need to be corrected to seasonal averages based on observations.
e.g. match average 30 January precipitation and compare that to your gridded product at that location.
If temperature is not a good fit, add or subtract the monthly difference to shift the monthly average values to the monthly average of the dataset you trust.
It addresses scale mismatch issues of downscaling general circuluatoin models (100 km) to a local monthly average at a 5 km grid.
This structure of the bias correction can be applied to future datasets downscaled with the same structure as the historic gridded products.
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