Developing a Remotely Sensed Rainfall Retrieval Algorithm using Multi-Spectral Information
Cecilia Hernandez-Aldarondo, NOAA/Cooperative Remote Sensing Science and Technology Center, New York, NY; and S. Mahani and R. Khanbilvardi
This study is for improving the accuracy of precipitation estimates using satellite-based cloud information from multi-channels, infrared (IR) and microwave. Application of remote sensing data for precipitation estimation is a challenging research area, particularly for the remote and mountainous regions, where ground-based gauge networks and radar sources cannot cover. Developing an artificial neural networks approach to estimate rainfall using cloud-top IR from geostationary operational environmental satellite (GOES) in conjunction with microwave from advanced microwave sounding units AMSU is the main objective of this research project. Remotely sensed infrared imagery provides brightness temperature only from the cloud top, but microwave spectrum can provide properties inside the clouds. Therefore, this model is expected to improve precipitation estimates, using both sources of IR and microwave information. Warm season storms over the western United States are considered for this project. NEXRAD stage IV data is used to train the ANN model and rain gauge data is used to validate this algorithm. Preliminary investigation indicates that the higher frequency microwave (AMSU-89 and -150) is more correlated with rainfall to be combined with the GOES infrared channel 4 and used as model input for precipitation estimation.
Poster Session 1, Retrievals and Cloud Products
Monday, 30 January 2006, 2:30 PM-2:30 PM, Exhibit Hall A2
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