CN117370714B - Representative station quantitative determination method - Google Patents

Representative station quantitative determination method Download PDF

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CN117370714B
CN117370714B CN202311666732.XA CN202311666732A CN117370714B CN 117370714 B CN117370714 B CN 117370714B CN 202311666732 A CN202311666732 A CN 202311666732A CN 117370714 B CN117370714 B CN 117370714B
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曾燕
邱新法
朱晓晨
陈兵
姜有山
吴泓
刘岩
王珂清
朱承瑛
许金萍
慕熙昱
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Nanjing Institute Of Meteorological Science And Technology Innovation
Nanjing University of Information Science and Technology
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Abstract

本发明公开了一种代表站定量确定方法,以相关系数与代表面积为指标,通过定量优化判识,确定最优代表站。该代表站确定方法具有定量、客观、可操作性强等优点,可用于气象、环境、水文、生态等领域的资源评价与科学研究等。

The invention discloses a method for quantitatively determining representative stations, which uses the correlation coefficient and the representative area as indicators to determine the optimal representative station through quantitative optimization and identification. This representative station determination method has the advantages of being quantitative, objective, and highly operable, and can be used for resource evaluation and scientific research in the fields of meteorology, environment, hydrology, ecology, etc.

Description

一种代表站定量确定方法A quantitative determination method for representative stations

技术领域Technical field

本发明属于气象环境(环保)技术领域,尤其涉及气象环境生态观测代表站的确定方法,可用于气象、环境、生态、水文等领域的资源评价与科学研究等。The invention belongs to the technical field of meteorological environment (environmental protection), and in particular relates to a method for determining a meteorological environment ecological observation representative station. It can be used for resource evaluation and scientific research in the fields of meteorology, environment, ecology, hydrology and other fields.

背景技术Background technique

气象、环境、生态、水文等部门通过观测站进行相关要素的连续监测与数据采集。如:气象站通过各种测量设备,对大气环境中的温度、湿度、气压、风速、风向、降雨量等气象要素进行观测采集,这些数据对气象预报、气候资源评价、气象防灾减灾等具有重要支撑作用。随着我国综合国力的提升,气象、环境、生态、水文等部门对观测站建设力度不断加大,各种观测站的布设密度也在逐渐增大,实际应用与科学分析中经常需要选择合适的代表站,即能够有效代表一定范围内某一观测要素总体情况的观测站。科学、合理的代表站选择具有事半功倍、举足轻重的意义。传统的代表站选择缺乏定量指标,实际应用时,有一定的局限性。Meteorological, environmental, ecological, hydrological and other departments conduct continuous monitoring and data collection of relevant elements through observation stations. For example, weather stations use various measurement equipment to observe and collect meteorological elements such as temperature, humidity, air pressure, wind speed, wind direction, and rainfall in the atmospheric environment. These data are of great significance for weather forecasting, climate resource evaluation, meteorological disaster prevention and reduction, etc. important supporting role. With the improvement of my country's comprehensive national strength, meteorological, environmental, ecological, hydrological and other departments have continued to increase their efforts in the construction of observation stations, and the density of various observation stations has gradually increased. In practical applications and scientific analysis, it is often necessary to select appropriate ones. A representative station is an observation station that can effectively represent the overall situation of a certain observation element within a certain range. Scientific and reasonable selection of representative stations is of great significance in getting twice the result with half the effort. Traditional representative station selection lacks quantitative indicators and has certain limitations in practical application.

发明内容Contents of the invention

发明目的:针对现有技术存在的问题,本发明设计了一种代表站定量确定方法,通过定量优化判识,确定最优代表站,本发明方法可用于气象、环境、生态、水文等领域的资源评价与科学研究等。Purpose of the invention: In view of the problems existing in the existing technology, the present invention designs a method for quantitative determination of representative stations. Through quantitative optimization and identification, the optimal representative station is determined. The method of the present invention can be used in the fields of meteorology, environment, ecology, hydrology and other fields. Resource evaluation and scientific research, etc.

