Abstract

Precipitation nowcasting play a vital role in preventing meteorological disasters and doppler radar data acts as an important input for nowcasting models. The traditional extrapolation method is difficult to model highly nonlinear echo movements. The key challenge of the nowcasting mission lies in achieving high-precision radar echo extrapolation. In recent years, machine learning has made a great progress in the extrapolation of weather radar echoes. However, most of models neglect the multi-modal characteristics of radar echo data, resulting in blurred and unrealistic prediction images. This paper aims to solve this problem by utilizing the feature of the GAN that can enhance the multi-modal distribution modelling, and design the radar echo extrapolation model of GAN-argcPredNet. The model composed of argcPredNet generator and a convolutional neural network discriminator. In generator, a gate control data memory and output are designed in the rgcLSTM prediction unit of the generator, thereby reducing the loss of spatiotemporal information. In discriminator, model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and has a more powerful discrimination ability. Through experiments on the radar data set of Shenzhen, China, the results show that the radar echo hit rate (POD) and critical success index (CSI) increased by 5.5 % and %10.4 % compared with rgcPredNet, the false alarm rate (FAR) is reduced by 15 %~20 %. From the comparison of the result graph and the evaluation index, we also found a problem. The recursive prediction method will produce the phenomenon that the prediction result will gradually deviate from the true value over time. In addition, the accuracy of high-intensity echo extrapolation is relatively low. This is a question worthy of further investigation. In the future, we will continue to conduct research from these two directions.

Highlights

  • Through experiments on the radar data set of Shenzhen, China, the results show that the radar echo hit rate (POD) and critical success index (CSI) increased by 5.5% and %10.4% compared with rgcPredNet, the false alarm rate (FAR) is reduced by 15%~20%

  • Since echo extrapolation can be considered 35 as a time series image prediction problem, these shortcomings of optical flow method are expected to be solved by recurrent neural network (RNN) (Giles et al 1994)

  • The argcPredNet generator is established based on the time and space characteristics of radar data. argcPredNet can predict future echo changes based on historical echo observations

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Summary

Introduction

Precipitation nowcasting refers to the prediction and analysis of rainfall in the target area in a short period of time (0-6 hours) (Bihlo et al 2019). Relevant departments can issue early warning information through accurate nowcasting to avoid 30 loss of economic life. This task is extremely challenging due to its very low tolerance to time and position errors (Sun et al 2014). The existing nowcasting systems mainly include two types, numerical weather prediction (NWP) and based on radar echo extrapolation (Chen et al 2020). The widely used optical flow method has problems such as poor capture of fast echo change regions, high complexity of the algorithm and low efficiency (Shang et al 2017). Since echo extrapolation can be considered 35 as a time series image prediction problem, these shortcomings of optical flow method are expected to be solved by recurrent neural network (RNN) (Giles et al 1994)

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