KR20120125900A - Method of classify meteorological and non-meteorological echoes using dual polarization radars - Google Patents

Abstract

PURPOSE: A method for classifying meteorological and non-meteorological echoes is provided to improve reliability of weather forecast by enhancing accuracy of measurable rainfall estimation while improving the quality of dual polarization radar data. CONSTITUTION: Dual polarization data is collected(S110). A fuzzy variable for meteorological and non-meteorological echoes is calculated from past dual polarization data(S120). A distribution function of the fuzzy variable for meteorological and non-meteorological echoes is drawn out(S130). A group function and a weighting factor are calculated from the distribution function of the fuzzy variable for the meteorological and non-meteorological echoes(S140). Echo classification is performed(S200) when the group function and the weighting factor are calculated(S100). [Reference numerals] (S100) Calculation of a weight value and a group function of a fuzzy variable; (S110) Past measuring data collection of dual polarization data; (S120) Calculation of the fuzzy variable(feature fields); (S130) Distribution function calculation of the fuzzy variable; (S140) Calculation of the group function and the weight value; (S200) Echo classification; (S210) Real time measuring data collection of the dual polarization data; (S220) Calculation of a total group value; (S230) Echo discrimination; (S240) Non-meteorological echo elimination

Description

Method of classify meteorological and non-meteorological echoes using dual polarization radars

The present invention relates to meteorological and non-memory echo classification methods, and more particularly, to a method for discriminating radar echoes into meteorological and non-memory echoes using fuzzy variables calculated from data observed in a dual polarized radar.

Dual polarization radar systems, also known as polarization radars, offer advantages in many ways over conventional radars. In addition to detecting storms and measuring Doppler velocity, dual polarization radars are excellent radar instruments for measuring rainfall rates (or cumulative quantities). Proven by scientists to determine the classification of hydrometeors such as small hail, large hail, graupel, light rain and heavy rain It became.

In addition, the dual polarization radar system can distinguish the insects, birds, as well as the precipitation, wind observation, the phase, shape, size of the hydrological particles using the horizontal and vertical polarization information. Such a dual polarized radar system can make a decisive contribution to the estimation of quantitative precipitation through the removal of non-precipitation echoes as well as the distinction between meteorological and non-climate echoes.

Accordingly, an object of the present invention is to provide a method for accurately determining weather and non-memory echo by applying fuzzy logic based on statistical analysis method to observation data of dual polarized weather radar.

One embodiment of the present invention for achieving the above object, the first step of calculating the membership function and weight of the fuzzy variable for each weather and non-weather echo from the past observation data collected in the dual polarization radar system; And calculating a total membership value by applying the membership function and weights of the fuzzy variables calculated to the observation data collected in real time in the dual polarization radar system, and comparing the calculated total membership value with a threshold to determine weather echoes and non-weather echoes. It includes a weather and non-weather echo classification method using a dual polarized radar including a second step.

In this case, the fuzzy variable includes at least a standard deviation of reflectivity, a standard deviation of differential reflectivity, a standard deviation of correlation coefficient, a standard deviation of differential phase difference, and a correlation coefficient.

The first step may further include calculating fuzzy variables for each of weather and non-weather echoes from past observation data of the dual polarization radar system; Calculating a distribution function of fuzzy variables for each of the calculated weather and non-weather echoes; And calculating a belonging function and a weight of each of the fuzzy variables from the calculated distribution function.

In addition, the membership function is defined as the ratio of the sum of the frequency values for the weather echo and the frequency values of the weather echo and the non-climate echo, and is calculated by the following equation.

Here, MF represents a membership function, P represents a fuzzy variable, F represents a frequency value of the fuzzy variable, PRE represents a weather (precipitation) echo, and GRE represents a non-weather (terrain) echo.

In addition, the weight is inversely proportional to a region where the distribution function of fuzzy variables for the weather and non-weather echoes overlap, and is calculated by the following equation.