技术方案:为实现上述发明目的,本发明采用以下技术方案:一种代表站定量确定方法,包括以下步骤:Technical solution: In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical solution: a representative station quantitative determination method, including the following steps:

步骤S1,选取目标站Step S1, select target station

在研究区域所有站中,随机选取一个目标站PiAmong all stations in the study area, a target station Pi is randomly selected;

步骤S2,确定目标站与周边站的相关性Step S2: Determine the correlation between the target station and surrounding stations

(1)以目标站Pi为中心,选取与目标站Pi距离最近的M个站,作为目标站Pi的周边站Pij(j=1…M);(1) With the target station Pi as the center, select the M stations closest to the target station Pi as the surrounding stations P ij of the target station Pi (j=1...M);

(2)计算目标站Pi与其周边站Pij的观测要素序列的相关系数,得到相关系数的平方序列rij 2(j=1…M);(2) Calculate the correlation coefficient of the observation element sequence of the target station Pi and its surrounding station P ij , and obtain the square sequence of the correlation coefficient r ij 2 (j=1...M);

步骤S3,确定目标站的有效周边站Step S3: Determine the effective surrounding stations of the target station

提取相关系数的平方序列rij 2(j=1…M)中大于等于给定阈值T的所有值rik 2(k=1…mi),其对应的周边站Pik(k=1…mi)作为目标站Pi的有效周边站;Extract all values r ik 2 (k=1...mi) that are greater than or equal to the given threshold T in the square sequence of correlation coefficients r ij 2 (j=1... M ), and their corresponding surrounding stations P ik (k=1... m i ) as an effective surrounding station of target station Pi ;

步骤S4,计算目标站的代表因子Step S4: Calculate the representative factor of the target station

计算目标站Pi 的代表性因子Ri(决定系数因子)与Gi(代表面积因子),计算式为:Calculate the representative factor R i (coefficient of determination factor) and G i (representative area factor) of the target station P i . The calculation formula is:

(1); (1);

(2); (2);

式中,Ri为决定系数因子;Gi为代表面积因子;rik为目标站Pi与其有效周边站Pik的观测要素序列的相关系数,mi为目标站Pi的有效周边站的站数;Si为目标站Pi对应的泰森多边形面积,Sik为有效周边站Pik对应的泰森多边形面积。In the formula, R i is the determination coefficient factor; G i is the representative area factor; r ik is the correlation coefficient of the observation element sequence of the target station P i and its effective surrounding station P ik , m i is the effective surrounding station of the target station P i Number of stations; S i is the Thiessen polygon area corresponding to the target station Pi , and S ik is the Thiessen polygon area corresponding to the effective surrounding station P ik .

步骤S5,选取新目标站,重复步骤S2-S4,遍历所有站,获得研究区域所有站的代表性因子;Step S5, select a new target station, repeat steps S2-S4, traverse all stations, and obtain the representative factors of all stations in the study area;

步骤S6,确定各站代表性指数Step S6: Determine the representativeness index of each station

(1)各站代表性因子的归一化处理,将目标站Pi的代表性因子Ri(决定系数因子)与Gi(代表面积因子)进行归一化处理,分别得到归一化后数值Ai和Bi(1) Normalization processing of the representative factors of each station. Normalize the representative factors R i (coefficient of determination factor) and G i (representative area factor) of the target station Pi , and obtain the normalized values respectively. Values A i and B i ;

(2)各站代表性指数Fi通过下式(3)计算得到,(2) The representativeness index F i of each station is calculated by the following formula (3),

(3) (3)

式中,Fi为目标站Pi的代表性指数,WA、WB分别为代表性因子的权重,N为研究区域内站点总数;In the formula, F i is the representative index of the target station Pi , W A and W B are the weights of the representative factors respectively, and N is the total number of stations in the study area;

遍历所有站,确定研究区每个站的代表性指数。All stations were traversed to determine the representativeness index of each station in the study area.

步骤S7,确定各站的代表性票数Step S7: Determine the number of representative votes for each station

将目标站Pi与其有效周边站Pik(k=1…mi)的代表性指数Fik进行比较,统计出现的次数Ci,作为目标站Pi的代表性票数。遍历所有站,确定每个站的代表性票数;Compare the representative index F ik of the target station P i with its effective surrounding station P ik (k=1...mi ) , and statistically The number of occurrences C i is used as the representative number of votes for the target station Pi . Traverse all stations and determine the number of representative votes for each station;

步骤S8,确定代表站Step S8: Determine the representative station

设定代表性票数阈值D,将目标站Pi的代表性票数Ci与D进行比较,若Ci≥D,则该目标站Pi即为代表站。遍历所有站,判定每个站是否为代表站。Set the representative vote threshold D, and compare the representative vote number C i of the target station Pi with D. If C i ≥ D, the target station Pi is the representative station. Traverse all stations and determine whether each station is a representative station.

