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Heavy rainfall and thunderstorm forecast method and system based on multi-scale convolutional neural network

A convolutional neural network and forecasting system technology, applied in the field of weather forecasting, can solve the problems of low accuracy, coarse granularity in time and space, etc.

Active Publication Date: 2020-12-15
南京云思创智信息科技有限公司
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Problems solved by technology

[0004] Aiming at the above-mentioned deficiencies existing in the prior art, the technical problem to be solved by the present invention is to provide a method and system for forecasting heavy rainfall and thunderstorms based on multi-scale convolutional neural networks, to solve the problems of traditional heavy rainfall and thunderstorm forecasting systems. Coarse granularity and low accuracy in time and space

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  • Heavy rainfall and thunderstorm forecast method and system based on multi-scale convolutional neural network
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  • Heavy rainfall and thunderstorm forecast method and system based on multi-scale convolutional neural network

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[0039] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments.

[0040] figure 1 It is a radar echo map of Jiangsu area at 8:06 on June 23, 2016, in which the reflectivity is marked with color, and the darker areas, such as red and purple, belong to strong reflectivity. Usually there is heavy rainfall or thunderstorms in this area, and the size of one pixel in the image represents the size of one kilometer on the ground surface. The generation cycle of the radar echo image is usually 5 minutes to obtain an echo image. Compared with the traditional meteorological index data, the data obtained by using the radar echo chart has the characteristics of better real-time performance, higher accuracy and lower cost in short-term weather forecasting.

[0041] Although the radar echo chart reflects the current weather conditions, we can form a space and time scale interrelated data through multiple correlated radar ...

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Abstract

The invention relates to a method and system for forecasting heavy rainfall and thunderstorms based on a multi-scale convolutional neural network. The present invention includes acquiring original radar echo data; preprocessing the original radar echo data to obtain radar echo time series images; taking any three consecutive radar echo time series images, and constructing radar echo time series images The convolutional neural network deduces the radar echo map of the next frame; through the optical flow field method and linear interpolation method, the radar echo change within one minute is inferred. The invention can forecast thunderstorm weather within 1 hour, and the space range can be reduced to within 1 kilometer.

Description

technical field [0001] The invention relates to the technical field of weather forecasting, in particular to a method and system for forecasting heavy rainfall and thunderstorms based on a multi-scale convolutional neural network. Background technique [0002] At present, the models that use machine learning models to forecast heavy rainfall or thunderstorms mainly include neural networks and support vector machines. These two models need to use some meteorological indicators when making forecasts, such as surface temperature, air pressure, high-altitude temperature, wind field, divergence, vertical velocity, etc., as well as some atmospheric instability factors calculated from meteorological indicators, such as K index, CT index, VT index, etc. The predicted results are relatively rough in time and space, and the accuracy is not high. In terms of time, it usually predicts whether there will be thunderstorms within 24 hours, and in terms of space, it usually forecasts the ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01W1/10G01S13/95G06N3/04
CPCG01S13/95G01W1/10G06N3/045Y02A90/10
Inventor 汪力
Owner 南京云思创智信息科技有限公司