CN116415408A - VOCs emission source list dynamic inversion method based on four-dimensional variation assimilation - Google Patents
VOCs emission source list dynamic inversion method based on four-dimensional variation assimilation Download PDFInfo
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Abstract
The invention discloses a dynamic inversion method of a VOCs emission source list based on four-dimensional variation assimilation. The method comprises the steps of establishing a VOCs observation operator aiming at ground monitoring data of an ecological environment department, researching and developing a VOCs related accompanying operator and a corresponding optimization algorithm, improving a WRF-CUACE accompanying model v1.0 and a WRF-CUACE-4DVar emission source list dynamic inversion system, realizing near real-time optimized inversion of a VOCs emission source list, solving the problem of difficult correction of the source list caused by rare VOCs observation data at present, and improving the hysteresis quality and uncertainty of the emission source list. The invention discloses a four-dimensional variation data assimilation method based on a reverse atmospheric chemistry accompanying model for carrying out dynamic inversion of a VOCs emission source list, and belongs to the technical field of emission source list inversion and air quality numerical weather forecast.
Description
Technical Field
The invention provides a near-real-time dynamic inversion optimization method technology of a VOCs (volatile organic compounds) emission source list, in particular relates to a dynamic inversion method of the VOCs emission source list based on four-dimensional variation data assimilation, carries out the dynamic inversion of the VOCs emission source list based on a four-dimensional variation data assimilation method of a reverse atmosphere gas accompanying model, and belongs to the technical field of emission source list inversion and air quality numerical weather forecast.
Background
In the technical field of numerical weather forecast, a four-dimensional variation assimilation system is built on the basis of an accompanying model, and the accompanying model is a reverse mode depending on a forward numerical weather forecast model. Existing atmospheric chemistry companion model techniques include the companion model of the new generation chemical weather system (WRF-CUACE-ADJ) described in literature (Quantification of SO Emission Variations and the Corresponding Prediction Improvements Made by Assimilating Ground-Based Observations, atmosphere, volume 13 of 2022), implementing SO with a four-dimensional variational assimilation system (WRF-CUACE-4 DVar) matched thereto 2 And NO 2 Is an assimilation inversion of (a). At present, the prior art can only realize the conventional gas pollutant SO 2 And NO 2 But not the inversion of volatile organics, VOCs.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a dynamic inversion method of a VOCs (volatile organic compounds) emission source list based on four-dimensional variation assimilation, which independently develops a WRF-CUACE-4DVar four-dimensional variation assimilation system based on parallel calculation, comprises the steps of establishing a VOCs observation operator aiming at ground monitoring data of an ecological environment part, developing a VOCs related accompanying operator and a corresponding optimization algorithm, and improving the WRF-CUACE accompanying model v1.0 and the WRF-CUACE-4DVar emission source list dynamic inversion system. The invention realizes near real-time optimized inversion of the VOCs emission source list, solves the problem of difficult correction of the source list caused by sparse current VOCs observation data, improves the hysteresis and uncertainty of the emission source list, and is particularly implementedAir quality model pair O is improved 3 And PM 2.5 And the effect of the prediction and simulation of (a).
For convenience, the present invention defines the following term designations:
WRF: weather Research and Forecasting model, weather research and forecast model;
CUACE China Meteorological Administration Unified Atmospheric Chemistry Environment, china weather office chemical weather model;
weather Research and Forecasting model coupled with China Meteorological Administration Unified Atmospheric Chemistry Environment, a forward China weather exchange chemical weather model coupled with WRF;
the adjoint model of WRF-CUACE, an accompanying code of a reverse China weather exchange chemical weather model coupled with WRF;
the WRF-CUACE accompanying model v1.0 is a China weather exchange chemical weather accompanying model (first edition) coupled with the WRF, and comprises the WRF-CUACE and the WRF-CUACE-ADJ;
WRF-CUACE-4DVar: four dimentional variational data assimilation inversion system based on WRF-CUACE, four-dimensional variation and assimilation emission source list dynamic inversion system of the China weather department.
