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Radar echo extrapolated method based on cycle dynamic convolution nerve network

A technology of convolutional neural network and radar echo, which is applied in radio wave measurement system, radio wave reflection/re-radiation, utilization of re-radiation, etc., can solve the problems of echo intensity, disordered vector interference, and large-scale precipitation forecast. Reliability and other issues

Inactive Publication Date: 2018-11-23
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

[0003] The traditional radar echo extrapolation methods are the centroid tracking method and the tracking radar echoes by correlation (TREC) method based on the maximum correlation coefficient, but the traditional methods have certain deficiencies. Strong and small-scale storm cells are unreliable for forecasting large-scale precipitation; TREC generally regards the echo as linearly changing, but in reality, the echo changes are more complex, and this method is vulnerable to vector field disordered vector interference
In addition, existing methods have a low utilization rate of radar data, while historical radar data contain important features of changes in local weather systems and have high research value

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  • Radar echo extrapolated method based on cycle dynamic convolution nerve network
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  • Radar echo extrapolated method based on cycle dynamic convolution nerve network

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[0054] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0055] Such as figure 1 As shown, the present invention discloses a radar echo extrapolation method based on a circular dynamic convolutional neural network, comprising the following steps:

[0056] Step 1, RDCNN offline training: input the training image set, perform data preprocessing on the training image set, obtain the training sample set, design the RDCNN structure, and initialize the network training parameters; use the training sample set to train the RDCNN, and the input ordered image sequence is passed through Forward propagation obtains a predicted image, calculates the error between the predicted image and the control label, and updates the weight parameters and bias parameters of the network through backpropagation, repeating this process until the predicted result reaches the training end condition, and a converged RDCNN is obtained Model...

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Abstract

The invention discloses a radar echo extrapolated method based on a cycle dynamic convolution nerve network; the method comprises the step of RDCNN online prediction: using a data preprocessing step to build a test sample set, using the test sample set to test the trained RDCNN, convoluting the final frame radar echo image in an input image sequence with a probability vector obtained in network forward propagation, thus obtaining a predicted radar echo image.

Description

technical field [0001] The invention belongs to the technical field of surface meteorological observation in atmospheric detection, and in particular relates to a radar echo extrapolation method based on a circular dynamic convolutional neural network. Background technique [0002] Nowcasting mainly refers to weather forecasting with a high temporal and spatial resolution of 0 to 3 hours, and the main forecasting objects include heavy precipitation, strong wind, hail and other disastrous weather. At present, many forecasting systems use numerical forecasting models, but due to the spin-up delay (spin-up) in numerical forecasting, their short-term nowcasting capabilities are limited. The new generation of Doppler weather radar has high sensitivity and resolution, the spatial resolution of its data can reach 200-1000m, and the time resolution can reach 2-15min. In addition, Doppler weather radar also has a reasonable working mode, comprehensive status monitoring and fault ala...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S13/95G01S7/41
CPCG01S7/417G01S13/95Y02A90/10
Inventor 李骞施恩马强马烁
Owner NAT UNIV OF DEFENSE TECH
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