• 한국환경과학회지
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• 제6권3호
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• pp.205-211
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• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• 대기
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• 제20권3호
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• pp.333-341
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• 2010

The stratiform rain fraction is investigated in the tropical boreal winter Madden-Julian oscillation (MJO) and summer intraseasonal oscillation (ISO) using Tropical Rainfall Measuring Mission (TRMM) Precipitation Rader data for the 11-yr period from 1998 to 2008. Composite analysis shows that the MJO/ISO produces larger stratiform rain rate than convective rain rate for nearly all phases following the propagating MJO/ISO deep clouds, with the greatest stratiform rainfall amount when the MJO/ISO center is located over the central-eastern Indian Ocean and the western Pacific. The fraction of the intraseasonally filtered stratiform rainfall compared to total rainfall (i.e., convective plus stratiform rainfall) amounts to 53~56%, which is 13~16% larger than the stratiform rain fraction estimated for the same data on seasonal-to-annual time scales by Schumacher and Houze. This indicates that the MJO/ISO exhibits the organized rainfall process which is characterized by the shallow convection/heating at the incipient phase and the subsequent flare-up of strong deep convection, followed by the development of stratiform clouds at the upper troposphere.