CN105224714B - The processing method and processing device of meteorological data - Google Patents
The processing method and processing device of meteorological data Download PDFInfo
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Abstract
The invention discloses a kind of processing method and processing devices of meteorological data.Wherein, which includes：Obtain Meso-scale meteorology data, wherein Meso-scale meteorology data by measured data carry out assimilation and numerical simulation obtain；Simulated domain corresponding with target area is determined based on the grid point distribution of Meso-scale meteorology data, wherein simulated domain includes multiple Grid datas in Meso-scale meteorology data；Multiple Grid datas are grouped according to the time parameter and wind direction parameter of multiple Grid datas, obtain multiple lattice point set；Count the corresponding atmospheric boundary layer parameter of each lattice point set, wherein atmospheric boundary layer parameter includes：At least one of planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.The present invention solves and generates the technical problem that the parameter of boundary condition only leans on subjective setting inaccurate in current CFD wind-resources assessments technology.
Description
Technical field
The present invention relates to modeling fields, in particular to a kind of processing method and processing device of meteorological data.
Background technology
Home and abroad all be using based on linear model algorithm or be based on CFD (computational fluid dynamics) nonlinear model
Numerical simulation technology carry out wind-resources assessment, based on CFD (computational fluid dynamics) nonlinear models numerical simulation technology also by
The referred to as wind-resources assessment technology based on CFD numerical simulations.Relative to the algorithm based on linear model, it is based on CFD numerical simulations
Wind-resources assessment technical know-how is more rigorous, error smaller, precision higher.In carrying out numerical simulation with CFD, generate close
The boundary condition of true stratification state is to ensure simulation precision, and then ensure the key link of wind-resources assessment quality.
At present, wind-resources assessment technology of the mainstream based on CFD numerical simulations generates certain wind direction with the following method in the world
The boundary condition of sector carries out numerical simulation in turn：It is assumed that or subjective setting planetary boundary layer (also known as atmospheric boundary layer, below
Abbreviation boundary layer) height and boundary layer at the top of wind speed；According to measured value or subjective judgement value setting roughness of ground surface length (with
Lower abbreviation roughness)；And calculate Wind outline according to digit rate semiempirical formula.
The boundary condition of prior art generation and the shortcoming of air boundary parameter are：Associated arguments are mostly
Default value or subjective setting value, without measurement data basis, in practice it has proved that, by the boundary condition that generates in this way under complicated landform
Error is larger.
Only lean on subjective setting inaccurate for the parameter for generating boundary condition in above-mentioned current CFD wind-resources assessments technology
The problem of, currently no effective solution has been proposed.
Invention content
An embodiment of the present invention provides a kind of processing method and processing devices of meteorological data, are provided at least solving current CFD wind
The technical problem that the parameter of boundary condition only leans on subjective setting inaccurate is generated in the assessment technology of source.
One side according to the ... of the embodiment of the present invention provides a kind of processing method of meteorological data, the processing method packet
It includes：Obtain Meso-scale meteorology data, wherein Meso-scale meteorology data be by measured data carry out assimilation and numerical simulation obtain
It arrives；Simulated domain corresponding with target area is determined based on the grid point distribution of Meso-scale meteorology data, wherein simulated domain packet
Include multiple Grid datas in Meso-scale meteorology data；According to the time parameter and wind direction parameter of multiple Grid datas to multiple lattice
Point data is grouped, and obtains multiple lattice point set；Count the corresponding atmospheric boundary layer parameter of each lattice point set, wherein big
Gas boundary layer parameters include：In planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length at least it
One.
Further, simulated domain packet corresponding with target area is determined based on the grid point distribution of Meso-scale meteorology data
It includes：Based on the simulated domain for determining coverage goal region on the map of the grid point distribution of Meso-scale meteorology data, wherein simulation region
Domain is horizontal rectangular region, and the boundary line in horizontal rectangular region is parallel with warp or weft.
Further, multiple Grid datas are grouped according to the time parameter of multiple Grid datas and wind direction parameter,
Obtaining multiple lattice point set includes：Calculate the mean wind direction angle of the corresponding Grid data of each time point；According to mean wind direction
The set identification of the corresponding Grid data of Each point in time is arranged in angle, wherein set identification is used to identify the category of Grid data
Property data belonging to lattice point set, lattice point set, which has, belongs to the mean wind direction angle of predetermined angle range.
Further, Grid data includes at least three layers of meteorological data, and every layer of layer Grid data includes layer position temperature
Parameter, layer horizontal wind speed and layer vertical velocity, wherein counting the corresponding atmospheric boundary layer parameter of each lattice point set includes：
It obtains each lattice point based on the layer position temperature parameter of each Grid data, layer horizontal wind speed, vertical velocity in lattice point set and is integrated into
Planetary boundary layer height, planetary boundary layer wind speed and the Monin-Obukhov length of different preset time periods；It calculates separately each
Planetary boundary layer height average, planetary boundary layer wind speed average value and Mo Ning-Austria of each preset time period of lattice point set
Cloth Hough length average value obtains planetary boundary layer height, planetary boundary layer wind speed and the Mo Ning-Ao Buhuo of each lattice point set
Husband's length.
