Method for observing and inverting regional methane emission based on satellite column concentration

Abstract

The invention discloses a method for observing and inverting regional methane emission based on satellite column concentration, which belongs to the technical field of atmospheric remote sensing monitoring, and comprises the following steps: step 1, modifying a CMAQ regional atmospheric chemical transmission model, adding CH4 species as trace gas to simulate an atmospheric transmission process, modifying an initial condition module and a boundary condition module in CMAQ, and establishing a CMAQ regional atmospheric chemical transmission model; the CH4 can be subjected to set simulation; 2, constructing a high-resolution nested emission inversion system based on a CMAQ region atmospheric chemical transmission model and an EnKF inversion algorithm; according to the high-resolution regional methane emission inversion system constructed through an innovative algorithm and program, satellite column concentration data can be directly assimilated, the uncertainty of CH4 emission estimation is remarkably reduced, and the CH4 simulation precision is improved. Meanwhile, the system has remarkable advantages in the aspects of CH4 emission monitoring and evaluation, and has important application value for realizing'carbon neutralization '.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric remote sensing monitoring, and particularly to a method for retrieving regional methane emissions based on satellite column concentration observations. Background Art

[0002] Methane (CH4) is the second largest greenhouse gas in the world, contributing 25% to global warming. Fossil fuel extraction and combustion, as well as land use change, are the main reasons for its growth.

[0003] Currently, multiple satellites have been launched globally to monitor changes in CH4 concentration. However, on the one hand, satellites cannot directly quantify the CH4 emission intensity. On the other hand, quantifying emissions at the regional scale faces challenges in improving resolution and is vulnerable to CH4 concentration errors at regional boundaries. For this reason, a method for retrieving regional methane emissions based on satellite column concentration observations is proposed.

[0004] The above information disclosed in this background art is only used to increase the understanding of the background art of the present invention. Therefore, it may include prior art that is not known to those of ordinary skill in the art. Summary of the Invention

[0005] The present invention aims to at least solve one of the technical problems existing in the prior art that although multiple satellites have been launched globally to monitor CH4 concentration, there are problems such as the inability to directly quantify the emission intensity and insufficient resolution and boundary errors at the regional scale. For this reason, an object of the present invention is to propose a method for retrieving regional methane emissions based on satellite column concentration observations, which can realize the monitoring of ground methane emission changes by satellites at the regional scale while considering the influence of CH4 concentration errors at regional boundaries.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for retrieving regional methane emissions based on satellite column concentration observations, comprising the following steps:

[0008] Step 1, modify the CMAQ regional atmospheric chemical transport model, add the CH4 species as a trace gas to simulate the atmospheric transport process, and modify the initial condition module and boundary condition module in CMAQ so that it can perform ensemble simulations on CH4;

[0009] Step 2, construct a high-resolution nested emission inversion system based on the CMAQ regional atmospheric chemical transport model and the EnKF inversion algorithm;

[0010] Step 3, use satellite CH4 column concentration data to correct the global CH4 concentration field simulated by the WACCM model, provide an optimized initial field and boundary field for the CMAQ regional simulation, and reduce the influence of boundary errors on emission inversion;

[0011] Step 4: Obtain the prior ensemble samples by Gaussian random perturbation of the prior CH4 emissions, input them into the CMAQ atmospheric model for CH4 concentration ensemble simulation, further match and assimilate the satellite column concentration data at the corresponding locations and times to obtain the simulated CH4 column concentration ensemble, and input the above data and their respective error information into the inversion system to obtain the optimized CH4 emissions;

[0012] Step 5: Adopt a two-step optimization strategy, re-input the optimized emissions into the CMAQ model for forward simulation to generate the boundary field of the inner high-resolution region of the current assimilation window and the optimized initial field of the next assimilation window. At the same time, use the optimized emissions as the prior emissions for the next window;

[0013] Step 6: Based on the optimized boundary field in Step 5, adopt a nested inversion strategy, repeat Step 5 for CH4 emission inversion in the inner high-resolution region, and finally, repeat Steps 5 and 6 for daily cycle assimilation.

