Method and computer program product for establishing carbon dioxide emission flux inversion model

Abstract

The content of the method and computer program product of the present application is a nested carbon dioxide flux inversion model that combines satellite observation of carbon dioxide and existing emission manifests for carbon dioxide carbon flux inversion. According to the method and the computer program product, the global model with the low resolution ratio and the regional model with the high resolution ratio are included, the global model can provide initial conditions and boundary conditions for the regional model, and therefore the nested carbon flux inversion model is formed. According to the method, the high-resolution near-real-time estimated carbon dioxide emission flux of the large bay area of China can be obtained, and the estimation can be provided for an environment management mechanism of the government, a data supplier of a carbon trading system and the industry of which products are influenced by carbon tax and carbon quota.

Description

Technical Field

[0001] The present application relates to the field of atmospheric science and technology, and more specifically, the present application aims to provide a method for quantifying carbon dioxide sources and sinks covering land and oceans and a corresponding computer program product. Background Art

[0002] Global warming associated with the increase of greenhouse gases has become the biggest threat to the earth's ecosystem and human life, and carbon dioxide (CO2) and methane (CH4) are two trace gases that contribute the most. The inventive concept of the present application focuses mainly on CO2 emissions. A comprehensive and accurate understanding of the sources and sinks of gas emissions is crucial to the development of effective climate change mitigation measures. The GDP of the Greater Bay Area of ​​China is close to US$2 trillion in 2022, and if it is regarded as an independent economy, it will become the ninth largest economy in the world. Therefore, accurate estimation of the CO2 and CH4 emission fluxes in the Greater Bay Area of ​​China is crucial to determining China's path to achieve carbon neutrality by 2060. However, due to limited measurements and inconsistent conversion methods, carbon emission data are quite scattered and lack consistency. CO2 or CH4 emission fluxes are referred to as carbon emissions hereinafter, and the inversion of their emission fluxes is referred to as carbon inversion.

[0003] Algorithms for inverting emission fluxes from atmospheric CO2 and CH4 concentrations using global chemical transport models for carbon sources and sinks (the most commonly used of which is GEOS-Chem, see https: / / geos-chem.seas.harvard.edu / ) combined with Bayesian inversion methods are common, as described in many published studies. For example, reference 3 published by Liu et al. in 2014; reference 2 published by Kunik et al. in 2019; and reference 5 published by Shen et al. in 2023. Inversion models include the GEOS-Chem adjoint model of the Jet Propulsion Laboratory (JPL), the linear Lagrangian backward trajectory model, and the Integrated Methane Inversion model developed by Harvard University. Such models have been widely used in academia to simulate global carbon fluxes and perform limited flux inversions for regional cases, and various error contributions have also been discussed in depth. However, such carbon inversion models have not been widely used to solve real-world problems outside of academic discussions.

[0004] The regional model WRF-Chem is integrated with the chemical modeling component in the Weather Research and Forecasting (WRF) model to achieve coupling between meteorology and atmospheric chemistry, see Grell et al., 2005 in the following reference 1. There are very limited research results on carbon inversion based on the WRF-Chem model, see Kunik et al., 2019 in reference 2.

[0005] Prior art references cited in this application include:

[0006] Reference 1: Grell, GA, SEPeckham, R. Schmitz, SAM McKeen, G. Frost, WC Skamarock and B. Eder, 2005, "Fully coupled on-line chemistry in the WRF model", Atmos. Environ., 39, 6957-6975.

[0007] Reference 2: Kunik, L et al., 2019, “Bayesian Inverse Estimation of Urban CO2 Emissions: Results from Synthetic Data Simulations for Salt Lake City, Utah”, Elem Sci Anth, 7: 36, DOI: https: / / doi.org / 10.1525 / elementa.375.

[0008] Reference 3: Liu, J., Bowman, KW, Lee, M., Henze, DK, Bousserez, N., Brix, H., Collatz, GJ, Menemenlis, D., Ott, L., Pawson, S., Jones, D. and Nassar, R., 2014, “Carbon Monitoring System Flux Estimation and Attribution: Impact of ACOS-GOSAT XCO2 Sampling on Inferred Terrestrial Biosphere Sources and Sinks”, Tellus B: Chemical and Physical Meteorology, 66(1), pp. 22486, DOI: https: / / doi.org / 10.3402 / tellusb.v66.22486 .

[0009] Reference 4: Pickett-Heaps, CA et al., 2011, “Validation of atmospheric CO2 inversions using vertical profile measurements: Analysis of four independent inversion models,” J. Geophys. Res., 116, D12305, doi: 10.1029 / 2010JD014887.

[0010] Reference 5: Shen, L., Jacob, DJ, Gautam, R. et al., “National quantification of methane emissions from fuel extraction using high-resolution satellite observation inversion,” Nat Commun 14, 4948 (2023), https: / / doi.org / 10.1038 / s41467-023-40671-6 .

