CN108052704A - Mesoscale photochemical pollution simulation and forecast algorithm with Grid Nesting function - Google Patents
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Abstract
The invention discloses a kind of Mesoscale photochemical pollution simulation and forecast algorithms with Grid Nesting function, the present invention adds nested grid function in CALGRID patterns, female region coarse grid is calculated within the entirely simulation period, and the value when each time step exports result by refined net subzone boundaries on corresponding mother's area grid stores, boundary value as subregion, value after mode operation on the mesh point in the corresponding female region of subregion is stored, and as the initial fields of subregion, when subregion is calculated, subregion provides more real primary condition and time-varying boundary condition.The present invention is using dull interpolation method, it is believed that the outermost grid values in zonule are equal to the value of the coarse grid at place.
Description
Technical Field
The invention belongs to the technical field of atmospheric environmental pollutant detection, and particularly relates to an improved prediction model for mesoscale atmospheric photochemical pollution.
Background
The air quality mode is a method for simulating and forecasting the concentration distribution condition and the variation trend of air pollutants on different spatial scales by utilizing the research methods and computer technologies of subjects such as meteorology, environment, physics, chemistry and the like on the basis of understanding a series of physical and chemical processes such as transmission, diffusion, conversion, removal and the like after the pollutants are discharged into the atmospheric environment, has important practical application value in the aspects of air quality forecasting, atmospheric pollution control, environmental planning and management, urban construction, public health and the like, and has wide development prospect.
At present, euler type medium-scale atmospheric photochemical mode, such as CALGRID mode of ARB, has better stability, has better effect on the simulation of secondary pollution sources such as ozone, is mainly suitable for the simulation of photochemical reaction under the clear sky condition, and comprises the processes of atmospheric transportation and diffusion, gas phase chemical reaction, artificially discharged point-plane line source, dry settlement and the like. However, the conventional CALGRID chemical model does not have the grid nesting function, so that a large amount of computing resources have to be consumed in the model to improve the simulation accuracy.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a mesoscale atmospheric photochemical pollution simulation prediction algorithm with a mesh nesting function, wherein the nesting mesh function is added in a mode, so that the mode improves the simulation precision and reduces the simulation time consumption.
In order to solve the technical problems, the invention adopts the following technical scheme: a simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with a grid nesting function comprises the following steps:
step 1: adopting CALCRID chemical mode, considering the influence of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation and bottom area overhead discharge source, and decomposing the average physical quantity in the original concentration of chemical species into pulsating quantity to obtain the chemical species concentration change equation as formula (1),
where C is the average concentration of the chemical species, V is the average magnitude of the three-dimensional wind vector, K is the turbulent diffusion coefficient, E is the pollutant emission,due to changes in the concentration of species, P, caused by sedimentation CHEM Is the chemical yield, LCHEM is the chemical loss rate; middle or second order in formula (1) turbulent diffusion term ·(K ^ C) obtained by closed transformation of the diffusion coefficient of turbulence K theory; the right polynomial in the formula (1) is respectively an advection term, a diffusion term, a source term, a sedimentation term and a chemical change term in sequence;
step 2: in the CALCHRD chemical mode, carrying out coarse grid nesting on the whole large area of a research area, and carrying out fine grid division on a target small area in the large area, wherein the large area is a mother area, the target small area is a sub area, and the mother area and the sub area adopt the same prediction mode, namely self-mode nesting;
and step 3: in the whole simulation time period, performing coarse grid calculation on a mother area, and storing a grid upper value of the mother area corresponding to the boundary of a fine grid sub-area as the boundary value of the sub-area when a result is output in each time step, wherein the boundary of the fine grid sub-area comprises a top boundary and a side boundary;
and 4, step 4: operating the CALCIRD chemical mode, storing values on grid points of the mother region corresponding to all the sub-regions after the concentrations of all the species are balanced and stable, and using the values as initial fields of the sub-regions;
and 5: and (4) taking the stored value obtained in the step (3) as a boundary value of the sub-region, taking the value obtained in the step (4) as an initial field condition of the sub-region, calculating the sub-region, and directly inserting the coarse grid into the corresponding fine grid during calculation.
