A method for analyzing the characteristics of temperature effect in local urban environment

Abstract

The present invention discloses a method for analyzing the characteristics of the temperature effect of local urban environment, using conventional weather stations to represent reference stations, and encrypted automatic weather stations to represent specific environmental stations. The representative range of the reference station is determined based on Thiessen polygons, and the characteristics of the temperature effect of the local environment are determined by comparing the temperatures of the encrypted stations and the reference stations. The data processing of the temperature observation data of the present invention includes: initial data quality control, temperature effect intensity calculation and ambiguous value screening, statistical time period determination, etc. The results can provide reference for the study of local environmental temperature effect and urban planning and construction.

Description

Technical Field

[0001] The invention belongs to the field of environmental meteorology and ecological protection science, and in particular relates to a data compilation method for analyzing the temperature effect of urban environment. Background Art

[0002] Urban construction land will cause the "urban heat island" phenomenon, that is, the "warming effect"; while vegetation and water bodies have a significant "cooling effect" during the day, so they will have a certain mitigating effect on the urban heat island. In recent years, there have been many studies on urban heat islands, but there are few studies on the temperature effect characteristics of different local environments, especially in an urbanized environment, with different underlying surface ratios of buildings, vegetation, and water bodies, how the temperature effect characteristics of the local environment are, there are currently no relevant reports.

[0003] In recent years, the dense deployment of automatic weather stations in the fields of meteorology, environment, ecology, hydrology, etc. has provided an important basis for studying the characteristics of the temperature effect of the local environment. Conventional meteorological stations (conventional stations) are set up in standard "meteorological observation fields". "Meteorological observation fields" have strict site setting specifications and are the station setting environment with the least human intervention. Their temperature observation values ​​represent the basic situation of the meteorological background field under the standard observation environment. The station setting environment of the encrypted automatic weather station (encrypted station) is different from that of conventional meteorological stations. Its station setting environment is complex and diverse. This feature can be used to reflect the characteristics of the temperature effect of different local environments. Therefore, how to effectively utilize the different characteristics of the station setting environment of conventional stations and encrypted stations, organically combine the observation data of the two, and then evaluate the characteristics of the temperature effect of the local environment is of great practical significance to urban planning and construction. In the study of the temperature effect of the local environment of the city, there is a lack of corresponding specifications and effective data analysis methods. Summary of the invention

[0004] Purpose of the invention: In view of the technical problems existing in the prior art, the present invention provides a method for analyzing the characteristics of the temperature effect of the local urban environment, which provides a reference for the research on the temperature effect of the local urban environment and urban planning and construction.

[0005] Technical solution: To achieve the above invention purpose, the present invention adopts the following technical solution: A method for analyzing the characteristics of the temperature effect of local urban environment, comprising the following steps:

[0006] Step S1, temperature data preparation: obtain daily and hourly temperature observation data of conventional stations and encrypted stations in the study area for more than one year of observation sequence, and remove ambiguous value data in the temperature observation data;

[0007] Step S2, establishing Thiessen polygons of conventional stations: taking conventional stations in the study area as reference stations, and establishing Thiessen polygons of conventional stations in the study area, thereby confirming the Thiessen polygon coverage of each conventional station, and taking the conventional station as the reference station of the encrypted stations within the coverage;

[0008] Step S3, temperature effect intensity calculation: The hourly temperature effect intensity of each encrypted station is calculated by the following formula: , and remove the ambiguous value data of temperature effect intensity;

[0009] (1)

[0010] In the formula, ΔT represents the temperature effect intensity of the encrypted station, T represents the temperature of the encrypted station, and T JZ represents the corresponding base station temperature;

[0011] Step S4, qualitative determination of hourly temperature effect: calculate the overall standard deviation of all samples of hourly temperature effect intensity of all encrypted stations in the study area , and the hourly temperature effect is determined according to the following formula:

[0012] (2)

[0013] In the formula, ΔT represents the intensity of the temperature effect at each encrypted station every day and hour; 0, 1, and 2 correspond to normal, warming effect, and cooling effect, respectively;

[0014] Step S5, qualitative determination of temperature effects in different time periods: qualitatively classify the temperature effects in the daytime and nighttime periods of each day according to the following rules, so as to obtain the qualitative results of the temperature effects in each time period of each encrypted station every day:

[0015] (1) If only one temperature effect lasts for more than three hours in a period, then that temperature effect is classified as the temperature effect of that period;

[0016] (2) If there are multiple temperature effects that last for three hours or more in a period, or if there is no continuous three hours, the temperature effect of the period will be classified according to the temperature effect with the largest cumulative hours;

[0017] (3) If under the conditions of (2), there are at least two temperature effects with the same cumulative duration, the period is classified as “atypical”;

[0018] Step S6, statistics of temperature effect characteristics of encryption stations: During the study period, statistics are made on the proportion of various temperature effects at the encryption stations. Combined with land use data or remote sensing image data, with the encryption stations as the center, statistical assessments are made on the proportion of vegetation, water bodies, and building underlying surfaces within a certain range around the encryption stations, so as to determine the corresponding relationship between the environmental characteristics of the encryption stations and their temperature effect characteristics.

