CN115146564A - Urban ground wind speed refined simulation method based on vertical hierarchical downscaling technology - Google Patents
Urban ground wind speed refined simulation method based on vertical hierarchical downscaling technology Download PDFInfo
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Abstract
The invention relates to a fine simulation method for urban ground wind speed based on a vertical hierarchical downscaling technology, which comprises the following steps: gridding the urban area, determining the height of a mixed layer of each grid point, and dividing the grid points into a coarse sublayer which is not higher than the height of the mixed layer and is relatively greatly influenced by a ground underlying surface and an inertia sublayer which is higher than the height of the mixed layer and is relatively less influenced by the ground underlying surface; calculating the wind speed at the designated height monitored by the meteorological station to the high altitude in the inertia sub-layer, then interpolating the wind speed of each station at the high altitude to all grid points or required partial grid points, then calculating the influence of the roughness on the wind speed in different ranges of the wind direction at different heights according to different underlying surfaces for the interpolated wind speed of each grid point, and calculating the wind speed to the surface simulation height respectively. The method can realize fine simulation of long-time scale and regional urban wind speed, and effectively solves the problem of observation of wind speed of a complex underlying surface.
Description
Technical Field
The invention relates to the technical field of meteorological monitoring simulation, in particular to a fine simulation method for urban ground wind speed based on a vertical hierarchical downscaling technology.
Background
The IPCC fifth evaluation report indicates that wind speeds in various regions around the world (especially urban regions) generally exhibit a decreasing trend. However, the wind extremes and dangerousness are not reduced, but rather increased. Although the quantity of typhoons affecting China is not changed greatly, the strength is obviously enhanced, and in nearly 10 years, about half of typhoons affecting China are strong typhoons or superstrong typhoons with wind power over 12 levels. It is well known that wind speed increases with increasing altitude. With the development of the urbanization process, the city range is larger and larger, the average building height is higher and higher, and the climate risk caused by strong wind is also larger and larger. The strong wind not only affects coastal ports and ships, but also brings huge weather risks to urban glass curtain walls, billboards, rail transit and the like, and affects the safe operation of cities. Therefore, the research on the wind speed in the urban area has important significance.
At present, the monitoring of urban wind speed mainly depends on instrument equipment such as a wind speed meter and a wind profile radar in meteorological stations (national weather stations and regional automatic stations) arranged in the Chinese meteorological office to carry out fixed-point observation. The biggest advantage of the observation of the station is that the obtained wind speed data is most reliable based on instrument observation, and the wind speed data of the long-time sequence of the station can be obtained through long-time continuous observation and accumulation. However, due to the complexity of the underlying surface of the city, the wind speed varies greatly locally. Even if more sites are arranged, observation density is increased, wind speed information of each position of a city cannot be obtained, and the requirement for fine depiction of city wind speed spatial distribution cannot be met.
In order to obtain the spatial distribution of the urban wind speed, a spatial statistical interpolation technology (linear and nonlinear interpolation methods) is applied to the analysis of the urban wind speed, and different statistical interpolation technologies have different adaptive ranges, but have the characteristics of easiness in operation, small calculated amount and the like. With the improvement of research and business requirements and computing power, a wind speed numerical simulation technology including a dynamic process is introduced into urban wind environment research and analysis, and mainly comprises a mesoscale meteorological numerical mode (such as WRF) and CFD simulation. Both in meteorological numerical mode and in CFD simulation, the computational power requirements on the computer are high, and each has different requirements in spatial extent and resolution. Specifically, the method comprises the following steps:
the statistical interpolation technique comprises the following steps: the statistical interpolation techniques include linear and nonlinear interpolation methods such as Kriging (Kriging), inverse Distance Weighting (IDW), spline interpolation (Spline), and the like. The statistical interpolation technique is a purely statistical method, based on certain data assumptions. If the inverse distance weighting method is based on the basic assumption of "first law of geography": i.e. the similarity of two objects decreases as their distance increases. Taking the distance between an interpolation point and a sample point as a weight to carry out weighted average, wherein the weight given to a sample which is closer to the interpolation point is larger; spline interpolation is a method of generating a smooth interpolation curve by polynomial fitting using a mathematical function, using some characteristic nodes, for some defined point values by controlling the estimated variance. And (4) carrying out statistical interpolation on each grid point in the required space by depending on the station wind speed observation data to obtain the spatial distribution of the wind speed. However, the statistical interpolation technique does not consider the variation of the urban underlying surface, and different urban roughnesses can be formed by different underlying surfaces, thereby causing the difference of the wind speed in the spatial distribution. Therefore, the accuracy thereof has a great disadvantage; on the other hand, the spatial resolution depends on the density of the stations, and the spacing between the general national-level meteorological stations is about 20km, so that the requirement of fine wind speed of a city cannot be met.