进一步的,所述站为气象观测站,环境观测站或水文观测站。Further, the station is a meteorological observation station, an environmental observation station or a hydrological observation station.

进一步的,步骤S2所述目标站Pi距离最近的搜索台站数M的值设为20~40;相关系数平方阈值T设为0.6~0.9。Further, in step S2, the value of the number M of search stations closest to the target station Pi is set to 20~40; the square correlation coefficient threshold T is set to 0.6~0.9.

进一步的,步骤S2所述目标站Pi与其周边站Pij的观测要素序列为气象观测站、环境观测站或水文观测站的观测要素序列,如:气象站的气温观测序列,环境观测站的PM2.5观测序列。Further, the observation element sequence of the target station Pi and its surrounding station P ij in step S2 is the observation element sequence of a meteorological observation station, an environmental observation station or a hydrological observation station, such as: the temperature observation sequence of a meteorological station, the observation sequence of an environmental observation station PM2.5 observation sequence.

进一步的,步骤S2所述目标站Pi与其周边站Pij的观测要素序列的相关系数,按下式计算:Further, the correlation coefficient of the observation element sequence of the target station P i and its surrounding station P ij in step S2 is calculated by the following formula:

(4); (4);

式中,xl(l=1…n)为目标站Pi的观测要素序列,为xl(l=1…n)的平均值;yl(l=1…n)为Pi的周边站Pij的观测要素序列,/>为yl(l=1…n)的平均值;n为观测要素序列的长度,即样本数。In the formula, x l (l=1...n) is the observation element sequence of target station Pi , is the average value of x l (l=1...n); y l (l=1...n) is the observation element sequence of P i 's surrounding station P ij ,/> is the average value of y l (l=1...n); n is the length of the observation element sequence, that is, the number of samples.

进一步的,步骤S6所述各站代表性因子按下式进行归一化处理,Further, the representative factors of each station described in step S6 are normalized according to the following formula,

(5); (5);

(6); (6);

式中,Rmin、Rmax分别为决定系数因子Ri(i=1…N)的最小值与最大值,N为研究区域内站点总数;Gmin、Gmax分别为代表面积因子Gi(i=1…N)的最小值与最大值。In the formula, R min and R max are respectively the minimum and maximum values of the determination coefficient factor R i (i=1...N), N is the total number of stations in the study area; G min and G max are respectively the representative area factor G i ( i=1…N) minimum and maximum values.

进一步的,步骤S6所述代表性因子的权重WA、WB,其数值范围为0.1~0.9,且应满足WA+WB=1.0,当两个因子为同等权重时,WA、WB同时取值0.5。Further, the weights W A and W B of the representative factors described in step S6 have a numerical range of 0.1~0.9, and should satisfy W A + W B =1.0. When the two factors have equal weights, W A and W B simultaneously takes the value 0.5.

有益效果:与现有技术相比,本发明提出了一种全新的科学合理的代表站定量确定方法,对资源评价与科学研究等具有重要意义。本发明给出的代表站确定方法,具有定量、客观、可操作性强、易于推广等优点,可广泛应用于气象、水文、环境、生态环保等领域。Beneficial effects: Compared with the existing technology, the present invention proposes a new scientific and reasonable quantitative determination method for representative stations, which is of great significance to resource evaluation and scientific research. The representative station determination method provided by the present invention has the advantages of quantitative, objective, strong operability, easy promotion, etc., and can be widely used in meteorology, hydrology, environment, ecological and environmental protection and other fields.

附图说明Description of the drawings

图1为本发明所述代表站定量确定方法的流程示意图;Figure 1 is a schematic flow chart of the representative station quantitative determination method according to the present invention;

图2为本发明实施例所述研究区气象站泰森多边形示意图;Figure 2 is a schematic diagram of the Tyson polygon of the weather station in the research area according to the embodiment of the present invention;

图3为本发明实施例所述目标站及其有效周边站泰森多边形示意图;Figure 3 is a Thiessen polygon schematic diagram of the target station and its effective surrounding stations according to the embodiment of the present invention;

图4为本发明实施例所述研究区降雨量代表站。Figure 4 is a representative station of rainfall in the study area according to the embodiment of the present invention.

具体实施方式Detailed ways

下面结合附图和具体实施例,进一步阐明本发明,应理解这些实施例仅用于说明本发明而不用于限制本发明的范围,在阅读了本发明之后,本领域技术人员对本发明的各种等价形式的修改均落于本申请所附权利要求所限定的范围。The present invention will be further clarified below in conjunction with the accompanying drawings and specific examples. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. After reading the present invention, those skilled in the art will be familiar with various aspects of the present invention. Modifications in the form of equivalents fall within the scope defined by the appended claims of this application.