VOCs: volatile Organic Compounds, volatile organic compounds.
According to the method, the atmospheric variable analysis values on the regular grid points are obtained according to the meteorological observation data distributed on the irregular observation points. And directly obtaining posterior weight through a variation method, and deducing analysis increment to obtain an emission source list after optimization adjustment. In general, the four-dimensional variation assimilation inversion method provided by the invention utilizes the observation data to carry out 'top-down' constraint updating on the pollution sources, can provide a rapid check sum updating method for an atmospheric pollution source list, and can also supplement more time-space distribution information for the 'bottom-up' pollution source list.
The invention provides a dynamic inversion method for a VOCs emission source list based on a WRF-CUACE-4DVar four-dimensional variation assimilation technology, which comprises the following steps: the establishment of VOCs observation operator, the storage of ground state value, the definition of cost function, the calculation of accompanying sensitivity and the optimization iteration method. The method comprises the following specific steps:
A. constructing an observation operator and a VOCs background field (VOCs original emission source list), and acquiring a ground state value of each VOCs gas and organic aerosol in the VOCs emission source list in a corresponding chemical process of each time step through an integral four-dimensional variation assimilation emission source list dynamic inversion model system; the implementation method comprises the following steps:
A1. constructing an observation operator, summing up the original emission source list of VOCs, and redistributing the VOCs in the original emission source list so as to be suitable for a gas CBM-IV chemical parameterization scheme; finally, interpolation is carried out to lead the obtained VOCs emission source list to be matched with the set grid points, and the processed emission source list x is obtained 0 ；
A2. Assimilation inversion is performed through a forward integration WRF-CUACE model system, and a processed emission source list x is used 0 Obtaining a ground state value and an analog value of each VOCs gas and organic aerosol in the corresponding chemical process of each time step, and storing the ground state value and the analog value;
B. the VOCs cost function is defined and constructed.
Constructing a cost function aiming at VOCs, and defining a background error covariance matrix, an observation error covariance matrix and a weight coefficient;
in the present invention, the cost function of VOCs is defined as follows:
wherein J (x) is a VOCs cost function; y is m For the simulation value of the WRF-CUACE forward model (O is adopted when the invention is embodied 3 Quantization of gain by analog value), y obs For observations at different moments, O in the present invention 3 Observing the concentration, wherein T is the transposition of the matrix; x is x a A priori emission source list of VOCs, x is a posterior emission source list of VOCs,
and->
Respectively is O 3 The observed error covariance matrix and the background error covariance matrix are diagonal matrices and gamma r For penalty terms, i.e. weighting coefficients, for adjusting the weights of the proportions of observed and background values, gamma r Is set to 1-100./>
Is the initial deviation of the prior emissions source list, +.>
Is the sum of the simulated bias and the observed error. Parameters in the above-mentioned VOCs cost function (+.>
And gamma r ) As the characteristics of VOCs gas species and the simulated bias of the inversion period vary;
C. the VOCs cost function is subjected to gradient calculation, and the accompanying sensitivity of the VOCs is calculated, wherein the realization method comprises the following steps:
C1. acquiring ground observation data, preprocessing the ground observation data, and interpolating to corresponding grid points by using a nearest neighbor interpolation method according to the longitude and latitude of a site to obtain observation values of the corresponding grid points;
C2. taking an error (an analog value minus an observation value) and a ground state value between the observation value of the corresponding grid point obtained by the C1 and the analog value of the WRF-CUACE model as input, and reversely integrating the four-dimensional variation assimilation emission source list dynamic inversion system to obtain VOCs concomitant sensitivity;
D. and solving the cost function and the accompanying sensitivity of the optimal estimated VOCs to obtain an optimized inversion source list. The implementation method comprises the following steps:
D1. inputting the sensitivity of the VOCs obtained by C2 into an L-BFGS quasi-Newton algorithm, calculating to obtain the gradient of a cost function, and inverting to obtain a discharge source list x 1 ；
D2. And judging the cost function difference proportion between two adjacent iterations.
