CN117370714A - Representative station quantitative determination method - Google Patents
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Abstract
The invention discloses a quantitative determination method for a representative station, which takes a correlation coefficient and a representative area as indexes to determine an optimal representative station through quantitative optimization judgment. The representative station determining method has the advantages of quantification, objectivity, strong operability and the like, and can be used for resource evaluation, scientific research and the like in the fields of meteorology, environment, hydrology, ecology and the like.
Description
Technical Field
The invention belongs to the technical field of meteorological environment (environmental protection), in particular to a method for determining an ecological observation representative station of meteorological environment, which can be used for resource evaluation, scientific research and the like in the fields of meteorology, environment, ecology, hydrology and the like.
Background
The departments of weather, environment, ecology, hydrology and the like carry out continuous monitoring and data acquisition of related elements through the observation station. Such as: the weather station observes and collects weather elements such as temperature, humidity, air pressure, wind speed, wind direction, rainfall and the like in the atmospheric environment through various measuring devices, and the data has important supporting effects on weather forecast, weather resource evaluation, weather disaster prevention, disaster reduction and the like. With the improvement of comprehensive national force in China, the construction force of departments such as weather, environment, ecology and hydrology on the observation stations is continuously increased, the arrangement density of various observation stations is gradually increased, and proper representing stations are often required to be selected in practical application and scientific analysis, namely, the observation stations capable of effectively representing the overall condition of a certain observation element in a certain range are also required to be selected. Scientific and reasonable representative station selection has the significance of twice the effort and the importance. The conventional representative station selection lacks quantitative indexes, and has certain limitations in practical application.
Disclosure of Invention
The invention aims to: aiming at the problems existing in the prior art, the invention designs a representative station quantitative determination method, and the optimal representative station is determined through quantitative optimization judgment.
The technical scheme is as follows: in order to achieve the above purpose, the present invention adopts the following technical scheme: a representative station quantitative determination method, comprising the steps of:
step S1, selecting a target station
Randomly selecting a target station P from all stations in a study 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
Calculating the destination P i Representative factor R of (2) i (determining coefficient factor) and G i (representing an area factor) 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 ThiessenPolygonal 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 following 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;
all stations are traversed and a representative index for each station in the study area is determined.
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 Is a representative ticket number of (a). Traversing all stations, determining a representative ticket number for each station;
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 I.e. the representative station. All stations are traversed and a determination is made as to whether each station is a representative station.
Further, the station is a weather observation station, an environmental observation station or a hydrological observation station.
Further, step S2 is performed by 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.
Further, step S2 is performed by 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, such as: an air temperature observation sequence of a weather station and a PM2.5 observation sequence of an environment observation station.
Further, step S2 is performed by the destination station P i With peripheral station P ij The correlation coefficient of the observation element sequence of (2) is calculated as follows:
（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.
Further, the representative factors of each station are normalized according to the following formula in step S6,
（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).
Further, the weight W of the representative factor in step S6 A 、W B The numerical range is 0.1-0.9, and W should be satisfied A +W B =1.0, when the two factors are equally weighted, W A 、W B And simultaneously takes a value of 0.5.
The beneficial effects are that: compared with the prior art, the invention provides a brand-new scientific and reasonable representative station quantitative determination method, which has important significance for resource evaluation, scientific research and the like. The representative station determining method provided by the invention has the advantages of quantification, objectivity, strong operability, easiness in popularization and the like, and can be widely applied to the fields of weather, hydrology, environment, ecological environment protection and the like.
Drawings
FIG. 1 is a flow chart of a representative station quantitative determination method according to the present invention;
FIG. 2 is a schematic view of a Thiessen polygon of a research area weather station according to an embodiment of the present invention;
FIG. 3 is a Thiessen polygon schematic diagram of a destination station and its active peripheral stations according to an embodiment of the present invention;
fig. 4 is a diagram of a region of investigation rainfall representation station according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated in the accompanying drawings and detailed description which are to be understood as being merely illustrative of the invention and not limiting of its scope, and various modifications of the invention, which are equivalent to those skilled in the art upon reading the invention, will fall within the scope of the invention as defined in the appended claims.