Further, it is obtained based on the layer position temperature parameter of each Grid data, layer horizontal wind speed, vertical velocity in lattice point set
Lattice point is taken to be integrated into planetary boundary layer height, planetary boundary layer wind speed and the Monin-Obukhov length of different preset time periods
Including：It obtains based on Grid data in the mean virtual position temperature of the layer position temperature parameter determination of preset time period, based on Grid data
The average level wind speed determined in the layer horizontal wind speed of preset time period and the layer based on Grid data in preset time period hang down
The average vertical wind speed that straight wind speed determines；Grid data is obtained respectively based on the determining mild average vertical wind speed in mean virtual position
It mildly pulses vertical position temperature in the pulsation virtual bit of preset time period, and is obtained based on pulsation virtual bit vertical position temperature of mildly pulsing
Friction velocity and virtual bit temperature flux of the Grid data in preset time period；Calculate mean virtual position temperature, friction velocity and void
The corresponding Monin-Obukhov length of quasi- position temperature flux, the corresponding planetary boundary layer height of calculating average level wind speed, Yi Jiji
Calculate planetary boundary layer height planetary boundary layer wind speed corresponding with average horizontal wind speed.
Another aspect according to the ... of the embodiment of the present invention additionally provides a kind of processing unit of meteorological data, the processing unit
Including：First acquisition module, for obtaining Meso-scale meteorology data, wherein Meso-scale meteorology data are by measured data
Carry out what assimilation was obtained with numerical simulation；Determining module is used for based on the grid point distribution determination of Meso-scale meteorology data and target
The corresponding simulated domain in region, wherein simulated domain includes multiple Grid datas in Meso-scale meteorology data；Grouping module,
For according to multiple Grid datas time parameter and wind direction parameter multiple Grid datas are grouped, obtain multiple lattice point collection
It closes；Statistical module, for counting the corresponding atmospheric boundary layer parameter of each lattice point set, wherein atmospheric boundary layer parameter includes：
At least one of planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.
Further, it is determined that module includes：Determination sub-module is used for the ground of the grid point distribution based on Meso-scale meteorology data
The simulated domain in coverage goal region is determined on figure, wherein simulated domain is horizontal rectangular region, the boundary in horizontal rectangular region
Line is parallel with warp or weft.
Further, grouping module includes：Computing module, for calculating being averaged for the corresponding Grid data of each time point
Wind direction angle；Setup module, the set identification for the corresponding Grid data of Each point in time to be arranged according to mean wind direction angle,
Wherein, set identification is used to identify the lattice point set belonging to the attribute data of Grid data, and lattice point set, which has, belongs to preset angle
Spend the mean wind direction angle of range.
Further, Grid data includes at least three layers of meteorological data, and every layer of layer Grid data includes layer position temperature
Parameter, layer horizontal wind speed and layer vertical velocity, wherein statistical module includes：Second acquisition module, for being based on lattice point set
In the layer position temperature parameter of each Grid data, layer horizontal wind speed, vertical velocity obtain each lattice point and be integrated into different preset times
Planetary boundary layer height, planetary boundary layer wind speed and the Monin-Obukhov length of section；Third acquisition module, based on respectively
Calculate the planetary boundary layer height average of each preset time period of each lattice point set, planetary boundary layer wind speed average value and not
Ning-Ao Bu Hough length average values, obtain planetary boundary layer height, planetary boundary layer wind speed and the Mo Ning-of each lattice point set
Cloth Hough length difficult to understand.
Further, the second acquisition module includes：First acquisition submodule, for obtaining based on Grid data when default
Between section layer position temperature parameter determine mean virtual position temperature, based on Grid data preset time period layer horizontal wind speed determination
Average level wind speed and the average vertical wind speed determined in the layer vertical velocity of preset time period based on Grid data；Second obtains
Submodule is taken, for obtaining Grid data respectively in preset time period based on the determining mild average vertical wind speed in mean virtual position
Pulsation virtual bit mildly pulse vertical position temperature, and Grid data is obtained pre- based on pulsation virtual bit vertical position temperature of mildly pulsing
If the friction velocity and virtual bit temperature flux of period；Computational submodule, for calculate mean virtual position temperature, friction velocity and
The corresponding planetary boundary layer height of the corresponding Monin-Obukhov length of virtual bit temperature flux, calculating average level wind speed and
Calculate planetary boundary layer height planetary boundary layer wind speed corresponding with average horizontal wind speed.
Using the present invention, after getting the Meso-scale meteorology data obtained based on the data measured in real time, based on having
The grid point distribution of the Meso-scale meteorology data on measurement data basis determines simulated domain, which covers preadmission sector-style resource
The target area of assessment, and simulated domain includes multiple Grid datas in Meso-scale meteorology data.Then according to multiple lattice
The time parameter and wind direction parameter of point data are grouped multiple Grid datas, obtain multiple lattice point set, and count each
The atmospheric boundary layers such as the corresponding planetary boundary layer height of lattice point set, planetary boundary layer wind speed and Monin-Obukhov length are joined
Number.Can objectively it be reflected since atmospheric boundary layer parameter is obtained based on the data actually measured by above-described embodiment
The weather condition of target area, to solve the parameter for generating boundary condition in current CFD wind-resources assessments technology only by master
The inaccurate problem of setting is seen, the accurate effect for obtaining atmospheric boundary layer parameter and boundary condition is realized.
Description of the drawings
Attached drawing described herein is used to provide further understanding of the present invention, and is constituted part of this application, this hair
Bright illustrative embodiments and their description are not constituted improper limitations of the present invention for explaining the present invention.In the accompanying drawings：
Fig. 1 is the flow chart of the processing method of meteorological data according to the ... of the embodiment of the present invention；
Fig. 2 is the schematic diagram of simulated domain according to the ... of the embodiment of the present invention；And
Fig. 3 is the schematic diagram of the processing unit of meteorological data according to the ... of the embodiment of the present invention.
