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.
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
68
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
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Product Details
| ISBN-13: | 9783319120812 |
|---|---|
| Publisher: | Springer-Verlag New York, LLC |
| Publication date: | 11/07/2014 |
| Series: | Springer Theses |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 68 |
| File size: | 3 MB |