Further, the boundaries of the fine mesh in step 3 include a top boundary and a side boundary.
Further, in step 1, the advection term of formula (1) adopts a high-order chapeau function numerical processing scheme, and combines with nonlinear local filtering (effectively ensuring material conservation and preventing negative concentration and low-value diffusion), the specific calculation method is as follows:
suppose K xx ＝K yy ＝K h The horizontal diffusion coefficient K can be determined by the following four methods h ；
In the method 1, diffusion parameters are determined by a P-G-T stability classification method in a boundary layer; above the boundary layer, the diffusion parameters are taken as fixed values and assigned by the user;
method 2, correcting the wind speed on the basis of the method 1;
the method 3, using a simplified Smagorinsky formula to solve:
K h ＝α 0 |D|Δt (3)
in the formula, alpha 0 =0.28; Δ t is the time step; | D | is the degree deformation tensor, u and v are the horizontal wind speeds in the x and y directions, respectively; the deformation and the shear of a horizontal wind field are considered;
method 4, the results of methods 2 and 3 are combined.
Further, in the vertical diffusion term of CALGRID chemical mode (1) in step 1, the vertical velocity W in the terrain correction coordinate is used as the vertical advection flux on the computational plane.
Further, in the step 1, the CALGRID chemical model (1) comprises a source item surface source, a fixed point source with periodic variation, a fixed point source without periodic variation and a flowing point source; artificial source is input in the form of surface source or fixed point source, and natural source is input in the form of surface source;
the CALGRID chemical model is divided into two steps, firstly, the default surface source discharge enters 100m, and then, the concentration is proportionally distributed to CALGRID vertical layers below 100m by using a distribution function.
Further, the CALGRID chemical mode aims at the vertical distribution of point source emission, and is determined by the height of a point source, the height of smoke flow lifting and the proportion of vertical layering of smoke flow in each mode.
Furthermore, the height delta h of the smoke stream lifting is calculated by a Briggs formula method, and when neutral and unstable layer knots are formed,
Δh＝min(Δh 1 ,Δh 2 )
X f ＝3.5X *
in the formula,. DELTA.h 1 Is the plume lifting height under the neutral condition, wherein u is the wind speed of the point source height layer; f is buoyancy flux, R 0 And w 0 Initial values of exit radius and exit velocity, respectively; t is s Is the flue gas temperature; t is the atmospheric temperature, X f The distance from the ultimate rise of the plume to the origin; s is a stability parameter;
considering the influence of the inverse temperature layer on the convection boundary layer on the smoke plume, using Briggs to provide a partial penetration equation, and calculating to obtain delta h 2 ：
In the formula, z b Is point source height h s Distance z to the top of the hybrid layer i (ii) a Beta' is a coiling parameter, and the value is 041314; s is a stability parameter; u is the flow rate, when h s ＞z i When the ratio is 1m/s.
z b ＝z i -h s Is point source height (h) s ) To the top of the mixed layer (z) i ) The distance of (d); beta' is a crimp parameter of 041314; s = (g/T) a ) (d θ/dz) stability parameter (d θ/dz is inverse temperature stratification temperature directly decreasing rate); when h is generated s >z i When u is equal to 1m/s.
Further, for a stable layer knot, the height Δ h of plume rise is calculated by the following formula:
wherein F is buoyancy flux, u is wind speed of a point source height layer, and s is a stability parameter.
Further, for the sedimentation term, for gas dry sedimentation, the sedimentation velocity V d Calculated by the following formula:
V d ＝(r a +r d +r c ) -1
in the formula, r a 、r d 、r c Respectively representing the impedance of the turbulent layer, the adhesion layer and the vegetation layer;
for dry settling of solid particles, the settling velocity V d Calculated by the following formula:
V d ＝(r a +r d +r a ·r d ·v g ) -1 +v g
in the formula, r a And r d Respectively representing the impedance, v, of the turbulent and adhesive layers g Is the gravity settling velocity.
