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Abstract In this study, we design a statistical method to couple observations with a physics-based tropical cyclone (TC) rainfall model (TCR) and engineered-synthetic storms for assessing TC rainfall hazard. We first propose a bias-correction method to minimize the errors induced by TCR via matching the probability distribution of TCR-simulated historical TC rainfall with gauge observations. Then we assign occurrence probabilities to engineered-synthetic storms to reflect local climatology, through a resampling method that matches the probability distribution of a newly proposed storm parameter named rainfall potential (POT) in the synthetic dataset with that in the observation. POT is constructed to include several important storm parameters for TC rainfall such as TC intensity, duration, and distance and environmental humidity near landfall, and it is shown to be correlated with TCR-simulated rainfall. The proposed method has a satisfactory performance in reproducing the rainfall hazard curve in various locations in the continental United States; it is an improvement over the traditional joint probability method (JPM) for TC rainfall hazard assessment.
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Abstract The parametric hurricane rainfall model (PHRaM), firstly introduced in 2007, has been widely used to forecast and quantify tropical-cyclone-induced rainfall (TC rainfall). The PHRaM is much more computationally efficient than global climate models, but PHRaM cannot be effectively utilized in the context of climate change because it does not have any parameters to capture the increase of tropospheric water vapor under the warming world. This study develops a new model that incorporates tropospheric water vapor to the PHRaM framework, named as the PHRaM with moisture (PHRaMM). The PHRaMM is trained to best fit the TC rainfall over the western North Pacific (WNP) unlike the PHRaM trained with the TCs over the continental US. The PHRaMM reliably simulates radial profile of TC rainfall and spatial distribution of accumulated rainfall during landfall in the present climate with the better prediction skills than existing statistical and operational numerical models. Using the PHRaMM, we evaluated the impacts of TC intensity and environmental moisture increase on TC rainfall change in a future climate. An increased TC intensity causes TC rainfall to increase in the inner-core region but to decrease in the outer region, whereas an increased environmental moisture causes the TC rainfall to increase over the entire TC area. According to the both effects of increased TC intensity and environmental moisture, the PHRaMM projected that the WNP TC rainfall could increase by 4.61–8.51% in the inner-core region and by 17.96–20.91% over the entire TC area under the 2-K warming scenario.more » « less
