Title :
Precipitation data merging using general linear regression
Author :
Turlapaty, Anish C. ; Younan, Nicolas H. ; Anantharaj, Valentine
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA
Abstract :
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.
Keywords :
atmospheric precipitation; atmospheric techniques; regression analysis; sensor fusion; weather forecasting; Earth climate system; GPM mission; Global Precipitation Measurement; data fusion; energy cycle; error propagation model; general linear regression; high resolution precipitation products; numerical weather prediction; precipitation data merging; satellite observations; water cycle; Australia; Energy resolution; Europe; Floods; Linear regression; Merging; Predictive models; Satellites; Spatial resolution; Weather forecasting; Data merging; Error propagation; Linear regression; Precipitation;
Conference_Titel :
Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009
Conference_Location :
Cape Town
Print_ISBN :
978-1-4244-3394-0
Electronic_ISBN :
978-1-4244-3395-7
DOI :
10.1109/IGARSS.2009.5417769