[0011] The entire contents of the above references 1-5 are incorporated herein by reference. From the above description, it can be understood that existing global inversion models have problems such as coarse spatial resolution and incomplete / inaccurate emission inventories, especially at the regional level. Summary of the invention

[0012] In order to apply top-down flux inversion technology to areas other than academic research, such as carbon monitoring and other important areas closely related to human society, the computer program product of the present application includes establishing a nested domain carbon flux inversion model, particularly a computer program product for establishing a nested CO2 emission flux inversion model using the GEOS-Chem model and the WRF-Chem model for the CO2 source-sink combination of the Greater Bay Area (GBA) of China.

[0013] The inversion model of the present invention is based on the coordinated use of satellite and ground-based CO2 databases and the established forward and reverse numerical models of emission flux, effectively combining the latest satellite observations of CO2 and existing emission inventories. The inversion model includes a coarser (or lower) resolution global model and a finer (or higher) resolution regional model, and the global model provides initial conditions and boundary conditions for the regional model to form a nested CO2 emission flux inversion model.

[0014] In a first aspect, the present invention provides a computer program product, comprising computer executable code, which is used to establish a CO2 emission flux inversion model when executed by a computer, the CO2 emission flux inversion model comprising: a first carbon inversion module, the first carbon inversion module performing global and low-resolution carbon dioxide inversion based on the GEOS-Chem model, the first carbon inversion module comprising a first top-down CO2 emission flux inversion module for obtaining a two-dimensional distribution of CO2 emission flux at a low resolution of 25-50 kilometers, and a second carbon inversion module, the second carbon inversion module performing regional and high-resolution carbon dioxide inversion based on the WRF-Chem model, the second carbon inversion module comprising a second top-down CO2 emission flux inversion module for obtaining a two-dimensional distribution of CO2 emission flux at a high resolution of 0.1-5 kilometers, wherein the first carbon inversion module and the second carbon inversion module are configured as numerical models for CO2 emission flux inversion that are regionally nested within the global.

[0015] In the computer program product, the first top-down CO2 emission flux inversion module includes the following submodules: 1) a flux inventory module; 2) a GEOS-Chem module, wherein the GEOS-Chem is a global chemical transport model; 3) a reanalysis global meteorological data input module; 4) an atmospheric CO2 bottom-up emission flux inventory integration module required for the CO2 emission flux inversion model; 5) an integration module for CO2 concentration fields measured by multi-source satellites; 6) a first reverse model module, wherein the core of the first reverse model in the first reverse model module is to minimize the Bayesian cost function; 7) and a posterior CO2 emission flux module. The flux inventory module in 1) inputs the flux inventory into the GEOS-Chem module as an estimate of the prior flux of the GEOS-Chem module, and obtains the atmospheric CO2 concentration from the GEOS-Chem module. Then, the atmospheric CO2 concentration output by the GEOS-Chem module and the integrated CO2 concentration measured by multi-source satellites in the CO2 concentration integration module are input into the first inverse model, and the first inverse model is run to obtain an estimate of the posterior CO2 emission flux in the posterior CO2 emission flux module.

[0016] In the computer program product, the flux inventory in the flux inventory module is updated with the estimate of the a posteriori CO2 emission flux to obtain the CO2 emission flux at a low resolution of 25-50 kilometers.

[0017] In the computer program product, the flux inventory at low resolution outputted by the first carbon inversion module is transmitted to the CO2 emission flux module in the second carbon inversion module to serve as a priori flux input of the second carbon inversion module.

[0018] In the computer program product, the second top-down CO2 emission flux inversion module includes: 1) a priori flux inventory module; 2) a forward model module, wherein the forward model in the forward model module adopts the WRF-Chem atmospheric transport model; 3) a WRF meteorological data prediction module; 4) a high-resolution satellite CO2 concentration observation data integration module; 5) a second reverse model module, wherein the core of the second reverse model in the second reverse model module is to minimize the Bayesian cost function; 6) and a posterior CO2 emission flux module; in the second top-down CO2 emission flux inversion module, the priori flux inventory The prior flux inventory in the module is input into the forward model as a prior flux estimate, and the meteorological data output from the meteorological data prediction module of WRF is input into the WRF-Chem forward model, the forward model is run to obtain the atmospheric CO2 concentration simulated by the forward model, and then the atmospheric CO2 concentration output by the forward model and the CO2 concentration of the high-resolution satellite CO2 concentration observation data integration module are input into the second reverse model together, the second reverse model is run to obtain the posterior high-resolution CO2 emission flux, and the data in the prior flux inventory is updated with the posterior CO2 emission flux.

