Abstract
Abstract. Precipitation nowcasting plays a vital role in preventing meteorological disasters, and Doppler radar data act as an important input for nowcasting models. When using the traditional extrapolation method it 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 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 features of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling, and design the radar echo extrapolation model GAN–argcPredNet v1.0. The model is composed of an argcPredNet generator and a convolutional neural network discriminator. In the generator, a gate controlling the memory and output is designed in the rgcLSTM component, thereby reducing the loss of spatiotemporal information. In the discriminator, the model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and it thus has a more powerful discrimination ability. Through experiments on a radar dataset from Shenzhen, China, the results show that the radar echo hit rate (probability of detection; POD) and critical success index (CSI) have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet under different intensity rainfall thresholds, and the false alarm rate (FAR) has decreased by an average of 17.9 %. We also found a problem during the comparison of the result graph and the evaluation index. 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
Precipitation nowcasting refers to the prediction and analysis of rainfall in the target area over a short period of time (0–6 h) (Bihlo, 2019; Luo et al, 2020)
A is set to 58.53, b is set to 1.56, Z represents the intensity of radar reflectivity, R represents the intensity of rainfall, and the corresponding relationship between rainfall and rainfall level is given in Table 4 (Shi et al, 2017)
This study demonstrated a radar echo extrapolation model
Summary
Precipitation nowcasting refers to the prediction and analysis of rainfall in the target area over a short period of time (0–6 h) (Bihlo, 2019; Luo et al, 2020). Relevant departments can issue early warning information through accurate nowcasting to avoid loss of life and destruction of infrastructure (Luo et al, 2021). This task is extremely challenging due to its very low tolerance for time and position errors (Sun et al, 2014). The existing nowcasting systems mainly include two types, numerical weather prediction (NWP) and radar echo extrapolation (Chen et al, 2020). The widely used optical flow method several problems, such as its poor capture quality in fast echo change regions, the high complexity of the algorithm and its low efficiency (Shangzan et al, 2017). Since echo extrapolation can be considered a time series imageprediction problem, these shortcomings of the optical flow
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