Abstract
Heavy rain associated with landfalling typhoons often leads to disasters in South China, which can be reduced by improving the accuracy of radar quantitative precipitation estimation (QPE). At present, raindrop size distribution (DSD)-based nonlinear fitting (QPEDSD) and traditional neural networks are the main radar QPE algorithms. The former is not sufficient to represent the spatiotemporal variability of DSDs through the generalized Z–R or polarimetric radar rainfall relations that are established using statistical methods since such parametric methods do not consider the spatial distribution of radar observables, and the latter is limited by the number of network layers and availability of data for training the model. In this paper, we propose an alternative approach to dual-polarization radar QPE based on deep learning (QPENet). Three datasets of “dual-polarization radar observations—surface rainfall (DPO—SR)” were constructed using radar observations and corresponding measurements from automatic weather stations (AWS) and used for QPENetV1, QPENetV2, and QPENetV3. In particular, 13 × 13, 25 × 25, and 41 × 41 radar range bins surrounding each AWS location were used in constructing the datasets for QPENetV1, QPENetV2, and QPENetV3, respectively. For training the QPENet models, the radar data and AWS measurements from eleven landfalling typhoons in South China during 2017–2019 were used. For demonstration, an independent typhoon event was randomly selected (i.e., Merbok) to implement the three trained models to produce rainfall estimates. The evaluation results and comparison with traditional QPEDSD algorithms show that the QPENet model has a better performance than the traditional parametric relations. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), the QPEDSD model shows a comparable performance to QPENet. Comparing the three versions of the QPENet model, QPENetV2 has the best overall performance. Only when the hourly rainfall intensity is less than 5 mm (R < 5 mm·h−1), QPENetV3 performs the best.
Highlights
Introduction conditions of the Creative CommonsHeavy rain from landfalling typhoons is one of the major natural disasters in SouthChina, which often causes life and economic losses [1]
The precipitation estimation accuracy of the parametric QPEDSD methods is determined by the physical model of DSD and the relationship between the physical model and the radar parameters [2,12,13]
We extended the deep learning applications for radar quantitative precipitation estimation (QPE) in south China, with an emphasis on typhoon events
Summary
Introduction conditions of the Creative CommonsHeavy rain from landfalling typhoons is one of the major natural disasters in SouthChina, which often causes life and economic losses [1]. It is difficult to present this functional relation in a simple form due to the complex spatiotemporal variability in DSDs [4,5], especially during typhoon events which are often characterized by complicated precipitation microphysical processes [6]. The precipitation estimation accuracy of the parametric QPEDSD methods is determined by the physical model of DSD and the relationship between the physical model and the radar parameters [2,12,13]. The parameter relationships between rain rate (R) and the polarimetric radar observables, including reflectivity (ZH ), differential reflectivity (ZDR ), and the specific differential phase (KDP ), are not sufficient to characterize the variation [14,15,16,17]. The traditional neural network method is limited by the number of network layers and the availability of training data, resulting in poor learning performance
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