,

,

Where W is the weight, P is the fuzzy variable, A, B, and C are the overlapping regions of the distribution function for each of the fuzzy variables of the meteorological and non-climate echoes, and D is the distribution function of the meteorological and non-climate echoes. It represents the sum of the inverses of the overlapped areas.

In the second step, after fuzzy variables are calculated in real time from the collected observation data in real time, the membership function and weights of the calculated fuzzy variables are applied to the fuzzy variables calculated in real time.

In the second step, when the calculated total belonging value is larger than the threshold value, it is determined as a non-weather echo, and when it is smaller, it is determined as a weather echo.

The second step may further include removing the determined non-weather echo.

The total belonging value is the sum of the belonging values of each fuzzy variable, and is calculated by the following equation.

Here, MV denotes the membership value of the fuzzy variable, MVtot denotes the total membership value, and W denotes the weight of the fuzzy variable.

According to the present invention, the quality of the dual polarized radar data can be improved, and the accuracy of quantitative precipitation estimation can be improved, thereby improving the reliability of the weather forecast.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the whole structure of the weather / non-weather echo classification system using the dual polarization radar which concerns on one Embodiment of this invention.
2 is a flowchart illustrating a weather / non-weather echo classification method using a dual polarization radar according to an embodiment of the present invention.
FIG. 3 is an exemplary diagram illustrating a process of calculating a membership function with respect to the standard deviation of reflectivity in the calculation of the membership function and the weight of the fuzzy variable shown in FIG. 2.
FIG. 4 is an exemplary diagram illustrating a weight calculation process for a standard deviation of reflectivity in a process of belonging and weighting a fuzzy variable shown in FIG. 2.
FIG. 5 is a flowchart for describing in detail an echo determination step illustrated in FIG. 2.
6 to 12 are exemplary views showing an example of applying the weather / non-climate echo classification method of the present invention to the actual case.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art to which the present invention pertains. As those skilled in the art would realize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Further, in order to clearly illustrate the present invention in the drawings, portions not related to the description are omitted. In addition, the same code | symbol is attached | subjected about the similar part throughout the specification. In the following description, well-known functions or constructions are not described in detail since they would obscure the invention in unnecessary detail.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the whole structure of the meteorological and non-memory echo classification system using the dual polarization radar which concerns on one Embodiment of this invention.

Non-climate echoes refer to radar observation echoes that are not associated with precipitation and clouds, such as terrain echo, chaff, and cheoncheon echo.

As shown in FIG. 1, the present meteorological and non-weather echo classification system uses fuzzy logic based on a statistical analysis method, and includes a data collecting unit 21, a fuzzy variable calculating unit 22, a fuzzy engine 23, and an echo. The determination and removal unit 24 is included.

The data collection unit 21 collects past observation information and real time observation information from the dual polarization radar system 1. Here, the observation information is dual polarization information including horizontal and vertical polarization information. When the horizontal and vertical polarization observation data are analyzed, it is very useful for precipitation observation, especially quantitative precipitation observation. .

The fuzzy variable calculating unit 22 calculates fuzzy variables from past observation data collected by the data collecting unit 21. The fuzzy variables include at least a standard deviation of reflectivity, a standard deviation of differential reflectivity, a standard deviation of correlation coefficient, a standard deviation of differential phase difference, and a correlation coefficient.

In addition, the fuzzy variable calculating unit 22 derives a distribution function of each of the fuzzy variables calculated for the weather echoes and the non-climate echoes, and obtains the meteorological and non-weather echoes for each of the derived fuzzy variables from the distribution function. Calculate membership function and weight. That is, the fuzzy variable calculation unit 22 calculates a membership function and weight by applying statistical analysis (fuzzy logic) from past observation data.

In addition, the fuzzy variable calculating unit 22 calculates fuzzy variables from observation data collected in real time by the data collecting unit 21. The fuzzy variables calculated in real time are then used for echo determination in the fuzzy engine 23.

