Question

When converting model output from a Gaussian grid to a regular latitude-longitude grid, what specific mathematical process is required to maintain the integrity of the data values without introducing interpolation bias?

Accepted Answer

To convert data from a Gaussian grid to a regular latitude-longitude grid without introducing interpolation bias, you must perform a spectral transformation rather than a spatial interpolation. A Gaussian grid is designed to support the spectral representation of fields, meaning the data is defined by a set of spherical harmonic coefficients rather than just isolated grid points. To move this data to a different grid, you first perform a spherical harmonic transform to convert the physical values on the Gaussian grid into the mathematical domain of spherical harmonics. This process calculates the exact amplitudes of the waves that compose the global data field. Once you have these coefficients, you perform an inverse spherical harmonic transform to map those waves onto the specific points of your desired regular latitude-longitude grid. Because this process relies on the mathematical properties of spherical harmonics—which represent the entire global field as a series of continuous functions—it preserves the integrity of the data across the entire sphere. Unlike spatial interpolation, which estimates values based on proximity to neighboring points and inherently smooths or distorts the underlying data, spectral transformation uses the exact global field definition to ensure that the values at the new grid points are mathematically consistent with the original model state.

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When converting model output from a Gaussian grid to a regular latitude-longitude grid, what specific mathematical process is required to maintain the integrity of the data values without introducing interpolation bias?

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