Premium
A coupled K‐nearest neighbour and Bayesian neural network model for daily rainfall downscaling
Author(s) -
Lu Y.,
Qin X. S.
Publication year - 2014
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.3906
Subject(s) - downscaling , environmental science , climatology , percentile , bayesian probability , meteorology , statistics , mathematics , geography , precipitation , geology
A coupled K‐nearest neighbour (KNN) and Bayesian neural network ( BNN ) model was developed for downscaling daily rainfall at a single site. The KNN was used for classification of dry/wet day and rainfall typing based on rainfall magnitude. The BNN was applied for prediction of rainfall amount. The proposed method was applied to rainfall downscaling at Singapore Island. The Climate Forecast System Reanalysis ( CFSR ) data were used for providing large‐scale predictors at a high spatial resolution; 31‐years daily rainfall record at two typical weather stations on the island was used as predictand. The performance of KNN–BNN was compared with two classical downscaling tools including automated statistical downscaling tool ( ASD ) and generalized linear model ( GLM ). The study results indicated that, the proposed model performed equally good or better than both ASD and GLM , in terms of prediction of basic statistical indicators (i.e. mean, SD, probability of wet days, 90th percentile rainfall amount, and maximum rainfall); it notably outperformed others in generating narrower uncertainty intervals for all indicators, especially for monthly mean and maximum rainfall. It was also demonstrated that separation of yearly data into monthly or seasonal could considerably enhance the performance of KNN–BNN .