Mountain disaster-causing storm identification method based on Doppler radar and deep learning

A technology of Doppler radar and deep learning, which is applied in the field of disaster-caused rainstorm identification in mountainous areas based on Doppler radar and deep learning, can solve the problem of failure to consider radar data and other meteorological elements, easily causing misjudgment, and echo shape Identify problems such as low accuracy and achieve the effects of fully automatic rapid identification, improved accuracy and efficiency, and efficient decision support

Active Publication Date: 2019-05-17
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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AI Technical Summary

Problems solved by technology

Using the physical parameter model to identify short-term heavy precipitation mainly relies on manual judgment, and fails to consider radar data and other meteorological elements
The short-term heavy precipitation identification method based on Doppler data is also limited to extracting a single echo feature from the radar basic reflectivity product, and the accuracy of the echo shape is not high, and it is all aimed at a certain feature ( Such as headwind area, monomer) to identify, and not to extract all the radar features related to heavy precipitation
[0006] However, there are many meteorological features related to short-term heavy precipitation, and the relationship between the physical quantity parameters used in weather forecasting is not clear. Using a single

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  • Mountain disaster-causing storm identification method based on Doppler radar and deep learning
  • Mountain disaster-causing storm identification method based on Doppler radar and deep learning
  • Mountain disaster-causing storm identification method based on Doppler radar and deep learning

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Embodiment 1

[0036] The technical solution adopted in the present invention is based on Doppler radar and deep learning mountain disaster-causing heavy rain identification method, the method is mainly divided into two main parts: one is to extract the strong rainfall related features of Doppler radar data, combined with multi-source Meteorological elements, national mountain torrent disaster survey and evaluation results, establish a mountainous disaster-caused rainstorm model feature database, and use fuzzy matching method to obtain all disaster-caused rainstorm characteristics in the radar strong echo area, and secondly build a regional mountainous torrential rainstorm based on deep learning methods The disaster-caused rainstorm identification model was developed, the model was tuned, and the model identification results were evaluated, so as to realize fully automatic and efficient identification of disaster-causing rainstorms in mountainous areas. Follow the steps below to implement:

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Abstract

The invention relates to a mountain disaster-causing storm identification method based on Doppler radar and deep learning. The method comprises the following steps: extracting heavy rainfall related features of Doppler radar data, establishing the mountain disaster-causing storm model feature database by combining multisource meteorological factors and the nationwide mountain torrents disaster research and appraisal result, and acquiring all disaster-causing storm features in a radar strong echo area by adopting a fuzzy matching method; and constructing a mountain disaster-causing storm identification model with the regional property based on the deep learning method, optimizing the model, evaluating a model identification result, thereby realizing full-automatic and efficient mountain disaster-causing storm identification.

Description

technical field [0001] The invention relates to the technical field of early warning and mountain torrent defense of disaster-causing rainstorms in mountainous areas, specifically a method for identifying disaster-causing torrential rains in mountainous areas based on Doppler radar and deep learning, which is mainly used for identifying short-term heavy rainfall in mountainous areas and torrential rain disasters in mountainous areas. Early warning and forecasting work. Background technique [0002] It is jointly affected by various factors such as special natural and geographical environment, extreme disastrous weather, and economic and social activities. Mountain torrent disasters have the characteristics of many points and wide areas, obvious seasonality and regionality, frequent bursts, rapid disaster formation, and strong destructive power. However, short-duration mountain torrential rain is the main factor causing mountain torrent disasters. Therefore, for mountain torr...

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Application Information

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IPC IPC(8): G01S13/95G01S7/41
CPCY02A90/10
Inventor 郭良刘启刘荣华田济扬王雅莉王丽珍翟晓燕张慧莉涂勇
Owner CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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