[0019] In the computer program product, the a posteriori CO2 emission flux is compared with the actual CO2 emission flux to evaluate the effectiveness of the computer program product for establishing the carbon dioxide emission flux inversion model.

[0020] In the computer program product, the a posteriori CO2 emission flux of the first carbon inversion module is transmitted to the second carbon inversion module, whereby the first carbon inversion module and the second carbon inversion module form a nested global to regional CO2 emission flux inversion numerical model.

[0021] In the computer program product, the flux inventory in the flux inventory module of the first carbon inversion module comes from an external inventory, or is data from land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM, wherein the data of the external inventory, land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM are output by a bottom-up numerical model operation.

[0022] In a second aspect, the present invention provides a method for establishing a CO2 emission flux inversion model, comprising: performing global and low-resolution CO2 inversion based on the GEOS-Chem model, the global and low-resolution CO2 inversion is a first top-down CO2 emission flux inversion, used to obtain a two-dimensional distribution of CO2 emission flux at a low resolution of 25-50 kilometers, and performing regional and high-resolution carbon dioxide inversion based on the WRF-Chem model, the regional and high-resolution CO2 inversion is a second top-down CO2 emission flux inversion, used to obtain a two-dimensional distribution of CO2 emission flux at a high resolution of 0.1-5 kilometers, wherein the first top-down CO2 emission flux inversion and the second top-down CO2 emission flux inversion are combined into a numerical model for CO2 emission flux inversion that is regionally nested within the global.

[0023] In the method, in a first top-down CO2 emission flux inversion based on the GEOS-Chem model, the a priori flux inventory is input as the a posteriori flux estimate of the first top-down CO2 emission flux inversion into the WRF-Chem model of the second top-down CO2 emission flux inversion, the WRF-Chem model is used as a transmission model with real meteorology to obtain the atmospheric CO2 concentration of the numerical model, the atmospheric CO2 concentration of the numerical model and the CO2 concentration measured by multi-source satellites are input into the second reverse model of the second top-down CO2 emission flux inversion, the core of the second reverse model is to minimize the Bayesian cost function, and the second reverse model is run to obtain an estimate of the a posteriori CO2 emission flux, and the a posteriori CO2 emission flux is stored in the a priori flux inventory.

[0024] In the method, the estimate of the a posteriori CO2 emission flux is stored in the a priori flux inventory to update it.

[0025] In the method, in a first top-down CO2 emission flux inversion, an a priori flux inventory is input into a forward model as an a priori flux estimate of the first top-down CO2 emission flux inversion, and GEOS meteorological data from a GEOS meteorological data module is input into the forward model, the forward model is run to obtain the atmospheric CO2 concentration simulated by the numerical model, the numerically simulated atmospheric CO2 concentration and the integrated satellite-measured CO2 concentration are input into the first reverse model, the first reverse model is run to obtain an estimate of the a posteriori CO2 emission flux, and the a posteriori CO2 emission flux is stored in the a priori flux inventory for updating.

[0026] In the method, the a posteriori CO2 emission flux is compared with the actual CO2 emission flux to evaluate the effectiveness of the method for establishing the CO2 emission flux inversion model.

[0027] In the method, the a posteriori CO2 emission flux is transmitted to the regional and high-resolution CO2 flux inversion as an output from the global and low-resolution CO2 flux inversion, thereby forming a nested first top-down CO2 emission flux inversion and a second top-down CO2 emission flux inversion.

[0028] In the method, the regional and high-resolution CO2 flux inversion is performed by an inverse model conventional algorithm, wherein the high resolution is 1 km.

[0029] In the method, the flux inventory in the prior flux inventory of the first top-down CO2 emission flux inversion comes from an external inventory, or is data from land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM, wherein the data of the external inventory, land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM are the output results of the bottom-up model.

[0030] In view of the fact that the new satellite carbon dioxide observations are more accurate and have higher resolution, the applicant expects that the accuracy of carbon flux estimates may be significantly improved according to the computer program product and method of the present invention. The physical model includes the WRF-Chem model and the GEOS-Chem model.

[0031] In addition, in order to help Hong Kong, China and the Greater Bay Area of ​​China achieve the goal of carbon neutrality by 2060, the computer program product and method of the present application are also intended to develop a numerical modeling framework to estimate carbon emissions from both land and ocean in the Greater Bay Area of ​​China. The Greater Bay Area of ​​China is also known as the Guangdong-Hong Kong-Macao Greater Bay Area, including the Hong Kong Special Administrative Region, the Macao Special Administrative Region and Guangzhou, Shenzhen, Zhuhai, Foshan, Huizhou, Dongguan, Zhongshan, Jiangmen and Zhaoqing in Guangdong Province. For example, this can be achieved in the following ways: In the above-mentioned computer program product and method of the present invention, by using data collected by various satellites over the Greater Bay Area of ​​China, a second carbon inversion module is used to perform regional and high-resolution CO2 inversion for the Greater Bay Area of ​​China based on the WRF-Chem model to obtain carbon emissions from land and / or ocean in the Greater Bay Area of ​​China.