Drawings
FIG. 1 is a schematic flow diagram of the addition of one-way nesting in the CALGRID chemistry model of the present invention;
FIG. 2 is a diagram of nested simulation zone locations in accordance with an embodiment of the present invention;
FIG. 3 illustrates nested front and rear east China areas O in an embodiment of the present invention 3 Concentration Profile (. Mu.g.m) -3 )；
FIG. 4 shows nested tandem east China NO's in an embodiment of the present invention x Concentration Profile (. Mu.g.m) -3 )；
FIG. 5 shows SO in the east China before and after nesting in accordance with an embodiment of the present invention 2 Concentration distribution (. Mu.g.m) -3 )；
FIG. 6 shows the distribution of CO concentration (. Mu.g.m) in east China before and after nesting in accordance with an embodiment of the present invention -3 )。
Detailed Description
The invention will be further elucidated with reference to the following description of an embodiment in conjunction with the accompanying drawing. It is to be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
In order to test the effect of the nested grid technology on improving the mode simulation performance, the concentration distribution of air pollutants in east China in summer is researched by using an improved CALGRID mode added with a nested function, and the simulation time is 7 months and 16-31 days in 2000. The improved CALGRID mode employs dual nesting, depending on the analog range and accuracy requirements. The simulation range of the mother area (DM 1) is 18.6 degrees N-53.5 degrees N and 90.6 degrees E-151.8 degrees E, the coordinates of the central point of the area are 40 degrees N and 113 degrees E, and the area approximately covers the China area; the horizontal grid is 66 x 56 and the grid resolution is 75km. The simulation range of the sub-region (DM 2) is 22.6 degrees N-38.2 degrees N and 108.6 degrees E-125.3 degrees E, the coordinates of the center point of the region are 45 degrees N and 94 degrees E, and the region comprises the east China region; the horizontal grid is 60 × 76, and the grid pitch is 25km. The coverage of the mother and sub-regions (DM 1 and DM 2) and their configuration are shown in fig. 2. The other settings of the two regions are the same: the vertical direction is divided into 10 layers which are respectively 20m, 80m, 160m, 260m, 410m, 660m, 1200m, 2200m, 3600m and 5000m; the selected gas phase chemistry mechanism is SAPRC-90; the chemical integration scheme is QSSA; the pollution source data comprises a surface source and an overhead point source. Both the side and top boundaries of the parent zone take small values close to 0, indicating that the contaminant concentration initial value is the background concentration of the species, regardless of the effect of extraneous input on the parent zone. The edge values of the subareas are provided by the mother area, and the initial values are the concentration values of all grid points after the mother area operates for three days.
The meteorological field driving the CALCRID is provided by a mesoscale meteorological model MM5, and double-layer one-way nesting is adopted. The cloud-accumulating parameterization scheme of the mother region (DM 1) adopts the Anthes-Kuo scheme suitable for the coarser resolution, and the sub region (DM 2) adopts the Grell parameterization scheme based on the single cloud assumption (generally suitable for 10-30 km grids). The other settings were taken to be consistent for both regions: the boundary layer scheme is a Blackadar high-precision scheme; the radiation scheme adopts a CCM2 scheme suitable for longer-time integration; the side boundary is a time varying boundary condition. The re-analysis data of global NCAR/NCEP for 4 times each day is adopted in the primary and side conditions of the meteorological model, the horizontal resolution is 2.5 degrees, and the vertical direction is 18 layers; sea Surface Temperature (SST) data was taken with Renolds SST with a horizontal resolution of 1 degree.