Numerical simulation technology: including mesoscale meteorological numerical patterns (e.g., WRF) and CFD simulations. The WRF (Weather Research and Forecasting Model) mode is a unified mesoscale Weather Forecasting mode developed by American scientific Research institute centers (NCEP), american national atmospheric Research center (NCAR) and other American scientific Research institute centers, and can perform simulation on all elements of the atmosphere including temperature, precipitation, wind and the like by solving an approximate solution of an atmospheric dynamics equation set. The method has the characteristics of portability, easiness in maintenance, expandability, high efficiency, convenience and the like, is widely applied to wind speed simulation, and obtains higher resolution by coupling with a city canopy mode. CFD is the abbreviation for Computational Fluid Dynamics (Computational Fluid Dynamics) in English. It has been developed with the development of computer technology and numerical calculation technology. In short, CFD is equivalent to "virtually" performing experiments in a computer to simulate actual fluid flow conditions. The basic principle is to solve the differential equation for controlling the fluid flow by numerical value to obtain the discrete distribution of the flow field of the fluid flow on a continuous area, thereby approximately simulating the fluid flow condition. CFD can be considered as one of the modern analog simulation techniques. However, the WRF model simulation technique in the numerical simulation technique is more suitable for spatial simulation of the mesoscale atmosphere, lacks the simulation capability of the atmospheric boundary layer, and cannot meet the requirement for refinement in spatial accuracy; on the other hand, due to the huge demand on the calculated amount, the wind speed cannot be simulated for more than 30 years in practical application such as city region feasibility demonstration work, and the average condition of the wind speed is generally replaced by the typical year. The applicable scene of the CFD simulation technology is the urban microscale wind speed simulation under a specific scene. Although capable of achieving simulations at the meter level of spatial resolution, it has certain limitations. On one hand, the method does not simulate the actual atmospheric condition, but realizes the flow simulation of the wind speed under the background wind condition by setting a background wind field, so that the real-time simulation of the regional historical wind speed cannot be realized; on the other hand, the spatial resolution is high, so that the method has huge demand on calculated amount, is only suitable for urban microscale (within 10km range), and cannot be suitable for the requirement on large-scale refined wind speed simulation.
At present, the lack of research on the long-time scale and large-range regional refined wind speed downscaling method is beneficial to the development of various scientific researches and services related to urban refined wind speed simulation, such as refined regional wind environment simulation, gale disaster forecast early warning service, urban ventilation environment research, building wind resource assessment and the like, and the lack of research on the wind speed downscaling method also provides higher requirements for the refined research on the wind speed.
Disclosure of Invention
In order to solve the problems, the invention provides a fine simulation method for the urban ground wind speed based on a vertical hierarchical downscaling technology, which can realize fine simulation for the urban wind speed with long time scale and regionality and effectively solve the problem of observation of the wind speed of a complex underlying surface.
The invention is realized by the following scheme: a fine simulation method for urban ground wind speed based on a vertical hierarchical downscaling technology comprises the following steps:
s1, acquiring urban geographic information data and meshing an urban area;
s2, determining the height of a mixed layer of each grid point according to the geographic information data, and dividing the space where the corresponding grid point is located into a coarse sublayer which is not higher than the height of the mixed layer and is relatively greatly influenced by a ground underlying surface and an inertia sublayer which is higher than the height of the mixed layer and is relatively less influenced by the ground underlying surface;
s3, acquiring wind speed and wind direction observation data of a meteorological station in an urban area, repeating the steps S31-S34 according to the observation data and the urban geographic information data, and simulating urban ground wind speed on the basis of the obtained data; wherein:
s31, calculating the height of a mixed layer at a grid point where a meteorological station is located from the specified height wind speed monitored by the meteorological station in the urban area to obtain the height wind speed of the mixed layer;
s32, calculating the height wind speed of the mixed layer to an interpolation height in the inertia sub-layer to obtain the interpolation height wind speed;
s33, interpolating the interpolated altitude wind speed to a position corresponding to each grid point by adopting a statistical interpolation method and obtaining the grid point interpolated altitude wind speed of each grid point;
s34, for each grid point, firstly calculating the corresponding grid point interpolation height wind speed to the corresponding mixed layer height to obtain the grid point mixed layer height wind speed, and then judging whether the building average height of the grid point is smaller than the simulation height:
if yes, directly calculating the grid point mixed layer height wind speed to the simulated height and obtaining the grid point simulated height wind speed;
if not, firstly calculating the grid point mixed layer height wind speed to the corresponding building average height to obtain the grid point building average height wind speed, and then calculating the grid point building average height wind speed to the simulated height to obtain the grid point simulated height wind speed.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that the step of determining the height of the mixed layer of each grid point comprises the following steps:
presetting a lowest height;
dividing all the lattice points into lattice points without buildings and lattice points with buildings;
for lattice points without buildings, directly taking the lowest height as the height of a mixed layer;
for the lattice points with buildings, firstly, calculating the average height of the buildings in the lattice points, and then judging whether the average height of the buildings is greater than the lowest height:
if so, taking 2-5 times of the average height of the building as the height of a mixed layer;
and if not, taking the lowest height as the height of the mixed layer.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S31, the logarithmic profile equation (1) is used for calculating the height wind speed of the mixed layer
