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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.
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.
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