If the cost function difference ratio between two adjacent iterations is largeAt 1%, go to step A2, update the inverted emission source list x 1 And inputting the integration simulation again into the forward model WRF-CUACE, and starting a new round of assimilation inversion. If 1% or less, the emission source list (x N ) I.e., the optimized inversion source list.
Through the steps, the dynamic inversion of the VOCs emission source list based on four-dimensional variation assimilation is realized.
As a preferable scheme, the invention also establishes a WRF-CUACE accompanying model v1.0, designs a parallel WRF-CUACE-4DVar four-dimensional variation assimilation system with variable time and spatial resolution, and realizes the dynamic inversion of the VOCs emission source list by using the VOCs emission source list dynamic inversion method based on four-dimensional variation assimilation provided by the invention through the processing of the four steps A, B, C, D. The variable-resolution parallel WRF-CUACE-4DVar four-dimensional variation assimilation system comprises a forward model WRF-CUACE and a reverse model WRF-CUACE-ADJ; the forward model WRF-CUACE is used for integrating and storing a ground state value and a simulation value, providing the ground state value and the simulation value for the WRF-CUACE-ADJ model, calculating the accompanying sensitivity of the VOCs, and finally solving the cost function and the accompanying sensitivity of the VOCs to obtain an optimized inversion source list.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a dynamic inversion method of a VOCs emission source list based on four-dimensional variation assimilation, which develops a WRF-CUACE-4DVar four-dimensional variation assimilation system based on parallel calculation, and establishes a system for realizing the three-dimensional environment part ground ozone (O) 3 ) The observation operator of the observation data, the cost function defining VOCs, the related accompanying sensitivity operator and the optimization algorithm improve the WRF-CUACE accompanying model v1.0 and the WRF-CUACE-4DVar emission source list dynamic inversion system. The invention realizes near real-time optimized inversion of the VOCs emission source list, solves the problem of difficult correction of the source list caused by sparse current VOCs observation data, improves the hysteresis and uncertainty of the source list, and promotes the O of an air quality model 3 And PM 2.5 And the effect of the prediction and simulation of (a).
Drawings
FIG. 1 is a schematic illustration of a WRF-CUACE-4DVar pollution emission source list dynamic inversion system inverting VOCs;
where J is a cost function, x is a source list, n is the number of iterations, x a For background source list, y m As analog value, y obs For observations, Φ is the forcing term. The diamond is used as a convergence criterion, the difference of the cost functions between two adjacent iterations is less than or equal to 1 percent and can be regarded as convergence, and the source list x of the last step is obtained at the moment n I.e., the optimized inversion source list.
FIG. 2 is a model integration and calculation step of a WRF-CUACE-4DVar pollution emission source inventory dynamic inversion system;
the method is divided into three integration modes of forward simulation, restarting simulation (arrow 1) and reverse simulation (arrow 2), wherein an assimilation window is 24 hours, and t is n Time of integration, X a The method comprises the steps that a background source list is adopted, and X is an inversion source list; after the observation value is calibrated, the simulation is restarted, the reverse simulation is carried out to store and read the ground state value, the simulation value is stored, the VOC is calculated to be the accompanying sensitivity, and meanwhile, the optimization algorithm solves the cost function of the VOCs to carry out assimilation inversion of the pollution source list.
FIG. 3 is a schematic diagram of a call framework and a module structure for reverse integration of the WRF-CUACE companion model v 1.0;
the method comprises a forward WRF-CUACE model and a reverse WRF-CUACE-ADJ model, wherein the WRF-CUACE integral stores a ground state value and a simulation value, and is provided for the WRF-CUACE-ADJ model, and the WRF-CUACE-ADJ model are continuously integrated. The WRF-CUACE comprises a transmission WRF module, a gas CBM-IV module and an aerosol CAM module; the WRF-CUACE-ADJ contains a reverse wind field module, a gas companion CBM-IV-ADJ module and an aerosol companion CAM-ADJ module.