[0032] In addition, the present invention can also be applied to other geographical regions. For example, the second carbon inversion module can be used to perform regional and high-resolution CO2 inversion for other regions based on the WRF-Chem model to obtain carbon emissions from land and / or ocean in the corresponding geographical region. The other geographical region can be, for example, East China or Northeast China, or a country or region in Southeast Asia, North America or Europe, as long as satellite-measured carbon emission data for the region can be obtained.

[0033] From the above aspects of the present invention, it can be understood that the first core concept of the present invention is to successfully develop a regional-level high-resolution and accurate computer program product and method for gaseous carbon inversion optimized for the Greater Bay Area of ​​China to estimate carbon emissions covering the entire land and sea surface of the Greater Bay Area of ​​China.

[0034] The second core concept of the present invention is to achieve operational carbon inversion to provide services to government and industrial partners.

[0035] According to the above solution of the present invention, the following objectives can be achieved, for example:

[0036] 1. A computer program product for global carbon inversion modeling optimized for the Greater Bay Area of ​​China.

[0037] 2. Computer program products for regional carbon inversion modeling of the Greater Bay Area of ​​China with high accuracy and resolution.

[0038] The main output of the invention is high-resolution, near-real-time estimates of carbon dioxide (CO2) emission fluxes in the Greater Bay Area of ​​China, which will be provided to government environmental management agencies, data suppliers to carbon trading systems, and industries whose products are affected by carbon taxes and carbon quotas.

[0039] The above-mentioned content of the present application and the following detailed description of the exemplary embodiments of the present application will be better understood by reading in conjunction with the accompanying drawings. To illustrate the present application, illustrative embodiments of the present disclosure are shown in the accompanying drawings. However, the present application is not limited to the arrangements and methods shown in the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of a computer program product for carbon inversion constructed according to the present application.

[0041] Figure 2 It is a schematic diagram comparing the emission flux error after July 2023 with the prior flux error in different regions of the world.

[0042] Figure 3 This is the curve of the regional average flux RMSE around the world. DETAILED DESCRIPTION

[0043] The following is a detailed description of the embodiments of the present invention by referring to the accompanying drawings. In the following description, detailed descriptions of general technical knowledge in the art are omitted to focus on the main contents of the invention solution of the present application.

[0044] The carbon budget estimation methods of terrestrial ecosystems can be roughly divided into two types: "bottom-up" and "top-down".

[0045] The "bottom-up" estimation method refers to the extension of ground observations and simulation results at the sample point or grid scale to the regional scale; commonly used "bottom-up" methods include inventory method, eddy covariance method and ecosystem process model simulation method. The inventory method estimates the carbon storage of terrestrial ecosystems by investigating the average carbon density of vegetation and soil in the sample plot. Although it has high accuracy, the application cost is also high. It is generally used to verify the carbon flux obtained by other methods. The eddy covariance method uses the principles of micrometeorology to directly measure the net CO2 exchange between terrestrial ecosystems and the atmosphere within a fixed coverage area, and estimates the net ecosystem productivity (NEP) at the regional scale through upscaling, that is, the net primary productivity (NPP) minus the carbon consumed by soil heterotrophic respiration (R h ), thereby estimating carbon absorption. The eddy covariance method can achieve long-term continuous positioning observation of ecosystem carbon flux at a fine time scale, but because it is easily affected by human factors at the regional scale, it is rarely used to directly estimate the size of carbon sinks at the regional scale. It is mainly used to understand the response process of carbon cycle to climate change at the ecosystem scale.

[0046] The "top-down" estimation method mainly refers to the inversion of carbon sources and sinks of terrestrial or marine ecosystems based on atmospheric CO2 concentration, that is, the atmospheric inversion method. The advantages and disadvantages and sources of uncertainty of different estimation methods are different. The atmospheric inversion method uses the atmospheric transport model to infer the regional carbon budget based on the temporal and spatial variation gradient of atmospheric CO2 concentration measured by the global observation network. It can estimate the real-time changes of large-scale carbon sinks, but it is limited by factors such as low spatial resolution and accuracy affected by the temporal and spatial distribution of atmospheric CO2 observations.

[0047] The computer program product and method for carbon inversion constructed in the present application include both a "top-down" estimation method and a "bottom-up" estimation method.

[0048] Figure 1 is a flow chart of a flow chart of a computer program product for carbon inversion constructed according to the present application for performing carbon inversion.

[0049] Figure 1The computer program product 10 for carbon inversion shown may be implemented as a nested global modeling computer program product and a regional modeling computer program product. The nested global modeling computer program product and the regional modeling computer program product may share similar components, see reference 4 published by Pickett-Heaps et al. in 2011, but using different transport models. The inversion models will also be developed separately.

