CN117668507B - Urban heat island effect evaluation method based on atmospheric analysis data - Google Patents
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Abstract
The invention discloses an urban heat island effect evaluation method based on atmospheric analysis data, which comprises the following steps: acquiring related data of the urban heat island, and preprocessing the related data; determining a typical heat island morphology based on the preprocessed data; based on the typical heat island morphology, obtaining a typical heat island mean value, and evaluating the typical heat island mean value; and acquiring a typical heat island area ratio based on the typical heat island morphology and the evaluation result, and acquiring the heat island range and degree of the urban influence according to the typical heat island area ratio. The heat island assessment method is simple, scientific and objective, and can provide a new angle and tool for urban meteorological research.
Description
Technical Field
The invention belongs to the technical field of urban heat islands, and particularly relates to an urban heat island effect evaluation method based on atmospheric analysis data.
Background
Urban heat island (Urban Heat Islands, UHI) refers to the phenomenon that when city is developed to a certain scale, urban air temperature is obviously higher than suburban area due to the change of urban sublevel surface property, atmospheric pollution, and the discharge of artificial waste heat, and the like, high-temperature island like phenomenon is formed (Guangzhou urban heat island detection gazette, 2021). UHI is the most pronounced example of the effect of human activity on local climate, and this concept was first proposed by Luke Howard (1833) in CLIMATE OF LONDON. UHI is specifically a phenomenon in which urban temperatures are higher than suburban temperatures due to UHI interaction with high temperature waves (Dong et al, 2018), and urban thermal environment assessment, urban design and planning, and heatEnvironmental improvement, supply and demand of urban energy, and the like have important roles, and research on UHI has been paid much attention. Oke (2017) and the like simply divide UHI into underground Urban Heat Islands (UHI) sub ) Urban Heat Island (UHI) surf ) Crown city heat island (UHI) ucl ) And boundary layer Urban Heat Island (UHI) ubl ）。
Concerning UHI ucl The intensity (Urban Heat Islands Intensity, UHII) is calculated by the weather station method and the remote sensing method, which are respectively focused on UHI ucl And UHI surf Study of intensity. Weather station method, namely, the difference between the average urban air temperature and the average suburban air temperature, but partial weather observation stations have station migration conditions, urban development causes the suburban stations to be insufficient in representativeness, and these factors lead to UHI ucl The intensity calculation results are greatly different. In addition, the timing air temperature of the weather station is generally measured every 6 hours for UHI ucl The resolution of the study of the daily variation characteristics is too low.
Disclosure of Invention
In order to solve the technical problems, the invention provides an urban heat island effect evaluation method based on atmospheric analysis data, which can effectively solve the problems that the constructed heat island index is not representative and the heat island range cannot be evaluated quantitatively and accurately due to urban development.
In order to achieve the above object, the present invention provides a method for estimating urban heat island effect based on atmospheric analysis data, comprising:
acquiring related data of the urban heat island, and preprocessing the related data;
determining a typical heat island morphology based on the preprocessed data;
based on the typical heat island morphology, obtaining a typical heat island mean value, and evaluating the typical heat island mean value;
and acquiring a typical heat island area ratio based on the typical heat island morphology and the evaluation result, and acquiring the heat island range and degree of the urban influence according to the typical heat island area ratio.
Optionally, the related data includes: air temperature, precipitation, wind speed, urban built-up area data and urban administrative division data.
Optionally, preprocessing the related data includes:
removing meteorological element data with default values, urban built-up area data and urban administrative division data;
and screening the air temperature, the precipitation and the wind speed according to preset standards.
Optionally, determining the typical heat island morphology includes:
based on the preprocessed data, acquiring suburban representative values by combining an area weighted average method;
acquiring the heat island strength based on the suburb representative value;
and determining the typical heat island shape according to the heat island intensity.
Optionally, the area weighted average method is as follows:
wherein,for suburban representative value->Represents the weights in the weft direction and the warp direction respectively, m and n are the number of weft and warp lattice points, < >>Is the background field lattice point temperature.
Optionally, evaluating the representative heat island mean includes: evaluating by using the grid point value and the regional grid point average value;
the lattice point values are:
wherein UHII-1 is a lattice value,for heat island intensity, subscript represents 3.8K contour;
the regional lattice point mean value is as follows:
wherein UHII-2 is the area lattice point mean value, and K and l respectively represent the lattice points in the weft direction and the warp direction in the 3.8K contour line range.
