Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery - Info and Reading Options

The Open Library:

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.

Search for “Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery” downloads:

Visit our Downloads Search page to see if downloads are available.

Find “Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery” in Libraries Near You:

Read or borrow “Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery” from your local library.

• The WorldCat Libraries Catalog: Find a copy of “Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery” at a library near you.