CN111723482A - Based on satellite CO2Method for observing and inverting surface carbon flux by using column concentration - Google Patents
Based on satellite CO2Method for observing and inverting surface carbon flux by using column concentration Download PDFInfo
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Abstract
The invention discloses a CO based on a satellite2A method for inverting surface carbon flux from column concentration observations, comprising: constructing an EnKF carbon flux inversion system and coupling the EnKF carbon flux inversion system to a MOZART-4 global atmospheric chemical transmission model; prior CO2Flux is input into an atmosphere model MOZART-4 to simulate CO by obtaining a set sample through Gaussian random disturbance2The concentration and assimilation of the observation data of the corresponding position and time to obtain the optimized CO2Flux; inputting the optimized flux into the MOZART-4 model again for operation, and generating a new initial field for the flux optimization of the next synchronization window; and (5) repeating the step (2) to carry out cyclic assimilation. The high-resolution carbon assimilation inversion system constructed by the novel algorithm and the novel program can directly assimilate the concentration data of the satellite column, and effectively corrects global and regional carbon flux errors and simulation accuracy.
Description
Technical Field
The invention belongs to the field of climate change, and particularly relates to CO based on a satellite2A method for inverting the carbon flux of the earth surface by observing the column concentration.
Background
Atmospheric CO2Is heavyOne of the major greenhouse gases, the combustion of fossil fuels and the change in land use are the major causes of their growth. Land ecosystems and oceans play a very important role in regulating atmospheric carbon dioxide concentrations. In the past half century, approximately 60% of anthropogenic carbon dioxide emissions have been absorbed by land ecosystems and the ocean. Therefore, to control global warming, it is very important to quantify global and regional carbon fluxes. However, due to the uneven distribution of ground observation, the uncertainty of the inversion result of the prior art is very large in the tropical, east asia, africa, south america and the like where the observation area is sparse. For compensating ground CO2The shortcomings of the observed data, such as GOSAT, OCO-2 and TanSat, are transmitted in Japan, America and China in sequence to monitor the change of the surface carbon flux. However, carbon satellites actually monitor atmospheric CO2The column concentration of (2) is needed to develop a technology for monitoring atmospheric CO by using massive satellites2The column concentration change is quantitatively inverted into the change of carbon flux of different earth surfaces in different areas.
Disclosure of Invention
The purpose of the invention is as follows: providing a CO based on satellite2The method for observing and inverting the carbon flux on the earth surface by the column concentration aims to solve the problems in the prior art.
The technical scheme is as follows: based on satellite CO2The method for observing and inverting the surface carbon flux by using the column concentration comprises the following working steps:
According to one aspect of the invention, the method for constructing the EnKF flux inversion system in the step 2 comprises the following steps:
step 2.1, generating a priori flux set sample:
wherein i is the disturbance sample identification,
represents the ith sample of the prior flux set,
respectively representing prior ecosystem, ocean and artificial fossil fuel carbon flux,bio，ocnandfossilgaussian disturbance random disturbance samples with the average number of 0 standard deviations of 1 respectively representing ecosystem carbon flux, ocean carbon flux and artificial carbon emission, lambdabio，λocnAnd λfossitRespectively representing the uncertainty estimation of the prior ecosystem carbon flux, ocean carbon flux and artificial carbon emission of the grid scale; n represents the sample size;
step 2.2, calculating the covariance of the background error:
wherein the content of the first and second substances,
represents a sample average of the collection;
step 2.3, satellite observation data and error calculation;
step 2.4, setting an assimilation window and a localization scheme;
step 2.5, combining the observation data y and the observation error R to obtain optimized pollutant emission
According to an aspect of the present invention, the step 1.3 is further a packagePair XCO2The data was processed as follows:
step 2.3.1, adopt wan _ levels and xco2Quality control is carried out on data by two parameters of quality _ flag; only xco2Data for quality flag greater than 0 is employed; the selected data is divided into three groups according to the value of war _ levels, which are respectively less than 8, more than 9, less than 12 and more than 13, and the smaller the value, the higher the data quality is.
