Feasibility Study Of Deriving Areal Reduction Factor For Storm Design Application In Malaysia Using Satellite Rainfall Products

Lariyah Mohd Sidek, KOK WANSIK, KIM JOOCHEOL, JUNG KWANSUE

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Abstract

This study presents the feasibility of deriving areal reduction factor (ARF) for storm design application in Malaysia using satellite rainfall
products. It is required to review the ARF estimations in Malaysia as some shortcomings have been noticed in the existing technical
manual. This study attempted to merge observed rainfall data from rainfall stations with satellite rainfall products for derivation of ARF
as conventional ground based rainfall monitoring networks have the major drawback of limited rainfall measuring coverage over large
spatial extent especially for sparse monitoring networks. To this end, annual maximum areal rainfall for interval of 1‐hour, 3‐hour, 6‐hour,
12‐hour, and 24‐hour have been computed from nine rainfall stations located within the study area and the corresponding satellite rainfall
products were also extracted for rainfall clustering analysis. The observed rainfall data were merged with satellite rainfall products of
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN‐CCS)
using several merging techniques which are Geographical Differential Analysis (GDA), distribution transformations, and regression analysis.
GDA approach was identified as the best merging technique over the others as it yielded most promising results when correlating with
observed rainfall data with average R2 of 0.603 and RMSE of 8.150. It is found out that GDA was able to model maximum rainfall amount as observed in the rainfall stations while the other merging techniques were failed to do so. It is noticed from the rainfall clustering analysis that the average threshold radius obtained for observed rainfall data of fixed boundary area of 1,024 km2 was smaller than the one obtained for satellite rainfall products. This implies that spatial distribution of satellite rainfall products was more homogenous than observed rainfall data. Thus higher values may be obtained from ARF derivation using satellite rainfall products.
Original languageEnglish
Article number0
Pages (from-to)399-409
Number of pages10
JournalJ. Korean Soc. Hazard Mitig.
Volume17
Issue number6
Publication statusPublished - 31 Dec 2001

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Rain
Satellites
Merging
Monitoring
Regression analysis
Spatial distribution

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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title = "Feasibility Study Of Deriving Areal Reduction Factor For Storm Design Application In Malaysia Using Satellite Rainfall Products",
abstract = "This study presents the feasibility of deriving areal reduction factor (ARF) for storm design application in Malaysia using satellite rainfallproducts. It is required to review the ARF estimations in Malaysia as some shortcomings have been noticed in the existing technicalmanual. This study attempted to merge observed rainfall data from rainfall stations with satellite rainfall products for derivation of ARFas conventional ground based rainfall monitoring networks have the major drawback of limited rainfall measuring coverage over largespatial extent especially for sparse monitoring networks. To this end, annual maximum areal rainfall for interval of 1‐hour, 3‐hour, 6‐hour,12‐hour, and 24‐hour have been computed from nine rainfall stations located within the study area and the corresponding satellite rainfallproducts were also extracted for rainfall clustering analysis. The observed rainfall data were merged with satellite rainfall products ofPrecipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN‐CCS)using several merging techniques which are Geographical Differential Analysis (GDA), distribution transformations, and regression analysis.GDA approach was identified as the best merging technique over the others as it yielded most promising results when correlating withobserved rainfall data with average R2 of 0.603 and RMSE of 8.150. It is found out that GDA was able to model maximum rainfall amount as observed in the rainfall stations while the other merging techniques were failed to do so. It is noticed from the rainfall clustering analysis that the average threshold radius obtained for observed rainfall data of fixed boundary area of 1,024 km2 was smaller than the one obtained for satellite rainfall products. This implies that spatial distribution of satellite rainfall products was more homogenous than observed rainfall data. Thus higher values may be obtained from ARF derivation using satellite rainfall products.",
author = "{Mohd Sidek}, Lariyah and KOK WANSIK and KIM JOOCHEOL and JUNG KWANSUE",
year = "2001",
month = "12",
day = "31",
language = "English",
volume = "17",
pages = "399--409",
journal = "J. Korean Soc. Hazard Mitig.",
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}

Feasibility Study Of Deriving Areal Reduction Factor For Storm Design Application In Malaysia Using Satellite Rainfall Products. / Mohd Sidek, Lariyah; WANSIK, KOK; JOOCHEOL, KIM; KWANSUE, JUNG.

In: J. Korean Soc. Hazard Mitig., Vol. 17, No. 6, 0, 31.12.2001, p. 399-409.

Research output: Contribution to journalArticle

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AU - Mohd Sidek, Lariyah

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AU - KWANSUE, JUNG

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AB - This study presents the feasibility of deriving areal reduction factor (ARF) for storm design application in Malaysia using satellite rainfallproducts. It is required to review the ARF estimations in Malaysia as some shortcomings have been noticed in the existing technicalmanual. This study attempted to merge observed rainfall data from rainfall stations with satellite rainfall products for derivation of ARFas conventional ground based rainfall monitoring networks have the major drawback of limited rainfall measuring coverage over largespatial extent especially for sparse monitoring networks. To this end, annual maximum areal rainfall for interval of 1‐hour, 3‐hour, 6‐hour,12‐hour, and 24‐hour have been computed from nine rainfall stations located within the study area and the corresponding satellite rainfallproducts were also extracted for rainfall clustering analysis. The observed rainfall data were merged with satellite rainfall products ofPrecipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN‐CCS)using several merging techniques which are Geographical Differential Analysis (GDA), distribution transformations, and regression analysis.GDA approach was identified as the best merging technique over the others as it yielded most promising results when correlating withobserved rainfall data with average R2 of 0.603 and RMSE of 8.150. It is found out that GDA was able to model maximum rainfall amount as observed in the rainfall stations while the other merging techniques were failed to do so. It is noticed from the rainfall clustering analysis that the average threshold radius obtained for observed rainfall data of fixed boundary area of 1,024 km2 was smaller than the one obtained for satellite rainfall products. This implies that spatial distribution of satellite rainfall products was more homogenous than observed rainfall data. Thus higher values may be obtained from ARF derivation using satellite rainfall products.

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