[0014] As a further optimization scheme of the present invention, in Step 2, the construction of the EnKF flux inversion system includes:

[0015] Step 201: The EnKF represents its uncertainty by perturbing the prior emissions and generates the ensemble samples of the prior emissions using the Monte Carlo method;

[0016] Step 202: Calculate the background error covariance, and the formula is:

[0017]

[0018] where, represents the ensemble sample average;

[0019] Step 203: Perform quality control on the satellite observation data and calculate the observation error;

[0020] Step 204: Assimilation window and localization scheme. The assimilation window is set to 1 day, the ensemble size is set to 50, the Gaspari-Cohn function is used as the localization function, the localization scale is set to 300 km, and only the emission grids that are significantly correlated with the concentration (p>0.05) are optimized;

[0021] Step 205: After obtaining the ensemble of the state vectors, CMAQ performs ensemble simulation so as to transmit these errors through each ensemble sample of the state vector in the model.

[0022] As a further optimization scheme of the present invention, in Step 201, the formula used to generate the ensemble samples of the prior emissions is:

[0023]

[0024] Among them, b represents the background field, i is the identifier of the perturbed sample, N is the set size, δX represents the random perturbed sample, and the sample is added to the prior emissions to generate the ensemble samples input into CMAQ The perturbation of the prior emissions in each grid is extracted according to a Gaussian distribution with a mean of zero and a standard deviation of the emission uncertainty.

[0025] As a further optimization scheme of the present invention, in step 203, the satellite observation data is processed as follows:

[0026] Step 2030: Only the data with qa_value > 0.5 is assimilated;

[0027] Step 2031: Resample the data to the grid resolution of the regional-scale model and perform weighted processing on multiple satellite observations under the same grid;

[0028] Step 2032: The observation error is estimated according to the given accuracy in the satellite data file.

[0029] As a further optimization scheme of the present invention, in step 205, combined with the observation vector y, the optimized emissions are updated by minimizing the analysis variance:

[0030]

[0031] Among them, H is the observation operator, which transforms the model state variables from the model space to the observation space, and can directly assimilate the satellite data. represents the mean value of the emission ensemble samples, and P b is the background error covariance matrix estimated based on the emission ensemble, and the observation error covariance R determines the relative contribution to the final analysis field. P b H T contains the response of the uncertainty of the simulated concentration to the emission uncertainty. In each assimilation window, As the optimal estimate of CH4 emissions, the observation operator serves as the conversion interface between the assimilation system and the satellite data;

[0032] The simulated column concentration data is calculated by performing pressure-weighted averaging on the CH4 concentration simulated by the CMAQ model in layers and interpolating it to the vertical stratification of the satellite.

[0033]

[0034] Among them, N is the number of satellite retrieval layers, and a i is the column average kernel of the satellite layer i, and VCH4 aprioriRepresents the prior CH4 column concentration, XCH4 ref,i Represents the mixing ratio of the i-th layer of the satellite corresponding to the model simulation, VAIR dry,ref And ΔVAIR dry,ref Respectively represent the total and stratified dry air columns of the simulation.

[0035] As a further optimization scheme of the present invention, in step 3, the specific steps of the initial field and boundary field for optimization are as follows:

[0036] Step 301: First, convert the WACCM stratified simulation concentration into a column concentration comparable to satellite data according to the above formula;

