Weather radar can offer synoptic measurement at a higher temporal and spatial resolution to extract the rain information. Rainfall can be inverted from the radar reflectivity using the power-law relation to ground rain gauge measurement. The relationship known as Z-R model has been established in many variants but the uncertainty from the sampling bias and the Z-R variability of single-polarization radar observation on monsoon rain becomes subject to research. This study reports a novel research framework to systematically estimate the monsoon rainfall using new Z-R model on the single-polarization weather radar in Kelantan. The sampling bias was quantified by the pixel matching procedure while the non-linear Levenberg Marquardt (LM) regression and the Artificial Neural Network (ANN) regression at different rain intensity and radar range were introduced to minimise the Spatio-temporal variability of the new Z-R model. This study uses 10-minute reflectivity data recorded in Kota Bahru radar station and hourly rain record at the nearby 58 gauge stations in 2013 to 2015. The three-dimensional nearest neighbour interpolation proves that the sampling bias can be quantified. The LM shows an improvement of about 12% if the spatial adjustment was applied in the regression. Unlike LM, the ANN is more robust and independent to the spatial adjustment thus it could provide more accurate and reliable monsoon rain information in heterogenous rainy condition. The ANN model provides accuracy of ± 0.4 mm/hr, ± 1.0 mm/hr and ± 8.2 mm/hr for low, medium and high rain intensity respectively with correlation coefficient > 0.7 (p 0.5 and accuracy improvement about 8 %, 10% and 5% for abovementioned rain intensity respectively. Radar derived rainfall maps present the rain distribution was more concentrated in all downstream but only covered 1/3 of the upstream in Kelantan rivers. Further research is needed before the technique could be applied to any single-polarization system in Southeast Asia to achieve better accuracy of rain information extraction