[0050] Figure 1 The lower half of the middle paper page is the first carbon inversion module 100 of the computer program product for carbon inversion of the present invention, which performs global and low-resolution carbon dioxide inversion based on GEOS-Chem, including a first top-down CO2 emission flux inversion module 101, which is used to obtain the two-dimensional distribution of CO2 emission flux at a low resolution of 25-50 kilometers.

[0051] GEOS-Chem is a global atmospheric chemistry 3-D (three-dimensional) model driven by meteorological inputs from NASA's (NASA) Global Modeling and Assimilation Office's Goddard Earth Observing System (GEOS). Research groups around the world apply it to a wide range of atmospheric composition problems. Scientific guidance for the model is provided by the international GEOS-Chem Steering Committee and Working Groups. The model is managed by the GEOS-Chem Support Team at Harvard University and the University of Washington, and is supported by NASA's Earth Science Division in the United States, the National and Engineering Research Councils of Canada, and Nanjing University of Information Science and Technology in China. GEOS-Chem is capable of simulating atmospheric composition from local to global scales. It can be used offline as a 3-D chemical transport model or online as a chemistry module coupled to weather and climate models. GEOS-Chem is a versatile tool developed and used by hundreds of research groups around the world and can be applied to a wide range of atmospheric composition problems. GEOS-Chem is publicly available for download via GitHub. GitHub is an online software source code hosting service platform for publicly available program or software code. It is also fully supported for use on the Amazon WebServices cloud.

[0052] like Figure 1As shown, in the first top-down CO2 emission flux inversion module 101 of the first carbon inversion module 100 based on the GEOS-Chem model, the priori flux inventory in the flux inventory module 104 is input into the GEOS-Chem module 107 as the optimized flux estimate 111 of the reverse model from the reverse model module 102, and the meteorological data from the reanalysis global meteorological data input module 108 is input into the global chemical transport model GEOS-Chem module 107 to obtain the atmospheric CO2 concentration 106 required by the reverse model of the present invention, and is transmitted to the reverse model 102 together with the satellite concentration observation 105. The core of the reverse model in the reverse model module 102 is to minimize the Bayesian cost function. Running the reverse model can obtain an estimate of the a posteriori CO2 emission flux in the a posteriori CO2 emission flux module 103. Then, the a posteriori CO2 emission flux is compared with the real CO2 emission flux to evaluate the effectiveness of the computer program product or method for establishing the CO2 emission flux inversion model of the present invention. Then, the a posteriori CO2 emission flux 103 is stored in the flux inventory module 104 as an update or supplement to the a priori flux inventory for later use. The a posteriori CO2 emission flux can also be transmitted to Figure 1 The CO2 emission flux module 211 in the second carbon inversion module based on the WRF-Chem model is shown in the upper half of FIG. The CO2 emission flux can provide initial conditions and boundary conditions to the second carbon inversion module based on the WRF-Chem model.

[0053] The data in the prior flux inventory in the flux inventory module 104 may also come from an external inventory 110, or may be output data from land 1CASA 109, land 2CASA-GFED 112, ocean 1ECCO2 114, or ocean 2GSFC / GMAO NOBM 113. The data of the external inventory, land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM come from the results of bottom-up model operation.

[0054] Figure 1 The upper part of the middle page is the second carbon inversion module 200 in the computer program product for the CO2 emission flux inversion model of the present invention, which performs regional and high-resolution CO2 inversion based on WRF-Chem and includes a second "top-down" CO2 emission flux inversion module 201 to obtain a two-dimensional distribution of regional CO2 emission flux at a high resolution of 0.1-5 kilometers. The high resolution is preferably 1 kilometer.

[0055] WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) is a Weather Research and Forecasting (WRF) model coupled with chemistry. The development of WRF-Chem is the result of the joint efforts of scientists in this field. The official website of WRF-Chem is located on the NOAA (National Oceanic and Atmospheric Administration) website, and scientists from NOAA / ESRL (National Oceanic and Atmospheric Administration / Environmental Research Laboratory) are the leaders and administrators of the model's code.

[0056] The WRF-Chem model is a widely used regional model that can simulate the direct and indirect climate effects of aerosols online. The model simultaneously simulates meteorological conditions and the emission, transport, mixing and chemical transformation of trace gases and aerosols. The model can be used for regional-scale air quality surveys, physical process analysis, and the interaction between clouds and chemistry at the cloud scale.

