Andere Kunden interessierten sich auch für
![Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery]( "Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery")
Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery74,99 €![Retrieval of Clouds and Precipitation from Satellite Observations]( "Retrieval of Clouds and Precipitation from Satellite Observations")
Retrieval of Clouds and Precipitation from Satellite Observations46,99 €![Wind Energy Meteorology]( "Wind Energy Meteorology")
Wind Energy Meteorology103,99 €![Cosmic Rays in the Earth¿s Atmosphere and Underground]( "Cosmic Rays in the Earth¿s Atmosphere and Underground")
Cosmic Rays in the Earth¿s Atmosphere and Underground221,99 €![Ocean Modelling for Beginners]( "Ocean Modelling for Beginners")
Ocean Modelling for Beginners88,99 €![Viscoelastic Interfaces Driven in Disordered Media]( "Viscoelastic Interfaces Driven in Disordered Media")
Viscoelastic Interfaces Driven in Disordered Media74,99 €![Viscoelastic Interfaces Driven in Disordered Media]( "Viscoelastic Interfaces Driven in Disordered Media")
Viscoelastic Interfaces Driven in Disordered Media74,99 €
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.
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.