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| Created: | Sep 26, 2019 at 7:39 p.m. | |
| Last updated: | Sep 27, 2019 at 10:09 p.m. | |
| Citation: | See how to cite this resource | |
| Content types: | Geographic Feature Content Geographic Raster Content |
| Sharing Status: | Discoverable |
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| Views: | 1326 |
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Abstract
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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Coverage
Spatial
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How to Cite
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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