Specific implementation mode
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The every other embodiment that member is obtained without making creative work should all belong to the model that the present invention protects
It encloses.
It should be noted that term " first " in description and claims of this specification and above-mentioned attached drawing, "
Two " etc. be for distinguishing similar object, without being used to describe specific sequence or precedence.It should be appreciated that using in this way
Data can be interchanged in the appropriate case, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " comprising " and " having " and their any deformation, it is intended that cover
It includes to be not necessarily limited to for example, containing the process of series of steps or unit, method, system, product or equipment to cover non-exclusive
Those of clearly list step or unit, but may include not listing clearly or for these processes, method, product
Or the other steps or unit that equipment is intrinsic.
Term is explained：
Meso-scale meteorology analyzes data, that is, uses assimilation technique that measurement data is converted to numeralization analysis data, then
NO emissions reduction processing is carried out to above-mentioned analysis data by Mesoscale Simulation technology, realizes temporal resolution and spatial resolution
Raising, the meteorological data of the numeralization obtained in this way is that Meso-scale meteorology analyzes data.
Lattice point and Grid data, i.e. quantified data are the data set that spatially position divides, the space of a discretization
Position is a lattice point, and each lattice point has one group and the relevant data in this spatial position and Grid data.It is involved in the present invention
Grid data be by Meso-Scale Analysis data conversion.
Horizontal lattice point group refers to the identical one group of lattice point in horizontal position, the lattice point number in horizontal lattice point group and vertical grid
The number of plies is identical.
Position refers to the horizontal geographical indication of the one group of lattice point determined by longitude and latitude.
Target area refers to the target area of wind-resources assessment, i.e., to use the wind-resources assessment based on CFD numerical simulations
Technology carries out the region of numerical simulation.
According to embodiments of the present invention, a kind of embodiment of the processing method of meteorological data is provided, it should be noted that
The step of flow of attached drawing illustrates can execute in the computer system of such as a group of computer-executable instructions, also,
It, in some cases, can be with different from shown in sequence execution herein although logical order is shown in flow charts
The step of going out or describing.
Fig. 1 is the flow chart of the processing method of meteorological data according to the ... of the embodiment of the present invention, as shown in Figure 1, the processing side
Method includes the following steps：
Step S102 obtains Meso-scale meteorology data, wherein Meso-scale meteorology data are same by being carried out to measured data
What change and numerical simulation obtained.
Step S104 determines simulated domain corresponding with target area based on the grid point distribution of Meso-scale meteorology data,
In, simulated domain includes multiple Grid datas in Meso-scale meteorology data.
Step S106 is grouped multiple Grid datas according to the time parameter and wind direction parameter of multiple Grid datas,
Obtain multiple lattice point set.
Step S108 counts the corresponding atmospheric boundary layer parameter of each lattice point set, wherein atmospheric boundary layer parameter packet
It includes：At least one of planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.
Using the present invention, after getting the Meso-scale meteorology data obtained based on the data measured in real time, based on having
The grid point distribution of the Meso-scale meteorology data on measurement data basis determines simulated domain, which covers preadmission sector-style resource
The target area of assessment, and simulated domain includes multiple Grid datas in Meso-scale meteorology data；Then according to multiple lattice
The time parameter and wind direction parameter of point data are grouped multiple Grid datas, obtain multiple lattice point set, pass through the above lattice
Points according to statistics with point set (set of the Grid data at each moment) corresponding planetary boundary layer height, row when calculating each
The atmospheric boundaries layer parameter such as star boundary layer wind speed and Monin-Obukhov length obtains annual be applicable in eventually by statistical method
Atmospheric boundary layer parameter.By above-described embodiment, since atmospheric boundary layer parameter is obtained based on the data actually measured,
The weather condition that can objectively reflect target area generates perimeter strip to solve in current CFD wind-resources assessments technology
The problem that the parameter of part only leans on subjective setting inaccurate realizes the accurate effect for obtaining atmospheric boundary layer parameter and boundary condition
Fruit.
Planetary boundary layer wind speed in the embodiment of the present application all can be：Wind speed at the top of planetary boundary layer.
Specifically, the Meso-scale meteorology analysis data that can obtain a complete year use after obtaining measured data
The measured data of measurement is converted to numeralization analysis data by assimilation technique, then by Mesoscale Simulation technology to above-mentioned
Numeralization analysis data carry out NO emissions reduction processing, realize the raising of temporal resolution and spatial resolution, the numerical value obtained in this way
The meteorological data of change is that Meso-scale meteorology analyzes data.These data can be stored in the form of netCDF files.In netCDF
In file, every group of Grid data includes to be not limited to following data item (following name and the netCDF files one in WRF patterns
Cause), it is that the English name of data item and corresponding Chinese are explained as follows：
Time：Date and time；bottom_top：Level serial number；south_north：The north and south serial number of lattice point；west_
east：The thing serial number of lattice point；U：The wind speed component of east-west direction；V：The wind speed component of North and South direction；W：The wind of vertical direction
Fast component；XLAT：Latitude；XLONG：Longitude；PH：Disturb potential；PHB：Ground state potential；HGT：Ground level；T：Disturbing potential temperature；
P：Disturbance；PB：Ground state air pressure；QVAPOR：Vapor-to-liquid ratio；And TH2：2 meters of ground height position temperature.
NetCDF (network Common Data Form) is network universal data format, be one kind towards digit group type simultaneously
The description of data suitable for network share and coding standard.
