NL2032264B1 - HASM-Based XCO2 Data Fusion Method And System - Google Patents

HASM-Based XCO2 Data Fusion Method And System Download PDF

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NL2032264B1
NL2032264B1 NL2032264A NL2032264A NL2032264B1 NL 2032264 B1 NL2032264 B1 NL 2032264B1 NL 2032264 A NL2032264 A NL 2032264A NL 2032264 A NL2032264 A NL 2032264A NL 2032264 B1 NL2032264 B1 NL 2032264B1
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xco
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Yue Tianxiang
Bao Zhengyi
Zhang Lili
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Aerospace Information Research Institute Chinese Academy Of Sciences
Inner Mongolia Normal Univ
Inst Geographic Sciences & Natural Resources Res Cas
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Abstract

The application provides a HASM—based XC02 data fusion method and system. The HASM—based XC02 data fusion method, comprises: step SlOl, simulating a concentration of atmospheric C02 according to the assimilation meteorological 5 observationdataobtainedbyaiGoddardearthobservationsystem basedrniaGEOS—ChanmodeltoobtainatmosphericXCOzspace—time continuous distribution.simulation.data; and step 5102, fusing XC02 observation data obtained. by the TanSat satellite observation and atmosphere XC02 space—time continuous 10 distribution simulation data based on a HASM method to obtain space—time continuous distribution XC02 fusion data. In this way, the XC02 fusion data with space—time continuous distribution has higher data accuracy and higher resolution, laying a foundation for the study of carbon flux.

Description

HASM-Based XCO2 Data Fusion Method And System
BACKGROUND Field of Invention
The application relates to the technical field of spatial information simulation, in particular to a HASM-based XCO: data fusion method and system.
Background of the Invention
In the aspect of space-time continuous simulation, the interpolation method based on satellite observation data is influenced by a satellite observation instrument and weather conditions, the simulation result has uncertainty, and the global XCO: data monitoring is influenced to a certain extent, so that a fusion method aiming at different satellite XCO: data is provided, and the atmospheric XCO: space simulation is realized.
Among them, there are many ways to realize XCO: spatial simulation, including the simulation of XCO: concentration spatial distribution based on atmospheric chemistry model, interpolation based on spatial observation points, and so on.
In the aspect of fusion, the previous studies are more focused on data fusion between satellites or different XCO: algorithm products, and lack of data fusion for different sources.
Therefore, there is a need to provide an improved technical solution to the above-mentioned deficiencies of the prior art.
Summary
It is an object of the present application to provide a
HASM-based XCO: data fusion method and system to solve or alleviate the above-mentioned problems in the prior art.
In order to achieve the above object, the present application provides the following technical solutions.
The application provides a HASM-based XCO: data fusion method, comprises: step S101, simulating a concentration of atmospheric CO: according to the assimilation meteorological observation data obtained by a Goddard earth observation system based on a GEOS-Chem model to obtain atmospheric XCO: space-time continuous distribution simulation data; and step S102, fusing
XCO: observation data obtained by the TanSat satellite observation and atmosphere XCO; space-time continuous distribution simulation data based on a HASM method to obtain space-time continuous distribution XCO: fusion data.
Preferably, in the step 5101, simulating the concentration of atmospheric CO: according to the assimilationmeteorological observation data obtained by the Goddard earth observation system based on the GEO-Chem model to obtain an atmospheric
CO, profile, and calculating the atmospheric CO: profile according to the pressure weight function of atmospheric CO: concentration to obtain atmospheric XCO: space-time continuous distribution simulation data: wherein, according to the formula: f J Le: pol, (Pes | Pes De LO & ( Pp mbes Jee n= | Pours calculating the pressure weight function of atmospheric CO:
concentration; in the formula, h represents a pressure weight function of atmospheric CO; concentration, 1 represents the number of layers for layering the atmosphere according to the equal air pressure surface, and 1 is a positive integer; Psut represents the surface pressure; Di, Pii represent pressures at the upper and lower boundaries of the i-th layer, respectively.