[0019] Furthermore, when the temperature data in the temperature observation data in step S1 meets the following conditions, it is determined to be ambiguous value data and is screened out:

[0020] (3)

[0021] Where x represents the temperature data in the temperature observation data, μ represents the mean of the temperature data, and σ represents the standard deviation of the temperature data.

[0022] Furthermore, when the temperature effect intensity in step S3 meets the following conditions, it is determined to be ambiguous value data and deleted:

[0023] (4)

[0024] In the formula, ΔT represents the temperature effect intensity of the encrypted station, μ Δ represents the mean value of the temperature effect intensity of the encrypted station, σ Δ Represents the standard deviation of the intensity of the temperature effect at the encrypted station.

[0025] Furthermore, the daytime period and nighttime period in step S5 are based on Beijing time, with the daytime period being (8:00-20:00] and the nighttime period being (20:00 - 8:00]. Based on this, the 24 hours of a day are divided into two periods, daytime and nighttime, each period being 12 hours.

[0026] Furthermore, the encryption station in step S6 has a certain range around it, and the specific range value is 100-3000m.

[0027] Beneficial effects: Compared with the prior art, the present invention uses conventional weather stations (conventional stations) to represent reference stations, and encrypted automatic weather stations (encrypted stations) to represent specific environmental stations. The representative range of the reference station is determined based on Thiessen polygons, and the characteristics of the local environmental temperature effect are determined by comparing the temperatures of the encrypted stations and the reference stations. The data collation involves initial data quality control, temperature effect intensity calculation and ambiguous value screening, statistical period determination, etc. This achievement can provide a reference for local environmental temperature effect research and urban planning and construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the method for analyzing the characteristics of the temperature effect of the local urban environment according to the present invention. DETAILED DESCRIPTION

[0029] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0030] Since conventional weather stations are set up in meteorological "observation fields", which have strict regulatory requirements, their temperature observation values ​​represent the basic situation of the meteorological background field and are also the temperature observation values ​​with the least interference from the artificial environment. The station setting environment of the encrypted automatic weather station is different from that of conventional weather stations. The station setting environment is complex and diverse. The present invention uses this feature to reflect the temperature effect characteristics of different local environments.

[0031] like Figure 1 As shown, the data collating method for analyzing the temperature effect of local urban environment of the present invention has the following steps:

[0032] S1, temperature data preparation

[0033] According to the study area, daily and hourly temperature observation data of conventional meteorological stations (conventional stations) and encrypted automatic meteorological stations (encrypted stations) with observation sequences of more than one year are prepared.

[0034] Boundary value check: Set the temperature boundary value, perform boundary value check on the original temperature data of conventional stations and encrypted stations, determine the data exceeding the boundary value as abnormal values, and remove them. Note: According to QX / T 118-2020 "Quality Control Ground for Meteorological Observation Data", the temperature boundary value is set. The minimum temperature boundary value is -80℃ and the maximum temperature boundary value is 60℃.

[0035] Ambiguous value screening: The original temperature data of each station is screened for ambiguous values ​​using the Raida criterion, and the formula is:

[0036] (1)

[0037] Among them, x is the temperature data, μ is the mean of the temperature data, and σ is the standard deviation of the temperature data. Data that meet this condition can be regarded as ambiguous value data and be eliminated. Note: Here, x, μ, and σ are all for a single weather station sample sequence.

[0038] For the temperature data of each meteorological station (including conventional stations and encrypted stations) in the study area, the data mean μ and standard deviation σ are statistically analyzed. According to formula (1), the ambiguous values ​​in the sample sequence are determined and eliminated.

[0039] S2, conventional station Thiessen polygon establishment

[0040] The conventional stations in the study area are used as the base stations, and the Thiessen polygons of the conventional stations in the study area are established. The base station corresponding to each encrypted station is determined based on the coverage of each conventional station's Thiessen polygon.

[0041] S3, Calculation of temperature effect intensity

[0042] The intensity of the temperature effect is calculated by the following formula (2):

[0043] (2)

[0044] Among them, ΔT represents the temperature effect intensity of the encrypted station, T represents the temperature of the encrypted station, and T JZ Indicates the temperature of the corresponding base station. Thus, the calculation results of the temperature effect intensity of each encrypted station are obtained.

