I have three-dimensional radar reflectivity data obtained as voxels (scans, rays, altitudes). The data has been sampled at irregular spacings and I want to convert this into a regular grid. In addition, I have 3-D arrays for the latitude, longitude, and altitude with the same shape(scans, rays, altitudes). Python is my programming language of choice. Is using scipy's
RegularGridInterpolator the best way to obtain a regular grid?
A colleague suggested that I first start with a 2-D interpolation (scans and rays for every altitude) and then proceed to do a 3-D interpolation. A bulk of the reflectivity data contains zeros(zero reflectivity) and NaNs(no data available) and whether any filter would be applicable to remove the zeros/NaNs would be nice to know. Any recommendations and references on this topic will be appreciated.
So for a sample data set - I have total of 22163680 points and out of which 266111 are finite values(greater than zero). The rest are zeros and NaNs.