Preferably, in step S101: obtaining the assimilation meteorological observation data according to the Goddard earth observation system based on the GEOS-Chem model, simulating the concentration of atmospheric CO; according to a preset step length, to obtain the atmospheric CO: profile.
Preferably, in step S102, fusing the mean value of the XCO: observation data of the TanSat satellite in the same grid with the atmospheric XC0O: space-time continuous distribution simulation data after the atmospheric grid division is performed, so as to obtain space-time continuous distribution
XCO, fusion data based on the HASM method; wherein the atmosphere is divided with a spatial resolution of 0.1 ° x0.1 °.
Preferably, the HASM-based XCO: data fusion method further comprises: verifying the space-time continuous distribution
XCO: fusion data according to the atmosphere XCO: ground observation data obtained by observation of the ground TCCON observation station.
The embodiment of present application further provides a
HASM-based XCO: data fusion system, comprises: a simulation unit configured to simulate a concentration of atmospheric CO: according to assimilation meteorological observation data obtained by the Goddard earth observation system based on the
GEOS-Chem model to obtain atmospheric XCO: space-time continuous distribution simulation data; and a fusion unit configured to fuse XCO: observation data obtained by the TanSat satellite observation and atmosphere XC0O; space-time continuous distribution simulation data based on a HASM method to obtain space-time continuous distribution XCO: fusion data.
Preferably, the simulation unit is further configured to, simulate the concentration of atmospheric CO: according to the assimilation meteorological observation data obtained by the
Goddard earth observation system based on the GEOS-Chem model, so as to obtain an atmospheric CO: profile; and calculate the atmospheric CO: profile according to the pressure weight function of atmospheric C02 concentration to obtain atmospheric XCO2 space-time continuous distribution simulation data; wherein, according to the formula: f 3 i
Pasa Pil | Pai — Pe} 1 fy = > Pete) + 2e) % nip JA ms Fa calculating the pressure weight function of atmospheric CO: concentration; in the formula, h represents the pressure weight function of atmospheric CO: concentration, 1 represents the number of layers for layering the atmosphere according to an equal air pressure surface, and i is a positive integer; psurr represents the surface pressure; p represents the pressure.
Preferably, the simulation unit is further configured to, obtain the assimilation meteorological observation data according to the Goddard earth observation system based on the
GEOS-Chem model, and simulate the concentration of atmospheric
CO; according to a preset step length to obtain the atmospheric
CO; profile.
Preferably, the fusion unit is further configured to , fuse the mean value of the XCO0; observation data of the TanSat 5 satellite in the same grid with the atmospheric XCO: space-time continuous distribution simulation data after the atmospheric grid division is performed, so as to obtain space-time continuous distribution XCO: fusion data based on the HASM method; wherein the atmosphere is divided with a spatial resolution of 0.1 ° x0.1 °.
Preferably, the HASM-based XCO- data fusion system further comprises: a verification unit configured to verify the space-time continuous distribution XCO: fusion data according to the atmosphere XCO: ground observation data obtained by observation of the ground TCCON observation station.
Compared with the closest prior art, the technical solutions of the embodiments of present application have the following beneficial effects:
In the cardinal scheme provided in the embodiments of present application, based on the GEOS-Chem model, according to the assimilation meteorological observation data obtained by the Goddard Earth observation system, the atmospheric CO: concentration is simulated, and the obtained simulation data of the atmospheric XCO: space-time continuous distribution simulation data is planar data, which simulates the overall distribution trend of the atmospheric XCO:; the observation data of XCO: observed by TanSat satellite are point data, which represent the local details of atmospheric XCO:; based on the
HASM method, the XCO; observation data obtained from the TanSat satellite observation and the atmospheric XCO: space-time continuous distribution simulation data are fused to obtain the data surface which not only retains the local details of the atmospheric XCO., but also reflects the overall distribution trend of the atmospheric XCO: , that is, the space-time continuous distribution XCO: fusion data; therefore, the space-time continuous distribution XCO: fusion data has higher data accuracy and higher resolution, which lays the foundation for the study of carbon flux.
