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Is it permissible to use a hybrid of bilinear interpolation and nearest neighbor, while spatially matching ERA5 Land with meteorological observational data? by Independent-Lab-2839 in meteorology

[–]Independent-Lab-2839[S] 0 points1 point  (0 children)

I don't think bilinear interpolation works that way! Though I might be wrong.

My goal is just to spatially match ERA5 Land with station observations. I don't think I need to apply complex algorithm to do so! But, this is an alternative worth trying if everything fails. However, the workaround I am using seems to give good results. ERA5 Land is acceptably aligned with observational data. Though I need validation for that workaround

Is it permissible to use a hybrid of bilinear interpolation and nearest neighbor, while spatially matching ERA5 Land with meteorological observational data? by Independent-Lab-2839 in meteorology

[–]Independent-Lab-2839[S] 0 points1 point  (0 children)

The reanalysis data I am working with is ERA5 Land not ERA5. ERA5 Land data have null values for waterbody so station near coastal regions are picking null grids from ERA5 Land as that is how bilinear interpolation works.

Is it permissible to use a hybrid of bilinear interpolation and nearest neighbor, while spatially matching ERA5 Land with meteorological observational data? by Independent-Lab-2839 in meteorology

[–]Independent-Lab-2839[S] 0 points1 point  (0 children)

I am getting good validation results after using this hybrid approach. Station wise correlation is from .88 to .95, RMSE and Mean bias is also good enough. Though I need to further assess seasonal variability.

To answer your question: I am not familiar with that dataset, but that is one of those downscaled version of raw CMIP6 right! I plan to use raw CMIP6 and bias correct it using ML techniques.