Wherein the content of the first and second substances,
specified altitude wind speed, H, for meteorological station monitoring h Height of mixing layer, Z d Is zero plane displacement, H zd To a specified height, Z land-Eff Is the effective roughness of the earth's surface.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S32, the logarithmic profile equation (2) is utilized to calculate the interpolation height wind speed
Wherein H cz In order to interpolate the height of the image,
effective roughness for the mixed layer.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S34, the logarithmic profile equation (3) is used for calculating the grid point mixed layer height wind speed
Wherein the content of the first and second substances,
interpolating the altitude wind speed for the grid points, H h For mixing layer height, H cz In order to interpolate the height of the image,
effective roughness of the mixed layer.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S34, when the average building height of the grid points is judged to be smaller than the simulated height, the logarithmic profile equation (4) is used for calculating the grid point simulated height wind speed
Wherein H mn To simulate height, Z d Is zero plane displacement, Z land-Eff Is the effective roughness of the earth's surface.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S34, when the building average height of the grid point is judged to be not smaller than the simulated height, the logarithm profile equation (5) is used for calculating the building average height wind speed of the grid point
Wherein the content of the first and second substances,
average height of buildings as lattice points, Z d Is zero plane displacement, Z land-Eff Is the effective roughness of the earth's surface.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that in the step S34, when the average building height of the grid points is judged to be not smaller than the simulated height, the exponential profile equation (6) is used for calculating the grid point simulated height wind speed at the grid points
Wherein λ is F Is the area parameter of the windward side of the building in the lattice points, sigma H The standard deviation of building height in the lattice points.
The urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology is further improved in that the effective roughness Z of the ground surface land-Eff And effective roughness of mixed layer
The calculation method of (2) is as follows:
dividing all grid points into city grid points and non-city grid points, and further dividing all the city grid points into building grid points and non-building grid points;
determining the aerodynamic roughness of the non-urban grid points by utilizing a surface roughness table of land utilization;
calculating the aerodynamic roughness Z of the non-architectural lattice points using equation (7) 0 ：
Wherein f is 0 The roughness coefficient is 0.1,
one coarse element corresponds to the underlying surface within one grid point for the average height of the coarse elements.
Calculating the aerodynamic roughness Z of the architectural lattice points using the calculation equation (8) 0 ：
Wherein λ is p The ratio of the top area of the building to the total area of the rough elements.
Calculating the effective roughness Z of the earth surface under different wind directions for each grid point by using the calculation formula (9) land-Eff And effective roughness of mixed layer
Wherein the content of the first and second substances,
is the average value of aerodynamic roughness in a certain range of the surface under a certain wind direction, D is the wind direction, theta l For the angle range of influence of the earth's surface, r l In order to influence the radius of the earth's surface,
is the average value of the aerodynamic roughness, theta, over a range of mixed layer heights in a certain wind direction bh For the mixing layer to influence the angular range highly, r bh The radius is highly influenced for the mixed layer.
The urban ground wind speed fine simulation method based on the vertical hierarchical downscaling technology is further improved in that the urban geographic information data comprise elevation data, land utilization data, urban building outline and height data.
The invention comprehensively considers the characteristics that the wind speed in the vertical direction has different changes at different heights in the atmospheric boundary layer and the roughness has different influences on the wind speed in different ranges at different heights in the horizontal direction, utilizes refined geographic information data, and can realize refined simulation on the regional urban wind speed in a long time scale based on the wind speed and wind direction observation data of the national weather station in a period of time scale, effectively solve the problem of complex underlying surface wind speed observation, and provide effective support for the refined regional wind environment simulation, the gale disaster forecast early warning service, the urban ventilation environment research, the building wind resource evaluation and other works.
Drawings
Fig. 1 shows a flow chart of the method of the invention.
FIG. 2 shows a schematic diagram of a method for calculating the area parameter of the windward side of the building in the grid point by the method.
Fig. 3 shows a technical flow diagram of an embodiment of the method of the present invention.
Detailed Description
In order to solve the problem that the existing urban refined wind speed simulation method has certain limitation and cannot meet the requirement of urban refined wind speed simulation with a complex underlying surface, the invention provides the urban ground wind speed refined simulation method based on the vertical hierarchical downscaling technology, which can realize refined simulation of long-time-scale and regional urban wind speeds and effectively solve the problem of complex underlying surface wind speed observation.
The detailed simulation method of the urban ground wind speed based on the vertical hierarchical downscaling technology is further described below with reference to the accompanying drawings.
Referring to fig. 1, a method for finely simulating urban ground wind speed based on a vertical hierarchical downscaling technology includes the steps of:
s1, acquiring urban geographic information data and meshing urban areas. Specifically, the city geographic information data mainly includes: building outline and height data, DEM digital elevation data, land use data and geographical information of each site in the area.
And S2, determining the height of a mixed layer of each grid point according to the geographic information data, and dividing the space where the corresponding grid point is located into a rough sublayer which is not higher than the height of the mixed layer and is relatively greatly influenced by the ground underlying surface and an inertia sublayer which is higher than the height of the mixed layer and is relatively less influenced by the ground underlying surface.
