Precipitation data merging using general linear regression
Precipitation is a key component of the water and energy cycles of the earth's climate system. Today, estimates of global precipitation are derived routinely from satellite observations and numerical weather prediction (NWP) models. Future satellite constellations from the Global Precipitation Measurement (GPM) mission will continue to provide high resolution precipitation products (HRPP) improved spatial and temporal resolutions. We have investigated a data fusion methodology, based on linear regression and an error propagation model, to merge different precipitation datasets in order to develop a fused product which is statistically superior to any individual data set or their average.