Optionally, determine that simulated domain corresponding with target area can wrap based on the grid point distribution of Meso-scale meteorology data
It includes：Based on the simulated domain for determining coverage goal region on the map of the grid point distribution of Meso-scale meteorology data, wherein simulation region
Domain is horizontal rectangular region, and the boundary line in horizontal rectangular region is parallel with warp or weft.
Determine that the simulated domain of CFD numerical simulations, specific method can be as described below according to the grid point distribution of mesoscale：
(1) a horizontal rectangular region, the horizontal rectangular are selected on the map of lattice point that Meso-scale meteorology data are distributed with
Region overlay will carry out the region (i.e. target area) of wind-resources assessment.
(2) side of the left and right of the target area two by the equal Meso-scale meteorology data of thing serial number lattice point connection and
At.
(3) two sides up and down of the target area by the equal Meso-scale meteorology data of north and south serial number lattice point connection and
At.
In this way, it includes several mesoscale lattice points (i.e. the lattice points of Meso-scale meteorology data) (to contain boundary) in simulated domain.
As shown in Fig. 2, irregular closed area indicates that target area, hollow dot indicate Meso-scale meteorology data
Lattice point, solid black dot indicates the lattice point of Meso-scale meteorology data, while the lattice point is also the lattice point of simulated domain, Fig. 2
Shown in dotted rectangle be simulated domain.
After the simulated domain for determining CFD numerical simulations, Grid data can be generated according to Meso-scale meteorology data, in
Scale meteorological data lattice site is identical as the lattice site of mesoscale, and the serial number of position coordinates is different, each Grid data packet
Multiple data item are included, the English name of data item and corresponding Chinese are explained as follows：
Specifically, t：Time phase；i：The North and South direction serial number of lattice point；j：The east-west direction serial number of lattice point；k：Lattice point hangs down
Histogram is to serial number；sec：Sector number, 1≤sec≤32；m：Position number in sector, reservation use when being divided to sector；u：North and south
Direction horizontal wind speed component；v：East-west direction horizontal wind speed component；uh：Horizontal maximum wind velocity component；p：Air pressure；θ：Position temperature；θv：
Virtual bit temperature；h：Ground.Lattice point in the embodiment is Grid data.
Above-mentioned data item can specifically be obtained by following method：
T=t ime；
I=south_north- (min (south_north)), wherein min (south_north) is lattice in simulated domain
Minimum the south_north of a point value, 1≤i≤I；
J=west_east- (min (west_east)), wherein min (west_east) is lattice point in simulated domain
A minimum west_east value, 1≤j≤J；
K=bottom_top, 1≤k≤K；
U=U, v=V, w=W；
P=PB+P；
θ=T+TH2；
θv=θ (1+0.61 (QVAPOR/ (QVAPOR+1)))；
H=PHB+PH-HGT (replaces geometric height) with geopotential unit herein.
Optionally, multiple Grid datas are grouped according to the time parameter of multiple Grid datas and wind direction parameter, are obtained
May include to multiple lattice point set：Calculate the mean wind direction angle of the corresponding Grid data of each time point；According to average wind
To the set identification of the corresponding Grid data of angle setting Each point in time, wherein set identification is for identifying Grid data
Lattice point set belonging to attribute data, lattice point set have the mean wind direction angle for belonging to predetermined angle range.
Lattice point collection in above-described embodiment is combined into the preset wind direction sector with different wind direction angular ranges.
Specifically, Grid data can be grouped according to its time parameter and wind direction parameter, specific method can be：
Wind direction (i.e. mean wind direction angle) is divided into 32, and (or 16 or customized sector number, this sentences 32 sectors
For, similarly hereinafter) sector (i.e. wind direction sector), each sector (i.e. wind direction sector) is from 0 ° to 11.25 °.
Calculate the mean wind direction angle of all lattice points of each time pointSpecific method can be：
Wherein, uijkIndicate that north and south serial number i, thing serial number j, level are the wind speed u, v of the lattice point of kijkIndicate north and south
Serial number i, thing serial number j, level are the wind speed v of the lattice point of k.
Further according to mean wind direction angleValue according to wind direction sector be grouped.It is divided into 32 lattice point group (i.e. lattice point collection in this way
Close), all Grid datas in each lattice point group (i.e. lattice point set) are the same time, and its attribute data is in same wind
To sector (i.e. lattice point set).A Grid data often is added in certain wind direction sector, then sets the data item sec of the Grid data
The sector numbers (i.e. set identification) are set to, the data item m of Grid data is set as on the sector addition Grid data
Data item m adds 1 again, and the m of the first addition lattice point in the sector is set as 1.
Optionally, Grid data includes at least three layers of meteorological data, and every layer of layer Grid data includes layer position temperature ginseng
Number, layer horizontal wind speed and layer vertical velocity, wherein counting the corresponding atmospheric boundary layer parameter of each lattice point set can wrap
It includes：Each lattice point set is obtained based on the layer position temperature parameter of each Grid data, layer horizontal wind speed, vertical velocity in lattice point set
In the planetary boundary layer height of different preset time periods, planetary boundary layer wind speed and Monin-Obukhov length；It calculates separately every
Planetary boundary layer height average, planetary boundary layer wind speed average value and the Mo Ning-of each preset time period of a lattice point set
Cloth Hough length average value difficult to understand obtains planetary boundary layer height, planetary boundary layer wind speed and the Mo Ning-Ao Bu of each lattice point set
Hough length.
