Objective

2

Radar echo extrapolation is an important method for short-term precipitation prediction. It can achieve faster and more accurate predictions compared with traditional methods

such as numerical weather forecast and optical flow method. Among them

numerical weather forecasting requires complex and meticulous simulations of physical equations in the atmosphere and then uses observation data as input to predict future weather conditions. The optical flow method is currently the mainstream method used by the meteorological department

but it has two inherent flaws. On the one hand

only two adjacent frames can be used to estimate the optical flow; on the other hand

the radar echo sequence cannot be fully used for prediction. Nevertheless

the radar echo extrapolation method based on deep learning can take full advantage of spatiotemporal sequence data to achieve faster and more accurate prediction. In addition

the echo extrapolation algorithm based on convolutional long short-term memory network (ConvLSTM) has been proved to be effective in real applications

and the effect is superior to other deep learning extrapolation algorithms. However

it ignores the limitations of ordinary convolution operations in the face of locally changing features

and in the extrapolation process

the loss function is simply defined as mean square error (MSE)

ignoring the distribution similarity between the extrapolated image and the original image

which is easy to cause information loss. To solve the above problems

an improved echo extrapolation algorithm based on adversarial long short-term memory network (LSTM) is proposed.

Method

2

First

in view of the local-invariance limitations of the traditional convolution kernel

we borrowed the idea of the dense optical flow method and constructed a two-dimensional instantaneous velocity field for all pixels to extract the motion information of each part of the object. Based on this idea

ConvLSTM is improved to form flow long short-term memory network (FLSTM)

which is an optical flow optimization extrapolation algorithm. The algorithm uses optical flow to track local features

breaking through the limitation of local invariance of general convolution kernels. Then

according to the characteristics of radar sequence data (high-dimensional spatiotemporal data)

the convolutional layer is used to extract effective spatial features to reduce spatial redundancy in the encoder

and then deconvolution is used in the decoder to amplify the generated decoded features to the size of the original image to form an output sequence. The convolutional layer and FLSTM are cross-stacked in depth to encode the input spatiotemporal sequence data into a fixed-length vector. The deconvolution and FLSTM are cross-stacked to decode the output sequence from the encoded vector. Finally

in order to obtain extrapolated images with higher accuracy

an adversarial generation network is introduced

and an extrapolation model forms an end-to-end game system deep convolutional generative adversarial flow-based long short-term memory network (DCF-LSTM). In this system

the generation network is the extrapolation model that tends to be stable after pre-training. Then

the pre-trained generation network continue to be alternately trained with the discriminator to further fit the extrapolated image distribution to the real image distribution

thereby improving the accuracy of the extrapolated image.

Result

2

Experiments were carried out under four different reflectance intensities. The DCF-LSTM model is compared with the flow based ConvLSTM (FLSTM) and DC-LSTM

which is an optimized convolutional LSTM by integrating deep convolutional generative adversarial network (DCGAN)

and three mainstream meteorological business algorithms. The experimental results show that DCF-LSTM had the best performance under all intensity thresholds. Its probability of detection (POD) and critical success index (CSI) are higher than the other two methods

and it has the lowest false alarm rate (FAR) and mean square error (MSE)

especially when the reflectivity is 35 dBZ. The higher the value of POD and CSI

the better the model performance; the lower the FAR value

the more accurate the model. Compared with FLSTM

DCF-LSTM has a 0.012 higher POD

0.02 lower FAR

0.015 higher CSI

and 0.115 lower MSE. Compared with DC-LSTM

DCF-LSTM has 0.035 higher POD

0.03 lower FAR

0.034 higher CSI

and 0.274 lower MSE. In addition

compared with TrajGRU

ConvLSTM

and Flow methods

DCF-LSTM has a 0.018

0.047

and 0.099 higher POD; 0.015

0.036

and 0.083 higher CSI; and 0.012

0.034

and 0.087 lower FAR

respectively.

Conclusion

2

The experimental results show that the optical flow method can enable the model to learn the dynamic changes of local features in the radar sequence

breaking through the limitation of local invariance of the convolution operation and making the model more resistant to distortion. In addition

the introduction of DCGAN module for further game training prediction model can further increase the accuracy of the results. Compared with the three mainstream meteorological business algorithms

the DCF-LSTM echo extrapolation algorithm proposed in this study has further improved the accuracy of radar extrapolation.