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...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/52844/1/ICMNS%202016.pdf http://irep.iium.edu.my/52844/ http://www.chem.itb.ac.id/icmns/ |
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| Summary: | 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. |
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