Optionally, obtaining the typical heat island area ratio includes:
acquiring the area of the typical heat island according to the form of the typical heat island and the evaluation result;
and acquiring the area ratio of the typical heat island with different intensities based on the area of the typical heat island and the area of the urban area.
Optionally, the method for obtaining the area of the typical heat island is as follows:
wherein UHIS is the typical heat island area,is the number of lattice points.
Compared with the prior art, the invention has the following advantages and technical effects:
the heat island assessment method is simple, scientific and objective, and can provide a new angle and tool for urban meteorological research. Compared with a remote sensing method, the method has the advantages that the atmospheric analysis data are convenient to obtain, the pretreatment is simple, and the method is suitable for the heat island effect evaluation of various time scale canopy and is more suitable for the heat island effect evaluation of mesoscale or urban scale; compared with the weather station method, the method has the advantages of easy acquisition of data, high time resolution, strong representativeness of the heat island strength index and more scientific and reasonable calculation method. The method can rapidly, objectively, scientifically and reasonably evaluate the daily change characteristics of the urban scale canopy heat island intensity and measure the influence of urbanization on the heat island range in the process of urban development.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a schematic diagram of a urban heat island effect assessment method based on atmospheric analysis data according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of ground temperature and wind farm of an embodiment of the present invention;
fig. 3 is a schematic diagram of the spatiotemporal evolution of heat island morphology (extent of coverage of the contour of the built-up area) according to an embodiment of the present invention, wherein fig. 3 (a) is 1 month 10 days 22:00 specific heat island ranges, fig. 3 (b) 1 month 11 day 00:00 specific heat island ranges, fig. 3 (c) 1 month 11 days 02:00 specific heat island ranges, fig. 3 (d) is 1 month 11 days 04:00 specific heat island ranges, fig. 3 (e) is 1 month 11 days 06:00 specific heat island ranges, fig. 3 (f) 1 month 11 days 07:00 specific heat island ranges, fig. 3 (g) is 1 month 11 days 08:00 specific heat island ranges, fig. 3 (h) is 1 month, 11 days, 10:00 specific heat island ranges are Beijing time;
fig. 4 is a schematic diagram of a typical heat island morphology (area contour coverage where UHII is located) according to an embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The invention provides an urban heat island effect evaluation method based on atmospheric analysis data, which is shown in figure 1 and specifically comprises the following steps:
acquiring related data of the urban heat island, and preprocessing the related data;
specifically, the relevant data of the urban heat island include: weather element data and urban administrative division data, eliminating default data, setting screening standards, and selecting weather conditions with almost no precipitation and small wind speed.
Determining a typical heat island morphology based on the preprocessed data;
specifically, referring to the urban administrative division boundary, a larger range of regular longitude and latitude grid area is selected as a background field (fig. 2), the average value of the temperature of the background field, namely the suburban representative value is estimated, and the difference value between the preprocessed temperature value of the grid point of the background field and the suburban representative value is utilized to define the night heat island shape (fig. 3). As can be seen from fig. 3, the characteristics of the extinction of the night heat island evolution over time, the heat island being generated in the central zone of the urban built-up area and having a multi-central characteristic, the typical heat island range is determined from the closed contour of the heat island intensity (UHII) of the outermost crown urban heat island (fig. 3 (f)). Further, the urban typical night heat island spatial morphology is depicted from standard characteristic contours (fig. 4).
Based on the typical heat island morphology, obtaining a typical heat island mean value, and evaluating the typical heat island mean value;
specifically, a typical heat island morphology (fig. 4) was selected and the UHII mean was evaluated. UHII mean value assessment includes: evaluation of the grid point value UHII-1 and the regional grid point mean UHII-2.
And obtaining a typical heat island area ratio according to the typical heat island morphology and the evaluation result, and obtaining a heat island range of the urban influence according to the typical heat island area ratio.
Further, the related data includes: air temperature, precipitation, wind speed, urban built-up area data and urban administrative division data.
Further, preprocessing the related data includes:
removing meteorological element data with default values, urban built-up area data and urban administrative division data;
and screening the air temperature, the precipitation and the wind speed according to preset standards.