Specifically, the present invention provides a preferred method of determining the height of the blend layer for each lattice point, comprising the steps of: first, a minimum height is preset (preferably, the minimum height is in the range of 30 to 50 meters). Then, all the grid points are divided into non-building grid points and building grid points according to the land utilization information. Then, respectively determining the heights of the mixed layers for the two types of lattice points, wherein for the lattice points without buildings, the lowest height is directly used as the height of the mixed layers; for the lattice points with buildings, the average height of the buildings in the lattice points is calculated firstly, and then whether the average height of the buildings is greater than the lowest height is judged: if so, taking 2-5 times of the average height of the building as the height of the mixed layer; if not, the lowest height is taken as the height of the mixed layer. By the method, the influence of the horizontal direction and the vertical direction on the wind speed is considered, the calculated amount is small, and the height of the mixed layer of each grid point can be determined quickly.
And S3, acquiring wind speed and wind direction observation data of a meteorological station in an urban area, repeating the steps S31 to S34 according to the observation data and the urban geographic information data, and simulating urban ground wind speed on the basis of the obtained data. Wherein:
and S31, calculating the height of the mixed layer at the grid point of the meteorological station from the specified height wind speed monitored by the meteorological station in the urban area, and obtaining the height wind speed of the mixed layer. Preferably, the mixed-layer height wind speed is calculated by using the logarithmic profile equation (1)
Wherein, the first and the second end of the pipe are connected with each other,
specified altitude wind speed, H, for meteorological station monitoring h For mixing layer height, Z d Is zero plane displacement, H zd To a specified height, Z land-Eff Is the effective roughness of the surface.
And S32, calculating the height wind speed of the mixed layer to an interpolation height in the inertia sublayer to obtain the interpolation height wind speed. Preferably, interpolation is calculated using the logarithmic profile equation (2)Value altitude wind speed
Wherein H cz In order to interpolate the height of the image,
effective roughness for the mixed layer.
And S33, interpolating the interpolated altitude wind speed to the position corresponding to each grid point by adopting a statistical interpolation method and obtaining the grid point interpolated altitude wind speed of each grid point. Preferably, the interpolation is performed using kriging interpolation.
Step S34, for each grid point, firstly, calculating the corresponding grid point interpolation height wind speed to the corresponding mixed layer height to obtain the grid point mixed layer height wind speed, and then judging whether the building average height of the grid point is smaller than the simulation height: if so, directly calculating the height wind speed of the lattice point mixing layer to the simulated height and obtaining the lattice point simulated height wind speed; if not, firstly calculating the grid point mixed layer height wind speed to the corresponding building average height to obtain the grid point building average height wind speed, and then calculating the grid point building average height wind speed to the simulated height to obtain the grid point simulated height wind speed.
Preferably, the lattice point mixed layer height wind speed is calculated by using a logarithmic profile equation (3)
Wherein the content of the first and second substances,
interpolating the altitude wind speed for the grid points, H h Height of the mixed layer, H cz In order to interpolate the height of the image,
effective roughness for the mixed layer.
When the average building height of the lattice point is judged to be smaller than the simulated height, the logarithmic profile equation (4) is utilized to calculate the wind speed of the simulated height of the lattice point
Wherein H mn To simulate height, Z d Is zero plane displacement, Z land-Eff Is the effective roughness of the earth's surface.
When the average building height of the lattice point is judged to be not less than the simulated height, the average building height wind speed of the lattice point is calculated by using a logarithmic profile equation (5)
Wherein, the first and the second end of the pipe are connected with each other,
average height of buildings as lattice points, Z d Is zero plane displacement, Z land-Eff Is the effective roughness of the surface.
When the average building height of the grid point is judged to be not less than the simulated height, the exponential profile equation (6) is used for calculating the wind speed of the simulated height of the grid point
Wherein λ is F Is the area parameter of the windward side of the building in the lattice points, sigma H The standard deviation of building height in the lattice points. Referring to fig. 2, the windward area parameter λ of the building F The calculation method of (2) is as follows:
wherein the content of the first and second substances,
the windward area of the building in a certain rough element (namely a lattice point),
is the total area of the coarse elements,
the length of the projection of the building on the vertical plane in the incoming wind direction,
and
respectively, the length and width of the asperity.
As a preferred embodiment, the above-mentioned surface effective roughness Z land-Eff And effective roughness of mixed layer
The calculation method of (2) is as follows: firstly, dividing all grid points into city grid points and non-city grid points, and further dividing all the city grid points into building grid points and non-building grid points; then, the aerodynamic roughness is calculated for each of the three grid points, so as to obtain an effective roughness more in line with the actual situation, specifically:
surface roughness by land utilizationTABLE 1.1 determination of the aerodynamic roughness Z of the non-urban grid points 0 。
TABLE 1.1 determination of surface roughness based on land utilization
Calculating the aerodynamic roughness Z of the non-architectural lattice points using equation (7) 0 ：
Wherein f is 0 The roughness coefficient is 0.1,
one asperity corresponds to the underlying surface within one grid point for the average height of the asperity.
