TY - BOOK ID - 4863399 TI - Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery PY - 2015 SN - 9783319120812 3319120808 9783319120805 3319120816 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Earth Sciences. KW - Atmospheric Sciences. KW - Geophysics and Environmental Physics. KW - Meteorology. KW - Environmental Physics. KW - Geography. KW - Physical geography. KW - Géographie KW - Géographie physique KW - Infrared detectors. KW - Precipitation (Meteorology) -- Remote sensing. KW - Earth & Environmental Sciences KW - Meteorology & Climatology KW - Clouds. KW - Remote-sensing images. KW - Images, Remote-sensing KW - Multispectral scanning images KW - Satellite images KW - Side-looking airborne radar images KW - SLAR (Side-looking airborne radar) KW - Aerology KW - Earth sciences. KW - Atmospheric sciences. KW - Geophysics. KW - Environmental sciences. KW - Earth sciences KW - Atmosphere KW - Cartographic materials KW - Meteorology KW - Atmospheric science KW - Geography KW - Environmental science KW - Science KW - Geological physics KW - Terrestrial physics KW - Physics KW - Atmospheric sciences UR - https://www.unicat.be/uniCat?func=search&query=sysid:4863399 AB - This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space.  Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved.  The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation. ER -