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.
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). The important data needed for this work come from Doppler weather radar with high temporal and spatial resolution (Wang et al., 2007). Relevant departments can issue early warning information through accurate nowcasting to avoid loss of life and destruction of infrastructure (Luo et al., 2021). However, 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 image-prediction problem, these shortcomings of the optical flow method are expected to be solved by using a recurrent neural network (RNN) (Giles et al., 1994).
With the continuous development of deep learning, more and more neural networks have been applied to the field of nowcasting. Forecast models such as ConvLSTM and EBGAN-Forecaster show that this new method's extrapolation effect is better than that of optical flow method (Shi et al., 2015; Chen et al., 2019). However, these models still have the problem of blurred and unrealistic prediction images (Tian et al., 2020; Xie et al., 2020; Jing et al., 2019). One of the main reasons is that radar echo maps are typically multi-modal data acquired by multiple sensors and different stations; some algorithms ignore this feature of radar echo maps, using the mean square error and mean absolute error as the loss function, which is better suited to a unimodal distribution.
The paper proposes a generative adversarial network-argcPredNet (GAN–argcPredNet) network model, which aims to solve this problem through GAN's ability to strengthen the characteristics of multi-modal data modeling. The generator adopts the same deep coding–decoding method as PredNet to establish a prediction model, and uses a new structure of convolutional long short-term memory (LSTM) as a predictive neuron, which can effectively reduce the loss of spatiotemporal information compared with rgcLSTM. The deep convolutional network is used as the discriminator to classify the distribution, and the dual-channel input mechanism is used to strictly judge the distribution of real radar echo images. Finally, based on the weather radar echo dataset, the generator and the discriminator are alternately trained to make the extrapolated radar echo map more real and precise.
The essence of radar echo image extrapolation is the problem of sequence image prediction, which can be solved by implementing an end-to-end sequence learning method (Shi et al., 2015; Sutskever et al., 2014). ConvLSTM introduces a convolution operation into the conversion of the internal data state of the LSTM, effectively utilizing the spatial information of the radar echo data (Shi et al., 2015). However, the trajectory gated recurrent unit (TraijGRU) has also been proposed as a solution (Shi et al. 2017) since the location-invariant nature of the convolutional recursive structure is inconsistent with the natural change motion. A GRU (gated recurrent unit), as a kind of recurrent neural network, performs a similar function to LSTM but is computationally cheaper (Group, 2017). Similarly, ConvGRU introduces convolution operations inside the GRU to enhance the sparse connectivity of the model unit and is used to learn video spatiotemporal features (Ballas et al., 2015). The RainNet network learns the movement and evolution of radar echo based on the U-NET convolutional network for extrapolation prediction (Ayzel et al., 2020). PredNet is based on a deep coding framework and adds error units to each network layer that can transmit error signals like the human brain structure (Lotter et al., 2016). In order to increase the depth of the network and the connections between modules, Skip-PredNet further introduces skip connections and uses ConvGRU as the core prediction unit. Experiments show that its effect is better than the TrajGRU benchmark (Sato et al., 2018). Although these networks can achieve echo prediction, they have problems with both blurring and producing unrealistic extrapolated images.
The generative adversarial network (GAN) consists of two parts: a generator and a discriminator (Goodfellow et al., 2014). GAN can be an effective model for generating images. Using an additional GAN loss, a model can better achieve multi-modal data modeling and each of its outputs will be clearer and more realistic (Lotter et al., 2016). Multiple complementary learning strategies show that generative adversarial training can maintain the sharpness of future frames and solve the problem of lack of clarity in prediction (Michael et al., 2015). In this regard, the extrapolators built a generative adversarial network to solve the problem of extrapolated image blur by trying to use this adversarial training to extrapolate more detailed radar echo maps (Singh et al., 2017). Similarly, an adversarial network with ConvGRU as the core was proposed, mainly to solve the problem of ConvGRU's inability to achieve multi-modal data modeling (Tian et al., 2020). There are also researchers working on the idea of a four-level pyramid convolution structure that propose four pairs of models to generate an adversarial network for radar echo prediction (Chen et al., 2019). It should be noted that the traditional GAN network has the problem that it uses unstable training, which will cause the model unable to learn. Therefore, the design of the nowcasting model should be based on a stable and optimized GAN network.
In this section, we describe the model both overall and in detail. Section 3.1 introduces the overall structure and training process of the model. In Sect. 3.2, we describe the structure of the argcPredNet generator and focus on the argcLSTM neuron. In Sect. 3.3, we introduce the design of the discriminator and the loss function of the model.
Radar echo extrapolation refers to the prediction of the dissipation and
distribution of future echoes based on the existing radar echo sequence
diagram. If the problem is formulated, then each echo map can be regarded
as a tensor, i.e.,
The schematic of the GAN–argcPredNet architecture.
GAN–argcPredNet training algorithm flow.
The internal structure of the argcLSTM neuron used in the model is shown in
Fig. 2. In order to provide better feature-extraction capabilities, the
structure contains two trainable gating units: the forget gate
The internal structure of argcLSTM.
The argcPredNet generator has the same structure as PredNet, which is
composed of a series of repeatedly stacked modules, with a total of three layers. The difference is that argcPredNet uses argcLSTM as the prediction
unit. As shown in Fig. 3, each layer of the module contains the following four units: the input convolutional layer (
Module expansion diagram of layer
The recursive prediction layer uses the argcLSTM loop unit, which is used to
generate the prediction of the next frame and the input of
Generator parameter settings.
