Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression

Having insufficient climate data is a critical problem in hydrological studies. Spatial interpolation methods were widely used to overcome the missing data problem. Previously, Advanced Weather Generator (AWE-GEN) parameters are only fitted for the specific rainfall stations at which meteorological...

Full description

Bibliographic Details
Main Authors:	Abdul Halim, Syafrina, Mohd Daud, Zalina
Format:	Conference or Workshop Item
Language:	English
Published:	2016
Subjects:	GE Environmental Sciences HA Statistics QA76 Computer software
Online Access:	http://irep.iium.edu.my/52844/ http://irep.iium.edu.my/52844/ http://irep.iium.edu.my/52844/1/ICMNS%202016.pdf

id	iium-52844
recordtype	eprints
spelling	iium-528442016-12-01T02:35:56Z http://irep.iium.edu.my/52844/ Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression Abdul Halim, Syafrina Mohd Daud, Zalina GE Environmental Sciences HA Statistics QA76 Computer software Having insufficient climate data is a critical problem in hydrological studies. Spatial interpolation methods were widely used to overcome the missing data problem. Previously, Advanced Weather Generator (AWE-GEN) parameters are only fitted for the specific rainfall stations at which meteorological observations exist. The spatial variability of AWE-GEN parameters are examined to overcome the problem of inadequate rainfall data at remote stations. The rainfall parameters estimated in AWE-GEN are interpolated using Locally Weighted Regression (LWR) model. This model was validated by comparing the observed and the interpolated parameters produced monthly. Results show that all rainfall parameters are well produced except for α and μ_c. The monthly statistics at different aggregation periods (i.e. 1, 24 and 48 hours) are also tested. The mean and variance are reproduced very closely to the observed mean and variance with the exception of the month of June. The lag-1 autocorrelation, the skewness, probability of no rainfall and the transition probability from a wet spells seem to be well reproduced at all aggregation periods. Generally, LWR is able to produce commendable result on rainfall simulation for ungauged sites in Peninsular Malaysia. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/52844/1/ICMNS%202016.pdf Abdul Halim, Syafrina and Mohd Daud, Zalina (2016) Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression. In: The 6th International Conference on Mathematics and Natural Sciences (ICMNS 2016), 2nd-3rd November 2016, Bandung, Indonesia. (Unpublished) http://www.chem.itb.ac.id/icmns/
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	GE Environmental Sciences HA Statistics QA76 Computer software
spellingShingle	GE Environmental Sciences HA Statistics QA76 Computer software Abdul Halim, Syafrina Mohd Daud, Zalina Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
description	Having insufficient climate data is a critical problem in hydrological studies. Spatial interpolation methods were widely used to overcome the missing data problem. Previously, Advanced Weather Generator (AWE-GEN) parameters are only fitted for the specific rainfall stations at which meteorological observations exist. The spatial variability of AWE-GEN parameters are examined to overcome the problem of inadequate rainfall data at remote stations. The rainfall parameters estimated in AWE-GEN are interpolated using Locally Weighted Regression (LWR) model. This model was validated by comparing the observed and the interpolated parameters produced monthly. Results show that all rainfall parameters are well produced except for α and μ_c. The monthly statistics at different aggregation periods (i.e. 1, 24 and 48 hours) are also tested. The mean and variance are reproduced very closely to the observed mean and variance with the exception of the month of June. The lag-1 autocorrelation, the skewness, probability of no rainfall and the transition probability from a wet spells seem to be well reproduced at all aggregation periods. Generally, LWR is able to produce commendable result on rainfall simulation for ungauged sites in Peninsular Malaysia.
format	Conference or Workshop Item
author	Abdul Halim, Syafrina Mohd Daud, Zalina
author_facet	Abdul Halim, Syafrina Mohd Daud, Zalina
author_sort	Abdul Halim, Syafrina
title	Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
title_short	Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
title_full	Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
title_fullStr	Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
title_full_unstemmed	Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression
title_sort	spatial interpolation of advanced weather generator parameters in peninsular malaysia using locally weighted regression
publishDate	2016
url	http://irep.iium.edu.my/52844/ http://irep.iium.edu.my/52844/ http://irep.iium.edu.my/52844/1/ICMNS%202016.pdf
first_indexed	2023-09-18T21:14:52Z
last_indexed	2023-09-18T21:14:52Z
_version_	1777411494711918592

Spatial interpolation of advanced weather generator parameters in Peninsular Malaysia using locally weighted regression

Similar Items