In above-described embodiment based on the layer position temperature parameter of each Grid data in lattice point set, layer horizontal wind speed, vertical
Wind speed obtains planetary boundary layer height, planetary boundary layer wind speed and the Mo Ning-Ao Buhuo that lattice point is integrated into different preset time periods
Husband's length may include：It obtains the mean virtual position temperature determined based on layer position temperature parameter respectively, determined based on layer horizontal wind speed
Average level wind speed and the average vertical wind speed determined based on layer vertical velocity；It is based respectively on mean virtual position temperature and obtains pulsation
Virtual bit temperature obtains vertical position temperature of pulsing based on average vertical wind speed；And it is mildly pulsed vertical position temperature based on pulsation virtual bit
Obtain friction velocity and virtual bit temperature flux；Mean virtual position temperature, friction velocity and virtual bit temperature flux is based respectively on to obtain
Monin-Obukhov length obtains planetary boundary layer height based on average level wind speed, and based on planetary boundary layer height and
Average level wind speed obtains planetary boundary layer wind speed.
Specifically, the data item of all Grid datas is counted and is calculated, respectively obtain the planet side in 32 directions
Interlayer height, planetary boundary layer wind speed and Monin-Obukhov length.For each lattice point group, specific statistic procedure is as follows：
First, the entire data period (such as 1 year) is divided into 2 hours as interlude section (i.e. preset time period),
It is flat in these periods (i.e. preset time period) to calculate several data item of lattice point of each position of each wind direction sector
Mean value, pulsating quantity and pulsation average value, the average value, pulsating quantity and pulsation average value may include：Mean virtual position temperatureIt is flat
Equal horizontal wind speedAverage vertical wind speedVirtual bit temperature of pulsing θ 'v, pulsation vertical velocity w ', friction friction u*And virtual bit temperature
Flux
(1) the mean virtual position temperature in each every 2 hours of position (i.e. preset time period) is calculated,
Wherein, subscript t indicates the serial number of this numerical value in every 2 hours, there is 12 values in 2 hours every in this way, i.e. 1≤t≤
12.K expression layer serial numbers, have three layers, are counted since ground floor altogether, that is, take nearest from the ground 3 layers.θvktRepresent each horizontal lattice point
Kth layer, t-th ten minutes virtual bit temperature θ in every 2 hoursvValue.
(2) average level wind speed of each position in every 2 hours is calculated
Wherein, subscript meaning is same as above, and details are not described herein.
(3) average vertical wind speed of each position in every 2 hours is calculated
Wherein, subscript meaning is same as above, and details are not described herein.
(4) pulsation virtual bit temperature θ ' of each position in every 2 hours is calculatedv,
Wherein, subscript k represents sequence number.Pulsation virtual bit temperature θ ' in every 2 hoursvHave 12 values, every 10 minutes 1.
(5) pulsation vertical velocity w ' of each position in every 2 hours is calculated,
Wherein, subscript k represents sequence number.Pulsation vertical velocity w ' in every 2 hours has 12 values, every 10 minutes 1.
(6) friction velocity u of each position in every 2 hours is calculated*,
(7) each virtual bit temperature flux of the horizontal lattice point in every 2 hours is calculated
Wherein subscript t indicates the serial number of this numerical value in every 2 hours, w 'tRepresent t-th of pulsation vertically-supplying air in every 2 hours
Fast w '.
Secondly, Monin-Obukhov length and other related parameters are calculated.
(1) each each 2 hours Monin-Obukhov lengths in position (Monin-Obukhov Length) are calculated, it should
The calculation formula of Monin-Obukhov length can be as follows：
Wherein, k is Feng Kaiman constants, takes k=0.4 herein；G is acceleration of gravity, takes g=9.8m/s herein2。
(2) each each 2 hours Boundary Layer Height H in position are calculatedmWith boundary layer highest wind velocity um。
Specifically, the every 2 hours average level wind speed in each position are calculatedThe average level wind speedCalculation formula
As follows：
Wherein, K is highest level.If uhkThe horizontal wind speed u of kth layerh, Δ uhkPoor, the u for interlayer horizontal wind speedhk-uhk-1,2
≤ k≤K inquires according to the ascending order of level serial number on each position, finds continuous 3 interlayers horizontal wind speed difference Δ uhk、Δ
uhk+1、Δuhk+2It is less thanLevel, then the height h of kth layer is exactly Boundary Layer Height Hm, this layer of wind speed uhIt is exactly boundary layer
Highest wind velocity um。
(3) annual applicable Monin-Obukhov length and other related parameters are calculated.
It is whole year to take each sector whole year and the average Monin-Obukhov length of each position and other related parameters herein
It is applicable in Monin-Obukhov length and other parameters.
The method for calculating Monin-Obukhov length can be as follows：
If LntFor certain sector nth position, the Monin-Obukhov length of t-th of 2 hour periods shares T time
Section and N number of position.Then annual applicable cloth Hough length L difficult to understand is：
The method for calculating Boundary Layer Height can be as follows：
If HmntFor certain sector nth position, the Boundary Layer Height of t-th of 2 hour periods shares T period and N
A position.Then annual applicable Boundary Layer Height HmFor：
The method for calculating boundary layer highest wind velocity can be as follows：
If umntFor certain sector nth position, the Boundary Layer Height of t-th of 2 hour periods shares T period and N
A position.Then annual applicable Boundary Layer Height HmFor：
So far, the Wind outline formula of non-neutral layer knot can be as follows：
Wherein, parameters, which all have calculated that, comes, empirical function ΨmIt is defined as：
U in above-mentioned formula is wind speed, and z is away from ground level (the planetary boundary layer height H i.e. in above-described embodimentm), u0
It is friction velocity (the friction velocity u i.e. in above-described embodiment*), z0It is Roughness Length (cloth Hough length L difficult to understand), k is Feng Kaiman
Constant usually takes 0.4, X to represent (z/L).