The fuzzy engine 23 applies fuzzy variables calculated from past observations to the fuzzy variables calculated from the real-time observation data in the fuzzy variable calculation unit 22 and applies them to the fuzzy variables of the weather echoes and non-weather echoes. The membership value is calculated, and the sum of the obtained membership values, that is, the total membership value is calculated.

The echo determination and removal unit 24 compares the total membership value calculated by the fuzzy engine 23 with a preset threshold value, and when the calculated total membership value is greater than or equal to the threshold value, determines the non-weather echo and calculates the total membership value. If it is below the threshold, it is determined by weather echo.

The echo discrimination and removal unit 24 removes the non-gassing echo when it is determined as the non-gassing echo.

A detailed description of the operation of the weather / non-weather echo classification system will be described with reference to FIGS. 2 to 13.

2 is a flowchart illustrating a weather / non-weather echo classification method using a dual polarization radar according to an embodiment of the present invention.

Referring to FIG. 2, the present meteorological / non-weather echo classification method is largely divided into two parts, and includes a membership function and weight calculation step S100 and an echo classification step S200 of fuzzy variables.

First, the belonging function and weight calculation step (S100) of the fuzzy variable is a process of calculating the belonging function and weight of the fuzzy variables for each of the weather echo and the non-climate echo using the previously observed data collected in advance from the dual polarization radar.

Specifically, the past observation data, that is, the dual polarization data observed from the dual polarized radar is collected (S110), and the fuzzy parameters (feature fields) for each of the weather / non-weather echoes are calculated from the collected historical dual polarized data (S120). ).

Here, the fuzzy variable includes at least a standard deviation of reflectance, a standard deviation of differential reflectivity, a standard deviation of correlation coefficient, a standard deviation of differential phase difference, and a correlation coefficient.

First, the reflectivity (Z _h ) is a variable representing the intensity of the signal returned by the precipitation particles in the atmosphere by the horizontal polarization of the radar, and is calculated by Equation 1 below.

Here, D represents the size of the particles, and N (D) represents the number of particles having a size of D. Thus, greater reflectivity means that the size of the precipitation particles is larger, which means that the precipitation is stronger.

Differential Reflectivity (Z _DR ) is a variable representing a ratio of reflectivity due to horizontal polarization Zh and vertical polarization Zv, and is calculated by Equation 2 below.

)는 이중편파 레이더가 관측하는 샘플부피 내에서 수평편파 및 수직편파에 의해 산란된 파워신호간의 상관도를 의미하는 변수로, 하기 수학식 3에 의해 계산된다.Correlation Coefficient

) Is a variable representing the correlation between the power signals scattered by the horizontal and vertical polarizations in the sample volume observed by the dual polarization radar, and is calculated by Equation 3 below.

Here, S _vv denotes a power signal transmitted as a vertical wave (V) and received as a vertical wave (V), and S _hh denotes a power signal transmitted as a horizontal wave (h) and received as a horizontal wave (h). Therefore, the correlation coefficient is information on the type of precipitation in the atmosphere. If the atmospheric precipitation is composed of liquid phase, that is, only rain, the correlation coefficient is high, and if the snow and rain are mixed in the atmosphere, the correlation coefficient is low.

)는 전자기파가 대기중의 강수에 의해 위상이 바뀌게 될 때, 수평편파 및 수직편파에 의한 위상 변화의 차이를 나타내는 변수로, 하기 수학식 4에 의해 계산된다.Differential Phase,

) Is a variable representing the difference in phase change due to horizontal and vertical polarization when the electromagnetic wave is changed in phase by atmospheric precipitation, and is calculated by Equation 4 below.

Here, HH represents a phase due to horizontal polarization and VV represents a phase due to vertical polarization. This differential phase difference increases with increasing precipitation because the shape of the precipitation particles is formed in an elliptical shape during strong precipitation.