如图1所示,本发明所述代表站定量确定方法的流程示意图,其方法步骤如下:As shown in Figure 1, a schematic flow chart of the quantitative determination method of representative stations according to the present invention, the method steps are as follows:

1.选取目标站1. Select target site

在研究区所有站中,随机选择一个目标站PiAmong all stations in the study area, a target station Pi is randomly selected.

2.计算目标站与周边站的相关性2. Calculate the correlation between the target station and surrounding stations

①以目标站Pi为中心,计算其它站与目标站的距离,取与目标站Pi距离最近的M个站,作为目标站Pi的周边站Pij (j=1…M)。① With the target station Pi as the center, calculate the distances between other stations and the target station, and select the M stations closest to the target station Pi as the surrounding stations P ij of the target station Pi (j=1...M).

②计算目标站Pi与其周边站Pij(j=1…M)观测要素序列的相关系数rij,按照相关系数公式计算:② Calculate the correlation coefficient r ij of the observation element sequence of the target station P i and its surrounding stations P ij (j=1...M), and calculate it according to the correlation coefficient formula:

(1); (1);

式中,xl(l=1…n)为目标站Pi的观测要素序列,为xl(l=1…n)的平均值;yl(l=1…n)为Pi的周边站Pij的观测要素序列,/>为yl(l=1…n)的平均值;n为观测要素序列的长度,即样本数。观测要素序列为气象观测站、环境观测站或水文观测站的观测要素序列,如:气象站的气温观测序列,环境观测站的PM2.5观测序列。In the formula, x l (l=1...n) is the observation element sequence of target station Pi , is the average value of x l (l=1...n); y l (l=1...n) is the observation element sequence of P i 's surrounding station P ij ,/> is the average value of y l (l=1...n); n is the length of the observation element sequence, that is, the number of samples. The observation element sequence is the observation element sequence of a meteorological observation station, an environmental observation station or a hydrological observation station, such as: the temperature observation sequence of a meteorological station, and the PM2.5 observation sequence of an environmental observation station.

在此基础上,获得相关系数平方序列rij 2(j=1…M)。On this basis, the correlation coefficient square sequence r ij 2 (j=1…M) is obtained.

3.确定目标站的有效周边站3. Determine the valid surrounding stations of the target station

提取rij 2(j=1…M)中大于等于给定阈值T的所有值rik 2(k=1…mi)及其对应的周边站Pik(k=1…mi),将Pik称为目标站Pi的“有效周边站”。Extract all values r ik 2 (k=1...mi) in r ij 2 (j=1...M ) that are greater than or equal to the given threshold T and their corresponding surrounding stations P ik (k=1...m i ), and then P ik is called the "effective surrounding station" of the target station Pi .

4.计算目标站的代表性因子4. Calculate the representative factor of the target station

计算目标站Pi 的代表性因子Ri(决定系数因子)与Gi(代表面积因子),计算式为:Calculate the representative factor R i (coefficient of determination factor) and G i (representative area factor) of the target station P i . The calculation formula is:

(2); (2);

(3); (3);

式中,Ri为决定系数因子;Gi为代表面积因子;rik为目标站Pi与其有效周边站Pik的观测要素序列的相关系数,mi为目标站Pi的有效周边站的站数;Si为目标站Pi对应的泰森多边形面积,Sik为有效周边站Pik对应的泰森多边形面积。研究区泰森多边形示意图见图2,目标站及其有效周边站泰森多边形示意图见图3。In the formula, R i is the determination coefficient factor; G i is the representative area factor; r ik is the correlation coefficient of the observation element sequence of the target station P i and its effective surrounding station P ik , m i is the effective surrounding station of the target station P i Number of stations; S i is the Thiessen polygon area corresponding to the target station Pi , and S ik is the Thiessen polygon area corresponding to the effective surrounding station P ik . The Thiessen polygon schematic diagram of the study area is shown in Figure 2, and the Thiessen polygon schematic diagram of the target station and its effective surrounding stations is shown in Figure 3.

5.选取新目标站5. Select new target site

选取新目标站,重复步骤2-4,遍历所有站,获得研究区所有站的代表性因子。Select a new target station, repeat steps 2-4, traverse all stations, and obtain the representative factors of all stations in the study area.