Calculating the aerodynamic roughness Z of the architectural lattice points by using the formula (8) 0 ：
Wherein λ is p The ratio of the top area of the building to the total area of the rough elements.
The effective surface roughness Z in different wind directions (including 16 wind directions) is calculated for each grid point by the calculation formula (9) land-Eff And effective roughness of mixed layer
Wherein, the first and the second end of the pipe are connected with each other,
is the average value of aerodynamic roughness in a certain range of the surface under a certain wind direction, D is the wind direction, theta l For the angular range of influence of the surface, r l In order to influence the radius of the earth's surface,
is the average value of the aerodynamic roughness, theta, over a certain range of mixed layer heights in a certain wind direction bh For the mixing layer to influence the angular range highly, r bh The radius is highly influenced for the mixed layer. Take east wind (D =90 °) as an example, let θ be l ＝30°，r l The average value of the aerodynamic roughness of all the rough elements in a fan-shaped range with the radius of 300 meters and the angle of 75-105 degrees (namely 15 degrees around 90 degrees) is that the average value of the rough elements is the effective surface rough element of the rough elements. The angle and radius parameters in the calculation formula (9) can be properly adjusted and valued according to the geographical position of the calculation region.
Preferably, the aerodynamic roughness Z is calculated for the three lattice points 0 Meanwhile, the zero plane displacement Z required in the logarithmic profile equations (1), (4) and (5) can be calculated together by using the same principle d The specific calculation formula is as follows:
for non-building lattice points
For having building lattice points
Wherein, f d The roughness coefficient was 0.67.
Calculating the effective roughness Z of the earth's surface by classification land-Eff And effective roughness of mixed layer
The influence of different underlying surfaces in different heights on the wind speed in the urban area is fully considered, so that the simulation method is more refined, excessive calculation amount is not needed, and the long-time scale and regional urban wind speed can be conveniently and finely simulated.
The method has the core idea that the urban area is gridded, the upper space of the urban area is divided into the rough sublayer and the inertia sublayer by taking each lattice point as a unit, the rough sublayer is influenced by the underlying surface of the ground, the airflow has great heterogeneity, the inertia sublayer has constant shear stress and horizontally uniform airflow, the height between the rough sublayer and the inertia sublayer is the height of a mixed layer, and in other words, each lattice point has the height of the mixed layer which is adaptive according to the condition of the underlying surface. Then, the wind speed at the designated height monitored by the weather station is calculated to the upper air in an inertia sub-layer which is less affected by the roughness of an underlying surface by using a wind profile fitting method of the wind speed at the boundary layer, the wind speed at each station at the upper air is interpolated to all grid points or required partial grid points by using a statistical interpolation method such as IDW/Kring and the like based on the wind speed at each station at the upper air, and then the influence of the roughness on the wind speed in different wind directions of different heights on the ground surface simulation height is respectively calculated according to the difference of the underlying surface for the interpolated wind speed at each grid point. In the calculation process, because the rough sublayer is greatly influenced by the bottom underlying surface, the airflow has great heterogeneity, and the inertia sublayer has constant shear stress and airflow with uniform level, the interpolation height wind speed, the mixed layer height wind speed and the wind speed which is higher than the average height of the building and is positioned in the inertia sublayer are calculated by adopting a logarithmic profile equation, and the wind speed which is lower than the average height of the building and is positioned in the rough sublayer is calculated by adopting an exponential profile equation, so that the refined simulation of the urban ground wind speed is finally realized.
The simulation method comprehensively considers that the wind speed in the vertical direction has different change characteristics at different heights in the atmospheric boundary layer, and the roughness of the wind speed in different ranges at different heights in the horizontal direction has different influences on the wind speed. The refinement and the accuracy of the method depend on refined urban underlying surface information such as digital elevation information, building form and height and the like, and the requirement on the computing capability is not high. Therefore, the method has the characteristics of quick calculation and high accuracy. Meanwhile, based on the wind speed and wind direction observation data of the long time scale of the national-level meteorological station, when the wind speed and wind direction data of the station are input, the historical wind speed and wind direction data observed by the national meteorological station are continuously updated into the model, and the long-time scale and regional urban wind speed are finely simulated. In addition, due to the fact that the calculation is fast, the method can be applied to real-time wind speed observation business, and fine simulation of real-time wind speed is achieved. The method effectively solves the problem of observing the wind speed of the complex underlying surface, and can provide effective support for the refined regional wind environment simulation, the gale disaster forecast and early warning service, the urban ventilation environment research, the building wind resource assessment and other works.
Taking the above sea as an example, by using the daily wind speed and wind direction data of the national weather station examined by the sea and the surrounding, the method of the present invention is adopted to perform a refined simulation on the spatial distribution of the average wind speed of the sea 10 meters above the ground (i.e. a simulated height, which is also a designated height because the height of the wind speed monitored by the weather station is usually 10 meters):
1. preparing data:
(1) Building outline and height data: the Shanghai city building data provided by the Shanghai surveying and mapping institute is adopted, and the data are vector data and comprise fields such as a face file of a building outline and a building height. The vector data is converted to lattice data on the basis of which it can be resampled to a resolution of 100 m.