[0037] There are usually missing values in the quality-controlled satellite data in space and time. To calculate the simulation deviation of the grid where the missing value is located, the simulated column concentration and the observed column concentration calculated in step 301 are smoothed in the radial, latitudinal, and time scales, and the latitudinal average value and the average deviation between the simulation and the observation are further calculated. Then, linear interpolation is used to fill the deviation missing values in the radial direction. For the deviation assumption of the grid where the missing value is located in the same latitude, it is assumed to be the same as the average deviation of the non-missing values in this latitude. Finally, the original CH4 concentration simulated by WACCM is corrected for deviation grid by grid, achieving the purpose of providing an optimized initial and boundary field for regional-scale inversion.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] The high-resolution regional methane emission inversion system constructed by the innovative algorithm and program of the present invention can directly assimilate satellite column concentration data, significantly reduce the uncertainty of CH4 emission estimation, and improve the accuracy of CH4 simulation. At the same time, the system has significant advantages in CH4 emission monitoring and assessment, and has important application value for achieving "carbon neutrality".

[0040] The above summary is only for the purpose of the specification and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 Is the flowchart of the CH4 emission inversion system of the present invention;

[0042] Figure 2 Is the change diagram of CH4 emission and column concentration before and after optimization of the nested inversion in the Shanxi region (9 km resolution) in the experimental example of the present invention;

[0043] Figure 3This is a comparison chart of prior and posterior simulations with the observed time series at five ground stations in the experimental examples of the present invention. Detailed implementation manners

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment

[0046] The present invention provides a technical solution: as Figure 1 shown, a method for retrieving regional methane emissions based on satellite column concentration observations includes the following steps:

[0047] First, a CH4 emission retrieval system is constructed based on the regional atmospheric chemical transport model CMAQ and EnKF. The CMAQ model adopts a highly modular design, can set different resolutions as needed, and can be driven by different meteorological data sets and emission inventories for simulation.

[0048] During the operation of the system, a nested inversion strategy is adopted. The resolution of the Chinese region is set to 27 km, the number of grids is 219×159, the inner nested region covers Shanxi and surrounding provinces, the resolution is set to 9 km, and the number of grids is 195×174. The vertical direction is set to 50 layers, from the ground to 100 Pa at the top of the atmosphere. The meteorological initial and boundary fields adopt the NCEP FNL global meteorological reanalysis data.

[0049] The CH4 boundary field outside the Chinese region adopts the optimized global WACCM boundary field of the present technology, and the boundary field of the inner region directly comes from the optimized CH4 concentration field in the Chinese region, thereby reducing the influence of boundary condition errors on the high-resolution emission inversion in the inner region. Except for the first window, the CH4 initial fields of other windows all come from the concentration fields generated by the posterior emissions in the previous window.

[0050] In this embodiment, prior flux ensemble samples are obtained by perturbation through the formula in step 201. Among them, the prior anthropogenic source CH4 emissions adopt the EDGAR v8 inventory with a resolution of 0.1°, the wetland emissions adopt the WetCHARTs v1.3 data set with a resolution of 0.5°, the wildfire emissions adopt the GFED v4.1s data set with a resolution of 0.25°, and the soil uptake is simulated according to the MeMo model with a resolution of 1°. All emission data are interpolated into the model matching resolution.

[0051] After obtaining the prior emission ensemble, it is input into the CMAQ model for ensemble simulation to obtain the response relationship between the simulated ensemble concentration and the ensemble emission. Based on the calculated background error covariance matrix, the simulated and observed CH4 column concentrations, and according to the formula in step 205, the "two-step optimization strategy" is adopted to assimilate and invert the CH4 emissions in the Chinese (outer layer) and the surrounding areas of Shanxi (inner layer) regions respectively.

[0052] Experimental example

[0053] The assimilation system runs from December 2021 to January 2022, and nested inversion of CH4 emissions in the Chinese and the surrounding areas of Shanxi is carried out every day under the same window, with December as the model initialization time. The prior uncertainty is set to 40%. The assimilated satellite observation data is the TROPOMI CH4 column concentration product. After emission optimization, the prior and posterior emissions are respectively re-input into the CMAQ model for CH4 concentration simulation, and the assimilation effect is evaluated by comparing with satellite and ground observations.