[0057] like Figure 1 As shown, in the second top-down CO2 emission flux inversion module 201 of the second carbon inversion module 200 based on the WRF-Chem model, the prior flux inventory in the prior flux inventory module 204 is input into the forward model module 207 as the optimized flux estimate 212 of the carbon inversion model of the present invention, and the forward model in the forward model module adopts the WRF-Chem atmospheric transport model. In the forward model module 207, the output of the WRF meteorological data prediction module 208 is combined with the prior flux input to simulate the output of the CO2 concentration 206 through the WRF-Chem model. The CO2 concentration 206 and the data of the high-resolution satellite CO2 concentration observation data integration module 205 are input into the reverse model module 202 of the present invention. The core of the reverse model in the reverse model module 202 is to minimize the Bayesian cost function. Running the reverse model in the reverse model module 202 can obtain an estimate of the posterior CO2 emission flux in the posterior CO2 emission flux module 203. Then, the a posteriori CO2 emission flux estimate is stored in the a priori flux inventory module 204 as an update or supplement to the a priori flux inventory for later use. The second top-down CO2 emission flux inversion module 201 can be run cyclically to obtain a two-dimensional distribution of CO2 emission flux that is closer to the actual situation.

[0058] The data in the prior flux inventory module 204 can also be obtained from the external inventory module 210. The external inventory module 210 can also include obtaining the CO2 emission flux of the precise region by running the shape file 209 for regional operation from top to bottom, and / or from Figure 1The lower part of the figure shows the a priori CO2 emission flux (originally 103) 211 in the first carbon inversion module based on GEOS-Chem. The a priori flux inventory 204 includes the CO2 a priori emission flux obtained from the shape file 209 for regional operation, and the a priori CO2 emission flux 103 includes the regional-level CO2 emission flux for the relevant region. The shape file 209 for regional operation can be obtained by downloading from the network.

[0059] like Figure 1 As shown, the computer program product 10 for the CO2 emission flux inversion model of the present invention includes a nested global modeling model (i.e., a first carbon inversion module 100) and a regional modeling model (i.e., a second carbon inversion module 200). The nested global modeling model and the regional modeling model may share similar components, but they use different transmission models, i.e., based on WRF-Chem and based on GEOS-Chem, respectively. The inversion models are also developed separately.

[0060] The WRF-Chem model and the GEOS-Chem model are respectively known in the art, and the use of the models is also a known technical means in the art, which will not be described in detail herein. The present invention is the first to combine the first carbon inversion module based on the GEOS-Chem model and the second carbon inversion module based on the WRF-Chem model to form a regional numerical model for the Greater Bay Area nested in the world, so as to form a nested CO2 emission flux inversion model.

[0061] Figure 2 Digitally marked Figure 3 Refers to various geographic regions, for example, 1 for Canada and 2 for the United States. Figure 3 This is a curve showing the RMSE (root mean square error) of the July (a priori and a posteriori) regional average flux of CO2 emission flux around the world as it changes with the marked area. Figure 3 The vertical axis in the figure represents the average flux RMSE of each region, and the unit is 10 -3 kgC / km 2 / s (kg carbon / km2 / sec). As an example of the inversion results of CO2 emission flux, in the a posteriori emission flux in July, several curves represent the differences in the inversion results of NASA's CMS-FLUX (Carbon Monitoring System Flux) model after increasing the number of satellite databases integrated. Several combinations are: using only OCO-2; using only GeoCARB; integrating OCO-2 and GeoCARB at the same time. OCO-2 is the abbreviation of Orbiting Carbon Observatory-2, which means the Orbiting Carbon Observatory-2 satellite. GeoCARB is the abbreviation of GEOlgical CARBon cycle, which means the Geostationary Carbon Cycle Observatory satellite, and CO2 refers to the carbon dioxide data in the data output by the satellite. This is the output result of the model simulation when the inventor team of this application was doing related project research at JPL (Jet Propulsion Laboratory).

[0062] Figure 3 Although the values ​​of the curves are different, their changing trends with regions are basically the same. But the core message of this figure is that the uncertainty of CO2 emissions will be significantly reduced when more satellite fusion data sets are used. It also shows that in the United States, where carbon emission inventories can be accurately provided, the results are better than in Asia.

[0063] The advantages of the computer program product of the present application over the existing carbon inversion system mainly include the following four aspects.

[0064] First, the computer program product and method for the CO2 emission flux inversion model of the present application will have the advantages of high resolution and high accuracy, which is crucial for identifying emission sources. Previous research results have shown that if the satellite data resolution is low and the carbon emission inventory is inaccurate, the inverted emission flux is of little use.

[0065] Secondly, the computer program product and method for the CO2 emission flux inversion model of the present application can be optimized for the Greater Bay Area of ​​China, which can be combined with known local carbon emission inventories to achieve near real-time monitoring of emission points in the Greater Bay Area of ​​China.