As described above, in the present embodiment, the standard deviation and correlation coefficient of the four variables described above are used as fuzzy variables.

In addition, the standard deviation SD (Z) indicates whether each variable is uniformly distributed and is calculated by Equation 5 below.

Here Z represents the reflectivity, and the standard deviation of the reflectivity is obtained by using the difference between the reflectivity located at the center of the five gates (meaning one pixel on the radar) and the reflectivity of each of the five gates for a given region.

This standard deviation results in a small standard deviation in the case of meteorological (precipitation) echoes, and a small standard deviation in the non-climate (terrain) echoes, resulting in a large standard deviation compared to the weather echo. However, the distribution of correlation coefficients is opposite to that of standard deviation. The membership function is calculated by using the standard deviation of these fuzzy variables and the distribution of correlation coefficients.

Then, a distribution function of fuzzy variables for each of the calculated weather / non-weather echoes is derived (S130). In the standard deviations of reflectivity, differential reflectance, correlation coefficient, and differential phase difference, the distribution function of fuzzy variable for meteorological echo is concentrated at low value, but the distribution function of fuzzy variable for non-climate echo is up to high value. It is widely distributed. On the other hand, in the case of the correlation coefficient, the distribution function of the fuzzy variable for the weather echo is concentrated at a high value, while the distribution function of the fuzzy variable for the non-climate echo is widely distributed to the low value.

Subsequently, the belonging function and the weight are calculated from the distribution function of the fuzzy variables for each of the derived weather and non-weather echoes (S140).

The membership function is defined as the ratio of the sum of the frequency values for the weather echo and the frequency values of the weather echo and the non-climate echo, and is calculated by Equation 6 below.

Here, MF represents a membership function, P represents a fuzzy variable, F represents a frequency value of the fuzzy variable, PRE represents a weather (precipitation) echo, and GRE represents a non-weather (terrain) echo.

The weight W is a variable that is inversely proportional to the overlapping region of the fuzzy variable for the weather echo and the fuzzy variable for the non-climate echo, and is defined as the inverse of the overlapping region. Is calculated by

Where P is the fuzzy variable, A, B, and C are the overlapping regions of the distribution function for each of the fuzzy variables of the meteorological and non-climate echoes, and D is the inverse of the overlapping region of the distribution functions for the meteorological and non-climate echoes. Represents the sum.

Next, when the membership function and weight of the fuzzy variable for each of the weather and non-weather echoes are calculated (S100), the echo classification is performed (S200).

The echo classification step (S200) includes a process of calculating a total belonging value from real-time observed observation data and determining an echo using the calculated total belonging value.

Specifically, after collecting real-time observation data, that is, dual polarization data observed in the dual polarization radar system (S210), and calculating fuzzy variables from the real-time collected dual polarization data, the calculated fuzzy variables are calculated in step S100. The total membership value is calculated by applying the membership function and the weight (S220).

Here, the total belonging value is defined as the sum of the product of the belonging value and the weight of each fuzzy variable and the weight sum ratio of each fuzzy variable, and is calculated by Equation 8 below.

This total membership has a value between 0 and 1, where 0 is 100% probability of weather echo and 1 is 100% probability of non-climate echo.

Then, echo determination is performed using the calculated total membership value (S230). In the present embodiment, 0.5 is set as a threshold, and if the calculated total belonging value is greater than or equal to the threshold, it is determined as a non-weather echo, and if the calculated total belonging value is less than or equal to the threshold, it is determined as a weather echo.

Next, if it is determined that the non-weather echoes, the non-weather echoes are removed (S240).

FIG. 3 is an exemplary diagram illustrating a process of calculating a membership function for the standard deviation of reflectivity, which is one of the fuzzy variables, in the step of calculating the membership function and the weight of the fuzzy variable shown in FIG. 2.