FIG. 4 is an example nesting arrangement, terrain elevation, and survey site distribution of a WRF-CUACE-4DVar pollution emission source list dynamic inversion system in northern China;
the left diagram is a schematic diagram of two layers of areas D1 (east Asia map) and D2 (northern China), and the right diagram is an enlarged display of the inner layer area D2, and white dots represent positions of apparent stations.
FIG. 5 is an optimized inversion effect of the WRF-CUACE-4DVar pollution emission source list dynamic inversion system on VOCs in northern China;
wherein (a) is the difference in units of mol/km between the inverse manifest (INVS) and the original Manifest (MEIC) 2 And/day, (b) is the ratio of the differences in%.
Detailed Description
The invention is further described by way of examples in the following with reference to the accompanying drawings, but in no way limit the scope of the invention.
Different from the conventional pollutants, the observed data of volatile organic compounds VOCs are quite rare, so that a source list is difficult to correct, the VOCs are important precursors for ozone generation, and the air quality model is determined for O 3 Is a simulation effect of (a). With PM in air 2.5 Concentration decrease, O 3 On the contrary, there is an upward trend, so inversion of the emission source list of volatile organic compounds VOCs is not acceptable. The present invention fuses together various known information (including models and observations) according to an "optimal" statistical method, resulting in a process that "best" describes the atmospheric conditions at a given resolution, different data assimilation methods using different criteria to define the so-called "best". And obtaining the atmospheric variable analysis value on the regular grid points according to the meteorological observation data distributed on the irregular observation points. And directly obtaining posterior weight through a variation method, and deducing analysis increment to obtain an emission source list after optimization adjustment. In general, the four-dimensional variation assimilation inversion method utilizes observation data to carry out 'top-down' constraint updating on pollution sources, can provide a rapid check sum updating method for an atmospheric pollution source list, and can also supplement more time-space distribution information for the 'bottom-up' pollution source list.
The invention develops a four-dimensional variation assimilation inversion algorithm of VOCs based on a WRF-CUACE-4DVar source list dynamic inversion system, performs near-real-time optimized inversion on the VOCs emission source list, reduces the hysteresis and uncertainty of the VOCs source list, and improves the O of an air quality model 3 Has important technical advancement and social application value. In particular, the invention proposes to use O 3 Assimilation inversion algorithm for indirectly inverting VOCs emission source by using conventional pollutant O of ecological environment department monitoring station 3 Is defined by the observed value of O 3 And (3) carrying out iterative optimization calculation on the VOCs emission source list by using a cost function through a VOCs sensitivity result calculated by a WRF-CUACE accompanying model v1.0 and a WRF-CUACE-4DVar four-dimensional variation source list dynamic inversion system. The core algorithm and the idea of the invention are described below through the attached drawings and specific implementation steps. The method mainly comprises observation data acquisition, cost function definition, concomitant sensitivity calculation method and optimization iteration method used in an assimilation inversion algorithm.
(1) The WRF-CUACE-4DVar four-dimensional variation assimilation system provided by the invention is used for background fields and observation data.
In the context of the original source inventory, the present invention first constructs a combined product of the MEIC and MIX source inventory (http:// www.meicmodel.org /) as the a priori source inventory. MEIC manual emission source definition in 2017 is adopted in a certain area of China, MIX source list in 2010 is adopted in east Asia, and the MIX source list comprises CH 4 、CO、SO 2 、NOx、NMVOC、NH 3 、PM 10 、PM 2.5 The emissions of BC and OC are strong, with a grid resolution of 0.25 deg. by 0.25 deg.. The VOCs source list in the invention comes from NMVOC, and the background field is the latest statistical data available in public channels.
The ground observation data adopted by the invention come from an environmental monitoring station (MEE) of the ecological environment department of the people's republic of China, comprising PM 10 、PM 2.5 、CO、NO 2 、O 3 And SO 2 Six conventional contaminants (https:// air. Cnemc. Cn: 18007). The invention has been applied in northern areas of China, with up to 245 monitoring stations in the assimilation area, each at more than 50 grids, as shown in FIG. 4. The invention adopts a super observation processing mode to uniformly process the pollutant concentrations of monitoring stations positioned in the same model grid. Finally O at the frequency of hours 3 And the observed value is input into a WRF-CUACE-4DVar pollution source list dynamic inversion system, and constraint correction of the VOCs priori emission source list is carried out.