[0066] Third, the computer program products and methods for carbon inversion of the present application may include measurement data from new satellites. For example, Figure 3It is shown that including more satellite measurement data will improve the accuracy of the inverted emission flux. The applicant hereby states that Hong Kong, China will launch a high-resolution point source carbon satellite in 2026, which will achieve more targeted carbon emission source detection for CO2 measurement in the Greater Bay Area of ​​China. The computer program product for carbon inversion of the present invention has the function of nesting global and regional, and combined with the enhanced satellite measurement strength, it will significantly enhance the ability of the computer program product and method of the present application to simulate carbon emissions in Asia, especially carbon emissions in the Greater Bay Area of ​​China.

[0067] The solution of the present invention can be implemented in two ways:

[0068] First, the nested global and regional models of the present invention can be incorporated into online carbon information systems, such as various carbon emission information systems, carbon emission assessment systems or carbon emission monitoring systems, from which carbon emission data can be downloaded, future forecasts and consultations can be conducted. This implementation method mainly serves the government in formulating policies, guiding the private sector in formulating forecasts of the costs required for carbon reduction measures, and conducting popular education and publicity for the public.

[0069] Secondly, the computer program product of the present application can be installed on a desktop computer, a personal computer, a server or a tablet computer, which will provide a numerical model package that can be carried by developers. This can also facilitate discussions and improvements among scientific and industrial partners.

[0070] Any example described herein, or its elements, or any combination of its elements, shall fall within the scope of protection of the claims of this application.

Claims

1. A computer program product, the computer program product (10) comprising computer executable code, the computer executable code being used to establish a carbon dioxide (CO2) emission flux inversion model when executed by a computer, the CO2 emission flux inversion model comprising: A first carbon inversion module (100), the first carbon inversion module performs global and low-resolution CO2 emission flux inversion based on the GEOS-Chem model, the first carbon inversion module comprising a first top-down CO2 emission flux inversion module (101) for obtaining a two-dimensional distribution of CO2 emission flux at a low resolution of 25-50 kilometers, and A second carbon inversion module (200), the second carbon inversion module performs regional and high-resolution CO2 inversion based on the WRF-Chem model, the second carbon inversion module comprises a second top-down CO2 emission flux inversion module (201), for obtaining a two-dimensional distribution of CO2 emission flux at a high resolution of 0.1-5 km, The first carbon inversion module and the second carbon inversion module are configured as a numerical model for CO2 emission flux inversion with a region nested within the globe.

2. The computer program product according to claim 1, wherein the first top-down CO2 emission flux inversion module (101) comprises the following submodules: A flux inventory integration module (104), GEOS-Chem module (107), said GEOS-Chem is a global chemical transport model, Reanalysis global meteorological data input module (108), CO2 concentration fields (106) output from the GEOS-Chem run, CO2 concentration field integration module measured by multiple satellite sources (105), A first reverse model module (102), wherein the core of the first reverse model in the first reverse model module is to minimize the Bayesian cost function, and A posteriori CO2 emission flux module (103), in, The flux inventory integration module inputs the integrated flux inventory into the GEOS-Chem module (107) as the prior flux estimate (111) of the GEOS-Chem module, and obtains the concentration of carbon dioxide in the atmosphere from the GEOS-Chem module as the atmospheric CO2 output concentration. The atmospheric CO2 output concentration of the GEOS-Chem module and the integrated CO2 concentration measured by multi-source satellites in the CO2 concentration integration module measured by multi-source satellites are input into the first inverse model, and the first inverse model is run to obtain an estimate of the posterior CO2 emission flux in the posterior CO2 emission flux module.

3. The computer program product according to claim 2, wherein: The flux inventory in the flux inventory module is updated with the estimate of the a posteriori CO2 emission flux to obtain the CO2 emission flux at a high resolution of 1 km.

4. The computer program product according to claim 2, wherein: The first carbon inversion module outputs flux to update the flux inventory at low resolution and transmits it to the CO2 emission flux module (211) in the second carbon inversion module to serve as a priori flux input of the second carbon inversion module.

5. The computer program product according to claim 2, wherein: The second top-down CO2 emission flux inversion module (201) comprises: A priori flux inventory module (204), A forward region model module (207), wherein the forward region model in the forward region model module adopts the WRF-Chem atmospheric transport model, WRF-Chem CO2 concentration prediction module (206), WRF meteorological data forecast and other boundary condition input modules (208) High-resolution satellite CO2 concentration observation data integration module (205), A second reverse model module (202), wherein the core of the second reverse model in the second reverse model module is to minimize the Bayesian cost function, and A posteriori CO2 emission flux module (203), Wherein, in the second top-down CO2 emission flux inversion module, the prior flux inventory in the prior flux inventory module is input into the forward model as a prior flux estimate, and the meteorological data obtained by WRF operation in the meteorological data prediction module (208) of WRF is input into the forward transmission model WRF-Chem, the forward model is run to obtain the forward model atmospheric CO2 concentration, the forward model atmospheric CO2 concentration and the CO2 concentration obtained by the high-resolution satellite CO2 concentration observation data integration module are input into the second reverse model, the second reverse model is run to obtain the posterior CO2 emission flux, and the posterior CO2 emission flux is stored in the prior flux inventory for updating.

