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CN114660610A - Convection development identification method based on networking wind profile radar - Google Patents

Convection development identification method based on networking wind profile radar Download PDF

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CN114660610A
CN114660610A CN202210300833.4A CN202210300833A CN114660610A CN 114660610 A CN114660610 A CN 114660610A CN 202210300833 A CN202210300833 A CN 202210300833A CN 114660610 A CN114660610 A CN 114660610A
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徐浩彬
刘显通
叶朗明
徐碧裕
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Jiangmen Xinhui District Meteorological Bureau Jiangmen Xinhui District Meteorological Observatory
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Abstract

The invention provides a convection development identification method based on a networking wind profile radar, which relates to the technical field of weather forecasting and comprises the following steps: s1, networking the wind profile radars, namely screening the wind profile radars for networking, and networking the wind profile radars by four or more than four. According to the invention, by utilizing wind profile radar mesoscale network observation, horizontal wind at different heights can be inverted into atmospheric power parameters with higher divergence, vorticity, wind shear, vertical speed and the like with higher space-time resolution, atmospheric vertical power change characteristics before convection triggering can be more accurately captured, real-time divergence and vorticity distribution in a three-dimensional space can be obtained, a weather forecaster can be helped to know the fine structure of a weather system, a strong ascending motion area can be rapidly found, the method is used for short-time near forecasting of strong rainfall, important references are provided for strong-convection weather monitoring early warning and short-time near forecasting, the practicability is higher, and the progress is obvious.

Description

一种基于组网风廓线雷达的对流发展识别方法A Convective Development Recognition Method Based on Networked Wind Profiler Radar

技术领域technical field

本发明涉及气象预报技术领域,尤其涉及一种基于组网风廓线雷达的对流发展识别方法。The invention relates to the technical field of weather forecasting, in particular to a method for identifying convection development based on a networked wind profile radar.

背景技术Background technique

强对流天气是一种因大气强烈垂直运动而产生的天气现象,具有很强的破坏力,常伴有强降水、大风和雷暴等灾害,比如短时强降水和对流性大风就是典型的强对流天气,应用多普勒、相控阵技术的天气雷达能够对其进行有效监测,目前,结合专业理论和方法能够分析出风暴发生的空间位置、移动速度和发展趋势,以及大风或降水的强度等大量有价值的天气信息,但是,对于强对流的发生发展,仍然缺乏有效的预测方法。Strong convective weather is a weather phenomenon caused by the strong vertical movement of the atmosphere. It has strong destructive power and is often accompanied by disasters such as heavy precipitation, strong winds and thunderstorms. For example, short-term heavy precipitation and convective strong winds are typical strong convection. Weather, weather radar using Doppler and phased array technology can effectively monitor it. At present, combined with professional theories and methods, it can analyze the spatial location, moving speed and development trend of storms, as well as the intensity of strong winds or precipitation, etc. There is a lot of valuable weather information, but there is still no effective prediction method for the occurrence and development of strong convection.

发明内容SUMMARY OF THE INVENTION

本发明的目的是为了解决现有技术中存在的缺点,而提出的一种基于组网风廓线雷达的对流发展识别方法。The purpose of the present invention is to propose a method for identifying convection development based on a networked wind profiler radar in order to solve the shortcomings in the prior art.

为了实现上述目的,本发明采用了如下技术方案:一种基于组网风廓线雷达的对流发展识别方法,所述基于组网风廓线雷达的对流发展识别方法包括如下步骤:In order to achieve the above purpose, the present invention adopts the following technical solutions: a method for identifying convection development based on a networked wind profiler radar, and the method for identifying convection development based on a networked wind profiler radar includes the following steps:

S1,风廓线雷达组网,首先筛选风廓线雷达组网,组网风廓线雷达数量四台或以上,同时使用三角形法确定最优组网站点;S1, wind profiler radar network, first screen the wind profiler radar network, network with four or more wind profiler radars, and use the triangle method to determine the optimal network site;

S2,计算数据,然后使用有限元法遍历三角形组网,计算分层水平散度和相对涡度,产生各高度层水平散度的空间分布图、产生散度和涡度垂直廓线图;S2, calculate the data, then use the finite element method to traverse the triangular network, calculate the layered horizontal divergence and relative vorticity, generate the spatial distribution map of the horizontal divergence at each height layer, and generate the vertical profile of the divergence and vorticity;

