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Review - Preprocessors #93
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Thanks for your questions! The first thing to say is that all of these parameters are flexible and the pipeline allows you to specify each of them as you require. We have made some initial choices for our current experiments but these are only one set of parameter choices with the pipeline.
We used an extent for kenya defined as this bounding box: Region(name='kenya', lonmin=33.501, lonmax=42.283,
latmin=-5.202, latmax=6.002) The resolution we are currently using is ~
The reference grid is a previous
This can be flexibly specified by the user from the following (see here for explanations): {'bilinear', 'conservative', 'nearest_s2d', 'nearest_d2s', 'patch'} We used
we are using the mean as a first implementation of the pipeline. It would be a quick fix to change this if required. However, it is worth noting that since all values are normalized to have mean 0 and std 1 before they are input to the machine learning models, whether the data is aggregated with a sum or mean doesn’t make a difference to what the models see. |
What resolution and extent did you use for the Unified Data Format?
The preprocessors use a reference grid, have you considered using epsg codes instead?
What remapping method is used?
CDS longitude range is [0, 360], while many other data providers use the range [-180, +180]. Is the preprocessor automatically rotating the layers, if needed?
How do you define how to do spatial aggregations for different variables? For instance for temperature you might want to use the mean, for precipitation you might want to use the sum (if you are converting to a coarser resolution). We can only see a MeanAggregator.
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