6. The computer program product according to claim 5, wherein: The a posteriori CO2 emission flux is compared with the actual CO2 emission flux to evaluate the effectiveness of the computer program product for establishing the carbon dioxide emission flux inversion model.

7. The computer program product according to claim 5, wherein: The a posteriori CO2 emission flux is transmitted to the second carbon inversion module as the CO2 emission flux output from the first carbon inversion module, whereby the first carbon inversion module and the second carbon inversion module form a nested global to regional numerical model for carbon dioxide emission flux inversion.

8. The computer program product of claim 5, wherein: The second top-down CO2 emission flux inversion module is completed by an iterative method or other conventional algorithms for solving the inverse model.

9. The computer program product of claim 5, wherein: The flux inventory in the flux inventory module (104) of the first carbon inversion module comes from an external inventory (110), or is data from land 1CASA (109), land 2CASA-GFED (112), ocean 1ECCO2 (113), or ocean 2GSFC / GMAO NOBM (114), wherein the data of the external inventory, land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM are output by running a bottom-up numerical model.

10. A method for establishing a CO2 emission flux inversion model, comprising: A global and low-resolution CO2 flux inversion based on the GEOS-Chem model, which is the first top-down CO2 emission flux inversion for obtaining a two-dimensional distribution of CO2 emission flux at a low resolution of 25-50 km, and Based on the WRF-Chem model, regional and high-resolution CO2 inversion is performed. The regional and high-resolution CO2 inversion is a second top-down CO2 emission flux inversion for obtaining a two-dimensional distribution of CO2 emission flux at a high resolution of 0.1-5 km. The first top-down CO2 emission flux inversion and the second top-down CO2 emission flux inversion are combined into a numerical model for carbon dioxide emission flux inversion with regions nested within the globe.

11. The method according to claim 10, wherein: In the first top-down CO2 emission flux inversion based on the GEOS-Chem model, the optimized flux estimate obtained from the first top-down CO2 emission flux inversion is input as a priori flux into the WRF-Chem model of the second top-down CO2 emission flux inversion. The WRF-Chem model is used as a transport model with realistic meteorology to obtain the atmospheric CO2 concentration of the numerical model. The atmospheric CO2 concentration of the numerical model and the CO2 concentration measured by multi-source satellites are input into a second inverse model for top-down CO2 emission flux inversion, wherein the core of the second inverse model is to minimize the Bayesian cost function, and The second inverse model is run to obtain an estimate of the a posteriori CO2 emission flux, and the a posteriori CO2 emission flux is stored in the a priori flux inventory.

12. The method according to claim 11, wherein: The estimate of the a priori CO2 emission flux is stored in the a priori flux inventory to update the original a priori flux.

13. The method according to claim 11, wherein: In a second top-down CO2 emission flux inversion, the a priori flux inventory is input into a forward model as an optimized flux estimate from the second top-down CO2 emission flux inversion, and GEOS meteorological data from a GEOS meteorological data module is input into the forward model, the forward model is run to obtain the atmospheric CO2 concentration of the numerical model, the atmospheric CO2 concentration of the numerical model and the integrated satellite-measured CO2 emission flux are input into the second reverse model, the second reverse model is run to obtain an estimate of the a posteriori CO2 emission flux, and the a posteriori CO2 emission flux is stored in the a priori flux inventory.

14. The method according to claim 13, wherein: The a posteriori CO2 emission flux is compared with the actual CO2 emission flux to evaluate the effectiveness of the method for establishing the CO2 emission flux inversion model.

15. The method according to claim 13, wherein: The a posteriori CO2 emission fluxes are transmitted to the regional and high-resolution CO2 flux inversion as an output from the global and low-resolution CO2 flux inversion, thereby forming a nested first top-down CO2 emission flux inversion and a second top-down CO2 emission flux inversion.

16. The method of claim 13, wherein the regional and high-resolution carbon dioxide (CO2) flux inversion is performed by an iterative method or other conventional algorithms for solving an inverse model, wherein the high resolution is 1 km.

17. The method according to claim 13, wherein: The flux inventory in the prior flux inventory of the first top-down CO2 emission flux inversion comes from an external inventory, or is data from land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM, wherein the data of the external inventory, land 1CASA, land 2CASA-GFED, ocean 1ECCO2, or ocean 2GSFC / GMAO NOBM are run from the bottom up.

18. A computer program product, comprising computer executable instructions, which, when executed by a computer, implement the steps of the method for establishing a CO2 emission flux inversion model according to any one of claims 10 to 17.

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