S3,数据集构建,将散度空间分布图、散、涡度垂直廓线图、风廓线图以及组网范围内地面气象站雨量、风力实测数据组成数据集;S3, data set construction, the scatter spatial distribution map, scatter and vorticity vertical profile map, wind profile map, and the measured data of rainfall and wind force of the ground meteorological stations within the network range are formed into a data set;

S4,构建深度神经网络模型,构建以卷积神经网络为基础的深度神经网络模型,通过大量样本“数据对”的迭代训练,拟合前一时刻散度图、廓线图等图像数据集与对流天气发展的函数映射关系;S4, build a deep neural network model, build a deep neural network model based on a convolutional neural network, and iteratively train a large number of sample "data pairs" to fit the image data sets such as the divergence map and the profile map at the previous moment. Function mapping relationship of convective weather development;

S5,识别,应用和改进拟合得出的函数映射关系,实现对流发展的识别。S5, identify, apply and improve the function mapping relationship obtained by fitting to realize the identification of convection development.

为了降低误差,本发明改进有,在S1步骤中,所述三角形法就是让站点构成等边三角形,等边三角形为最优组网形式。In order to reduce the error, the improvement of the present invention is that in step S1, the triangle method is to make the stations form an equilateral triangle, and the equilateral triangle is the optimal networking form.

为了能够准确计算,本发明改进有,在S2步骤中,计算的具体方式为:根据确定好的三角形3个顶点测站位置的经纬度和地球平均半径R计算该三角形三边的分量,结合各层3个测站的水平风数据计算该高度三角形内的水平散度D和相对涡度ζ;In order to be able to calculate accurately, the present invention is improved as follows: in step S2, the specific method of calculation is: calculate the components of the three sides of the triangle according to the determined latitude and longitude of the station positions of the three vertices of the triangle and the average radius R of the earth, combine each layer Calculate the horizontal divergence D and relative vorticity ζ within the altitude triangle from the horizontal wind data of the three stations;

随后遍历已经组网的三角形,获得各三角形上空各高度层的水平散度和相对涡度数值,绘制成水平散度的空间分布图,然后使用各高度层计算得到的水平散度和相对涡度数值,绘制成散度和涡度垂直廓线图。Then traverse the triangles that have been networked, obtain the horizontal divergence and relative vorticity values of each altitude layer above each triangle, draw a spatial distribution map of horizontal divergence, and then use the horizontal divergence and relative vorticity calculated by each altitude layer. Numerical values, plotted as vertical profiles of divergence and vorticity.

为了保证深度神经网络模型的预测效果,本发明改进有,在S4步骤中,所述深度神经网络模型包括2个卷积层、2个最大池化层和2个全连接层。In order to ensure the prediction effect of the deep neural network model, the present invention improves that, in step S4, the deep neural network model includes 2 convolution layers, 2 max pooling layers and 2 fully connected layers.

为了优化数据集,本发明改进有,在S4步骤中,所述图像数据集可采用几何变换方式进行增强优化。In order to optimize the data set, the present invention improves that, in step S4, the image data set can be enhanced and optimized by means of geometric transformation.

为了进一步优化深度神经网络模型的预测效果,本发明改进有,在S5步骤中,通过网络拓扑结构进行合理修改配置,进一步提升模型识别能力。In order to further optimize the prediction effect of the deep neural network model, the present invention improves that, in step S5, the network topology structure is used to reasonably modify the configuration to further improve the model identification ability.

相比于现有技术,本发明利用风廓线雷达中尺度网观测,不同高度处的水平风可反演出时空分辨率更高的散度、涡度、风切变和垂直速度等大气动力参数,能够更准确捕捉对流触发前大气垂直动力变化特征,能够得到实时三维空间的散度、涡度分布,有助于气象预报员了解天气系统的精细结构,迅速找到强上升运动区域,用于强降水的短时临近预报,为强对流天气监测预警和短期临近预报提供重要参考,实用性较高,进步性显著。Compared with the prior art, the present invention utilizes wind profiler radar mesoscale network observation, and the horizontal wind at different heights can invert atmospheric dynamic parameters such as divergence, vorticity, wind shear and vertical velocity with higher spatial and temporal resolution. , which can more accurately capture the characteristics of atmospheric vertical dynamic changes before convection triggering, and can obtain real-time three-dimensional space divergence and vorticity distribution, which is helpful for meteorologists to understand the fine structure of the weather system and quickly find areas of strong upward motion for strong Short-term nowcasting of precipitation provides an important reference for monitoring and early warning of severe convective weather and short-term nowcasting, with high practicability and remarkable progress.

