Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]

In Malaysia, there are two types of flood that normally occur namely, monsoon flood and flash flood. Floods associated with the monsoonal rainfall events are common occurrences on the eastern coast of Peninsular Malaysia during the northeast monsoon season. Every year tropical monsoon storms result...

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Main Authors:	Tahir, Wardah, Atan, Ismail, Abu Bakar, Sahol Hamid, Aminuddin, Ahmad Kamil
Format:	Research Reports
Language:	English
Published:	Research Management Institute (RMI) 2012
Subjects:	Instruments and machines
Online Access:	http://ir.uitm.edu.my/id/eprint/23725/ http://ir.uitm.edu.my/id/eprint/23725/1/LP_WARDAH%20TAHIR%20RMI%2012_5.pdf

id	uitm-23725
recordtype	eprints
spelling	uitm-237252019-04-05T07:03:23Z http://ir.uitm.edu.my/id/eprint/23725/ Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.] Tahir, Wardah Atan, Ismail Abu Bakar, Sahol Hamid Aminuddin, Ahmad Kamil Instruments and machines In Malaysia, there are two types of flood that normally occur namely, monsoon flood and flash flood. Floods associated with the monsoonal rainfall events are common occurrences on the eastern coast of Peninsular Malaysia during the northeast monsoon season. Every year tropical monsoon storms result in severe flooding and causes enormous economic damage, social disruption, and sometimes loss of lives. Extreme monsoon storm weather phenomenon is the most destructive natural disaster afflicting Malaysia with respect to the cost, damages to properties and the area of extent (Keizrul and Chong, 2002). Given the sparseness of ground based observations, missing records and uneven distribution of the existing raingauge network, there is no adequate and timely information about rainfall pattern in Malaysia. An alternative source of quantitative precipitation information is from the Numerical Weather Prediction (NWP) model products. However, the accuracy of quantitative precipitation forecast produced by the Malaysian Meteorological Department (MMD) is still lacking even though significant progress has been made on the technical aspects (Low, 2006) . The study examined the effectiveness of two high resolution Numerical Weather Prediction (NWP) models namely the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) in predicting Quantitative Precipitation Forecast (QPF) over a tropical region. In this study, Kelantan River Basin has been selected as the case study to evaluate the performance and accuracy of precipitation forecast produced by the NWP models for monsoon flood events in the catchment area. Hourly and daily total rainfall data in year 2009 had been analysed. The rainfall events were further classified into low, moderate and heavy rainfall by using Drainage and Irrigation Department (DID) Malaysia standard. The performance and accuracy of the NWP model outputs against rainfall amount was verified using Root Mean Square Error (RMSE) and correlation (r). Notably, the statistical verification shows that there is quite strong correlation for 24 hourly rainfall forecast and the RMSE values are smaller for short range forecast (hourly up to 24 hourly). It is also noted that the longer the rainfall forecast duration, the higher probability of detection (POD) and the lesser probability of the false alarm ratio (FAR). Research Management Institute (RMI) 2012 Research Reports NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/23725/1/LP_WARDAH%20TAHIR%20RMI%2012_5.pdf Tahir, Wardah and Atan, Ismail and Abu Bakar, Sahol Hamid and Aminuddin, Ahmad Kamil (2012) Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]. [Research Reports] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Instruments and machines
spellingShingle	Instruments and machines Tahir, Wardah Atan, Ismail Abu Bakar, Sahol Hamid Aminuddin, Ahmad Kamil Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
description	In Malaysia, there are two types of flood that normally occur namely, monsoon flood and flash flood. Floods associated with the monsoonal rainfall events are common occurrences on the eastern coast of Peninsular Malaysia during the northeast monsoon season. Every year tropical monsoon storms result in severe flooding and causes enormous economic damage, social disruption, and sometimes loss of lives. Extreme monsoon storm weather phenomenon is the most destructive natural disaster afflicting Malaysia with respect to the cost, damages to properties and the area of extent (Keizrul and Chong, 2002). Given the sparseness of ground based observations, missing records and uneven distribution of the existing raingauge network, there is no adequate and timely information about rainfall pattern in Malaysia. An alternative source of quantitative precipitation information is from the Numerical Weather Prediction (NWP) model products. However, the accuracy of quantitative precipitation forecast produced by the Malaysian Meteorological Department (MMD) is still lacking even though significant progress has been made on the technical aspects (Low, 2006) . The study examined the effectiveness of two high resolution Numerical Weather Prediction (NWP) models namely the Fifth Generation Penn State/NCAR Mesoscale (MM5) and Weather Research and Forecasting (WRF) in predicting Quantitative Precipitation Forecast (QPF) over a tropical region. In this study, Kelantan River Basin has been selected as the case study to evaluate the performance and accuracy of precipitation forecast produced by the NWP models for monsoon flood events in the catchment area. Hourly and daily total rainfall data in year 2009 had been analysed. The rainfall events were further classified into low, moderate and heavy rainfall by using Drainage and Irrigation Department (DID) Malaysia standard. The performance and accuracy of the NWP model outputs against rainfall amount was verified using Root Mean Square Error (RMSE) and correlation (r). Notably, the statistical verification shows that there is quite strong correlation for 24 hourly rainfall forecast and the RMSE values are smaller for short range forecast (hourly up to 24 hourly). It is also noted that the longer the rainfall forecast duration, the higher probability of detection (POD) and the lesser probability of the false alarm ratio (FAR).
format	Research Reports
author	Tahir, Wardah Atan, Ismail Abu Bakar, Sahol Hamid Aminuddin, Ahmad Kamil
author_facet	Tahir, Wardah Atan, Ismail Abu Bakar, Sahol Hamid Aminuddin, Ahmad Kamil
author_sort	Tahir, Wardah
title	Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
title_short	Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
title_full	Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
title_fullStr	Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
title_full_unstemmed	Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]
title_sort	prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / wardah tahir … [et al.]
publisher	Research Management Institute (RMI)
publishDate	2012
url	http://ir.uitm.edu.my/id/eprint/23725/ http://ir.uitm.edu.my/id/eprint/23725/1/LP_WARDAH%20TAHIR%20RMI%2012_5.pdf
first_indexed	2023-09-18T23:11:15Z
last_indexed	2023-09-18T23:11:15Z
_version_	1777418817335459840

Prognostic equation based on artificial neural network for quantitative rainfall forecast using numerical weather prediction model products / Wardah Tahir … [et al.]

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