BRIEF DESCRIPTION OF THE DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this application, are included to provide a further understanding of the application, and the description of the exemplary embodiments and illustrations of the application are intended to explain the application and are not intended to limit the application. Wherein:
Fig.l is a schematic flow diagram of the HASM-based XCO: data fusion method according to some embodiments of the present application;
Fig.2 is a logical block diagram of the HASM-based XCO: data fusion method provided in accordance with some embodiments of the present application;
Fig.3 is a schematic illustration of the distribution of atmospheric CO; concentration from January to December 2018 provided in accordance with some embodiments of the present application;
Fig.4 is a schematic diagram of the distribution of XCO: observed by the TanSat nadir mode in the global land area from
:
January to December 2018 according to some embodiments of the present application;
Fig.5 is a schematic diagram of the HASM fusion results of
TanSat and GEOS-Chem from January to December 2018 provided in accordance with some embodiments of the present application;
Fig.6 is a 3D schematic diagram of the spatiotemporal distribution of errors in TanSat and GEOS-Chem fusion results from January to December 2018 provided in accordance with some embodiments of the present application;
Fig.7 is a schematic diagram of the HASM-based XCO: data fusion system provided in accordance with some embodiments of the present application.
DETAILED DESCRIPTION OF THE INVENTION
The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments. The various embodiments are provided by way of interpretation of the application and not limiting the application. Indeed it will be apparent to those skilled in the art that modifications and variations may be made in the present application without departing from the scope or spirit of the application. For example features shown or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. It is therefore desirable that the application encompass such modifications and variations falling within the scope of the appended claims and their equivalents.
Fig.l is the schematic flow diagram of the HASM-based XCO: data fusion method according to some embodiments of the present application; Fig.2 is the logical block diagram of the
HASM-based XCO: data fusion method provided in accordance with some embodiments of the present application; As shown in Fig.l and Fig.2, the HASM-based XCO: data fusion method comprises: step S101 and step S102.
Step S101 is simulating the concentration of atmospheric
CO, according to the assimilation meteorological observation data obtained by the Goddard earth observation system based on the GEOS-Chem model to obtain atmospheric XCO: space-time continuous distribution simulation data.
In the embodiment of the present application, large-scale (long time series) meteorological element (atmospheric CO: concentration) data is simulated by the atmospheric chemical transmission model GEOS-Chem according the assimilation meteorological observation data obtained by the Goddard earth observation system, and XCO: space-time continuous distribution data (planar data) required for observation time is obtained to reflect a spatial distribution trend of the atmospheric XCO: (as shown in Fig. 3).
In the embodiment of the present application, particularly, simulating the concentration of atmospheric CO: according to the assimilation meteorological observation data obtained by the Goddard earth observation system based on the GEO-Chem model to obtain an atmospheric CO: profile, and calculating the atmospheric CO: profile according to the pressure weight function of atmospheric CO; concentration to obtain atmospheric
XCO; space-time continuous distribution simulation data.
Wherein, the pressure weight function of atmospheric
COzconcentration is calculated according to the formula (1), and the formula (1) is as follows: / | Ë % he = [otitis … (1) \ n= / \ n= J Bsurs in the formula, h represents the pressure weight function of atmospheric CO: concentration, 1 represents the number of layers for layering the atmosphere according to the equal air pressure surface, and 1 is a positive integer; psurr represents the surface pressure; pi, Pii represent pressures at the upper and lower boundaries of the i-th layer, respectively.
In the embodiments of the present application, according to the isobaric surface, the atmosphere is divided into n lavers i€{l, 2, 3...n), where i and n are both positive integers.
Wherein, the pressure weight function of atmospheric CO; concentration for the first layer of the atmosphere is: 3 Ba 2 1
BTR In 22 ours
Pi
The pressure weight function of atmospheric CO: concentration for the n-th layer of the atmosphere is:
in nti Bours 2 xn ;
Where Pn +1 is the pressure of the upper boundary layer of the n-th layer, and p. + = 0.
