Precipitation estimation is a challenge for atmospheric remote sensing: a number of satellite sensors, with different sensitivity to precipitation, are commonly used to feed estimation techniques. Precipitation signature in the radiation measured from an orbiting sensor varies across the wavelength: is generally low in the visible-infrared and higher in the microwave. On the other hand, due to diffraction reasons, microwave sensors are only operated on low orbit satellites, resulting in high revisiting time and large footprint at the ground. To overcome these limitations, multisensor approaches are pursued, combining microwave and VIS-IR measures in order to mutually mitigate disadvantages and enhance capabilities. In the frame of CEOP-AEGIS an effort was undertaken to perform precipitation estimation on the Tibetan Plateau, where the knowledge of precipitation systems is very low and the ground-based observation system is poor. A summary of the results achieved in the Project is presented, with emphasis on satellite precipitation estimation, showing advantages and drawbacks of the considered techniques. A new Artificial Neural Network multisensor technique has been implemented on the Plateau, by using infrared METEOSAT-7 channels, ground radar rainrate measurements and microwave satellite estimates. Comparison with ground data and global scale precipitation products are considered and the role of orography and diurnal cycle on the precipitation intensity and spatial distribution is evaluated. The results are also considered with a look in the next future, when other sensors, dedicated to precipitation measurement, such as the Dual-frequency Precipitation Radar, on board the GPM Core Observatory, will be available.
Satellite Precipitation Estimation over the Tibetan Plateau and Perspectives for new Satellite Missions
PORCU', Federico;GJOKA, Uarda;
2013
Abstract
Precipitation estimation is a challenge for atmospheric remote sensing: a number of satellite sensors, with different sensitivity to precipitation, are commonly used to feed estimation techniques. Precipitation signature in the radiation measured from an orbiting sensor varies across the wavelength: is generally low in the visible-infrared and higher in the microwave. On the other hand, due to diffraction reasons, microwave sensors are only operated on low orbit satellites, resulting in high revisiting time and large footprint at the ground. To overcome these limitations, multisensor approaches are pursued, combining microwave and VIS-IR measures in order to mutually mitigate disadvantages and enhance capabilities. In the frame of CEOP-AEGIS an effort was undertaken to perform precipitation estimation on the Tibetan Plateau, where the knowledge of precipitation systems is very low and the ground-based observation system is poor. A summary of the results achieved in the Project is presented, with emphasis on satellite precipitation estimation, showing advantages and drawbacks of the considered techniques. A new Artificial Neural Network multisensor technique has been implemented on the Plateau, by using infrared METEOSAT-7 channels, ground radar rainrate measurements and microwave satellite estimates. Comparison with ground data and global scale precipitation products are considered and the role of orography and diurnal cycle on the precipitation intensity and spatial distribution is evaluated. The results are also considered with a look in the next future, when other sensors, dedicated to precipitation measurement, such as the Dual-frequency Precipitation Radar, on board the GPM Core Observatory, will be available.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