(2) DEM digital elevation data: SRTM data measured jointly by the United states aeronautics and space administration (NASA) and the National Institute of Mapping (NIMA) of defense are used with a spatial resolution of 30m.
(3) Land utilization data: 2015 year land use type data provided by the Chinese academy and interpreted through satellite remote sensing is adopted.
(4) Data of each site in the area: the method comprises the geographical information of sites such as longitude and latitude, altitude and the like of 36 national-level meteorological sites around Shanghai, and the daily wind speed and the main wind direction monitoring data of each site.
2. The technical process comprises the following steps:
(1) Determination of urban aerodynamic roughness: determining the aerodynamic roughness Z by using the calculation formulas (7) - (8) and the table 1.1 for the Shanghai non-urban mat surface and the urban mat surface respectively according to the Shanghai building data and the land utilization type distribution 0 And forming a Shanghai city aerodynamic roughness distribution map.
(2) Establishing effective roughness characteristic libraries with different wind directions and different heights: the effective roughness Z of the surface of 16 wind directions can be determined according to the calculation formula (9) land-Eff And effective roughness of mixed layer
Wherein: effective roughness of earth's surface Z land-Eff In the middle, the ground surface influences the angular range theta l Take 30 degrees, influence radius r of earth surface l Taking 300 meters; effective roughness of mixed layer
In the calculation, the height of the mixed layer affects the angular range θ bh Taking 45 degrees, the height of the mixed layer influences the radius r bh Taking 5000 meters. And respectively forming a distribution diagram of the effective roughness of the earth surface and the effective roughness of the mixed layer aiming at 16 wind directions.
(3) Building windward area parameter lambda of different wind direction rough elements F Determination of (1): the area parameter lambda of the windward side of the building under 16 wind directions can be determined according to the calculation formula (6-1) F And respectively forming a building windward area parameter distribution diagram aiming at 16 wind directions.
(4) And (3) finely simulating the ground wind speed of the Shanghai city:
by adopting wind speed and wind direction data of 36 national weather stations in Shanghai and surrounding areas, monthly wind speed fine simulation of Shanghai ground is carried out, and a grid point data set is generated, which is specifically shown in figure 3.
An input layer: counting 36 monthly average wind speeds of Shanghai and surrounding areas and a monthly dominant wind direction of a Baoshan station as a meteorological condition input field; the parameter files of the Shanghai city roughness multidimensional database, the windward side and the like are obtained through the steps.
Calculating a layer: carrying out monthly wind speed refinement simulation on the Shanghai ground according to the urban ground wind speed refinement simulation method based on the vertical hierarchical downscaling technology, acquiring monthly ground refinement wind speed data of the Shanghai, and forming a related simulation diagram;
an output layer: outputting the detected Shanghai near-ground monthly wind speed, and bringing the Shanghai near-ground wind speed into a data set.
While the present invention has been described in detail and with reference to the embodiments thereof as illustrated in the accompanying drawings, it will be apparent to one skilled in the art that various changes and modifications can be made therein. Therefore, certain details of the embodiments should not be construed as limitations of the invention, except insofar as the following claims are interpreted to cover the invention.
Claims (10)
1. A fine simulation method for urban ground wind speed based on a vertical hierarchical downscaling technology is characterized by comprising the following steps:
s1, acquiring urban geographic information data and meshing urban areas;
s2, determining the height of a mixed layer of each grid point according to the geographic information data, and dividing the space where the corresponding grid point is located into a rough sublayer which is not higher than the height of the mixed layer and is relatively greatly influenced by a ground underlying surface and an inertia sublayer which is higher than the height of the mixed layer and is relatively less influenced by the ground underlying surface;
s3, acquiring wind speed and wind direction observation data of a meteorological station in an urban area, repeating the steps S31-S34 according to the observation data and the urban geographic information data, and simulating urban ground wind speed on the basis of the obtained data; wherein:
s31, calculating the height of a mixed layer to a grid point where a meteorological station is located from the designated height wind speed monitored by the meteorological station in the urban area, and obtaining the height wind speed of the mixed layer;
s32, calculating the height wind speed of the mixed layer to an interpolation height in the inertia sub-layer to obtain the interpolation height wind speed;
s33, interpolating the interpolated altitude wind speed to a position corresponding to each grid point by adopting a statistical interpolation method and obtaining the grid point interpolated altitude wind speed of each grid point;
s34, for each grid point, firstly calculating the corresponding grid point interpolation height wind speed to the corresponding mixed layer height to obtain the grid point mixed layer height wind speed, and then judging whether the building average height of the grid point is smaller than the simulation height:
if so, directly calculating the height wind speed of the lattice point mixing layer to the simulated height and obtaining the lattice point simulated height wind speed;
if not, firstly calculating the grid point mixed layer height wind speed to the corresponding building average height to obtain the grid point building average height wind speed, and then calculating the grid point building average height wind speed to the simulated height to obtain the grid point simulated height wind speed.