That is, when 1 =1, h; takes a value of a parenthesized function on the right side of the plus sign in the absolute value sign in formula (1), when i = n, h, takes a value of a parenthesized function on the left side of the plus sign in the absolute value sign in formula (1), and when i€ (2, 3, n-1), h, takes a value of formula (1).
In the embodiments of the present application, further, obtaining the assimilation meteorological observation data according to the Goddard earth observation system based on the
GEOS-Chem model, simulating the concentration of atmospheric
CO: according to a preset step length to obtain the atmospheric
CO: profile.
In the embodiments of the present application, the preset step length is 6 hours, namely based on the GEO5-Chem model, according to the assimilation meteorological observation data obtained by the Goddard earth observation system, the concentration of atmospheric CO: is simulated by taking hours as a unit, and a simulation result is output every 6 hours on average. Wherein, the smaller the preset step length is, the higher the output accuracy of the simulation result is.
Step S102 is fusing XCO: observation data obtained by the
TanSat satellite observation and atmosphere XCO: space-time continuous distribution simulation data based on the HASM method to obtain space-time continuous distribution XCO: fusion data.
In the embodiments of the present application, although the large-scale observation can be realized by observing the space-time distribution of XCO; through satellites, due to the limitations of satellite operating orbits and data resolution, there are many vacancies between the observation data of each orbit, and the types of satellite observation data are in the spatial data vector format belongs to "point data”, that is, the XCO: observation data obtained by the TanSat satellite is point data (as shown in Fig.4), although it can represent the local details of atmospheric XCO:, it cannot obtain continuous spatial distribution information.
While through the atmospheric chemical transmission model, large-scale {long time series) meteorological element (atmospheric CO: concentration) data is simulated to obtain
XCO: space-time continuous distribution data (planar data) required for observation time, to reflect the spatial distribution trend of the atmospheric XCO;.
Based on HASM (High Accuracy Surface Modeling) method, taking XCO: observation data obtained from TanSat satellite observation of point data as optimal control condition, taking the atmospheric XCO, space-time continuous distribution simulation data as the driving field, to fuse the XCO: observation data obtained by TanSat satellite and the atmospheric XCO: space-time continuous distribution simulation data, which can not only retain the spatial distribution trend of atmospheric XCO: space-time continuous distribution simulation data, but also retain the characterization of the local detail information of atmospheric XCO: from the XCO; observations obtained from
TanSat satellite observations, and generate a data surface that retains sufficient detail information and reflects the overall trend, that is, XCO: fusion data of space-time continuous distribution (as shown in Fig.5). Therefore, the XC0, fusion data with space-time continuous distribution has higher data accuracy and higher resolution, which lays a foundation for the study of carbon flux.
In the embodiments of the present application, particularly, based on the HASM method, fusing the mean value of the XCO; observation data of the TanSat satellite in the same grid with the atmospheric XCO; space-time continuous distribution simulation data after the atmospheric grid division is performed, so as to obtain space-time continuous distribution
XCO: fusion data; wherein the atmosphere is divided with a spatial resolution of 0.1° x0.1°.
In the embodiments of the present application, particularly, the atmosphere 1s divided with a spatial resolution of 0.1°x0.1°, and there may be multiple observations in the same grid, the mean value of the multiple observations is extracted as the optimal control condition of the corresponding grid, and is fused with atmospheric XCO: space-time continuous distribution simulation data in the corresponding grid.
In some alternative embodiments, the HASM-based XCO: data fusion method further comprises: verifying the space-time continuous distribution XCO: fusion data according to the atmosphere XCO: ground observation data obtained by observation of the ground TCCON observation station.
In the embodiments of the present application, the atmospheric XCO: ground observation data obtained by the ground TCCON observation station has high accuracy, and the use of it to verify the XCO: fusion data with space-time continuous distribution can effectively test the validity and accuracy of the HASM-based XCO: data fusion method , and lay the foundation for the research of carbon flux. Specifically, by comparing the grid value of XCO: extracted from the corresponding ground TCCON observation station in the space-time continuous distribution XCO: fusion data with the ground observation data of atmospheric XCO:, the error in the
XCO: fusion data with space-time continuous distribution is analyzed to verify the space-time continuous distribution XCO: fusion data (as shown in Fig.6).