As shown in (a) of FIG. 3, the distribution function of the standard deviation of the reflectivity for the weather echo is shown by the black line, and the standard deviation of the reflectivity for the non-climate echo is shown by the red line, respectively. In addition, it can be seen that the distribution function of the weather echo has a small standard deviation, and the non-climate echo has a large standard deviation.

As shown in (b) of FIG. 3, the membership function (MF) is defined as the ratio of one frequency value to the sum of two frequency values with respect to the standard deviation of reflectance, and the membership function for non-weather echoes. Is the ratio of the frequency values of the non-climate echoes to the sum of the frequencies of the meteorological and non-climate echoes, respectively. The membership function (MF _GRE (SDZ _i )) of the reflectance standard deviation with respect to the non-climate echo is calculated by the equation shown in (b) of FIG. 3 to obtain a graph of the membership function as shown in (c) of FIG. .

As shown in (c) of FIG. 3, in the graph of the membership function, the x-axis represents the standard deviation (dB) of the reflectivity, the y-axis represents the membership value, and when the distribution for the weather echo is 0, the value of the membership function is It becomes 1.

FIG. 4 is an exemplary diagram illustrating a weight calculation process for a standard deviation of reflectivity in a process of belonging and weighting a fuzzy variable shown in FIG. 2.

As shown in FIG. 4, the weight W of the fuzzy variable is a region where the distribution function (black line) of the fuzzy variable for the weather echo overlaps with the distribution function (red line) of the fuzzy variable for the non-climate echo. The overlapping area (A, B, C) refers to the area where the belonging function does not become 1 because the belonging function does not become 1 in the area where the weather echoes and the non-climate echoes are mixed. It means that the better the distinction between weather echo and non-weather echo. In other words, a large weight is given to the variable SDZ where the overlap region is small.

FIG. 5 is a flowchart for describing in detail an echo determination step illustrated in FIG. 2.

As shown in FIG. 5, the total membership value is calculated by applying the membership function and weights calculated to the observation data of the real-time collected dual polarization radar (S220).

The calculated total belonging value MV _tot is compared with the set threshold value MV _thresh to determine a weather echo or a non-weather echo (S231).

If the calculated total belonging value is larger than the threshold value, it is determined as a non-weather echo (S232). If the calculated total belonging value is smaller than the threshold value, it is determined as a weather echo (S233).

Then, the determined non-weather echo is removed (S240).

6 to 12 are exemplary views showing an example in which the weather / non-weather echo classification method of the present invention is applied to an actual case.

6 is an exemplary diagram showing an example of a distribution function and membership function between weather echoes and non-weather echoes with respect to the standard deviation of reflectivity. Here, the distribution function and membership function between the weather echoes and the non-climate echoes with respect to the standard deviation SD (Z) of the reflectivity are obtained as shown in FIG. 3.

6 (a) shows a distribution function, and (b) shows a belonging function, and the reflectance (Z) is classified into five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z>. 0, 0> Z), and the distribution function and membership function are subdivided, and the weather echo is shown by the dotted line and the non-weather echo is shown by the solid line.

In the membership function graph shown in (b) of FIG. 6, when the membership value is 1, the non-meteorological echo is a perfect value.

7 is an exemplary diagram illustrating an example of a distribution function and membership function between weather echoes and non-weather echoes with respect to the standard deviation of differential reflectivity. Here, the distribution function and the membership function between the weather echoes and the non-climate echoes with respect to the standard deviation SD (Zdr) of the differential reflectance are obtained in the same manner as the method shown in FIG.

(A) of FIG. 7 shows a distribution function, (b) shows a belonging function, and the reflectance (Z) has five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z> 0, 0> Z), the distribution function and the membership function for the standard deviation of the differential reflectance are subdivided, and the weather echo is indicated by the dotted line and the non-weather echo is represented by the solid line.

In the membership function graph shown in FIG. 7B, if the membership value is 1, the non-weather echo is a perfect value, and if the membership value is 0, it is a complete weather echo.