[0054] Changes in CH4 emissions and column concentrations before and after optimization in the Chinese region (27 km resolution) Compared with the prior emissions, the posterior emissions increase in the northeast and northwest regions of China, while in most parts of the central and eastern regions of China, especially in the North China Plain, the Yangtze River Delta and the Pearl River Delta regions, the emissions decrease significantly.

[0055] The total CH4 emissions in January 2022 were 3.7 Tg, a decrease of 24.6% compared with the EDGAR inventory. Compared with the TROPOMI satellite observations, the CH4 column concentrations simulated by the prior emissions are significantly overestimated by more than 40 ppb in some parts of the central and eastern regions, while the column concentrations simulated by the posterior emissions are more consistent with the satellite observation results, significantly reducing the overestimation in the central and eastern regions.

[0056] Figure 2 Show the changes in CH4 emissions and column concentrations before and after optimization in the nested inner layer of Shanxi region (9 km resolution). In the figure, (a) prior emissions, (b) posterior emissions and (c) the difference between the two, (d) TROPOMI satellite observation concentration, (e) concentration simulated by prior emissions and (f) concentration simulated by posterior emissions.

[0057] Compared with the prior emissions, the emissions in almost the entire Shanxi Province decrease significantly. However, as the main coal-producing areas in Shanxi Province, Jincheng and Yangquan have increased emissions, indicating that there may be uncounted omission sources in the EDGAR emissions. Statistically, the CH4 emissions in the EDGAR inventory of Shanxi Province in January were 756 thousand tons, and after optimization, it was 506 thousand tons, a decrease of 33.1%. After optimization, the simulated CH4 column concentration decreased from 1924.4 ppb to 1896.4 ppb, and the posterior simulation is more consistent with the TROPOMI observation result (1894.7 ppb), and the average deviation reduction reaches 94%.

[0058] Figure 3 It shows the comparison of the time series of ground CH4 concentrations simulated by prior and posterior emissions with the observed concentrations at five sites. Except for the RYO site located in the simulated boundary area, the performance of the posterior simulations at the other four sites has improved. Compared with the AMY, ULD, GSN, and YON sites, the average biases of the posterior simulations have decreased by 63.1%, 81.4%, 94.4%, and 66.4% respectively. This shows that the uncertainty of the emission inventory optimized by this system has been significantly reduced, and the CH4 simulation can be effectively improved.

[0059] Parts not involved in the present invention are the same as or can be implemented by the prior art. Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for inverting regional methane emissions based on satellite column concentration observations, characterized in that: The following steps are involved: Step 1: Modify the CMAQ regional atmospheric chemical transport model, add CH4 species as a trace gas to simulate the atmospheric transport process, and modify the initial condition module and boundary condition module in CMAQ to enable ensemble simulation of CH4; Step 2: Based on the CMAQ regional atmospheric chemical transport model and the EnKF inversion algorithm, a high-resolution nested emission inversion system is constructed; Step 3: Use satellite CH4 column concentration data to correct the global CH4 concentration field simulated by the WACCM model, provide optimized initial and boundary fields for CMAQ regional simulation, and reduce the impact of boundary errors on emission inversion; Step 4: Obtain a priori ensemble samples by Gaussian random perturbation of prior CH4 emissions, input them into the CMAQ atmospheric model for CH4 concentration ensemble simulation, further match and assimilate the satellite column concentration data of the corresponding position and time to obtain the simulated CH4 column concentration ensemble, combine the above data and their respective error information, and input them into the inversion system to obtain optimized CH4 emissions; Step 5: Using a two-step optimization strategy, re-input the optimized emissions into the CMAQ model for forward simulation to generate the boundary field of the inner high-resolution region of the current assimilation window and the optimized initial field of the next assimilation window. At the same time, the optimized emissions are used as the prior emissions of the next window. Step 6: Based on the optimized boundary field in step 5, a nested inversion strategy is adopted to repeat step 5 for the inner high-resolution regional CH4 emission inversion. Finally, steps 5 and 6 are repeated for the daily cycle assimilation.