本发明的其它特征和优点将在随后的说明书中阐述,并且,部分地从说明书中变得显而易见,或者通过实施本发明而了解。本发明的目的和其他优点可通过在所写的说明书、权利要求书、以及附图中所特别指出的结构来实现和获得。Other features and advantages of the present invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description, claims, and drawings.

下面通过附图和实施例,对本发明的技术方案做进一步的详细描述。The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments.

附图说明Description of drawings

为了更清楚地说明本发明实施例或现有技术中的技术方案,下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

图1为本发明提出一种基于组网风廓线雷达的对流发展识别方法的流程示意图;1 is a schematic flowchart of a method for identifying convection development based on a networked wind profiler radar proposed by the present invention;

图2为本发明提出一种基于组网风廓线雷达的对流发展识别方法中深度神经网络模型架构图。FIG. 2 is an architecture diagram of a deep neural network model in a method for identifying convection development based on a networked wind profiler radar proposed by the present invention.

具体实施方式Detailed ways

下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例仅仅是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

实施例一Example 1

参阅图1-2,一种基于组网风廓线雷达的对流发展识别方法,基于组网风廓线雷达的对流发展识别方法包括如下步骤:Referring to Figure 1-2, a method for identifying convective development based on a networked wind profiler radar, the method for identifying convective development based on a networked wind profiler radar includes the following steps:

S1,风廓线雷达组网,首先筛选风廓线雷达组网,组网风廓线雷达数量四台或以上,同时使用三角形法确定最优组网站点;S1, wind profiler radar network, first screen the wind profiler radar network, network with four or more wind profiler radars, and use the triangle method to determine the optimal network site;

S2,计算数据,然后使用有限元法遍历三角形组网,计算分层水平散度和相对涡度,产生各高度层水平散度的空间分布图、产生散度和涡度垂直廓线图;S2, calculate the data, then use the finite element method to traverse the triangular network, calculate the layered horizontal divergence and relative vorticity, generate the spatial distribution map of the horizontal divergence at each height layer, and generate the vertical profile of the divergence and vorticity;

S3,数据集构建,将散度空间分布图、散、涡度垂直廓线图、风廓线图以及组网范围内地面气象站雨量、风力实测数据组成数据集;S3, data set construction, the scatter spatial distribution map, scatter and vorticity vertical profile map, wind profile map, and the measured data of rainfall and wind force of the ground meteorological stations within the network range are formed into a data set;

S4,构建深度神经网络模型,构建以卷积神经网络为基础的深度神经网络模型,通过大量样本“数据对”的迭代训练,拟合前一时刻散度图、廓线图等图像数据集与对流天气发展的函数映射关系;S4, build a deep neural network model, build a deep neural network model based on a convolutional neural network, and iteratively train a large number of sample "data pairs" to fit the image data sets such as the divergence map and the profile map at the previous moment. Function mapping relationship of convective weather development;

S5,识别,应用和改进拟合得出的函数映射关系,实现对流发展的识别。S5, identify, apply and improve the function mapping relationship obtained by fitting to realize the identification of convection development.

本实施例中,在S1步骤中,三角形法就是让站点构成等边三角形,等边三角形为最优组网形式,因为当三角形最大内角增大时,产生数据与形状相关性误差也将增大,所以等边三角形为最优组网形式。In this embodiment, in step S1, the triangle method is to make the sites form an equilateral triangle, and the equilateral triangle is the optimal networking form, because when the maximum interior angle of the triangle increases, the correlation error between the generated data and the shape will also increase. , so the equilateral triangle is the optimal networking form.