(2) And constructing a cost function of VOCs in the WRF-CUACE-4DVar assimilation system.
The invention constructs a cost function in a VOCs source list assimilation algorithm based on a WRF-CUACE-4DVar assimilation system. The cost function is a functional for measuring the difference degree between the simulated value of the WRF-CUACE forward model and the observed field at the corresponding moment, namely, the weighted sum of the analysis field and the observed field. In the invention, the WRF-CUACE forward model is used for O 3 Establishing an assimilation algorithm for constraint conditions by using the simulation values and O in the mode 3 Physical and chemical dynamics associated with VOCs to constrain all O's during inversion time period 3 And (5) observing data. Such as three-dimensional wind field transmission process, VOCs and NOx generation O 3 And the like, so that complex physical and chemical relations among different elements are included, and the accompanying sensitivity obtained through calculation of the WRF-CUACE accompanying mode v1.0 is represented. The gas mechanism of WRF-CUACE companion mode v1.0 employs a CBM-IV mechanism (Carbon Bond Mechanism IV), and the aerosol mechanism employs CAM (China Aerosol Model), as shown in fig. 3. The information accompanying the sensitivity includes both the backward trajectory and the chemical relationship between VOCs gas and organic aerosols and the respective contaminants. The cost function of VOCs in the present invention is defined as follows:
wherein y is m O for WRF-CUACE forward model 3 Analog value, y obs For observations at different moments, O in the present invention 3 Observing the concentration, x a Is a VOCs prior source list, x is a VOCs posterior source list,
and->
Respectively is O 3 The error covariance matrixes of the observation field and the background field are diagonal matrixes and gamma r For penalty terms, i.e. weighting coefficients, for adjusting the weights of the proportions of observed and background values, gamma r Is set to 1-100./>
Is the initial bias of the a priori source list, +.>
Is the sum of the simulated bias and the observed error. The assimilation parameters described above vary with the characteristics of the VOCs gas species and the simulated bias of the inversion period.
(3) And (3) calculating the VOCs concomitant sensitivity in the WRF-CUACE-4DVar four-dimensional variation assimilation system.
The accompanying mode is a sensitivity analysis method, the main function is to realize the calculation of variable gradients, and the four-dimensional variation assimilation system is established on the basis of solving the gradient by the accompanying mode to carry out optimization estimation and adjustment on errors. Different from the common forward sensitivity analysis method, the calculation cost of the backward method is not increased along with the increase of the types of pollutants, and the space sensitivity distribution of the cost function on the change of all pollutants along with time can be obtained by the backward integration of the accompanying model only once, which is the greatest advantage of the accompanying model in the pollutant traceability analysis in the invention. The invention develops a dynamic inversion system of a 4DVar pollution source list of VOCs by utilizing the accompanying sensitivity of ozone to the VOCs in a WRF-CUACE accompanying mode v 1.0. The information on the concomitant sensitivity of VOCs includes both the backward trajectory of the gas field and the atmospheric chemical reaction relationship between the various contaminants. The cost function (1) is used for carrying out gradient on the VOCs emission source, and the following formula can be obtained:
wherein,,
gradient of emission source as a cost function; />
For the sake of forcing assimilation, i.e. input, it can be noted +.>
K T Is an accompanying operator of the chemical reaction of VOCs; the 4DVar assimilation of VOCs is essentially a minimum process of finding a cost function (1), equivalent to solving the cost function to zero in the gradient (2) of the emissions source.