Step 2.3.2, resampling the data to the grid resolution of 1 degree multiplied by 1 degree, and preferentially selecting high-quality data for averaging in each grid;
step 2.3.3 to make XCO2The observation and the observation error can be mutually independent and are based on the optimal estimation theory to XCO2And (3) carrying out super observation processing on the observed value:
where j is the observation identifier, m is the number of observations of a super observation grid, yjRepresentative of an observation, rjIs yjObservation error of (2), xijIndicating the ith exhaust drive and the jth observation yjCorresponding analog concentration;
is a weight factor, ynewIs a super observed value, rnewFor corresponding observation errors, xnew，iAn analog value driven for the ith set of samples; as the number of observations introduced in the super-observation increases, the error is continuously reduced;
according to one aspect of the invention, said step 2.4 further employs one week as an assimilation window.
According to one aspect of the invention, the optimized pollutant emission in step 2.5
The calculation model of (a) is:
K＝pbHT(HpbHT+R)-1(7)
h is an observation operator, model state variables are interpolated into an observation space from a model space, and K is a gain matrix, and the weights of the background field and the observation are determined. In each of the analysis steps, it is preferred that,
as an optimal estimate of flux.
According to an aspect of the invention, the XCO for model simulation2(i.e. the
) First interpolation to satellite XCO2Then calculated according to the following equation:
j represents the search level and x represents the simulated CO2Profile, A (x) represents a mapping matrix,
representing a priori XCO2，hjRepresenting a pressure weight function, ajIs the average kernel of the satellite column,
is a priori CO of the search2A profile line. Removing simulated CO2Outside the contour, other parameters come from XCO2Producing a product; it is the assimilation system and the assimilation system of the present inventionAnd the MOZART-4 and satellite data construct a data conversion interface, so that an assimilation system directly assimilates the concentration of the satellite column to invert the ground carbon flux.
According to one aspect of the invention, the method further introduces a localization technique to reduce the false correlation effect, the localization scale being set to 3000 km; perturbed flux samples and calculated atmospheric CO over an observed 500km range2Fluxes with concentration dependence greater than 0 are optimized; within the range of 500-3000km, the correlation is significant (p)<0.05) flux is also optimized; the flux in the range over 3000km is not optimized.
Has the advantages that: the high-resolution carbon assimilation inversion system constructed by the novel algorithm and the novel program can directly assimilate the concentration data of the satellite column, and effectively corrects global and regional carbon flux errors and simulation accuracy.
Drawings
Fig. 1 is a schematic diagram of the principle of the present invention.
FIG. 2 is a diagram of CO simulation and observation at different latitudes before and after optimization2Concentration deviation, where (a) is XCO inverted from satellite2Comparing the images; in the figure, (b) is CO observed from the ground2Concentration comparison graph.
FIG. 3 is a global CO for a posteriori carbon flux simulation2Concentration and ground station CO2And satellite XCO2Spatial distribution of mean deviation of concentration.
In fig. 4, (a) is a spatial distribution diagram of the posterior and prior carbon flux differences of the annual average carbon flux of the land ecosystem and the ocean, and (b) is a spatial distribution diagram of the posterior and prior carbon flux differences of the carbon emission of fossil fuels.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
As shown in figure 1, an EnKF flux inversion system is firstly constructed and coupled to a MOZART-4 atmospheric chemical transmission model, the MOZART-4 is flexible to operate, different resolutions can be set according to requirements, and the model can be driven and simulated by any meteorological data set and emission list. In the system, the MOZART-4 resolution is set to be 1 degree multiplied by 1 degree, the grid number is 360 multiplied by 180 degrees, and the system covers the whole world. The vertical direction was set to 28 layers, 2.5hPa from the ground to the top of the atmosphere. ERA-Interim reanalysis data from European ECMWF was used as meteorological input.
The EnKF generates a group of random samples of a finite set according to the mode input background field to represent the uncertainty of the random samples, and the background error is evolved along with the update of the power mode through mode simulation, so that a background error covariance matrix is calculated. This embodiment is achieved by
Wherein i is the disturbance sample identification,
represents the ith sample of the prior flux set,
respectively representing prior ecosystem, ocean and artificial fossil fuel carbon flux,bio，ocnandfossilgaussian disturbance random disturbance samples with the average number of 0 standard deviations of 1 respectively representing ecosystem carbon flux, ocean carbon flux and artificial carbon emission, lambdabio，λocnAnd λfossilRespectively representing the uncertainty estimation of the prior ecosystem carbon flux, ocean carbon flux and artificial carbon emission of the grid scale; n represents the sample size; and (3) carrying out disturbance by using the formula (1) to obtain a priori flux set sample.