本实施例中,在S2步骤中,计算的具体方式为:根据确定好的三角形3个顶点测站位置的经纬度和地球平均半径R计算该三角形三边的分量,结合各层3个测站的水平风数据计算该高度三角形内的水平散度D和相对涡度ζ;In this embodiment, in step S2, the specific method of calculation is: calculate the components of the three sides of the triangle according to the determined latitude and longitude of the positions of the three vertices of the triangle and the average radius R of the earth, and combine the three measuring stations of each layer. The horizontal wind data calculates the horizontal divergence D and relative vorticity ζ within the altitude triangle;

随后遍历已经组网的三角形,获得各三角形上空各高度层的水平散度和相对涡度数值,绘制成水平散度的空间分布图,然后使用各高度层计算得到的水平散度和相对涡度数值,绘制成散度和涡度垂直廓线图。Then traverse the triangles that have been networked, obtain the horizontal divergence and relative vorticity values of each altitude layer above each triangle, draw a spatial distribution map of horizontal divergence, and then use the horizontal divergence and relative vorticity calculated by each altitude layer. Numerical values, plotted as vertical profiles of divergence and vorticity.

本实施例中,在S4步骤中,深度神经网络模型包括2个卷积层、2个最大池化层和2个全连接层;In this embodiment, in step S4, the deep neural network model includes 2 convolutional layers, 2 maximum pooling layers and 2 fully connected layers;

搭建适用于对流发展识别的深度神经网络模型,其中,输入是一个长宽均为100像素,由3幅图像(1幅水平散度空间分布图像、1幅散度和涡度垂直廓线图像和1幅风廓线图)构成的三维向量;Build a deep neural network model suitable for convective development identification, where the input is a length and width of 100 pixels, which consists of 3 images (1 horizontal divergence spatial distribution image, 1 divergence and vorticity vertical profile image and A three-dimensional vector composed of a wind profile map);

经过2次卷积和2次池化,最后送入2层全连接层,其中卷积层和全连接层所采用的激活函数均为ReLU函数,另外为避免过拟合现象,全连接层使20%神经元休眠。After 2 times of convolution and 2 times of pooling, it is finally sent to the 2-layer fully connected layer. The activation functions used in the convolutional layer and the fully connected layer are both ReLU functions. In addition, in order to avoid over-fitting, the fully connected layer uses 20% of neurons dormant.

本实施例中,在S4步骤中,图像数据集可采用几何变换方式进行增强优化,几何变换是指从具有几何结构之集合至其自身或其他此类集合的一种对射,通过对图形作一定的变换,这样将有利于发现问题的隐含条件。In this embodiment, in step S4, the image data set can be enhanced and optimized by means of geometric transformation. Geometric transformation refers to an anti-projection from a set with geometric structure to itself or other such sets. Certain transformation, which will help to find the hidden conditions of the problem.

本实施例中,在S5步骤中,通过网络拓扑结构进行合理修改配置,进一步提升模型识别能力,提高模型识别准确度。In this embodiment, in step S5, the configuration is reasonably modified through the network topology structure, so as to further improve the model identification capability and the model identification accuracy.

从上述实施例可以看出,本发明利用风廓线雷达中尺度网观测,不同高度处的水平风可反演出时空分辨率更高的散度、涡度、风切变和垂直速度等大气动力参数,能够更准确捕捉对流触发前大气垂直动力变化特征,能够得到实时三维空间的散度、涡度分布,有助于气象预报员了解天气系统的精细结构,迅速找到强上升运动区域,用于强降水的短时临近预报,为强对流天气监测预警和短期临近预报提供重要参考,实用性较高,进步性显著。It can be seen from the above embodiments that the present invention uses wind profiler radar mesoscale network observation, and the horizontal wind at different heights can invert atmospheric dynamics such as divergence, vorticity, wind shear and vertical velocity with higher spatial and temporal resolution. parameters, can more accurately capture the characteristics of atmospheric vertical dynamic changes before convection triggering, and can obtain the real-time three-dimensional space divergence and vorticity distribution, which is helpful for meteorologists to understand the fine structure of the weather system and quickly find areas of strong upward motion for use in Short-term nowcasting of heavy precipitation provides an important reference for monitoring and early warning of severe convective weather and short-term nowcasting, with high practicability and remarkable progress.