[0045] On this basis, it is necessary to conduct quality control on the intensity of temperature effect. The temperature effect intensity data of each encrypted station in the study area are counted and its data mean μ Δ With standard deviation σ Δ , according to formula (3), the ambiguous values ​​in the sample sequence are determined and eliminated. That is, the quality control of the intensity of the temperature effect of the encrypted station,

[0046] (3)

[0047] Among them, ΔT represents the temperature effect intensity of the encrypted station, μ Δ represents the mean value of the temperature effect intensity of the encrypted station, σ Δ It represents the standard deviation of the temperature effect intensity of the encrypted station. Note: Here ΔT, μ Δ , σ Δ All of them refer to the sample sequence of temperature effect intensity at a single encrypted station.

[0048] S4, qualitative determination of hourly temperature effect

[0049] Calculate the overall standard deviation of all samples of hourly temperature effect intensity at all encrypted stations in the study area , refer to GB / T 35562-2017 "Temperature Evaluation Level", when When , the temperature effect is characterized as “normal”; when When , the temperature effect is characterized as being high, that is, the “warming effect”; when When , the temperature effect is characterized as being on the low side, that is, the "cooling effect".

[0050] The intensity of the hourly temperature effect of each encrypted station is determined according to the above standards, thereby obtaining the qualitative results of the hourly temperature effect of each encrypted station.

[0051] S5, Qualitative determination of daytime and nighttime temperature effects

[0052] Due to different underlying surfaces, the environmental temperature effects caused by them may be characterized differently during the day and at night. For example, water bodies have a warming effect during the day and a cooling effect at night. According to certain rules, 24 hours a day is divided into two periods, day and night, namely day and night. Day and night are defined according to national meteorological standards. Taking Beijing time as the standard, daytime time is (8:00-20:00], and night time is: (20:00 - 8:00]. Based on this, 24 hours a day is divided into two periods, daytime and night, each period is 12 hours.

[0053] The temperature records of the weather station are from 20:00 to 20:00 across the day. The corresponding time for the 24 temperature data every hour of the day is from 21:00 of the previous day to 20:00 of the current day. The corresponding time for the hourly record of the night period is from 21:00 of the previous day to 08:00 of the current day, with a total of 12 data. The corresponding time for the hourly record of the day period is from 09:00 to 20:00 of the current day, with a total of 12 data.

[0054] Since the temperature monitored by the encrypted station is affected by many factors such as the environment and weather (such as wind, rain, and sunshine), in order to eliminate the randomness of the temperature characteristics caused by complex factors as much as possible, the intensity of the hourly temperature effect at the encrypted station is qualitatively classified according to day and night time periods.

[0055] The temperature effects are qualitatively classified for each period of the day (i.e., daytime and nighttime) according to the following rules:

[0056] (1) If only one type of temperature effect lasts for three hours or more in a period, the period is classified as that type of temperature effect.

[0057] (2) If there are multiple temperature effects that last for three hours or more in a period, or if there are no temperature effects that last for three hours in a period, the period will be classified according to the temperature effect with the largest cumulative number of hours.

[0058] (3) If, under the conditions of (2), the cumulative duration of various temperature effects is the same, the period is classified as “atypical”.

[0059] The above standards are used to judge the temperature effect of each encrypted station during the day and night, thereby obtaining qualitative results of the temperature effect of each encrypted station during each period of the day.

[0060] S6, statistics of temperature effect characteristics of encrypted stations

[0061] During a certain research period, the qualitative judgment results of the temperature effects during the day and at night at each encrypted station are statistically analyzed, that is, the respective proportions of "warming effect", "cooling effect", "normal" and "atypical" are statistically analyzed.

[0062] Finally, combined with land use data or remote sensing image data, with the encryption station as the center, a statistical assessment is conducted on the proportion of vegetation, water bodies, and building underlying surfaces within a certain range around the encryption station, and then the correspondence between the environmental characteristics of the underlying surface of the encryption station and its temperature effect characteristics is determined.

[0063] Embodiment: Taking the daily and hourly temperature data of 135 encrypted automatic weather stations (encrypted stations) and 5 conventional weather stations (conventional stations) in Nanjing from 2018 to 2020 as an example, the implementation process of the present invention is described. The original temperature data of conventional stations and encrypted stations are quality controlled to eliminate ambiguous values. Conventional stations have standard meteorological observation fields, which are used as reference stations. According to the coverage range of Thiessen polygons, the reference station (conventional station) corresponding to each encrypted station is determined. The daily hourly temperature effect intensity of all encrypted stations is calculated, and the temperature effect intensity is quality controlled to eliminate ambiguous values. Qualitative judgment is made on the hourly temperature effect of each encrypted station, and then qualitative judgment is made on the temperature effect during the day and night of each day.

