Multi-sensor data inputs rainfall estimation for flood simulation and forecasting

2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER) Pub Date : 2012-12-01 DOI:10.1109/CHUSER.2012.6504342

T. Wardah, R. Suzana, S. Huda, A. Kamil

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引用次数: 10

Abstract

The research project focused on new techniques in rainfall forecasting and flood monitoring, using multi-sensor data rainfall inputs from the Doppler weather radar, geostationary meteorological satellite and numerical weather prediction (NWP) models. Improved Z-R equations for radar rainfall have been derived for category monsoon and category rain-rate with bias ranging from 1.1 to 1.3. In addition, the rainfall forecasts produced from two NWP models namely the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) are statistically verified with the observed rain for case studies of Kelantan River basin and Klang River basin. The research also investigated the correlation between the images of visible and infrared geostationary meteorological satellite (metsat) to rainfall depth and developed a satellite-based rainfall estimation. Finally, a hydrodynamic model of case study river basin had been developed for an integrated hydro-meteorological flood monitoring system, using one of the multi sensor data rainfall inputs.

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多传感器数据输入降雨估计，用于洪水模拟和预报

该研究项目侧重于降雨预报和洪水监测的新技术，利用多普勒天气雷达、地球同步气象卫星和数值天气预报(NWP)模式输入的多传感器降雨数据。改进了雷达降雨的Z-R方程，适用于季风类别和雨率类别，偏差范围为1.1至1.3。此外，以吉兰丹河流域和巴生河流域为例，对两个NWP模式即第五代Penn State/NCAR中尺度(MM5)和天气研究与预报(WRF)所产生的降雨预报进行了统计验证。研究了可见光和红外地球同步气象卫星(metsat)图像与降雨深度的相关性，开发了基于卫星的降雨估算方法。最后，利用其中一种多传感器降水数据输入，为水文气象综合洪水监测系统开发了案例流域的水动力模型。

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2012 IEEE Colloquium on Humanities, Science and Engineering (CHUSER)

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