Specifically, the Wind outline formula of non-neutral layer knot is obtained by following process, due to being passed through according to digit rate half
Testing formula calculating Wind outline is：In this, as the boundary condition of CFD numerical simulations.U is wind speed in formula, z be away from
Ground level, u0It is friction velocity, z0It is Roughness Length, k is Feng Kaiman constants, usually takes 0.4.On Boundary Layer Height, boundary
In the case that layer top wind speed, Roughness Length determine, friction velocity can be calculated.
Above method is only applicable to neutral line knot.For non-neutral layer knot, i.e., stable or unstable layer knot needs to consider not
Influence of the Ning-Ao Bu Houghs length (Monin-Obukhov Length) to Wind outline, above formula are revised as：
Wherein ΨmFor empirical function, L is Monin-Obukhov length.L in the embodiment of the present invention is based on practical inspection
The meteorological data of survey is calculated, so as to avoid in wind-resources assessment technology of the mainstream in the world based on CFD numerical simulations,
L values pass through the inaccurate problem of the subjective calculating set caused by assignment.
According to embodiments of the present invention, a kind of embodiment of the processing unit of meteorological data is additionally provided, as shown in figure 3, should
Processing unit includes：First acquisition module 20, determining module 40, grouping module 60 and statistical module 80.
Wherein, the first acquisition module 20, for obtaining Meso-scale meteorology data, wherein Meso-scale meteorology data are to pass through
Assimilation is carried out to measured data and numerical simulation obtains.
Determining module 40 determines simulation region corresponding with target area for the grid point distribution based on Meso-scale meteorology data
Domain, wherein simulated domain includes multiple Grid datas in Meso-scale meteorology data.
Grouping module 60 is used to carry out multiple Grid datas according to the time parameter and wind direction parameter of multiple Grid datas
Grouping, obtains multiple lattice point set.
Statistical module 80, for counting the corresponding atmospheric boundary layer parameter of each lattice point set, wherein atmospheric boundary layer is joined
Number includes：At least one of planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.
Using the present invention, after getting the Meso-scale meteorology data obtained based on the data measured in real time, based on having
The grid point distribution of the Meso-scale meteorology data on measurement data basis determines simulated domain, which covers preadmission sector-style resource
The target area of assessment, and simulated domain includes multiple Grid datas in Meso-scale meteorology data.Then according to multiple lattice
The time parameter and wind direction parameter of point data are grouped multiple Grid datas, obtain multiple lattice point set, and count each
The atmospheric boundary layers such as the corresponding planetary boundary layer height of lattice point set, planetary boundary layer wind speed and Monin-Obukhov length are joined
Number.Can objectively it be reflected since atmospheric boundary layer parameter is obtained based on the data actually measured by above-described embodiment
The weather condition of target area realizes accurate acquisition to solve the problems, such as the parameter of the boundary condition of acquisition inaccuracy
The effect of atmospheric boundary layer parameter and boundary condition.
Optionally it is determined that module may include：Determination sub-module, for the grid point distribution based on Meso-scale meteorology data
The simulated domain in coverage goal region is determined on map, wherein simulated domain is horizontal rectangular region, the side in horizontal rectangular region
Boundary line is parallel with warp or weft.
Optionally, grouping module may include：Computing module and setup module.
Wherein, computing module, the mean wind direction angle for calculating the corresponding Grid data of each time point；
Setup module, for the corresponding Grid data of Each point in time to be divided to different lattice according to mean wind direction angle
In point set, wherein multiple lattice point collection are combined into the preset wind direction sector with different wind direction angular ranges.
Optionally, Grid data includes at least three layers of meteorological data, and every layer of layer Grid data includes layer position temperature ginseng
Number, layer horizontal wind speed and layer vertical velocity, wherein statistical module may include：Second acquisition module and third obtain mould
Block.
Wherein, the second acquisition module, for based on the layer position temperature parameter of each Grid data, the horizontal wind of layer in lattice point set
Speed, vertical velocity obtain planetary boundary layer height, planetary boundary layer wind speed and Mo Ning-that lattice point is integrated into different preset time periods
Cloth Hough length difficult to understand.
Third acquisition module, the planetary boundary layer height of each preset time period for calculating separately each lattice point set
Average value, planetary boundary layer wind speed average value and Monin-Obukhov length average value obtain the planet side of each lattice point set
Interlayer height, planetary boundary layer wind speed and Monin-Obukhov length.
Optionally, the second acquisition module may include：First acquisition submodule, the second acquisition submodule and calculating submodule
Block.
Wherein, the first acquisition submodule, for obtaining the layer based on the Grid data in the preset time period
It is mean virtual position temperature that the warm parameter in position determines, true in the layer horizontal wind speed of the preset time period based on the Grid data
It fixed average level wind speed and is determined in the layer vertical velocity of the preset time period based on the Grid data flat
Equal vertical velocity.
Second acquisition submodule, for mildly the average vertical wind speed to obtain respectively based on the determining mean virtual position
The Grid data is taken mildly to pulse vertical position temperature in the pulsation virtual bit of the preset time period, and virtual based on the pulsation
It is logical in the friction velocity and virtual bit temperature of the preset time period that the mild vertical position temperature of pulsing in position obtains the Grid data
Amount.