FIG. 3 is a simulation of time frame east China area O 3 The distribution of the mean concentration is the result of the mother region of coarse resolution on the left and the result of the sub-region of fine resolution after the nested calculation is used on the right. On the whole, the spatial distribution of ozone is consistent before and after nesting, but the detailed structure has obvious difference and is closely related to the distribution of a flow field and a pollution source: the Shandong, shanxi, hebei and Henan have higher O 3 Concentration of more than 150 μ g.m -3 (ii) a Southern, especially southeast coastal zone, O 3 The concentration is lower than 80 mu g.m -3 . Most of the provinces in the simulation area belong to the areas with fast economic development in China, and have developed industrial, agricultural and traffic areas and correspond to O 3 Precursor NO x The VOC emission is also large, and in summer with high temperature and strong radiation, the provinces can form high-concentration ozone, but at the same time, the southeast monsoon on the high-pressure west side of the adverse tropical zone of the northwest Pacific ocean can also influence the southeast of China, so that a simulation area O is formed 3 The distribution is high in the north, low in the south and east. It can also be seen from FIG. 3 that because of the higher horizontal resolution of the sub-regions, O is the ratio of the total number of sub-regions 3 The regional differences in the spatial distribution of concentrations are more pronounced than in the parent region. In the northern area with higher concentration, the nested result can clearly show that the central area with high concentration value is in the north of river and east of mountain, and the concentration is more than 250 mu g.m -3 . In the lower southeast coastal zone, although O 3 The overall level is still lower, but the amount of precursors is greater in urban areas such as Nanjing, shanghai, hangzhou, fuzhou, etc., O 3 The concentration is obviously higher than that of the cities in the surrounding areas. In addition, O in cities such as Xian, zhengzhou, wuhan, changsha, and Nanchang 3 The concentration is also higher than the ambient environment.
FIG. 4 shows simulated time periods NO in east China with and without consideration of nesting x Distribution of average concentration. In generalIt appears that the spatial distribution before and after nesting is consistent, and the large value area is mainly distributed in Shandong, shanxi, hebei, henan, hubei, hunan, anhui, guangdong provinces, and NO of Shanghai, jiangsu and Zhejiang x The concentration is also greater. Wherein the east China part of the mother zone simulates NO x The concentration distribution is relatively uniform, and the average concentration in North China, two lakes and Guangdong region is more than 10 mu g.m -3 Partially greater than 20 μ g/m -3 The average concentration of Jiangzhe area is 5-10 mu g.m -3 In the meantime. The locality of the concentration distribution simulated by the nested subareas is very obvious, and the results after nesting can obviously show that NO is in the urban areas with large artificial source emission, such as Shijiazhuang, taiyuan, jinan, zhengzhou, xian, wuhan, hefei, nanjing, shanghai, hangzhou, nanchang, changsha, fuzhou, guangzhou and the like x The concentration is obviously higher than that of the surrounding areas and is all more than 50 mu g.m -3 . In addition, NO in the atmosphere near some large point source emission sources (primarily power plants) x The concentration is also higher. Due to NO x The life in the atmosphere is short, so the distribution is mainly influenced by the emission of the source, and the simulation result of the subarea is consistent with the distribution condition of the source in east China. Therefore, nested grid calculation is carried out, the distribution details of the pollutant concentration can be simulated, and the simulation precision is greatly improved.
FIG. 5 is a diagram of SO in the east China area under simulation of both cases of presence and absence of nesting 2 Distribution of mean concentration. In general, the spatial distribution before and after nesting is consistent, the large-value areas are mainly distributed in provinces such as Shandong, hebei and Guangdong, and the concentration of the Jiangsu part areas is also larger. But the results of the mother area simulation are relatively uniformly distributed, and the average concentration of the large areas in North China and Shandong China is more than 40 mu g.m -3 Concentration of higher than 30 μ g/m in some regions of Jiangsu and Guangdong -3 . The spatial nonuniformity of the concentration distribution simulated by the nested subareas is strong, and the SO of a plurality of areas 2 The concentration values are all larger than 75 mu g.m -3 Even more than 100 μ g/m in some regions -3 The areas are distributed in a dotted way and mainly in urban areas with large artificial emission, such as Shijiazhuang, taiyuan, jinan, zhengzhou, xian and WuHan, hefei, nanjing, shanghai, hangzhou, nanchang, changsha, fuzhou, guangzhou and the like, and in the vicinity of some larger point source emission sources (mainly power plants). Therefore, the simulation precision of the pollutant concentration is improved by adopting a nested calculation scheme.