显然,本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样,倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内,则本发明也意图包含这些改动和变型在内。It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention. Thus, provided that these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims (6)

1.一种基于组网风廓线雷达的对流发展识别方法,其特征在于,所述基于组网风廓线雷达的对流发展识别方法包括如下步骤:1. a convection development identification method based on networking wind profiler radar, is characterized in that, the described convection development identification method based on networking wind profiler radar comprises the steps: S1,风廓线雷达组网,首先筛选风廓线雷达组网,组网风廓线雷达数量四台或以上,同时使用三角形法确定最优组网站点;S1, wind profiler radar network, first screen the wind profiler radar network, network with four or more wind profiler radars, and use the triangle method to determine the optimal network site; S2,计算数据,然后使用有限元法遍历三角形组网,计算分层水平散度和相对涡度,产生各高度层水平散度的空间分布图、产生散度和涡度垂直廓线图;S2, calculate the data, then use the finite element method to traverse the triangular network, calculate the layered horizontal divergence and relative vorticity, generate the spatial distribution map of the horizontal divergence at each height layer, and generate the vertical profile of the divergence and vorticity; S3,数据集构建,将散度空间分布图、散、涡度垂直廓线图、风廓线图以及组网范围内地面气象站雨量、风力实测数据组成数据集;S3, data set construction, the scatter spatial distribution map, scatter and vorticity vertical profile map, wind profile map, and the measured data of rainfall and wind force of the ground meteorological stations within the network range are formed into a data set; S4,构建深度神经网络模型,构建以卷积神经网络为基础的深度神经网络模型,通过大量样本“数据对”的迭代训练,拟合前一时刻散度图、廓线图等图像数据集与对流天气发展的函数映射关系;S4, build a deep neural network model, build a deep neural network model based on a convolutional neural network, and iteratively train a large number of sample "data pairs" to fit the image data sets such as the divergence map and the profile map at the previous moment. Function mapping relationship of convective weather development; S5,识别,应用和改进拟合得出的函数映射关系,实现对流发展的识别。S5, identify, apply and improve the function mapping relationship obtained by fitting to realize the identification of convection development. 2.根据权利要求1所述的基于组网风廓线雷达的对流发展识别方法,其特征在于:在S1步骤中,所述三角形法就是让站点构成等边三角形,等边三角形为最优组网形式。2. the method for identifying convection development based on networked wind profiler radar according to claim 1, is characterized in that: in step S1, described triangle method is to let site form equilateral triangle, and equilateral triangle is optimal group. web form. 3.根据权利要求1所述的基于组网风廓线雷达的对流发展识别方法,其特征在于:在S2步骤中,计算的具体方式为:根据确定好的三角形3个顶点测站位置的经纬度和地球平均半径R计算该三角形三边的分量,结合各层3个测站的水平风数据计算该高度三角形内的水平散度D和相对涡度ζ;3. the convective development identification method based on networked wind profile radar according to claim 1, is characterized in that: in S2 step, the concrete mode of calculation is: according to the longitude and latitude of the determined 3 vertex station positions of triangle Calculate the components of the three sides of the triangle with the average radius of the earth R, and calculate the horizontal divergence D and relative vorticity ζ in the height triangle in combination with the horizontal wind data of the three stations at each layer; 随后遍历已经组网的三角形,获得各三角形上空各高度层的水平散度和相对涡度数值,绘制成水平散度的空间分布图,然后使用各高度层计算得到的水平散度和相对涡度数值,绘制成散度和涡度垂直廓线图。Then traverse the triangles that have been networked, obtain the horizontal divergence and relative vorticity values of each altitude layer above each triangle, draw a spatial distribution map of horizontal divergence, and then use the horizontal divergence and relative vorticity calculated by each altitude layer. Numerical values, plotted as vertical profiles of divergence and vorticity. 4.根据权利要求1所述的基于组网风廓线雷达的对流发展识别方法,其特征在于:在S4步骤中,所述深度神经网络模型包括2个卷积层、2个最大池化层和2个全连接层。4. the method for identifying convection development based on networking wind profile radar according to claim 1, is characterized in that: in step S4, described deep neural network model comprises 2 convolution layers, 2 maximum pooling layers and 2 fully connected layers. 5.根据权利要求1所述的基于组网风廓线雷达的对流发展识别方法,其特征在于:在S4步骤中,所述图像数据集可采用几何变换方式进行增强优化。5 . The method for identifying convection development based on a networked wind profiler radar according to claim 1 , wherein in step S4 , the image data set can be enhanced and optimized by means of geometric transformation. 6 . 6.根据权利要求1所述的基于组网风廓线雷达的对流发展识别方法,其特征在于:在S5步骤中,通过网络拓扑结构进行合理修改配置,进一步提升模型识别能力。6 . The method for identifying convection development based on networked wind profiler radar according to claim 1 , wherein in step S5 , the configuration is reasonably modified through the network topology structure to further improve the model identification capability. 7 .
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