For background (a priori flux), fossil fired carbon emissions from noba carbon tracker (CT2017), a product of Carbon Dioxide Information Analysis Center (CDIAC) and open-source anthropogenic CO2Data Inventory (ODIAC) product averaging; biomass combustion emissions were also from CT2017, a global fire emissions database (GFEDv4) and NASA carbon monitoring systemThe fire discharge database (GFED _ CMS) is obtained on average. Ocean CO2Flux from pCO2The loss of data for a part of the sea (e.g., the japanese sea) is filled by flux simulated by global ocean circulation (OPA) and a biogeochemical model (PISCES-T). terrestrial ecosystem carbon flux is the most important a priori carbon flux, and this case is simulated by a BEPS model. BEPS is a process-based, remote sensing data driven mechanical ecosystem model. the BEPS mode is also driven by ERA-Interim reanalysis data, with a resolution of 1 ° × 1 °. the required meteorological variables include relative humidity, wind speed, air temperature, ground atmospheric pressure, incident solar shortwave flux and total precipitation.
After acquiring the prior flux set, inputting the prior flux set into MOZART-4 for simulation, thereby calculating a background error covariance matrix,
wherein the content of the first and second substances,
represents a sample average of the collection; further obtaining the response relation of the simulated concentration to the emission, and finally assimilating observation data to invert and optimize CO2Flux (equation 6, 7). Despite the longer assimilation window of CO2With longer transmission distance, more observations can perceive flux changes in a certain place, i.e., more observations are assimilated to optimize flux at a farther place, but flux information perceived by the farther observation is weaker and can introduce false correlation. Therefore, the invention comprehensively considers the calculated amount and the transmission distance and adopts one circle as an assimilation window. While localization techniques were further introduced to reduce the false correlation effect, with the localization scale set at 3000 km. Perturbed flux samples and calculated atmospheric CO over an observed 500km range2Fluxes with concentration dependence greater than 0 are optimized; within the range of 500-3000km, the correlation is significant (p)<0.05) flux is also optimized; the flux in the range over 3000km is not optimized.
The optimized pollutant emission is obtained by combining the observation data y and the observation error R
K＝PbHT(HPbHT+R)-1(7)
H is an observation operator, model state variables are interpolated into an observation space from a model space, and K is a gain matrix, and the weights of the background field and the observation are determined. In each of the analysis steps, it is preferred that,
as an optimal estimate of flux. XCO for model simulation2(i.e. the
) First interpolation to satellite XCO2Then calculated according to the following equation:
where j represents the search layer and x represents the simulated CO2Profile, A (x) represents a mapping matrix,
representing a priori XCO2，hjRepresenting a pressure weight function, αjIs the average kernel of the satellite column,
is a priori CO of the search2A profile line. Removing simulated CO2Outside the contour, other parameters come from XCO2And (5) producing the product. The assimilation system of the invention, MOZART-4 and satellite data construct a data conversion interface, so that the assimilation system directly assimilates the concentration of the satellite column to invert the carbon flux on the ground.
Examples
The assimilation system performed the inversion of global land ecosystem, ocean carbon flux and anthropogenic carbon emissions from month 1 in 2010 to month 12 in 2015 with annual a priori uncertainties set at 100%, 40% and 20%, respectively, and uncertainties for the scaling factor γ set at 3, 5 and 0.5, respectively. Assimilated XCO2The observation data are GOSAT ACOS v7.3 products. CO with Prior and posterior fluxes, respectively2And (4) concentration simulation, namely verifying the assimilation effect by comparing with observation.
FIG. 2 shows the weft averaged XCO2Simulated deviations at different latitudes. And GOSAT XCO2Retrieval compares, basically, a priori XCO at different latitudes2All mean deviations in weft direction of greater than 1ppm, with a global mean of 1.8. + -. 1.3ppm (mean. + -. standard deviation), but for the posterior XCO2Most of the mean deviations were within. + -. 0.5ppm, with a global mean of-0.0. + -. 1.1 ppm. XCO retrieved by simulation and GOSAT2The global mean RMSE between concentrations also decreased from the previous 2.2ppm to 1.1ppm, indicating simulated and retrieved XCO2The model data mismatch error between them is significantly reduced. In general, the southern hemisphere is less divergent than the northern hemisphere. In the same hemisphere, the deviation of the low latitude area is smaller than that of the high latitude area.
