![]() Networks have been applied to the field of nowcasting. With the continuous development of deep learning, more and more neural Since echo extrapolation can be consideredĪ time series image-prediction problem, these shortcomings of the opticalįlow method are expected to be solved by using a recurrent neural network (RNN) The widely used optical flow method several problems, such as its poor capture quality inįast echo change regions, the high complexity of the algorithm and its lowĮfficiency (Shangzan et al., 2017). Prediction (NWP) and radar echo extrapolation (Chen et al., 2020). The existing nowcasting systems mainly include two types, numerical weather Task is extremely challenging due to its very low tolerance for time and Nowcasting to avoid loss of life and destruction of infrastructure (Luo et al., 2021). Relevant departments can issue early warning information through accurate Weather radar with high temporal and spatial resolution (Wang et al., 2007). The important data needed for this work come from Doppler In the target area over a short period of time (0–6 h) (Bihlo, 2019 Precipitation nowcasting refers to the prediction and analysis of rainfall To conduct research from these two directions. This isĪ question worthy of further investigation. The accuracy of high-intensity echo extrapolation is relatively low. Result will gradually deviate from the true value over time. Recursive prediction method will produce the phenomenon that the prediction We also found a problem during the comparison of the (FAR) has decreased by an average of 17.9 %. Under different intensity rainfall thresholds, and the false alarm rate Have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet ![]() Show that the radar echo hit rate (probability of detection POD) and critical success index (CSI) Through experiments on a radar dataset from Shenzhen, China, the results The true echo distribution, and it thus has a more powerful discrimination ability. In the discriminator, the model uses aĭual-channel input method, which enables it to strictly score according to In the generator, a gate controlling the memory and output isĭesigned in the rgcLSTM component, thereby reducing the loss of Is composed of an argcPredNet generator and a convolutional neural networkĭiscriminator. This paper aims to solve this problem by utilizing theįeatures of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling,Īnd design the radar echo extrapolation model GAN–argcPredNet v1.0. However, most of models neglect the multi-modalĬharacteristics of radar echo data, resulting in blurred and unrealistic Years, machine learning has made great progress in the extrapolation of Mission lies in achieving high-precision radar echo extrapolation. When using the traditional extrapolation method it is difficult to Meteorological disasters, and Doppler radar data act as an important inputįor nowcasting models. ![]() Precipitation nowcasting plays a vital role in preventing
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