As shown in fig. 1, the representative station quantitative determination method of the present invention comprises the following steps:
1. selecting a destination station
Randomly selecting a target station P among all stations in a study area i 。
2. Calculating correlation of target station and peripheral station
(1) By destination station P i For the center, calculate the distance between other stations and the target station, and take the distance from the 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 (j= … M) correlation coefficient r of observation element sequence ij Calculating according to a correlation coefficient formula:
（1）；
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. The observation element sequence is an observation element sequence of a meteorological observation station, an environmental observation station or a hydrological observation station, for example: an air temperature observation sequence of a weather station and a PM2.5 observation sequence of an environment observation station.
On the basis, a correlation coefficient square sequence r is obtained ij 2 （j=1…M）。
3. Determining an active peripheral station of a destination station
Extracting r ij 2 All values r in (j= … M) equal to or greater than a given threshold T ik 2 （k=1…m i ) Peripheral station P corresponding thereto ik （k=1…m i ) Will P ik Called destination station P i Is an effective peripheral station.
4. Calculating a representative factor for a destination
Calculating the destination P i Representative factor R of (2) i (determining coefficient factor) and G i (representing an area factor) calculated as:
（2）；
（3）；
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. The Thiessen polygon schematic diagram of the investigation region is shown in FIG. 2, and the Thiessen polygon schematic diagram of the destination station and its effective peripheral stations is shown in FIG. 3.
5. Selecting a new destination station
And selecting a new target station, repeating the steps 2-4, traversing all stations, and obtaining the representative factors of all stations in the research area.
6. Calculating representative index of each station
(1) Normalization of representative factors of each station
Will target station P i Representative factor R of (2) i (determining coefficient factor) and G i The normalization (representative area factor) is performed as follows:
（4）；
（5）；
wherein A is i To determine the coefficient factor R i Normalized value of B i Is representative of the face factor G i Normalized value of R min 、R max Respectively determining coefficient factors R i Minimum and maximum values of (i= … N), N being the grinding timeThe total number of sites in the area is explored. G min 、G max Respectively representing the factors G of the face i Minimum and maximum values of (i= … N).
(2) Representative index calculation for each station
Representative index F of each station i Is calculated by the following formula (6),
（6）；
F i for destination station P i Representative index of W A 、W B Is a weight of a representative factor, has a value of 0.1 to 0.9, and satisfies W A +W B =1.0, when the two factors are equally weighted, W A 、W B And simultaneously takes a value of 0.5. All stations are traversed and a representative index for each station in the study area is determined.
7. Determining representative ticket numbers for each station
For destination station P i Representing the index F i And an effective peripheral station P ik （k=1…m i ) Representative index F of (2) ik （k=1…m i ) Comparing and countingNumber of occurrences C i ，C i I.e. the destination station P i Is a representative ticket number of (a). A representative number of votes for all stations of the study area is determined by traversing all stations.
8. Determining representative stations
A representative ticket threshold D is set for the destination station P i Representative ticket number C i Comparing with D, ifP is then i I.e. the representative station, and vice versa.
All representative stations of the study area can be selected by traversing all stations. By adjusting the size of the representative ticket number threshold D, the representative station density condition can be adjusted.
Implementation case:
200 weather stations in Nanjing are taken as an example to illustrate the implementation process of the invention. According to the steps of the invention, a rainfall representing station in Nanjing city is determined. The value of the search station number M is set to 20, the correlation coefficient square threshold T is set to 0.85, and the representative factor weight W A 、W B Meanwhile, the value of 0.5 is obtained, the correlation coefficient of the target station and peripheral stations of the target station is calculated by using a month-by-month rainfall observation sequence of 200 meteorological stations 2018-2022 in Nanjing city, the Thiessen polygons of each meteorological station are generated by using ArcGIS, the area of the Thiessen polygon corresponding to each station is further obtained, and according to the implementation steps of the invention, the determined rainfall representation station of the research area is shown in figure 4.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to 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 description, but must be determined according to the scope of claims.
Claims (7)
1. A representative station quantitative determination method, characterized by comprising the steps of:
step S1, selecting a target station
Randomly selecting a target station P from all stations in a study 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 ) For it is toPeripheral 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: the station is a weather observation station, an environmental observation station or a hydrological observation station.
3. 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.
4. 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.
5. The representative station quantitative determination method according to claim 1, wherein: step S2 the destination station P i With peripheral station P ij The correlation coefficient of the observation element sequence of (2) is calculated as follows:
（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.
6. 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).
7. According to claim 1The representative station quantitative determination method is characterized in that: 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 the two factors are equally weighted, W A 、W B And simultaneously takes a value of 0.5.
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