Low-rank tensor structure of precipitation and its application to satellite-reference merging
The study uses low-rank tensor structure to model daily precipitation as a spatiotemporal tensor, showing that preserving its native spatial and temporal modes is superior to simple matrix factorization. The resulting TMerge framework merges satellite and sparse reference data using shared latent factors, significantly improving precipitation estimates by up to 85% in certain conditions.