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Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir

The nature of flash flood which can be dreadfully torrential in a very short time requires an improvement in its flood forecasting-and-warning system (FFWS) to further mitigate flood damages and loss of life. The use of geostationary meteorological satellite images in estimating rainfall has become...

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Main Author: Tahir, Wardah
Format: Thesis
Language:English
Published: 2008
Subjects:
Flood forecasting
Rain and rainfall
Online Access:https://ir.uitm.edu.my/id/eprint/65317/1/65317.pdf
https://ir.uitm.edu.my/id/eprint/65317/
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spelling my.uitm.ir.653172024-09-20T03:49:24Z https://ir.uitm.edu.my/id/eprint/65317/ Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir Tahir, Wardah Flood forecasting Rain and rainfall The nature of flash flood which can be dreadfully torrential in a very short time requires an improvement in its flood forecasting-and-warning system (FFWS) to further mitigate flood damages and loss of life. The use of geostationary meteorological satellite images in estimating rainfall has become an attractive option in improving the performance of FFWS. Although the estimates are indirect, meteorological satellites with fine temporal and spatial resolution cover broader areas that may be inaccessible or that may cause difficulties with the traditional rainfall measurement such as the oceans or rigid mountains, therefore should be taken as complementary to radar and rain gage measurements. In this study, a rainfall estimation algorithm using the information from the geostationary meteorological satellite infrared (IR) images is developed for potential input to a flood forecasting system. Data from the records of Geostationary Meteorological Satellite-5 (GMS-5) IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation Artificial Neural Network (ANN). The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three Numerical Weather Prediction (NWP) products namely the precipitable water content, relative humidity and vertical wind are also included as inputs; to provide some considerations on the meteorological factors that physically contribute to rain formation. The algorithm is applied for areal-averaged rainfall estimation in the upper Klang River Basin and compared with another technique, which uses power law regression between the IR cloud top brightness temperature and radar rain-rate. Results from both techniques are validated against recorded Thiessan areal-averaged rainfall with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the ANN-based technique, respectively This satellite-based quantitative rainfall estimation using the ANN technique is then transformed to a satellite-based quantitative precipitation forecast (QPF) by using the cross correlation technique to track the direction of movement of the convective cloud cells. In addition, an example rainfall-runoff model is developed, namely the unit hydrograph technique to be linked with the satellite-based QPF to become a coupled hydro-meteorological FFWS. An extra lead time of around two hours is gained when the coupled hydro-meteorological model is applied to forecast several flash-flood events in the upper Klang River Basin. 2008 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/65317/1/65317.pdf Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir. (2008) PhD thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/65317.pdf>
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Flood forecasting
Rain and rainfall
spellingShingle Flood forecasting
Rain and rainfall
Tahir, Wardah
Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
description The nature of flash flood which can be dreadfully torrential in a very short time requires an improvement in its flood forecasting-and-warning system (FFWS) to further mitigate flood damages and loss of life. The use of geostationary meteorological satellite images in estimating rainfall has become an attractive option in improving the performance of FFWS. Although the estimates are indirect, meteorological satellites with fine temporal and spatial resolution cover broader areas that may be inaccessible or that may cause difficulties with the traditional rainfall measurement such as the oceans or rigid mountains, therefore should be taken as complementary to radar and rain gage measurements. In this study, a rainfall estimation algorithm using the information from the geostationary meteorological satellite infrared (IR) images is developed for potential input to a flood forecasting system. Data from the records of Geostationary Meteorological Satellite-5 (GMS-5) IR images have been retrieved for selected convective cells to be trained with the radar rain rate in a back-propagation Artificial Neural Network (ANN). The selected data as inputs to the neural network, are five parameters having a significant correlation with the radar rain rate: namely, the cloud top brightness-temperature of the pixel of interest, the mean and the standard deviation of the temperatures of the surrounding five by five pixels, the rate of temperature change, and the sobel operator that indicates the temperature gradient. In addition, three Numerical Weather Prediction (NWP) products namely the precipitable water content, relative humidity and vertical wind are also included as inputs; to provide some considerations on the meteorological factors that physically contribute to rain formation. The algorithm is applied for areal-averaged rainfall estimation in the upper Klang River Basin and compared with another technique, which uses power law regression between the IR cloud top brightness temperature and radar rain-rate. Results from both techniques are validated against recorded Thiessan areal-averaged rainfall with coefficient correlation values of 0.77 and 0.91 for the power-law regression and the ANN-based technique, respectively This satellite-based quantitative rainfall estimation using the ANN technique is then transformed to a satellite-based quantitative precipitation forecast (QPF) by using the cross correlation technique to track the direction of movement of the convective cloud cells. In addition, an example rainfall-runoff model is developed, namely the unit hydrograph technique to be linked with the satellite-based QPF to become a coupled hydro-meteorological FFWS. An extra lead time of around two hours is gained when the coupled hydro-meteorological model is applied to forecast several flash-flood events in the upper Klang River Basin.
format Thesis
author Tahir, Wardah
author_facet Tahir, Wardah
author_sort Tahir, Wardah
title Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
title_short Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
title_full Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
title_fullStr Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
title_full_unstemmed Satellite based quantitative rainfall estimation for flash flood forecasting / Wardah Tahir
title_sort satellite based quantitative rainfall estimation for flash flood forecasting / wardah tahir
publishDate 2008
url https://ir.uitm.edu.my/id/eprint/65317/1/65317.pdf
https://ir.uitm.edu.my/id/eprint/65317/
_version_ 1811596674314797056
score 13.211869

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