6.计算各站代表性指数6. Calculate the representativeness index of each station

①各站代表性因子进行归一化处理①The representative factors of each station are normalized.

将目标站Pi的代表性因子Ri(决定系数因子)与Gi(代表面积因子)按下式进行归一化处理:The representative factors R i (coefficient of determination factor) and G i (representative area factor) of the target station Pi are normalized according to the following formula:

(4); (4);

(5); (5);

式中,Ai为决定系数因子Ri的归一化数值,Bi为代表面子因子Gi的归一化数值,Rmin、Rmax分别为决定系数因子Ri(i=1…N)的最小值与最大值,N为研究区域内站点总数。Gmin、Gmax分别为代表面子因子Gi(i=1…N)的最小值与最大值。In the formula, A i is the normalized value of the determination coefficient factor R i , B i is the normalized value representing the face factor G i , R min and R max are respectively the determination coefficient factor R i (i=1...N) The minimum and maximum values of , N is the total number of sites in the study area. G min and G max represent the minimum and maximum values of the face factor G i (i=1...N) respectively.

②各站代表性指数计算② Calculation of representativeness index of each station

各站代表性指数Fi通过下式(6)计算得到,The representativeness index F i of each station is calculated by the following formula (6),

(6); (6);

Fi为目标站Pi的代表性指数,WA、WB为代表性因子的权重,其数值为0.1~0.9,且应满足WA+WB=1.0,当两个因子为同等权重时,WA、WB同时取值0.5。遍历所有站,确定研究区每个站的代表性指数。F i is the representative index of the target station P i , W A and W B are the weights of the representative factors, their values are 0.1~0.9, and should satisfy W A + W B =1.0, when the two factors have equal weights , W A and W B take the value 0.5 at the same time. All stations were traversed to determine the representativeness index of each station in the study area.

7.确定各站代表性票数7. Determine the number of representative votes for each station

对于目标站Pi,将其代表指数Fi与其有效周边站Pik(k=1…mi)的代表性指数Fik(k=1…mi)进行比较,统计出现的次数Ci,Ci即为目标站Pi的代表性票数。遍历所有站,确定研究区所有站的代表性票数。For the target station P i , compare its representative index F i with the representative index F ik (k=1...mi ) of its effective surrounding stations P ik (k=1...mi ) , and statistically The number of occurrences C i , C i is the representative number of votes for the target station Pi . All stations were traversed to determine the representative number of votes for all stations in the study area.

8.确定代表站8. Determine representative station

设定代表性票数阈值D,对于目标站Pi,将其代表性票数Ci与D进行比较,若,则Pi即为代表站,反之则不是。Set the representative vote threshold D. For the target station Pi , compare its representative vote C i with D. If , then Pi is the representative station, and vice versa.

遍历所有站,即可选出研究区所有代表站。通过调整代表性票数阈值D的大小,可以调整代表站密度情况。By traversing all stations, all representative stations in the study area can be selected. By adjusting the size of the representative vote threshold D, the density of representative stations can be adjusted.

实施案例:Implementation case:

以南京市200个气象站为例,说明本发明实施过程。按照本发明步骤,确定南京市降雨量代表站。将搜索台站数M的值设为20,相关系数平方阈值T设为0.85,代表性因子权重WA、WB同时取值0.5,用南京市200个气象站2018-2022年逐月降雨量观测序列,计算目标站与其周边站的相关系数,用ArcGIS生成各气象站泰森多边形,进而获得各站对应泰森多边形面积,按照本发明实施步骤,确定的研究区降雨量代表站见图4。Taking 200 weather stations in Nanjing City as an example, the implementation process of the present invention is explained. According to the steps of the present invention, the rainfall representative station in Nanjing is determined. Set the value of the number of search stations M to 20, the square correlation coefficient threshold T to 0.85, and the representative factor weights W A and W B to take the value 0.5 at the same time. Use the monthly rainfall of 200 weather stations in Nanjing from 2018 to 2022. Observe the sequence, calculate the correlation coefficient between the target station and its surrounding stations, use ArcGIS to generate the Tyson polygon of each weather station, and then obtain the corresponding Tyson polygon area of each station. According to the implementation steps of the present invention, the determined rainfall representative station in the study area is shown in Figure 4 .

以上述依据本发明的理想实施例为启示,通过上述的说明内容,相关工作人员完全可以在不偏离本项发明技术思想的范围内,进行多样的变更以及修改。本项发明的技术性范围并不局限于说明书上的内容,必须要根据权利要求范围来确定其技术性范围。Taking the above-mentioned ideal embodiments of the present invention as inspiration and through the above description, relevant workers can make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the content in the description, and must be determined based on the scope of the claims.