WRF-CUACE-4DVar four-dimensional variation method and WRF-CUACE companion mode v1.0 companion operator (K T ) In connection, the companion operator, also referred to as VOCs companion sensitivity, represents the degree of response of the contaminant concentration to the relevant emission variable, and the sensitivity provided by the companion mode directly affects the inversion performance of VOCs. VOCs companion operator K T The discrete computation process of (2) is as follows, and the code of the forward WRF-CUACE model is expressed as (3):
y (t) ＝Aa·Cc·Xx(y (t-1) ) (3)
wherein y is (t) The basic state value of the integration time of the WRF-CUACE model is; y is (t-1) The last integrated analog value; aa is a advection item, which is the transmission process of VOCs in air; cc is a chemical field term indicating that VOCs participate in the production of O 3 Is chemically reacted with the atmosphere; xx is an emission source term representing the intensity of VOCs released into the air; t represents a time step of 5 minutes. Respectively deriving Aa, cc and Xx operators in the formula (3) to obtain a tangential model (TLM), and writing the tangential model into a matrix form, namely a Jacobian operator K, which can be expressed as the formula (4):
wherein the method comprises the steps of
The tangential model (TLM) is transposed to obtain the code ((6) of the accompanying model ADJ, namely the accompanying sensitivity operator K of the VOCs T . Accompanying sensitivity operator K of VOCs T I.e. the transpose of the jacobian K.
(4) VOCs inversion algorithm operation flow based on WRF-CUACE-4DVar four-dimensional variation system
In specific implementation, the WRF-CUACE-4DVar pollution source list dynamic inversion system passes through O 3 And (3) carrying out constraint of observation data and calculation of VOCs concomitant sensitivity, and then carrying out continuous iterative correction through an L-BFGS quasi-Newton algorithm to obtain an optimal estimated inversion emission source list so as to approximate the real situation, wherein the assimilation flow of the VOCs inversion algorithm is shown in a figure 1. First, an initial source list, namely a VOCs source list x to be assimilated is input 0 The forward integration atmospheric chemical model stores the ground state value and the analog value at the corresponding moment; calculating an evaluation cost function J; inverse integral WRF-CUACE-ADJ model, (input observations, ground state values and simulations, calculate the gradient of cost function with respect to VOCs emissions source)
Solving a source list x of the next step by using a nonlinear optimization algorithm L-BFGS quasi-Newton algorithm 1 The method comprises the steps of carrying out a first treatment on the surface of the Updated inverted VOCs source list x 1 The forward model is input for re-simulation, and a new round of assimilation inversion is started. When the difference of the cost function between two adjacent iterations is less than or equal to 1%, the convergence can be considered, and the last step source list x is obtained N I.e., the optimized inversion source list. In the actual calculation of the cost function, loop iteration typically takes seven eight steps to more than ten steps. The assimilation inversion calculation flow for each round is shown in FIG. 2. The blue rectangular box represents a single assimilation (DA) window set for 1 day (24 hours) to get a daily resolution pollution source list, resolution of 30X 30km. Three types of simulations are included: forward simulation, restart simulation, and reverse simulation. First, a WRF-CUACE forward simulation of spin-up was performed for 10 days, at t n The moment forms a daily restart file. Next, after interpolation correction is performed on the restart initiation field by the observation data, a restart simulation is performed at Xn, which is indicated by an arrow 1. In the restart simulation, the ground state value and the simulation value are stored once every time step for driving the operation of the corresponding time-associated mode. Third, reverse WRF-CUACE-ADJ integration, indicated by arrow 2, is performed. Finally, the L-BFGS quasi-Newton optimization algorithm is applied to minimize the cost function J by continuous 1+2 forward and reverse iterative loops. When +.>
When convergence is reached (less than 1%), the best estimated list of VOCs pollution sources for the assimilation window is obtained.