Fig. 6 is the distribution of the average CO concentration in east china before and after nesting. The results of the mother zone simulation are relatively uniformly distributed, except that the average concentration in the southeast coastal zone is less than 50 mu g.m -3 In addition, the concentration is 100 μ g.m in most regions -3 Above, the maximum value is in Zhengzhou area of Henan province, and the average concentration is more than 750 mu g.m -3 . The concentration distribution simulated by the nested subareas is very strong in locality, and the average concentration of CO in Henan province is more than 1000 mu g.m -3 In some regions even greater than 3000 μ g m -3 (ii) a The concentration is more than 500 μ g.m in Shandong, hubei, jiangsu and Zhejiang -3 In a distribution zone in which the concentration in the vicinity of urban areas is greater than 1000. Mu.g.m -3 (ii) a Other areas also have large value areas distributed in a point shape, mainly in the vicinity of larger cities and power plants, and the concentration of part of the areas can exceed 2000 mu g m -3 . In general, the spatial distribution of the CO concentration in the east China area before and after nesting is consistent, but the calculated value after nesting is relatively large and the spatial non-uniformity of the distribution is strong. The CO belongs to a long-life species, the simulation result of the general long-life species is greatly influenced by initial and boundary conditions, the initial condition of a mother area is set, and the influence of peripheral areas on Chinese areas is not considered, so the simulation result is relatively low. The boundary condition of the nested sub-region is the simulation value of the mother region, and the simulation value is relatively real and considers the influence of pollutants outside the east China on the region, so that the simulation value is closer to the reality. Nested computations not only improve the resolution of the patterns, but also improve the accuracy of long-lived species simulations.
In conclusion, the nested grid technology effectively controls the calculated amount while improving the CALCRID simulation precision, enhances the authenticity of the initial value of the region of interest, and reduces the influence of the manually set side boundary and top boundary conditions on the research region. The simulation method can simulate the primary and secondary photochemical reactions on multiple scales, and improves the application performance of CALGRID.
Claims (9)
1. A simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with a grid nesting function comprises the following steps:
step 1: adopting CALGRID chemical mode, considering the influence of atmospheric chemical reaction, atmospheric transportation and diffusion, sedimentation and bottom area overhead discharge source, and decomposing the average amount of physical quantity in the original concentration of chemical species into pulsating quantity to obtain the chemical species concentration change equation as formula (1),
where C is the average concentration of the chemical species, V is the average magnitude of the three-dimensional wind vector, K is the turbulent diffusion coefficient, E is the pollutant emission,due to changes in the concentration of species, P, caused by sedimentation CHEM Is the chemical generation rate, L CHEM Is the chemical loss rate; the second-order turbulent diffusion term · (K · (C)) in formula (1) is obtained by a turbulent diffusion coefficient K theoretic closed transformation; the right polynomial in the formula (1) is respectively an advection term, a diffusion term, a source term, a sedimentation term and a chemical change term in sequence;
step 2: in the CALCHRD chemical mode, carrying out coarse grid nesting on the whole large area of a research area, and carrying out fine grid division on a target small area in the large area, wherein the large area is a mother area, the target small area is a sub area, and the mother area and the sub area adopt the same prediction mode, namely self-mode nesting;
and 3, step 3: in the whole simulation time period, performing coarse grid calculation on a mother area, and storing a grid upper value of the mother area corresponding to the boundary of a fine grid sub-area as the boundary value of the sub-area when a result is output in each time step, wherein the boundary of the fine grid sub-area comprises a top boundary and a side boundary;
and 4, step 4: operating the CALCIRD chemical mode, storing values on grid points of the mother region corresponding to all the sub-regions after the concentrations of all the species are balanced and stable, and using the values as initial fields of the sub-regions;
and 5: and (4) taking the stored value obtained in the step (3) as a boundary value of the sub-region, taking the value obtained in the step (4) as an initial field condition of the sub-region, calculating the sub-region, and directly inserting the coarse grid into the corresponding fine grid during calculation.
2. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 1, characterized in that: the boundaries of the fine mesh in step 3 include top boundaries and side boundaries.
3. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 1, characterized in that: in the step 1, a high-order chapeau function numerical processing scheme is adopted for the advection item of the formula (1), and nonlinear local filtering is combined (material conservation is effectively ensured and negative concentration and low numerical diffusion are prevented), and the specific calculation method is as follows:
suppose K xx ＝K yy ＝K h The horizontal diffusion coefficient K can be determined by the following four methods h ；
The method 1, in the boundary layer, the diffusion parameters are determined by a P-G-T stability classification method; above the boundary layer, the diffusion parameters are taken as fixed values and assigned by the user;
method 2, correcting the wind speed on the basis of the method 1;
the method 3, using a simplified Smagorinsky formula to solve:
K h ＝α 0 |D|Δt (3)
in the formula, alpha 0 =0.28; Δ t is the time step; d is degree deformation sheetAmount, u and v are the horizontal wind speeds in the x and y directions, respectively; the deformation and the shear of a horizontal wind field are considered;
method 4, the results of methods 2 and 3 are combined.
4. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 1, characterized in that: in the vertical diffusion term of CALGRID chemical model (1) in step 1, the vertical velocity W in the terrain correction coordinate is used as the vertical advection flux on the computational level.
5. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 1, characterized in that: a CALCRID chemical model (1) in the step 1 comprises a source item surface source, a periodically-changed fixed point source, a non-periodically-changed fixed point source and a flowing point source; the artificial source is input in a surface source or fixed point source mode, and the natural source is input in a surface source mode;
the CALGRID chemical model is divided into two steps, firstly, the default surface source discharge enters 100m, and then, the concentration is proportionally distributed to CALGRID vertical layers below 100m by using a distribution function.
6. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 4, wherein the simulation and prediction algorithm comprises: the CALCRID chemical mode aims at the vertical distribution of point source emission and is determined by the height of a point source, the height of smoke flow lifting and the proportion of vertical layering of smoke flow in each mode.
7. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 5, wherein the simulation and prediction algorithm comprises: the height deltah of the plume rise is calculated by the Briggs formula method, and when the neutral and unstable layer is formed,
Δh＝min(Δh 1 ,Δh 2 )
X f ＝3.5X *
in the formula,. DELTA.h 1 Is the plume lifting height under the neutral condition, wherein u is the wind speed of the point source height layer; f is buoyancy flux, R 0 And w 0 Respectively the initial values of the exit radius and the exit speed; t is s Is the flue gas temperature; t is the atmospheric temperature, X f The distance from the ultimate rise of the plume to the origin; s is a stability parameter;
considering the influence of the inverse temperature layer on the convection boundary layer on the smoke plume, using Briggs to provide a partial penetration equation, and calculating to obtain delta h 2 ：
In the formula, z b Is point source height h s Distance z to the top of the mixed layer i (ii) a Beta' is a rolling parameter, and the value is 041314; s is a stability parameter; u is the flow rate, when h s ＞z i When the ratio is 1m/s.
z b ＝z i -h s Is point source height (h) s ) To the top of the mixed layer (z) i ) The distance of (d); beta' is a crimp parameter of 041314; s = (g/T) a ) (d θ/dz) stability parameter (d θ/dz is inverse temperature stratification temperature directly decreasing rate); when h is generated s >z i When u is equal to 1m/s.
8. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 5, wherein the simulation and prediction algorithm comprises: for a stable nodule, the height Δ h of plume rise is calculated by:
wherein F is buoyancy flux, u is wind speed of a point source height layer, and s is a stability parameter.
9. The simulation and prediction algorithm for mesoscale atmospheric photochemical pollution with grid nesting function according to claim 1, characterized in that: for the sedimentation term, for gas dry sedimentation, the sedimentation velocity V d Calculated by the following formula:
V d ＝(r a +r d +r c ) -1
in the formula, r a 、r d 、r c Respectively representing the impedance of the turbulent layer, the adhesion layer and the vegetation layer;
for dry settling of solid particles, the settling velocity V d Calculated by the following formula:
V d ＝(r a +r d +r a ·r d ·v g ) -1 +v g
in the formula, r a And r d Respectively representing the impedance, v, of the turbulent and adhesive layers g Is the gravity settling velocity.
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