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Existing studies on radar rainfall uncertainties were performed to reduce the uncer-tainty for each stage by using bias correction during the quantitative radar rainfall estimationprocess. However, the studies do not provide quantitative comparison with the uncertainties forall stages. Consequently, this study proposes a suitable approach that can quantify the uncer-tainties at each stage of the quantitative radar rainfall estimation process. First, the newapproach can present initial and final uncertainties, increasing or decreasing the uncertainty,and the uncertainty percentage at each stage. Furthermore, Maximum Entropy (ME) wasapplied to quantify the uncertainty in the entire process. Second, for the uncertainty quantifica-tion of radar rainfall estimation at each stage, this study used two quality control algorithms,two rainfall estimation relations, and two bias correction techniques as post-processing andprogressed through all stages of the radar rainfall estimation. For the proposed approach, thefinal uncertainty (ME = 3.81) from the ME of the bias correction stage was the smallest whilethe uncertainty of the rainfall estimation stage was higher because of the use of an unsuitablerelation. Additionally, the ME of the quality control was at 4.28 (112.34%), while that of therainfall estimation was at 4.53 (118.90%), and that of the bias correction at 3.81 (100%). How-ever, this study also determined that selecting the appropriate method for each stage wouldgradually reduce the uncertainty at each stage. Finally, the uncertainty due to natural variabil-ity was 93.70% of the final uncertainty. Thus, the results indicate that this new approach cancontribute significantly to the field of uncertainty estimation and help with estimating moreaccurate radar rainfall.Key words: Radar rainfall estimation, uncertainty quantification, uncertainty propagation,maximum entropy

Uncertainty Analysis of Quantitative Radar Rainfall Estimation Using the Maximum Entropy