For example, the minimum error (MIN), the maximum error (MAX), the mean error (ME), the absolute mean error (MAE) and the standard error (ESD) of the ground TCCON observation station within a predetermined time are counted. Specifically, under the same space-time matching rule, the atmospheric XCO: ground observation data of the ground TCCON observation station are matched with the XCO; observation data of the TanSat satellite, the GOSAT satellite and the OCO-2 satellite in a synchronous space-time manner, and the atmospheric XCO: ground observation data of the ground TCCON observation station are compared with the mean values of the atmospheric XCO: ground observation data of the TanSat satellite, the GOSAT satellite and the OCO-2 satellite under the same space-time respectively, so as to obtain the error results of the atmospheric XCO 2 ground observation data of the TanSat satellite, the GOSAT satellite and the OCO-2 satellite and the atmospheric XCO: ground observation data of the ground TCCON observation station under the same space-time, to extract the maximum error, the minimum error, the mean error, the absolute mean error and the standard error.
In the embodiments of the present application, under the space-time matching rules of + 2h and + 1 °, the same space-time matching is performed on the atmospheric XCO: ground observation data of the ground TCCON observation station and the XCO: observation data of the TanSat satellite, the GOSAT satellite and the OCO-2 satellite, respectively.
Table 1 shows the error statistics of the atmospheric XCO: space-time continuous distribution simulation data and the atmospheric XCO: ground observation data obtained based on the
GEOS-Chem model, and error statistics of the obtained XCO: fusion data with space-time continuous distribution and atmospheric XCO: ground observation data after the atmospheric
XCO: space-time continuous distribution simulation data being fused with the XCO: observation data obtained by the TanSat satellite observation. As shown in Table 1, the error statistics of the space-time continuous distribution XCO: fusion data and the atmospheric XCO; ground observation data are greatly improved compared with the error statistics of the driving field GEOS-Chem and the TCCON observation station. For example, the ME and MAE of the driving field are 1.51 ppm and 2.45 ppm, respectively. the ME and MAE after TanSat fusion are reduced to 0.64 ppm and 1.62 ppm, respectively, in the XCO: fusion data of space-time continuous distribution , MAE can better reflect the error change, and the accuracy after fusion is improvedby 0.83 ppm. In terms of standard deviation of error, the mean value of ESD after fusion is 1.9 ppm, which is 0.7 ppm lower than that of driving field of 2. 6ppm. Table 1 is shown below:
Tablel GEOS-Chem driving field and fusion result error statistics
Data MIN MAX ME MAE ESD
GEOS-Chem -5.94 6.64 1.51 2.45 2.6
TanSat -5.05 4.94 0.64 1.62 1.9
Note:Unit (ppm)
Fig.7 is the schematic diagram of the HASM-based XCO: data fusion system provided in accordance with some embodiments of the present application; As shown in Fig.”7, the HASM-based XCO: data fusion system comprises: simulation unit 701 and fusion unit 702. The simulation unit 701 is configured to simulate the concentration of atmospheric CO; according to the assimilation meteorological observation data obtained by the
Goddard earth observation system based on the GEOS-Chem model to obtain atmospheric XCO: space-time continuous distribution simulation data; and the fusion unit 702 is configured to fuse XCO: observation data obtained by the TanSat satellite observation and atmosphere XCO: space-time continuous distribution simulation data based on the HASM method to obtain space-time continuous distribution XCO: fusion data.
In some alternative embodiments, the simulation unit 701 is further configured to, simulate the concentration of atmospheric CO: according to the assimilation meteorological observation data obtained by the Goddard earth observation system based on the GEOS-Chem model, so as to obtain the atmospheric CO: profile; and calculate the atmospheric CO: profile according to the pressure weight function of atmospheric CO, concentration to obtain atmospheric XCO; space-time continuous distribution simulation data; wherein, according to the formula: nh vo A] fy = (+ + Mn 2 + [> - Bir |L 3 piri } 4 Intl Ji Dory
FN By J De Ji calculating the pressure weight function of atmospheric CO: concentration; in the formula, h represents the pressure weight function of atmospheric CO: concentration, 1 represents the number of layers for layering the atmosphere according to the equal air pressure surface, and 1 is a positive integer; psurr represents the surface pressure; p represent the pressure.