Computational submodule, for calculating mean virtual position temperature, the friction velocity and the virtual bit temperature flux
The corresponding Monin-Obukhov length, calculate the corresponding planetary boundary layer height of the average level wind speed and
Calculate the planetary boundary layer height and the corresponding planetary boundary layer wind speed of the average level wind speed.
In particular it relates to a kind of method generating parameter needed for boundary condition for the simulation of CFD wind-resources, this method
Following steps realization may be used：The Meso-scale meteorology for obtaining a complete year analyzes data；According to the grid point distribution of mesoscale
Determine the simulated domain in target area CFD numerical simulations；Plaid matching point is grouped according to time and wind direction；To the data of lattice point composition
Collection (i.e. lattice point set) is counted and is calculated, and obtains simulating parameter needed for generation boundary condition for CFD wind-resources.
The above method can also include the following steps：Each Grid data that data are analyzed according to Meso-scale meteorology generates physics
The average magnitude of amount and the statistical method of pulsating quantity；Changed according to the mean wind speed of different height lattice point and calculates Boundary Layer Height.Root
According to the computational methods of Monin-Obukhov length.
The purpose of the present invention is generating the parameter with measurement data foundation using mesoscale numerical value meteorologic analysis data, use
To generate annual applicable boundary condition.Meso-scale meteorology analysis data are obtained by assimilation to measured data and numerical simulation
It arrives, has more objectivity.But if since Meso-scale meteorology analyzes data (the Meso-scale meteorology number i.e. in above-described embodiment
According to) be made of the data of different discrete time points, it cannot be directly as wind-resources assessment of the mainstream in the world based on CFD numerical simulations
Technology uses, and the present invention is solved by the way that mesoscale meteorologic analysis data are counted, handle and calculated, led in the world
Each parameter of boundary condition is generated used in the wind-resources assessment technology based on CFD numerical simulations of stream.
The embodiments of the present invention are for illustration only, can not represent the quality of embodiment.
In the above embodiment of the present invention, all emphasizes particularly on different fields to the description of each embodiment, do not have in some embodiment
The part of detailed description may refer to the associated description of other embodiment.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, for example, the unit division, Ke Yiwei
A kind of division of logic function, formula that in actual implementation, there may be another division manner, such as multiple units or component can combine or
Person is desirably integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Between coupling, direct-coupling or communication connection can be INDIRECT COUPLING or communication link by some interfaces, unit or module
It connects, can be electrical or other forms.
The unit illustrated as separating component may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, you can be located at a place, or may be distributed over multiple
On unit.Some or all of unit therein can be selected according to the actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, it can also
It is that each unit physically exists alone, it can also be during two or more units be integrated in one unit.Above-mentioned integrated list
The form that hardware had both may be used in member is realized, can also be realized in the form of SFU software functional unit.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can be stored in a computer read/write memory medium.Based on this understanding, technical scheme of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned includes：USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic disc or CD etc. are various can to store program code
Medium.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. a kind of processing method of meteorological data, which is characterized in that including：
Obtain Meso-scale meteorology data, wherein the Meso-scale meteorology data are by carrying out assimilation and numerical value to measured data
What simulation obtained；
Simulated domain corresponding with target area is determined based on the grid point distribution of the Meso-scale meteorology data, wherein the mould
Quasi- region includes multiple Grid datas in the Meso-scale meteorology data；
The multiple Grid data is grouped according to the time parameter and wind direction parameter of the multiple Grid data, is obtained more
A lattice point set；
The corresponding atmospheric boundary layer parameter of each lattice point set of statistics, wherein the atmospheric boundary layer parameter includes：Planet
At least one of Boundary Layer Height, planetary boundary layer wind speed and Monin-Obukhov length.
2. processing method according to claim 1, which is characterized in that the grid point distribution based on the Meso-scale meteorology data
Determine that simulated domain corresponding with target area includes：
The simulated domain of the target area is covered based on determination on the map of the grid point distribution of the Meso-scale meteorology data,
In, the simulated domain is horizontal rectangular region, and the boundary line in the horizontal rectangular region is parallel with warp or weft.
3. processing method according to claim 1, which is characterized in that according to the multiple Grid data time parameter and
Wind direction parameter is grouped the multiple Grid data, obtains multiple lattice point set and includes：
Calculate the mean wind direction angle of the corresponding Grid data of each time point；
The set identification of corresponding Grid data of each time point is set according to the mean wind direction angle, wherein described
Set identification is used to identify the lattice point set belonging to the attribute data of the Grid data, and the lattice point set, which has, belongs to default
The mean wind direction angle of angular range.
4. processing method according to claim 1, which is characterized in that the Grid data includes at least three layers of meteorology
Data, every layer of layer Grid data includes layer position temperature parameter, layer horizontal wind speed and layer vertical velocity, wherein counts each institute
Stating the corresponding atmospheric boundary layer parameter of lattice point set includes：
It is obtained based on the layer position temperature parameter of each Grid data, layer horizontal wind speed, vertical velocity in the lattice point set each
A lattice point is integrated into planetary boundary layer height, planetary boundary layer wind speed and the Mo Ning-Ao Bu Houghs of different preset time periods
Length；
Calculate separately planetary boundary layer height average, the planet side of each preset time period of each lattice point set
Interlayer wind speed average value and Monin-Obukhov length average value, obtain each lattice point set planetary boundary layer height,
Planetary boundary layer wind speed and Monin-Obukhov length.