2. The method for inverting regional methane emissions based on satellite column concentration observations according to claim 1, characterized in that: In step 2, the construction of the EnKF flux inversion system includes: Step 201, EnKF generates a set sample of prior emissions by perturbing the prior emissions to represent their uncertainty, using the Monte Carlo method; Step 202: Calculate the background error covariance, the formula is: in, represents the average of the sample set; Step 203: Perform quality control on satellite observation data and calculate observation errors; Step 204, assimilation window and localization scheme, the assimilation window is set to 1 day, the ensemble size is set to 50, the Gaspari-Cohn function is used as the localization function, the localization scale is set to 300 km, and only the emission grids with significant correlation between ensemble emission and concentration (p>0.05) are optimized; Step 205: After obtaining the ensemble of state vectors, CMAQ performs ensemble simulation to propagate these errors through each ensemble sample of the state vector in the model.

3. The method for inverting regional methane emissions based on satellite column concentration observations according to claim 2, characterized in that: In step 201, the formula used to generate the set sample of prior emissions is: Where b represents the background field, i is the identifier of the perturbed sample, N is the set size, δX represents a random perturbed sample, and the sample is added to the prior emission to generate the ensemble samples that are input to CMAQ The perturbation of the a priori emission in each grid is extracted according to a Gaussian distribution with mean zero and standard deviation equal to the emission uncertainty.

4. The method for inverting regional methane emissions based on satellite column concentration observations according to claim 2, characterized in that: In step 203, the satellite observation data is processed as follows: Step 2030, only the data with qa_value>0.5 are assimilated; Step 2031, resampling the data to the regional scale model grid resolution, and performing weighted processing on multiple satellite observations in the same grid; Step 2032: The observation error is estimated based on the accuracy given by the satellite data file.

5. The method for inverting regional methane emissions based on satellite column concentration observations according to claim 2, characterized in that: In step 205, the optimized emission is combined with the observation vector y. Update by minimizing the analytical variance: Among them, H is the observation operator, which converts the model state variables from the model space to the observation space to realize the direct assimilation of satellite data. represents the average value of the emission ensemble sample, P b The background error covariance matrix based on the emission ensemble estimate and the observation error covariance R determine the relative contribution to the final analysis field. b H T Contains the response of the uncertainty in the simulated concentrations to the uncertainty in the emissions, in each assimilation window, As the optimal estimate of CH4 emissions, the observation operator serves as the conversion interface between the assimilation system and satellite data; The simulated column concentration data is calculated by weighting the CH4 concentration simulated by the CMAQ model layer by layer and interpolating it to the vertical layer of the satellite. Where N is the number of satellite retrieval layers, a i is the column average kernel of satellite layer i, VCH4 apriori represents the prior CH4 column concentration, XCH4 ref,i represents the mixing ratio of the satellite layer i corresponding to the model simulation, VAIR dry,ref and ΔVAIR dry,ref Represent the simulated total and layered dry air columns, respectively.

6. The method for inverting regional methane emissions based on satellite column concentration observations according to claim 1, characterized in that: In step 3, the specific steps for optimizing the initial field and boundary field are: Step 301: First, the WACCM stratified simulation concentration is converted into a column concentration comparable to the satellite data according to the formula in claim 5; Step 302: The satellite data after quality control has missing values ​​in time and space. In order to calculate the simulation deviation of the grid where the missing value is located, the simulated column concentration calculated in step 301 and the observed column concentration are smoothed in radial, latitudinal and time scales, and the latitudinal average value and the average deviation of simulation and observation are further calculated. Then, linear interpolation is used to fill the missing values ​​of radial deviation. The deviation of the grid where the missing value is located in the same latitudinal direction is assumed to be consistent with the average deviation of the non-missing value in the latitudinal direction. Finally, the original CH4 concentration simulated by WACCM is corrected for grid-by-grid deviation, so as to provide optimized initial and boundary fields for regional scale inversion.

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