Use of NWP model products and metsat images data for quantitative precipitation forecast
Quantitative Precipitation Forecast (QPF) from Numerical Weather Prediction (NWP) model products combined with geostationary meteorological satellite (metsat) data as input to a flood forecasting system has great potential to provide improved lead time for warning. In this study, a QPF Model is deve...
| Published in: | Journal of Engineering and Applied Sciences |
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| Main Author: | |
| Format: | Article |
| Language: | English |
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Medwell Journals
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026380282&doi=10.3923%2fjeasci.2017.2248.2253&partnerID=40&md5=5c592ebbab12e306df3b77a19679755a |
| Summary: | Quantitative Precipitation Forecast (QPF) from Numerical Weather Prediction (NWP) model products combined with geostationary meteorological satellite (metsat) data as input to a flood forecasting system has great potential to provide improved lead time for warning. In this study, a QPF Model is developed using the artificial multilayer neural network with data inputs from selected NWP model products combined with the metsat image features such as cloud top brightness temperature and albedo to forecast precipitation for a flood-prone area in a tropical region. The model was used to forecast intense rainfall episodes in Kelantan and Klang River Basins of Peninsular Malaysia. The results indicate that the model can satisfactorily produce 1h forecast with improved accuracy for larger forecast area. Performance of the model is better for Klang River Basin with r2 of 0.89 as compared to Kelantan River Basin with r2 of 0.67. © Medwell Journals, 2017. |
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| ISSN: | 1816949X |
| DOI: | 10.3923/jeasci.2017.2248.2253 |