5. processing method according to claim 4, which is characterized in that based on each lattice point number in the lattice point set
According to layer position temperature parameter, layer horizontal wind speed, vertical velocity obtain the planet boundary that the lattice point is integrated into different preset time periods
Layer height, planetary boundary layer wind speed and Monin-Obukhov length include：
Obtain based on the Grid data the preset time period the layer position temperature parameter determine mean virtual position temperature, base
In the Grid data in the average level wind speed of the layer horizontal wind speed determination of the preset time period and based on described
The average vertical wind speed that Grid data is determined in the layer vertical velocity of the preset time period；
Based on the determining mean virtual position, mildly the average vertical wind speed obtains the Grid data described pre- respectively
The vertical position temperature if the pulsation virtual bit of period is mildly pulsed, and based on the mild vertical position temperature of pulsing of the pulsation virtual bit
Obtain the Grid data the preset time period friction velocity and virtual bit temperature flux；
Calculate mean virtual position temperature, the friction velocity and the corresponding Mo Ning-Ao Bu of the virtual bit temperature flux
Hough length calculates the corresponding planetary boundary layer height of the average level wind speed and calculates the planetary boundary layer
The height planetary boundary layer wind speed corresponding with the average level wind speed.
6. a kind of processing unit of meteorological data, which is characterized in that including：
First acquisition module, for obtaining Meso-scale meteorology data, wherein the Meso-scale meteorology data are by surveying number
According to carrying out assimilation and numerical simulation obtains；
Determining module determines simulation region corresponding with target area for the grid point distribution based on the Meso-scale meteorology data
Domain, wherein the simulated domain includes multiple Grid datas in the Meso-scale meteorology data；
Grouping module, for according to the multiple Grid data time parameter and wind direction parameter to the multiple Grid data into
Row grouping, obtains multiple lattice point set；
Statistical module, for counting the corresponding atmospheric boundary layer parameter of each lattice point set, wherein the atmospheric boundary layer
Parameter includes：At least one of planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.
7. processing unit according to claim 6, which is characterized in that the determining module includes：
Determination sub-module, for covering the target area based on determining on the map of the grid point distribution of the Meso-scale meteorology data
The simulated domain in domain, wherein the simulated domain is horizontal rectangular region, the boundary line in the horizontal rectangular region and warp or
Weft is parallel.
8. processing unit according to claim 6, which is characterized in that the grouping module includes：
Computing module, the mean wind direction angle for calculating the corresponding Grid data of each time point；
Setup module, the set mark for corresponding Grid data of each time point to be arranged according to the mean wind direction angle
Know, wherein the set identification is used to identify the lattice point set belonging to the attribute data of the Grid data, the lattice point set
With the mean wind direction angle for belonging to predetermined angle range.
9. processing unit according to claim 6, which is characterized in that the Grid data includes at least three layers of meteorology
Data, every layer of layer Grid data includes layer position temperature parameter, layer horizontal wind speed and layer vertical velocity, wherein the statistics mould
Block includes：
Second acquisition module, for based on the layer position temperature parameter of each Grid data, the horizontal wind of layer in the lattice point set
Speed, vertical velocity obtain each lattice point and are integrated into the planetary boundary layer height of different preset time periods, planetary boundary layer wind
Speed and Monin-Obukhov length；
Third acquisition module, the planetary boundary layer of each preset time period for calculating separately each lattice point set
Height average, planetary boundary layer wind speed average value and Monin-Obukhov length average value obtain each lattice point set
Planetary boundary layer height, planetary boundary layer wind speed and Monin-Obukhov length.
10. processing unit according to claim 9, which is characterized in that second acquisition module includes：
First acquisition submodule, it is true in the layer position temperature parameter of the preset time period based on the Grid data for obtaining
Fixed mean virtual position temperature, the average water of the layer horizontal wind speed determination based on the Grid data in the preset time period
Flat wind speed and the average vertical wind speed determined in the layer vertical velocity of the preset time period based on the Grid data；
Second acquisition submodule, for mildly the average vertical wind speed to obtain institute respectively based on the determining mean virtual position
It states Grid data mildly to pulse vertical position temperature in the pulsation virtual bit of the preset time period, and is based on the pulsation virtual bit temperature
With the vertical position temperature of the pulsation obtain the Grid data the preset time period friction velocity and virtual bit temperature flux；
Computational submodule is corresponded to for calculating mean virtual position temperature, the friction velocity and the virtual bit temperature flux
The Monin-Obukhov length, calculate the corresponding planetary boundary layer height of the average level wind speed and calculate
The planetary boundary layer height and the corresponding planetary boundary layer wind speed of the average level wind speed.
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| CN107544098B (en) * | 2017-07-24 | 2020-04-07 | 中国华能集团清洁能源技术研究院有限公司 | Surface roughness generation method and device, storage medium and processor |
| CN109961207B (en) * | 2017-12-26 | 2021-04-13 | 北京金风科创风电设备有限公司 | Method and device for determining wind resources |
| CN109783965B (en) * | 2019-01-25 | 2022-08-02 | 中国空气动力研究与发展中心计算空气动力研究所 | Automatic block encryption method for structural grid |
| CN111460637B (en) * | 2020-03-20 | 2023-07-21 | 成都市环境保护科学研究院 | Urban ventilation potential quantitative evaluation method based on numerical method |
| CN111860996B (en) * | 2020-07-15 | 2021-09-03 | 中科三清科技有限公司 | Emission source data generation method and device of air quality model and medium |
| CN112149365B (en) * | 2020-09-29 | 2023-06-30 | 华能新能源股份有限公司 | Micro-scale wind model system and method |
| CN112699102B (en) * | 2021-03-23 | 2021-06-29 | 北京简巨科技有限公司 | Processing method of lattice point data and related equipment thereof |
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