The purpose of the discriminator is to recognize images, which similar to the purpose to the classifier. In the GAN–argcPredNet model, a double-channel convolutional neural network (DC-CNN) network is designed for discrimination. The process is shown in Fig. 4. It is a four-layer convolution model with a dual-channel input method.
The DC-CNN network extracts a pair of images from the three pairs of images, inputs them to the fully connected layer through a four-layer
convolution transformation and finally generates a probability output
through the Sigmoid function, indicating the possibility that the input
image is from a real image. When the input is a real image, the
discriminator will maximize the probability, and thus the value will approach 1.
If the input is a generator-synthesized image, the discriminator will
minimize the probability, and thus the value will approach
The DC-CNN structure.
Discriminator parameter settings.
The generative adversarial network relies on the distribution of simulated
data to generate images. It can retain more echo details, thereby realizing
the modeling of multi-modal data. A gradient penalty term is added to
GAN–argcPredNet, and the loss function of the discriminator is shown in Eq. (11).
In order to verify the effectiveness of the model, the paper uses the radar echo data from January to July 2020 in Shenzhen, China, to conduct experiments on the four models of ConvGRU, rgcPredNet, argcPredNet, and GAN–argcPredNet. All experiments are implemented in Python and based on the Keras deep-learning library with TensorFlow as the back end for model training and testing.
This experiment uses the radar echo data of Shenzhen, China. The dataset is
made up of rain images after quality control. The reflectivity range is 0–80 dBZ,
the amplitude limit is between 0 and 255, and the data are collected every 6 min, with a total of one layer. The height above sea level is 3 km. A total of
600 000 echo images were collected, of which 550 000 were used as the
training set and 50 000 were used as the test set. Each set of
data contained 12 consecutive images. The horizontal resolution of the radar
echo maps is 0.01
In order to evaluate the accuracy of the model for precipitation nowcasting,
the experiment uses three evaluation indicators to evaluate the prediction
precision of the model, critical success index (CSI, Eq. 14), false
alarm rate (FAR, Eq. 15) and hit rate (probability of detection, POD; Eq. 16).
CSI index score.
POD index score.
FAR index score.
The experiment comprehensively evaluates the prediction accuracy of
precipitation with different thresholds. The radar reflectivity and rainfall
intensity refer to the
Rain level
Figures 5, 6 and 7 compare the CSI, POD and FAR index scores, respectively in detail for each model at different rainfall thresholds.
Four prediction examples for the precipitation nowcasting problem. From top to bottom: ground truth frames, prediction by GAN–argcPredNet, prediction by argcPredNet, prediction by rgcPredNet, prediction by ConvGRU.
This result is calculated based on 50 000 test pictures (more
than 4000 test sets), which is taken as a representative number. It can be seen that when the
rainfall rate increases from 0.5 to 30 mm h
To compare the three methods more intuitively, Fig. 8 shows the image prediction results of the three models on the same piece of test data.
Compared with the other three models, GAN–argcPredNet generates better image clarity and shows more detailed features on a small scale. The contrast between the areas marked by the red circle in Fig. 8 is more obvious. GAN–argcPredNet made the best prediction of the shape and intensity of the echo. The area selected by the rectangle mainly shows the echo changes in the northern region within 30 min. Both models correctly predict the movement of the echo to a certain extent, and the prediction process shown by GAN–argcPredNet is the most complete. In some mixed intensity and edge areas, our model clearly predicts the echo intensity information, which clearly shows the effect of the confrontation training.
In order to compare the prediction results more specifically, the experiment uses mean square error (MSE) and mean structural similarity (MSSIM) to evaluate the quality of the generated images (Wang et al., 2004). The MSE and MSSIM index scores of the images generated by each model are shown in Table 5. ConvGRU has the lowest two indexes. Although the MSE index of rgcPredNet is slightly lower than that of the argcPredNet and GAN–argcPredNet models, the MSSIM index of the argcPredNet and GAN–argcPredNet models is 0.066 and 0.109 higher than that of the rgcPredNet network model, respectively.
MSE and MSSIM index scores of each model.
This study demonstrated a radar echo extrapolation model. The main innovations are summarized as follows. First, the argcPredNet generator is established based on the time and space characteristics of radar data. The argcPredNet generator can predict future echo changes based on historical echo observations. Second, our model uses adversarial training methods to try to solve the problem of blurry predictions.
Based on the evaluation indicators and qualitative analysis results, GAN–argcPredNet has achieved excellent results. Our model can reduce the prediction loss in a small-scale space so that the prediction results have more detailed features. However, the recursive extrapolation method causes the error to accumulate as time goes by, and the prediction result deviates more and more from the true value. In addition, when the amount of high-intensity echo data is small, the prediction of high-risk and strong convective weather through machine learning is also a problem that we are very concerned about because it is more realistic. Therefore, we will carry out research into these two issues in the future.
The GAN–argcPredNet and argcPredNet models are
free and open source. The current version number is GAN–argcPredNet v1.0,
and the source code is provided through a GitHub repository
KZ was responsible for developing the models and writing the manuscript. YL and QT conducted the model experiments and co-authored the manuscript. JZ, CL, ST, HR, YY and XR were responsible for data screening and preprocessing.
The contact author has declared that neither they nor their co-authors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This research was funded by Science and Technology Planning Project of Guangdong Province, China (grant no. 2018B020207012).
This research has been supported by the Science and Technology Planning Project of Guangdong Province (grant no. 2018B020207012).
This paper was edited by David Topping and reviewed by one anonymous referee.