Further, determining a typical heat island morphology includes:
based on the preprocessed data, acquiring suburban representative values by combining an area weighted average method;
acquiring the heat island strength based on suburb representative values;
based on the heat island intensity, a typical heat island morphology is determined.
Further, evaluating the representative heat island mean includes: evaluating by using the grid point value and the regional grid point average value;
the output results of the lattice values are shown in Table 1, and the maximum value of the lattice values, i.e., the typical center value of the heat island, is 4.32K, at 116E, 40N. The distribution of the heat island space can be further evaluated by using a box diagram. The area lattice point average value is 4.06K, and the heat island is determined to be a super heat island according to the intensity classification of the heat island in table 2. These two indices can be used for heat island center and heat island intensity analysis.
TABLE 1
TABLE 2
Further, obtaining a typical heat island area ratio includes:
acquiring a typical heat island area according to an evaluation result;
based on the areas of the typical heat islands (table 2) and urban areas of different grades, the typical heat island area ratio was obtained.
Specifically, the sum of all grid points in the range of the typical heat island closed contour is the area of UHI.
In order to better verify the evaluation method of the invention, take the Beijing city heat island effect as an example:
(1) And (3) data acquisition: downloading ERA5-land 0.1 DEG at the European mid-term weather forecast center2m air temperature, precipitation and 10m wind speed data of 0.1 DEG hour by hour; downloading 2020 city built-up area data; and downloading the urban administrative division data of the natural resource department standard map service system.
(2) Data preprocessing: removing meteorological element data with default values, urban built-up area data and administrative division data; because the shape and strength of the heat island are obviously influenced by wind speed and precipitation, weather conditions with almost no precipitation and small wind speed are screened out, and the specific standard is P10mm/24h，V/>The method comprises the steps of carrying out a first treatment on the surface of the The canopy heat island reaches the maximum at night, so that the night air temperature is screened as a study object;
(3) Area weighted averaging
（1）
Wherein,for (FIG. 2) background field lattice point temperature +.>I.e., suburban representative,representing the weights in the weft and warp directions, respectively, since the data selected is regular 0.1 +.>0.1 degree longitude and latitude grid, so the weight average value is 1.m and n are the number of the weft and warp lattice points.
(4) Definition of heat island intensity UHII at a certain moment:
（2）
(5) Judging the center of a heat island: in fig. 3, a significant seasonal variation signal exists in the temperature of the south area of the urban built-up area, and in combination with the wind field, the central intensity of the heat island of 39 degrees 40' N is considered to be possibly influenced by the southeast side warmer advection, so that the central of the heat island is removed, and the central of the heat island of the vicinity of 40 degrees N is defined as the central of the urban heat island, and the area is basically consistent with the population density large-value area of the urban built-up area. Fig. 3 shows the formation (fig. 3 (a) - (e)) and the tripod (fig. 3 (f)) and the disappearance (fig. 3 (g) - (h)) of the urban heat island, so that the formation process of the heat island is slower and the disappearance process is rapid.
(6) Judging the typical heat island shape: the heat island shape when the central intensity of the urban heat island reaches the maximum is defined as a typical heat island according to the heat island center selected in the previous step. Selected in this example is Beijing time (CST) 07: the heat island at time 00 closes the range of 3.8K of the contour line, the average hour-by-hour air temperature of the corresponding urban and suburban areas at the time is the lowest, and the temperature difference is the largest.
(7) Typical heat island mean value assessment
The following two indexes are adopted:
（3）
（4）
wherein UHII-1 is a lattice point value, specifically referring to the lattice point heat island intensity value of the typical heat island 3.8K closed contour line demarcating range in FIG. 4, see Table 1.UHII-2 is the arithmetic mean of UHII-1, and k and l are the number of grid points in the weft and warp directions within the 3.8k contour line range, respectively. And outputting UHII-1, and performing heat island intensity spatial distribution characteristic analysis by using a box diagram. These two indices can evaluate the intensity of a typical heat island.
(8) UHI area assessment
The sum of all grid points within a typical heat island. In this embodiment, a typical heat island shape at the moment of CST 07:00 (FIG. 4) is selected, and the heat island intensity UHII-1 is not less than the sum of grid numbers of 3.8K within the range of 39.9 DEG N-41 DEG N,116.2 DEG E-116.8 DEG E, specifically shown in the formula 5. The index may evaluate typical heat island intensities.
（5）
Wherein,representing the number of grid points.
