Abstract
The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2.
Highlights
The heterogenous of rain intensity that Southeast Asia (SEA) experiences throughout the northeast monsoon (NEM) and southwest monsoon (SWM) and transition monsoon every year, generally refers to the “monsoon rainfall”
The matching sample between the reflectivity pixel and the gauge vector was used as an input to the non-linear least square regression and artificial neural network (ANN) models to estimate the rainfall at the gauge location
ANN was able to significantly reduce the error difference in the radar rainfall estimate and provide efficient way to estimate the hourly rainfall without gauge adjustment
Summary
The heterogenous of rain intensity that Southeast Asia (SEA) experiences throughout the northeast monsoon (NEM) and southwest monsoon (SWM) and transition monsoon every year, generally refers to the “monsoon rainfall”. The region often receives mean annual rainfall of more than 2500 mm/year. The NEM brings in more rainfall (37–52% of total annual rainfall) than of the SWM (33–40%) [1,2]. The impact of extreme rainfall events in SEA is eventually the episodes of severe floods. A massive flood event in Kelantan, Malaysia in 2014 resulted in huge economic loss about USD 0.3 million [4]. Measuring accurate, reliable, and real time rainfall is crucial in flood management and mitigation specially across SEA [1]
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