2. The urban ground wind speed refinement simulation method based on the vertical hierarchical downscaling technology according to claim 1, wherein the step of determining the height of the mixed layer of each grid point comprises:
presetting a lowest height;
dividing all the lattice points into lattice points without buildings and lattice points with buildings;
for lattice points without buildings, directly taking the lowest height as the height of a mixed layer;
for the lattice points with buildings, calculating the average height of the buildings in the lattice points, and then judging whether the average height of the buildings is greater than the lowest height:
if so, taking 2-5 times of the average height of the building as the height of a mixed layer;
and if not, taking the lowest height as the height of the mixed layer.
3. The urban ground wind speed refinement simulation method based on vertical hierarchical downscaling technology according to claim 1, wherein step S31 is implemented by using logarithmic profile equation (1) to calculate mixed layer height wind speed
Wherein the content of the first and second substances,
wind speed, H, at a given altitude monitored for a weather station h Height of mixing layer, Z d Is zero plane displacement, H zd To a specified height, Z land-Eff Is the effective roughness of the surface.
4. The method for refining and simulating urban ground wind speed based on vertical hierarchical downscaling technology according to claim 3, wherein the step S32 is implemented by using logarithmic profile equation (2) to calculate interpolated altitude wind speed
Wherein H cz In order to interpolate the height of the image,
effective roughness of the mixed layer.
5. The method for refining and simulating urban ground wind speed based on vertical hierarchical downscaling technology according to claim 1, wherein step S34 is implemented by using logarithmic profile equation (3) to calculate grid point hybrid layer height wind speed
Wherein, the first and the second end of the pipe are connected with each other,
interpolating the altitude wind speed for the grid points, H h For mixing layer height, H cz In order to interpolate the height of the image,
effective roughness of the mixed layer.
6. The method for refining and simulating wind speed on urban ground based on vertical hierarchical downscaling technology according to claim 5, wherein the step S34 is implemented by calculating a grid point simulated height wind speed by using a logarithmic profile equation (4) when the building average height of the grid point is judged to be smaller than the simulated height
Wherein H mn To simulate height, Z d Zero plane displacement, Z land-Eff Is the effective roughness of the surface.
7. The method as claimed in claim 5, wherein in step S34, when it is determined that the average building height of the grid point is not less than the simulated height, the logarithmic profile equation (5) is used to calculate the average building height wind speed of the grid point
Wherein the content of the first and second substances,
average height of buildings as lattice points, Z d Is zero plane displacement, Z land-Eff Is the effective roughness of the earth's surface.
8. The method for refining and simulating wind speed on urban ground based on vertical hierarchical downscaling technology of claim 7, wherein the step S34 is implemented by utilizing an exponential profile equation (6) to calculate a grid point simulated height wind speed and wind speed when the building average height of the grid point is judged to be not less than the simulated height
Wherein λ is F Is the area parameter of the windward side of the building in the lattice points, sigma H The standard deviation of building height in the lattice points.
9. The method for finely simulating the wind speed on the ground of an urban area based on the vertical hierarchical downscaling technology according to any one of claims 3 to 7, wherein the effective roughness Z of the ground surface land-Eff And effective roughness of mixed layer
The calculation method of (2) is as follows:
dividing all grid points into city grid points and non-city grid points, and further dividing all the city grid points into building grid points and non-building grid points;
determining the aerodynamic roughness of the non-urban grid points by utilizing a surface roughness table of land utilization;
calculating the aerodynamic roughness Z of the non-architectural lattice points using equation (7) 0 ：
Wherein f is 0 The roughness coefficient is 0.1,
one asperity corresponds to the underlying surface within one grid point for the average height of the asperity.
Calculating the aerodynamic roughness Z of the architectural lattice points using the calculation equation (8) 0 ：
Wherein λ is p The ratio of the top area of the building to the total area of the rough elements.
Calculating the effective roughness Z of the earth surface under different wind directions for each grid point by using a calculation formula (9) land-Eff And effective roughness of mixed layer
Wherein the content of the first and second substances,
is the average value of aerodynamic roughness in a certain range of the surface under a certain wind direction, D is the wind direction, theta l For the angle range of influence of the earth's surface, r l In order to influence the radius of the earth's surface,
is the average value of the aerodynamic roughness, theta, over a range of mixed layer heights in a certain wind direction bh For the mixing layer to influence the angular range highly, r bh The radius is highly influenced for the mixed layer.