FIG. 4 is a simulation area of a WRF-CUACE-4DVar pollution source list dynamic inversion system, wherein the D1 area covers the middle east of China, the resolution is 90km, and the method is sufficient for considering the transportation of pollutants outside the North China and reducing the uncertainty of boundary conditions. The D2 area is an inner layer area operated by an assimilation inversion algorithm, and the resolution is 30km. The vertical direction comprises 32 layers, and the highest air pressure is 100hPa. Taking the ozone pollution process (23 to 26 days) of Jinjinj region once from 21 to 26 days in 2021 summer, the assimilation parameters adopted in the inversion experiment of VOCs are
γ r =100. The emissions of VOCs after iterative optimization of the WRF-CUACE-4DVar contaminated source list dynamic inversion system are reduced by 30-60% compared with the original source list, as shown in FIG. 5. The O of all measuring stations of WRF-CUACE is obviously improved by using the inverted VOCs source list 3 At the simulation level, the correlation coefficient is increased from 0.7 to 0.8, and the root mean square error is reduced from 44.4 to 37.3 mug/m 3 。
It should be noted that the purpose of the disclosed embodiments is to aid further understanding of the present invention, but those skilled in the art will appreciate that: various alternatives and modifications are possible without departing from the scope of the invention and the appended claims. Therefore, the invention should not be limited to the disclosed embodiments, but rather the scope of the invention is defined by the appended claims.
Claims (5)
1. A dynamic inversion method of VOCs emission source list based on four-dimensional variation assimilation obtains atmospheric variable analysis values on regular grid points according to meteorological observation data distributed on irregular observation points, obtains posterior weight and analysis increment through a variation method, and obtains an emission source list after optimization and adjustment; the method comprises the following steps:
A. constructing an observation operator and a VOCs background field, wherein the VOCs background field is an original emission source list; acquiring a ground state value and an analog value of each VOCs gas and organic aerosol in the emission source list in a corresponding chemical process of each time step; the implementation method comprises the following steps:
A1. constructing an observation operator, summing up original VOCs emission source lists of the sub-departments, and redistributing original VOCs gas so as to be suitable for a gas CBM-IV chemical parameterization scheme; interpolation is carried out again, so that the obtained VOCs emission source list is matched with the set grid points, and a processed emission source list x is obtained 0 ；
A2. Assimilation inversion is carried out through a forward integration four-dimensional variation assimilation emission source list dynamic inversion WRF-CUACE model system, and a processed emission source list x is used 0 Obtaining a ground state value and an analog value of each VOCs gas and organic aerosol in the emission source list in a corresponding chemical process of each time step, and storing the ground state value and the analog value;
B. defining and constructing VOCs cost functions;
constructing a VOCs cost function, and defining a background error covariance matrix, an observation error covariance matrix and a weight coefficient in the VOCs cost function; the VOCs cost function is defined as equation (1):
wherein the cost function of J (x) VOCs; y is m The simulation value of the WRF-CUACE forward model is obtained; y is obs The observation data are the observation data at different moments; t is the transpose of the matrix; x is x a A prior emission source list for VOCs; x is VOCs posterior emission source list;
and->
Respectively is O 3 The observation error covariance matrix and the background error covariance matrix are diagonal matrices; gamma ray r The weight coefficient is used for adjusting the weight of the proportion of the observed value and the background value; />
Is the initial deviation of the prior emissions source list, +.>
Is the sum of the simulated deviation and the observed error;
C. solving a gradient based on the VOCs cost function, and calculating to obtain the VOCs concomitant sensitivity; comprising the following steps:
C1. acquiring ground observation data, preprocessing the ground observation data, interpolating to corresponding grid points by using a nearest neighbor interpolation method according to the longitude and latitude of a site, and obtaining observation values of the corresponding grid points;
C2. taking the difference and the ground state value between the observed value of the corresponding grid point obtained by C1 and the analog value of the WRF-CUACE model as input, and reversely integrating the WRF-CUACE-ADJ model to obtain the accompanying sensitivity information of the VOCs; the accompanying sensitivity information of the VOCs comprises backward track of the gas image field and the atmospheric chemical reaction relation between each VOCs gas and organic aerosol pollutant;