Due to the fact that the satellite observation data volume is large, the data quality of different regions is greatly different, and the satellite observation data quality is guaranteed and the calculation cost is reduced. The invention is to XCO2The data was processed as follows: 1) using the wan _ levels and xco2Two parameters, quality _ flag, are used to quality control the data. Only xco2Data with quality flag greater than 0 is employed, in addition, the selected data is divided into three groups based on the value of war _ levels, less than 8, greater than 9, less than 12, and greater than 13, with smaller values indicating higher data quality.2) the data is resampled to a grid resolution of 1 deg. × 1 deg. with each grid having a preference for high quality data to be averaged.3) to make XCO the data more likely to be averaged2The observation and the observation error can be mutually independent and are based on the optimal estimation theory to XCO2And (3) carrying out super observation processing on the observed value:
where j is the observation identifier, m is the number of observations of a super observation grid, yjRepresentative of an observation, rjIs composed of
Observation error of (2), xijIndicating the ith exhaust drive and the jth observation yjThe corresponding analog concentration.
Is a weight factor, ynewIs a super observed value, rnewFor corresponding observation errors, xnew，iAnalog values driven for the ith set of samples. As the number of observations introduced in the super-observation increases, the error decreases continuously.
FIG. 3 shows XCO2Spatial distribution of a posteriori simulations. The results show that in most grids, the error is within + -1 ppm (-80%). In the tropical pacific, north atlantic and tropical land, most are positive deviations, while in the northern temperate land, more negative deviations predominate. This mode may be related to inversion errors, and large deviations in high latitude areas may be due to large satellite retrieval errors due to low solar altitude. In general, the present assimilation systems and methods were related to GOSAT XCO in the present study2The retrieval is more consistent.
Fig. 4 shows the difference in the 2010-2015 annual average prior and posterior land ecosystem, ocean carbon flux, and artificial fossil fuel carbon emissions. For a priori terrestrial ecosystem carbon flux, carbon sequestration occurs primarily in the eastern North America,Amazon, southern brazil, western europe, southern russia, eastern china, southern asia, and the archipelagic zone; carbon sources are mainly distributed in western north america, eastern amazon, argentina, major africa, central and south peninsula, and parts of eastern europe and russia. For a priori ocean flux, carbon sequestration occurs primarily in mid-latitude regions, while carbon sources are distributed primarily in tropical and south oceans. In GOSAT XCO2Under the constraints, the overall pattern of carbon sink and carbon source is similar a priori. But land ecosystems in east, central america, amazon east, tropical africa, central-south peninsula, russian southwest, etc. have significantly increased carbon sink, while in north america west, south american temperate, africa outside the subtropics, south asia, south southwest china, north china, siberian, and parts of europe south and north, the carbon source has increased. For ocean carbon flux, carbon sink is slightly increased in most tropical and northern hemisphere oceans, while carbon source is slightly increased in most southern hemisphere oceans. For artificial carbon emissions, the optimized carbon emissions are lower than the prior value in the east of china, the east of the united states, europe, etc., which indicates that in these areas, the prior carbon emissions may be overestimated, the overestimated magnitude being most significant in the united states, and the second being weakest in china. In contrast, there is a significant underestimation in india, western united states, argentina, brazil, chile, and western china.
It should be noted that, in the above embodiments, the parameters can be freely selected according to different situations without contradiction. The present invention is not further described with respect to various possible parameter schemes in order to avoid unnecessary repetition.
Claims (7)
1. Based on satellite CO2The method for observing and inverting the earth surface carbon flux by using the column concentration is characterized by comprising the following working steps of:
step 1, constructing an EnKF carbon flux inversion system and coupling the EnKF carbon flux inversion system to a MOZART-4 global atmospheric chemical transmission model;
step 2, prior CO2Flux is input into an atmosphere model MOZART-4 to simulate CO by obtaining a set sample through Gaussian random disturbance2The concentration of the active ingredients in the mixture is,assimilating the observation data of corresponding position and time to obtain optimized CO2Flux;
step 3, inputting the optimized flux into the MOZART-4 model again for operation, and generating a new initial field for the flux optimization of the next synchronization window; and (5) repeating the step (2) to carry out cyclic assimilation.