8 is an exemplary diagram showing an example of a distribution function and membership function between weather echoes and non-weather echoes with respect to the standard deviation of correlation coefficients. Here, the distribution function and membership function between the weather echo and the non-climate echo with respect to the standard deviation SD (Rhv) of the correlation coefficient are obtained in the same manner as the method shown in FIG.

(A) of FIG. 8 shows a distribution function, (b) shows a belonging function, and the reflectance (Z) has five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z> 0, 0> Z), the distribution function and the membership function for the standard deviation of the correlation coefficient are subdivided, and the weather echo is indicated by the dotted line and the non-weather echo is represented by the solid line.

In the membership function graph shown in (b) of FIG. 8, if the membership value is 1, the non-weather echo is a perfect membership.

9 is an exemplary diagram illustrating an example of a distribution function and membership function between weather echoes and non-weather echoes for standard deviation of differential phase differences. Here, the distribution function and membership function between the weather echoes and the non-climate echoes with respect to the standard deviation SD (Pdp) of the differential phase difference are obtained in the same manner as the method shown in FIG.

9 (a) shows a distribution function, and (b) shows a belonging function, and the degree of reflectance (Z) is classified into five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z> 0, 0> Z), the distribution function and membership function for the standard deviation of the differential phase difference are subdivided, and the weather echo is indicated by the dotted line and the non-weather echo is represented by the solid line.

In the membership function graph shown in FIG. 9B, if the belonging value is 1, the non-weather echo is a complete belonging value.

10 is an exemplary diagram illustrating an example of a distribution function and membership function between weather echoes and non-weather echoes for correlation coefficients. Here, the distribution function and membership function between the weather echo and the non-climate echo with respect to the correlation coefficient Rhv are obtained in the same manner as the method shown in FIG.

(A) of FIG. 10 shows a distribution function, (b) shows a belonging function, and the reflectance (Z) has five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z> 0, 0> Z), the distribution function and the membership function for the correlation coefficient are subdivided, and the weather echo is indicated by the dotted line and the non-weather echo is represented by the solid line.

In the membership function graph illustrated in FIG. 10B, if the membership value is 1, the non-weather echo is a complete non-weather echo.

11 is an exemplary diagram illustrating an example of weights for each of the five fuzzy variables. Here, the weight for each fuzzy variable is obtained as described in FIG.

As shown in FIG. 11, weights are subdivided by classifying the reflectance Z into five grades (Z> 30, 30> Z> 20, 20> Z> 10, 10> Z> 0, 0> Z). It was obtained.

)의 가중치가 가장 작으며, 차등위상차의 표준편차(SD(

))의 가중치가 가장 높다.If the reflectance (Z) is 20 dBZ or more, the correlation coefficient (

) Has the smallest weight, and the standard deviation of the differential phase difference (SD (

)) Has the highest weight.

This result implies that the standard deviation of the differential phase difference is the most important variable in determining the meteorological and non-weather echoes in the reflectance region of 20 dBZ or more.

When the reflectance Z is 20 dBZ or less, the weight of the standard deviation SD (Z) of reflectance is the smallest.

This result implies that the fuzzy variable of the dual polarization except the standard deviation of the reflectivity among the five fuzzy variables plays an important role in discriminating weather echoes and non-weather echoes in the reflectance region of 20 dBZ or less.

Therefore, it can be seen that the fuzzy variable calculated from the double polarization variable in the present invention is an important variable for accurately determining weather echoes and non-weather echoes.

FIG. 12 is an exemplary view showing an example in which a weather echo and non-weather echo classification method using a dual polarization radar shown in FIG. 2 is applied in practice, (a) is an image showing the observed reflectivity, and (b) The reflectance image after applying the weather echo and non-weather echo classification method using the dual polarization radar according to the present invention is shown. Here, the actual case is observational data for August 10, 2010 (0515 KST).

