This is an unofficial implementation for the paper below. The acceleration is mainly owing to the ufuncs in PyGEOS and NumPy, which tries to avoid iteration to provide a better performance (~10 times the speed over the official one under a two-core test).
In addition, the geometry operation seems to be more intuitive and easier to understand in the code snippets.

Sun, K., Zhu, L., Cady-Pereira, K., Chan Miller, C., Chance, K., Clarisse, L., Coheur, P.-F., González Abad, G., Huang, G., Liu, X., Van Damme, M., Yang, K., and Zondlo, M.: A physics-based approach to oversample multi-satellite, multispecies observations to a common grid, Atmos. Meas. Tech., 11, 6679–6701, https://doi.org/10.5194/amt-11-6679-2018, 2018.

• Handle the global grids (longitude bounds: 0~360/±180, overlaps in high-latitude areas)
• Add ellipsoid projection
• More dataset will be ingested by pre-defined YAML files if possible