Claims (6)

1. A representative station quantitative determination method, characterized by comprising the steps of:
step S1, selecting a target station
The stations are weather observation stations, environment observation stations or hydrologic observation stations, and one target station P is randomly selected from all stations in a research area i
Step S2, determining the relativity of the target station and the peripheral station
(1) By destination station P i For the center, select and target station P i M stations closest to the station as destination stations P i Peripheral station P of (a) ij (j=1…M);
(2) Calculating the destination P i With peripheral station P ij The correlation coefficient of the observation element sequence of (2) to obtain a square sequence r of the correlation coefficient ij 2 (j=1…M);
Step S3, determining the effective surrounding station of the target station
Extracting square sequence r of correlation coefficient ij 2 All values r in (j= … M) equal to or greater than a given threshold T ik 2 (k=1…m i ) Its corresponding peripheral station P ik (k=1…m i ) As destination station P i Is effective for the peripheral station;
step S4, calculating the representative factor of the target station
By destination station P i Square sequence r of correlation coefficients with peripheral stations ij 2 Average value R of (2) i And destination station P i Is the representative area factor G of (2) i As a destination station P i Is calculated as:
(1)
(2)
wherein R is i Determining coefficient factors; g i Is a representative area factor; r is (r) ik For destination station P i And an effective peripheral station P ik Correlation coefficient of observation element sequence, m i For destination station P i The number of active peripheral stations; s is S i For destination station P i Corresponding Thiessen polygonal area, S ik For effective peripheral station P ik Corresponding Thiessen polygonal area;
step S5, selecting a new target station, repeating the steps S2-S4, traversing all stations, and obtaining representative factors of all stations in a research area;
step S6, determining representative index of each station
(1) Normalization processing of representative factors of each station, and target station P i Representative factor R of (2) i (determining coefficient factor) and G i Normalized (representing area factor) to obtain normalized value A i And B i
(2) Representative index F of each station i Is calculated by the formula (3),
(3)
wherein F is i For destination station P i Representative index of W A 、W B Weights of representative factors respectively, wherein N is the total number of sites in a research area;
traversing all stations, and determining a representative index of each station in the study area;
step S7, determining representative ticket numbers of each station
Will target station P i And an effective peripheral station P ik (k=1…m i ) Representative index F of (2) ik Comparing and countingNumber of occurrences C i As a destination station P i Representative ticket numbers of (a); traversing all stations, and determining a representative ticket number of each station in a study area;
step S8, determining representative station
Setting a representative ticket number threshold D to enable the target station P to be i Representative ticket number C i Comparing with D, if C i Not less than D, the destination station P i Namely a representative station; all stations are traversed and a determination is made as to whether each station of the study area is a representative station.
2. The representative station quantitative determination method according to claim 1, wherein: step S2 the destination station P i Setting the value of the number M of the nearest searching stations to be 20-40; the correlation coefficient square threshold T is set to 0.6-0.9.
3. The representative station quantitative determination method according to claim 1, wherein: step S2 the destination station P i With peripheral station P ij The observation element sequence of (a) is an observation element sequence of a weather observation station, an environment observation station or a hydrological observation station.
4. The representative station quantitative determination method according to claim 1, wherein: step S2 the destination station P i With peripheral station P ij Is a part of the observation element of (a)The correlation coefficient of the sequence is calculated as:
(4)
wherein x is l (l= … n) is the destination station P i Is characterized in that the sequence of the observation elements of (a),is x l An average value of (l= … n); y is l (l= … n) is P i Peripheral station P of (a) ij Is a sequence of observation elements,/->Is y l An average value of (l= … n); n is the length of the sequence of observation elements, i.e. the number of samples.
5. The representative station quantitative determination method according to claim 1, wherein: the representative factors of each station in step S6 are normalized as follows,
(5)
(6)
wherein R is min 、R max Respectively determining coefficient factors R i (i= … N) minimum and maximum values, N being the total number of sites in the investigation region; g min 、G max Respectively representing area factors G i Minimum and maximum values of (i= … N).
6. The representative station quantitative determination method according to claim 1, wherein: step S6 weight W of the representative factor A 、W B The numerical range is 0.1-0.9, and W should be satisfied A +W B =1.0, when two factorsWhen the weights are equal, W A 、W B And simultaneously takes a value of 0.5.
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