(9) UHIS calculation method
According to the heat island intensity classification table 2, the ratio UHIS of the number of weak heat islands, strong heat islands and super heat islands in the administrative region to the number of grids in the urban region is counted, expressed in percentage, and two decimal places are reserved.
（6）
Wherein UHIS (i) represents a certain type of heat island area ratio,representing the number of the heat island lattice points of the type S2 i The number of grid points in the administrative range of the urban area is represented, and i takes values of 1, 2 and 3, which correspond to the weak heat island, the strong heat island and the super heat island in the table 2 respectively. UHIS ∈Temp corresponding to three heat islands through calculation>The method comprises the following steps:
（7）
（8）
（9）
the method is suitable for analyzing various time scales of the urban scale or the mesoscale heat island, but the determination of the heat island shape is to be noted that the determination of the heat island shape has large difference due to data and research areas and is to be modified according to actual conditions.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily conceivable by those skilled in the art within the technical scope of the present application should be covered in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (3)
1. The urban heat island effect evaluation method based on the atmospheric analysis data is characterized by comprising the following steps of:
acquiring related data of the urban heat island, and preprocessing the related data;
determining a typical heat island morphology based on the preprocessed data;
determining the typical heat island morphology includes:
based on the preprocessed data, acquiring suburban representative values by combining an area weighted average method;
acquiring the heat island strength based on the suburb representative value;
determining the typical heat island morphology according to the heat island intensity;
the area weighted average method is as follows:
，
wherein,for suburban representative value->、/>Represents the weights in the weft direction and the warp direction respectively, m and n are the number of weft and warp lattice points respectively, and +.>The temperature of the background field lattice point;
based on the typical heat island morphology, obtaining a typical heat island mean value, and evaluating the typical heat island mean value;
evaluating the representative heat island mean includes: evaluating by using the grid point value and the regional grid point average value;
the lattice point values are:
，
wherein UHII-1 is a lattice value,for heat island intensity, subscript represents 3.8K contour;
the regional lattice point mean value is as follows:
，
wherein UHII-2 is the average value of grid points in the area, and K and l respectively represent the grid point numbers in the weft direction and the warp direction in the 3.8K contour line range;
the heat island strength is:
，
wherein,is a heat islandIntensity of (I)>For the background field lattice temperature, +.>Is a suburb representative value;
based on the typical heat island morphology and the evaluation result, obtaining a typical heat island area ratio, and obtaining the heat island range and degree of the urban influence according to the typical heat island area ratio;
obtaining the typical heat island area ratio includes:
acquiring the area of the typical heat island according to the form of the typical heat island and the evaluation result;
acquiring the area ratio of the typical heat island with different intensities based on the area of the typical heat island and the area of the urban area;
the method for obtaining the typical heat island area comprises the following steps:
，
wherein UHIS is the typical heat island area,for the number of lattice points, the subscript represents a 3.8K contour.
2. The urban heat island effect evaluation method based on atmospheric analysis data according to claim 1, wherein the related data comprises: air temperature, precipitation, wind speed, urban built-up area data and urban administrative division data.
3. The urban heat island effect evaluation method based on atmospheric analysis data according to claim 2, wherein preprocessing the related data comprises:
removing meteorological element data with default values, urban built-up area data and urban administrative division data;
and screening the air temperature, the precipitation and the wind speed according to preset standards.
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| CN109002627A (en) * | 2018-07-28 | 2018-12-14 | 南京林业大学 | Urban planning scheme heat island simulating and predicting method based on grey neural network CA model |
| CN110727984A (en) * | 2019-09-29 | 2020-01-24 | 天津大学 | Method for researching mechanism between land utilization change and urban thermal environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107678075A (en) * | 2017-11-13 | 2018-02-09 | 深圳先进技术研究院 | A kind of urban heat land effect monitoring method and system based on domestic satellite |
| CN108549858A (en) * | 2018-04-08 | 2018-09-18 | 武汉理工大学 | A kind of quantitative evaluation method of urban heat land effect |
| CN109002627A (en) * | 2018-07-28 | 2018-12-14 | 南京林业大学 | Urban planning scheme heat island simulating and predicting method based on grey neural network CA model |
| CN110727984A (en) * | 2019-09-29 | 2020-01-24 | 天津大学 | Method for researching mechanism between land utilization change and urban thermal environment |
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