Preferred Remapping technique for different variables
Added by Divyam Garg 7 months ago
Hi,
I am working with temperature, precipitation data (0.1 degree from CDS store) and population density data (2.5 min from SEDAC) and often have to change the resolution of either data to achieve a common resolution. I do this using cdo but often get confused on which interpolation method would be more appropriate or give accurate results for a particular variable. And does the choice of method also depends on whether we are interpolating from coarser to finer grid or vice-versa? What method would be used for population data?
Can you guide me on this or provide some good references?
cdo remapbil,target_file_name temp.nc outfile.nc
cdo remapcon,target_file_name prec.nc outfile.nc
Replies (1)
RE: Preferred Remapping technique for different variables - Added by Leo Crown 5 months ago
Hello,
Choosing the appropriate interpolation method for your data can indeed be challenging.
Use Case: Suitable for smoothly varying variables like temperature.
Description: This method uses the four nearest grid points to estimate the value at a new point. It works well for continuous data but may not be ideal for variables with sharp gradients or discontinuities.
Conservative Remapping (remapcon):
Use Case: Ideal for variables like precipitation where conservation of the total quantity is important.
Description: This method ensures that the total amount of the variable is conserved during the interpolation process. It is particularly useful when interpolating from a coarser to a finer grid.