[0064] Two typical encrypted stations located near large water bodies are selected to illustrate the characteristics of their environmental temperature effects. One station is located on the Yangtze River (referred to as the Yangtze River Station), and the other station is located on the Xuanwu Lake (referred to as the Xuanwu Lake Station). As can be seen from Table 1, affected by the water body, both stations showed very significant warming effect characteristics at night, with the warming effect accounting for more than 80%; during the day, the cooling effect of the Yangtze River Station accounted for 49.4%, the highest among the four qualitative proportions of temperature effects, and the cooling effect of the Xuanwu Lake Station accounted for 33.1%, the second highest value among the four qualitative proportions of temperature effects, which also reflects the cooling effect of the water body to a certain extent.

[0065] Table 1 Proportion of temperature effect characteristics of typical encrypted stations

[0066]

[0067] Using land use data, the underlying land use is classified into three categories: vegetation, water bodies, and buildings. Taking the encrypted station as the center, the proportion of vegetation, water bodies, and building underlying surfaces within a certain range around the encrypted station is statistically evaluated. Table 2 shows the proportion of various underlying surfaces in the surrounding areas of two typical encrypted stations. It can be seen that within 1500m, the proportion of water bodies has an absolute advantage at the Yangtze River Station and Xuanwu Lake Station. The statistical results indirectly illustrate the local environment of different underlying surfaces, and the influence range of the temperature effect is within 1500m.

[0068] Table 2 Proportion of various underlying surfaces within the perimeter of typical encrypted stations

[0069]

[0070] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for analyzing the characteristics of the temperature effect of local urban environment, characterized in that The following steps are involved: S1, temperature data preparation: obtain daily and hourly temperature observation data of conventional stations and encrypted stations in the study area with an observation sequence of more than one year, and remove ambiguous value data in the temperature observation data; S2, establish Thiessen polygons of conventional stations: take conventional stations in the study area as reference stations, and establish Thiessen polygons of conventional stations in the study area, so as to confirm the Thiessen polygon coverage of each conventional station, and use the conventional station as the reference station of the encrypted stations within the coverage; S3, Calculation of temperature effect intensity: The hourly temperature effect intensity of each encrypted station is obtained by the following formula: , and remove the ambiguous value data of temperature effect intensity; （1） In the formula, ΔT represents the temperature effect intensity of the encrypted station, T represents the temperature of the encrypted station, and T JZ represents the corresponding base station temperature; S4, qualitative determination of hourly temperature effect: calculate the overall standard deviation of all samples of hourly temperature effect intensity of all encrypted stations in the study area , and the hourly temperature effect is qualitatively determined according to the following formula: （2） In the formula, ΔT represents the daily hourly temperature effect intensity of each encrypted station; 0, 1, and 2 correspond to normal, warming effect, and cooling effect, respectively; S5, qualitative determination of temperature effects at different time periods: qualitatively classify the temperature effects at daytime and nighttime according to the following rules, so as to obtain qualitative results of temperature effects at each encrypted station at each time period of each day: (1) If only one temperature effect lasts for more than three hours in a period, then that temperature effect is classified as the temperature effect of that period; (2) If there are multiple temperature effects that last for three hours or more in a period, or if there is no continuous three hours, the temperature effect of the period will be classified according to the temperature effect with the largest cumulative hours; (3) If under the conditions of (2), there are at least two temperature effects with the same cumulative duration, then the period is classified as "atypical"; S6, statistics of temperature effect characteristics of encrypted stations: During the study period, statistics are made on the proportion of various temperature effects at encrypted stations. Combined with land use data or remote sensing image data, with the encrypted station as the center, statistical assessment is made on the proportion of vegetation, water bodies, and building underlying surfaces within a certain range around the encrypted station, so as to determine the corresponding relationship between the environmental characteristics of the encrypted station and its temperature effect characteristics.

2. The method for analyzing the characteristics of the temperature effect of local urban environment according to claim 1 is characterized by: When the temperature data in the temperature observation data in step S1 meets the following conditions, it is determined to be ambiguous value data and is screened out. （3） Where x represents the temperature data in the temperature observation data, μ represents the mean of the temperature data, and σ represents the standard deviation of the temperature data.

3. The method for analyzing the characteristics of the temperature effect of local urban environment according to claim 1 is characterized in that: When the temperature effect intensity in step S3 meets the following conditions, it is determined to be ambiguous value data and deleted. （4） In the formula, ΔT represents the temperature effect intensity of the encrypted station, μ Δ represents the mean value of the temperature effect intensity of the encrypted station, σ Δ Represents the standard deviation of the intensity of the temperature effect at the encrypted station.

4. The method for analyzing the characteristics of the temperature effect of local urban environment according to claim 1 is characterized in that: The daytime and nighttime periods described in step S5 are based on Beijing time, with the daytime period being (8:00-20:00] and the nighttime period being (20:00-8:00].

5. The method for analyzing the characteristics of the temperature effect of local urban environment according to claim 1 is characterized by: The encryption station in step S6 is within a certain range around the encryption station, and the specific range value is 100-3000m.

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