2. The satellite CO-based system of claim 12The method for observing and inverting the earth surface carbon flux by using the column concentration is characterized in that the method for constructing the EnKF flux inversion system in the step 2 comprises the following steps:
step 2.1, generating a priori flux set sample:
wherein i is the disturbance sample identification,
represents the ith sample of the prior flux set,
respectively representing prior ecosystem, ocean and artificial fossil fuel carbon flux,bio，Ocnandfossilgaussian disturbance random disturbance samples with the average number of 0 standard deviations of 1 respectively representing ecosystem carbon flux, ocean carbon flux and artificial carbon emission, lambdabio，λocnAnd λfossilRespectively representing the uncertainty estimation of the prior ecosystem carbon flux, ocean carbon flux and artificial carbon emission of the grid scale; n represents the sample size;
step 2.2, calculating the covariance of the background error:
wherein the content of the first and second substances,
represents a sample average of the collection;
step 2.3, satellite observation data and error calculation;
step 2.4 assimilation window and localization scheme setting;
step 2.5, combining the observation data y and the observation error R to obtain optimized pollutant emission
3. The satellite CO-based system of claim 22The method for observing and inverting the surface carbon flux by column concentration is characterized in that the step 2.3 further comprises the step of inverting XCO2The data was processed as follows:
step 2.3.1, adopt wan _ levels and xco2Quality control is carried out on data by two parameters of quality _ flag; only xco2Data for quality flag greater than 0 is employed; the selected data is divided into three groups according to wam _ levels, which are less than 8, greater than 9, less than 12, and greater than 13, respectively, with smaller values indicating higher data quality.
Step 2.3.2, resampling the data to the grid resolution of 1 degree multiplied by 1 degree, and preferentially selecting high-quality data for averaging in each grid;
step 2.3.3 to make XCO2The observation and the observation error can be mutually independent and are based on the optimal estimation theory to XCO2And (3) carrying out super observation processing on the observed value:
where j is the observation identifier, m is the number of observations of a super observation grid, yjRepresentative of an observation, rjIs yjObservation error of (2), xijIndicating the ith exhaust drive and the jth observation yjCorresponding analog concentration;
is a weight factor, ynewIs a super observed value, rnewFor corresponding observation errors, xnew，iAn analog value driven for the ith set of samples; as the number of observations introduced in the super-observation increases, the error decreases continuously.
4. The satellite CO-based system of claim 22The method for inverting the surface carbon flux by column concentration observation is characterized in that the step 2.4 further adopts one week as an assimilation window.
5. The method for inverting the surface carbon flux based on the satellite CO2 column concentration observation according to claim 2, wherein the pollutant emission optimized in the step 2.5 is
The calculation model of (a) is:
K＝PbHT(HPbHT+R)-1(7)
wherein H is an observation operator, model state variables are interpolated from the model space to the observation space, K is a gain matrix, the weights of the background field and the observation are determined, in each analysis step,
as an optimal estimate of flux.
6. The satellite CO-based system of claim 32Method for inverting the surface carbon flux from column concentration observations, characterized by XCO for model simulations in the method2(i.e. the
) First interpolation to satellite XCO2Then calculated according to the following equation:
j represents the search level and x represents the simulated CO2Profile, A (x) represents a mapping matrix,
representing a priori XCO2，hjRepresenting a pressure weight function, ajIs the mean kernel of the satellite column, ya，jIs a priori CO of the search2Profile, excluding simulated CO2Outside the contour, other parameters come from XCO2And (5) producing the product.
7. The satellite CO-based system of claim 22The method for observing and inverting the carbon flux on the earth surface by using the column concentration is characterized in that a localization technology is further introduced to reduce false correlation influence, and the localization scale is set to be 3000 km; perturbed flux samples and calculated atmospheric CO over an observed 500km range2Fluxes with concentration dependence greater than 0 are optimized; flux with significant correlation (p < 0.05) was also optimized in the 500-3000km range; the flux in the range over 3000km is not optimized.
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