10. The method for refining and simulating the wind speed on the ground of an urban based on the vertical hierarchical de-scaling technology as claimed in claim 1, wherein the geographic information data of the urban comprises elevation data, land utilization data, contour and height data of urban buildings.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634342A (en) * | 2023-11-22 | 2024-03-01 | 中国电建集团西北勘测设计研究院有限公司 | Simulation method and simulation system for refined wind field |
| CN118069958A (en) * | 2024-04-18 | 2024-05-24 | 南京信息工程大学 | Atmospheric environment capacity calculation method and device |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104392113A (en) * | 2014-11-11 | 2015-03-04 | 宁波市气象台 | Method for estimating wind speed of cold air wind on offshore sea surface |
| WO2016023527A1 (en) * | 2014-08-14 | 2016-02-18 | 国家电网公司 | Method based on anemometer tower measurement data for determining wind farm discarded wind power |
| CN107390298A (en) * | 2017-07-19 | 2017-11-24 | 云南电网有限责任公司电力科学研究院 | A kind of analogy method and device of Complex Mountain underlying surface strong wind |
| CN107704641A (en) * | 2017-05-27 | 2018-02-16 | 福州大学 | Fine simulation of wind method based on outdoor scene vegetation spatial distribution roughness |
| US20200018666A1 (en) * | 2019-07-02 | 2020-01-16 | Shanghai Typhoon Institute, CMA | Wind field dynamic downscaling method based on aerodynamic parameters of simplified terrain |
| CN113987779A (en) * | 2021-10-22 | 2022-01-28 | 中国建筑科学研究院有限公司 | Urban canopy model building method based on tropical island climate characteristics |
| CN114357571A (en) * | 2021-12-14 | 2022-04-15 | 华南理工大学 | Inversion method and system for atmospheric boundary layer wind field characteristics in constructed building environment |
| CN114386215A (en) * | 2021-11-18 | 2022-04-22 | 中国气象局上海台风研究所(上海市气象科学研究所) | Urban three-dimensional wind field rapid analysis method capable of identifying scale of building |
| CN114580310A (en) * | 2020-11-28 | 2022-06-03 | 北京零壹橡树科技有限公司 | Method for realizing scale reduction processing of WRF (hand-wrenching simulation) wind field based on PALM (PALM fiber laser) |
| KR20220100751A (en) * | 2021-01-08 | 2022-07-18 | 부경대학교 산학협력단 | Wind speed analyzing system in urban area based on inflow wind speed and morphological parameters, and wind speed analyzing method using the same |
-
2022
- 2022-08-02 CN CN202210920740.1A patent/CN115146564A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2016023527A1 (en) * | 2014-08-14 | 2016-02-18 | 国家电网公司 | Method based on anemometer tower measurement data for determining wind farm discarded wind power |
| CN104392113A (en) * | 2014-11-11 | 2015-03-04 | 宁波市气象台 | Method for estimating wind speed of cold air wind on offshore sea surface |
| CN107704641A (en) * | 2017-05-27 | 2018-02-16 | 福州大学 | Fine simulation of wind method based on outdoor scene vegetation spatial distribution roughness |
| CN107390298A (en) * | 2017-07-19 | 2017-11-24 | 云南电网有限责任公司电力科学研究院 | A kind of analogy method and device of Complex Mountain underlying surface strong wind |
| US20200018666A1 (en) * | 2019-07-02 | 2020-01-16 | Shanghai Typhoon Institute, CMA | Wind field dynamic downscaling method based on aerodynamic parameters of simplified terrain |
| CN114580310A (en) * | 2020-11-28 | 2022-06-03 | 北京零壹橡树科技有限公司 | Method for realizing scale reduction processing of WRF (hand-wrenching simulation) wind field based on PALM (PALM fiber laser) |
| KR20220100751A (en) * | 2021-01-08 | 2022-07-18 | 부경대학교 산학협력단 | Wind speed analyzing system in urban area based on inflow wind speed and morphological parameters, and wind speed analyzing method using the same |
| CN113987779A (en) * | 2021-10-22 | 2022-01-28 | 中国建筑科学研究院有限公司 | Urban canopy model building method based on tropical island climate characteristics |
| CN114386215A (en) * | 2021-11-18 | 2022-04-22 | 中国气象局上海台风研究所(上海市气象科学研究所) | Urban three-dimensional wind field rapid analysis method capable of identifying scale of building |
| CN114357571A (en) * | 2021-12-14 | 2022-04-15 | 华南理工大学 | Inversion method and system for atmospheric boundary layer wind field characteristics in constructed building environment |
Non-Patent Citations (3)
| Title |
|---|
| 史军;徐家良;谈建国;刘坚刚;: "上海地区不同重现期的风速估算研究", 地理科学, no. 09, 30 September 2015 (2015-09-30) * |
| 谈建国;常远勇;韩志惠;唐玉琪;岳彩军;彭杰;: "一种基于粗糙度分布的大风风速降尺度方法初步研究――以上海市域为例", 气象科学, no. 06, 18 October 2017 (2017-10-18) * |
| 谈建国等: "一种基于粗糙度分布的大风风速降尺度方法初步研究——以上海市域为例", 气象科学, no. 06, 18 October 2017 (2017-10-18), pages 1 - 3 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117634342A (en) * | 2023-11-22 | 2024-03-01 | 中国电建集团西北勘测设计研究院有限公司 | Simulation method and simulation system for refined wind field |
| CN118069958A (en) * | 2024-04-18 | 2024-05-24 | 南京信息工程大学 | Atmospheric environment capacity calculation method and device |
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