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Nominated by the University of California, Irvine, USA, as an outstanding Ph.D. thesis
Presents data sets that reduce false rain signals in satellite precipitation measurements
Provides advances in the accuracy of satellite-based precipitation estimation
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.
Content Level »Research
Keywords »CloudSat precipitation data - CloudSat texts - MODIS satellite observations - award-winning thesis - current satellite precipitation products - false rain reduction - introduction to satellite precipitation - multi-spectral satellite imagery - precipitation retrival algorithms - satellite precipitation data - satellite precipitation measurements - satellite-based precipitation estimation