As shown in (a) of FIG. 12, it can be seen that the non-climate echo corresponds to a region indicated in red color and unlike the weather echo, discontinuously strong reflectivity is compared to the surrounding echo.

On the other hand, as shown in Figure 12 (b), as a result of applying the weather echo and non-weather echo classification method using the dual polarization radar of the present invention, it can be seen that all the parts that are determined to be non-weather echo are removed. .

So far I looked at the center of the preferred embodiment for the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Accordingly, the disclosed embodiments are to be considered in an illustrative rather than a restrictive sense, and all differences within the scope of equivalents thereof should be construed as being included in the present invention.

In addition, the apparatus and method according to the present invention can be embodied as computer readable codes on a computer readable recording medium. A computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored. Examples of the recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like, and a carrier wave (for example, transmission via the Internet). The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

1. Dual polarized radar system 2. Weather / non-weather echo classification system
21. Data collector 22. Fuzzy variable calculator
23. Fuzzy engine 24. Echo discrimination and removal unit

Claims (10)

Calculating a belonging function and weight of fuzzy variables for each of weather and non-weather echoes from past observation data collected by a dual polarization radar system; And
A total membership value is calculated by applying the membership function and weights of the fuzzy variables calculated to the observation data collected in real time by the dual polarization radar system, and the weather echo and non-weather echoes are determined by comparing the calculated total membership value with a threshold. Meteorological and non-memory echo classification method using a dual polarization radar comprising a.
The method of claim 1,
The fuzzy variable includes a standard deviation of reflectivity, a standard deviation of the differential reflectance, a standard deviation of the correlation coefficient, a standard deviation of the differential phase difference, and a correlation coefficient.
The method of claim 2,
The first step includes calculating fuzzy variables for each of weather and non-weather echoes from past observations of the dual polarization radar system;
Calculating a distribution function of fuzzy variables for each of the calculated weather and non-weather echoes; And
Computing the belonging function and the weight of each of the fuzzy variables from the calculated distribution function; Weather and non-weather echo classification method using a dual polarized radar comprising a.
The method of claim 3,
The membership function is defined as the ratio of the sum of the frequency values for the weather echo and the frequency values of the weather echo and the non-climate echo, and the weather and non-weather echo classification using the dual polarized radar, characterized by the following equation: Way.

Here, MF represents a membership function, P represents a fuzzy variable, F represents a frequency value of the fuzzy variable, PRE represents a weather (precipitation) echo, and GRE represents a non-weather (terrain) echo. 5. The method of claim 4,
The weights are inversely related to the region where the distribution function of the fuzzy variables for the meteorological and non-climate echoes overlaps, and is calculated by the following equation.

,

Where W is the weight, P is the fuzzy variable, A, B, and C are the overlapping regions of the distribution function for each of the fuzzy variables of the meteorological and non-climate echoes, and D is the distribution function of the meteorological and non-climate echoes. It represents the sum of the inverses of the overlapped areas.
The method of claim 1,
In the second step, after the fuzzy variables are calculated in real time from the collected observation data in real time, the belonging function and weights of the calculated fuzzy variables are applied to the fuzzy variables calculated in real time. Meteorological and non-climate echo classification methods.
The method according to claim 6,
In the second step, if the calculated total belonging value is larger than the threshold value, it is determined as a non-weather echo, and if it is smaller, it is determined as a weather echo.
The method according to claim 6,
The second step further comprises the step of removing the determined non-weather echoes.
7. The method according to claim 2 or 6,
The total belonging value is the sum of the belonging values of each fuzzy variable, and the weather and non-weather echo classification method using a dual polarized radar, characterized in that calculated by the following equation.

Here, MV denotes the membership value of the fuzzy variable, MVtot denotes the total membership value, and W denotes the weight of the fuzzy variable.
A recording medium having recorded thereon a program which can be read and executed by a digital signal processing apparatus as a program for performing the method of any one of claims 1 to 9.

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