I have a downscaled precipitation data, which needs to be bias-corrected. For the process, I tried the non-linear correction technique, which does one station at a time.
So is there any guiding idea, code, manual, or reference, which is done by using the SVM principle to apply for large data and many stations at a time?
In addition, I am also seeking the application of machine learning with SVM in climate data dis-aggregation into fine temporal resolution such as an hourly basis.
You can choose any spatial or space-time deconvolution algorithm. If you are inclined towards traditional spatial/space-time models, you can use area-to-area/area-to-point geostatistics. If you have access to high resolution correlated secondary information, you can use any kind of machine learning or deep learning approach. Thank You!
ANN returned good results in the annual downscaling model of TRMM: Article Downscaling TRMM Monthly Precipitation Using Google Earth En...
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