C21. the cost function is subjected to strong gradient on the VOCs pollution source, and the following formula is obtained:
wherein,,
gradient of cost function to pollution source; />
Forced, i.e., input, for concomitant assimilation; k (K) T The concomitant sensitivity of VOCs, also known as VOCs, is an indicator of the extent to which the concentration of VOCs gas and organic aerosol contaminants respond to relevant emission variables;
searching to obtain the minimum value of the cost function through the four-dimensional variation assimilation process of the VOCs, namely solving the gradient of the cost function to the pollution source to be zero;
c22.vocs companion operator K T The discrete calculation process of (2) is as follows:
the forward WRF-CUACE model is expressed as formula (3):
y (t) ＝Aa·Cc·Xx(y (t-1) ) (3)
wherein y is (t) The basic state value of the integration time of the WRF-CUACE model is; y is (t-1) The last integrated analog value; aa is a advection item, which is the transmission process of VOCs in air; cc is a chemical field term indicating that VOCs participate in the production of O 3 Is chemically reacted with the atmosphere; xx is an emission source term representing the intensity of VOCs released into the air; t represents a time step;
respectively deriving Aa, cc and Xx operators in the formula (3) to obtain a tangential model, and writing the tangential model into a matrix form, namely a Jacobian operator K, wherein the Jacobian operator K is expressed as the formula (4):
wherein:
transpose the tangential model to obtain the accompanying sensitivity operator K of VOCs T ：
Accompanying sensitivity operator K of VOCs T I.e. Jacobian KTranspose matrix;
D. and solving the cost function and the accompanying sensitivity of the optimal estimated VOCs to obtain an optimized inversion source list. The implementation method comprises the following steps:
D1. the L-BFGS quasi-Newton algorithm is adopted, the gradient of the cost function is calculated and inverted according to the accompanying sensitivity of the VOCs obtained by C2, and the emission source list x is obtained 1 ；
D2. Setting a threshold value, and carrying out optimization judgment according to the cost function difference proportion between two adjacent iterations;
if the cost function difference ratio between two adjacent iterations is greater than the threshold, turning to step A2, and updating the inversion emission source list x 1 Inputting a forward model WRF-CUACE to perform integration simulation again, and starting a new round of assimilation inversion;
if the cost function difference ratio between two adjacent iterations is smaller than or equal to a threshold value, the emission source list obtained in the last iteration is the optimized inversion source list;
through the steps, the dynamic inversion of the VOCs emission source list based on four-dimensional variation assimilation is realized.
2. The dynamic inversion method of VOCs emission source list based on four-dimensional variation assimilation as claimed in claim 1, wherein the cost function is constructed for VOCs species, wherein the simulation value of WRF-CUACE forward model is specifically O 3 To quantize the gain.
3. The dynamic inversion method for the VOCs emission source list based on four-dimensional variation assimilation according to claim 2, wherein the observation data at different moments is specifically O 3 The concentration was observed.
4. The dynamic inversion method of the VOCs emissions source list based on four-dimensional variation assimilation of claim 1, wherein the weight coefficient is set to 1-100.
5. The dynamic inversion method of VOCs emissions source list based on four-dimensional variation assimilation according to claim 1, wherein the threshold value set in step D2 is 1%.
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| CN117217027A (en) * | 2023-11-02 | 2023-12-12 | 中国人民解放军国防科技大学 | Pollutant point source profile emission estimation method and device based on four-dimensional variation assimilation |
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| CN116739224A (en) * | 2023-08-14 | 2023-09-12 | 中科三清科技有限公司 | Method, apparatus and storage medium for determining contribution concentration of heavy point pollution source |
| CN116739224B (en) * | 2023-08-14 | 2023-11-03 | 中科三清科技有限公司 | Method, apparatus and storage medium for determining contribution concentration of heavy point pollution source |
| CN117217027A (en) * | 2023-11-02 | 2023-12-12 | 中国人民解放军国防科技大学 | Pollutant point source profile emission estimation method and device based on four-dimensional variation assimilation |
| CN117217027B (en) * | 2023-11-02 | 2024-01-30 | 中国人民解放军国防科技大学 | Pollutant point source profile emission estimation method and device based on four-dimensional variation assimilation |
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