In a specific example, the simulation unit 701 is further configured to, based on the GEOS-Chem model, according to the assimilation meteorological observation data obtained by the
Goddard earth observation system, simulate the concentration of atmospheric CO: according to a preset step length to obtain the atmospheric CO: profile.
In some alternative embodiments, the fusion unit 702 is further configured to, based on the HASM method, fuse the mean value of the XCO: observation data of the TanSat satellite in the same grid with the atmospheric XCO: space-time continuous distribution simulation data after the atmospheric grid division is performed, so as to obtain space-time continuous distribution XCO: fusion data based on the HASM method; wherein the atmosphere is divided with a spatial resolution of 0.1 ° x0.1 °.
In some alternative embodiments, the HASM-based XCO: data fusion system further comprises: a verification unit configured to verify the space-time continuous distribution
XCO: fusion data according to the atmosphere XCO: ground observation data obtained by observation of the ground TCCON observation station.
The HASM-based XCO: data fusion system provided by the embodiments of the present application can implement the processes and steps of any of the above-mentioned HASM-based
XCO: data fusion method embodiments, and achieve the same technical effect, which is not repeated here.
The above descriptions are only preferred embodiments of the present application, and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included within the protection scope of this application.

Claims (10)

CONCLUSIESCONCLUSIONS 1. Werkwijze voor het fuseren van HASM-gebaseerde XCOs-data, omvattende: stap S101: het simuleren van een concentratie van at- mosferisch CO: volgens assimilatiedata van meteorologische waarnemingen die door een Goddard-aardobservatiesysteem, gebaseerd op een GEOS-Chem-model, verkregen worden om si- mulatiedata van continue verspreiding van atmosferisch XCO; in ruimte-tijd te verkrijgen; en stap S102: het fuseren van waarnemingsdata van XCO:, verkregen door waarnemingen van een TanSat-satelliet, en simulatiedata van continue verspreiding van atmosferisch XCO2 in ruimte-tijd, gebaseerd op een HASM-werkwijze, om fusiedata van continue verspreiding van XCO: in ruimte- tijd te verkrijgen.1. Method for fusing HASM-based , are obtained to simulate data of continuous distribution of atmospheric XCO; obtainable in space-time; and step S102: fusing observational data of XCO:, obtained by observations from a TanSat satellite, and simulation data of continuous distribution of atmospheric obtain space-time. 2. Werkwijze voor het fuseren van HASM-gebaseerde XCO:- data volgens conclusie 1, met het kenmerk dat stap S101 gespecificeerd wordt door: het simuleren van de concentratie van atmosferisch CO: volgens assimilatiedata van meteorologische waarnemingen die door het Goddard-aardobservatiesysteem, gebaseerd op het GEOS-Chem-model, verkregen worden om een atmosferisch CO:-profiel te verkrijgen, en het berekenen van het atmos- ferische CO:-profiel volgens de druk-gewicht-functie van atmosferische CO:-concentratie om simulatiedata van conti- nue verspreiding van atmosferisch XCO: in de ruimte-tijd te verkrijgen: waarbij volgens de formule:A method for fusing HASM-based on the GEOS-Chem model, are obtained to obtain an atmospheric CO: profile, and calculating the atmospheric CO: profile according to the pressure-weight function of atmospheric CO: concentration to obtain simulation data of continuous current distribution of atmospheric XCO: in space-time can be obtained: where according to the formula: Di+1 — Pi Di+1 7 Pi 1 ssh Int In== [| Dsurf Di Di de druk-gewicht-functie van atmosferische CO:- concentratie berekend wordt; waarbij in de formule, h de druk-gewicht-functie van atmosferische CO: concentratie vertegenwoordigt, i het aantal lagen voor de gelaagdheid van atmosfeer volgens een oppervlak van gelijke luchtdruk vertegenwoordigt, en i een positief geheel getal is; pues oppervlakdruk vertegenwoor- digt, pi, Pii drukken bij respectievelijk de bovenste en onderste grenzen van de i-th-laag vertegenwoordigen.Di+1 — Pi Di+1 7 Pi 1 ssh Int In== [| Dsurf Di Di the pressure-weight function of atmospheric CO: concentration is calculated; where in the formula, h represents the pressure-weight function of atmospheric CO: concentration, i represents the number of layers for the stratification of atmosphere along a surface of equal air pressure, and i is a positive integer; pues represents surface pressure, pi, Pii represent pressures at the upper and lower boundaries of the i-th layer, respectively. 3. Werkwijze voor het fuseren van HASM-gebaseerde XCO:-data volgens conclusie 2, met het kenmerk dat stap S101 omvat: het verkregen van de assimilatiedata van meteorolo- gische waarnemingen volgens het Goddard-aardobservatie- systeem, gebaseerd op het GEOS-Chem-model, het simuleren van de concentratie van atmosferisch CO: volgens een vooraf ingestelde staplengte om het atmosferi- sche CO;-profiel te verkrijgen. Method for fusing HASM-based model, simulating the concentration of atmospheric CO: according to a preset step length to obtain the atmospheric CO profile. 4, Werkwijze voor het fuseren van HASM-gebaseerd XCO:- data volgens conclusie 1, met het kenmerk dat stap S102: het fuseren van de gemiddelde waarde van de waarne- mingsdata van XCO; van de TanSat-satelliet in hetzelfde raster met simulatiedata van continue verspreiding van het atmosferische XCO: in ruimte-tijd, nadat de atmosferische rasterverdeling uitgevoerd is, om fusiedata van continue verspreiding van XCO:s in ruimte-tijd, gebaseerd op de HASM-werkwijze, te verkrijgen, waarbij de atmosfeer met een ruimteresolutie van 0,1 ° x 0.1 ° verdeeld is.A method for fusing HASM-based XCO: data according to claim 1, characterized in that step S102: fusing the average value of the observation data of XCO; of the TanSat satellite in the same grid with simulation data of continuous distribution of the atmospheric XCO:s in space-time, after the atmospheric grid division has been performed, to merge data of continuous distribution of , where the atmosphere is divided with a spatial resolution of 0.1° x 0.1°. 5. Werkwijze voor het fuseren van HASM-gebaseerde XCOz;-data volgens één van de conclusiel 1-4, met het ken-5. Method for fusing HASM-based XCO2 data according to any one of claims 1 to 4, with the characteristic merk dat de werkwijze voor het fuseren van HASM-gebaseerde xCO:-data verder: het verifiëren omvat van fusiedata van continue ver- spreiding van XCO: in ruimte-tijd volgens de grondwaarne- mingsdata van atmosferisch XCO:, verkregen door waarnemin- gen van het TCCON-grondwaarnemingsstation.note that the method for fusing HASM-based xCO: data further includes: verifying fusion data of continuous distribution of XCO: in space-time according to the ground observation data of atmospheric the TCCON ground observation station. 6. HASM-gebaseerd XCO:-datafusiesysteem, omvattende een simulatie-eenheid die geconfigureerd is om een concentratie van atmosferisch CO: volgens een assimilatie- data van meteorologische waarnemingen te simuleren die door het Goddard-aardobservatiesysteem, gebaseerd op het GEOS-Chem-model, verkregen worden om simulatiedata van continue verspreiding van atmosferisch XCO; in ruimte-tijd te verkrijgen; en een fusie-eenheid die geconfigureerd is om waarne- mingsdata van XCO:, verkregen door waarnemingen van de TanSat-satelliet, en simulatiedata van continue versprei- ding van atmosferisch XCO: in ruimte-tijd, gebaseerd op een HASM-werkwijze, te fuseren om fusiedata van continue verspreiding van XCO: in ruimte-tijd te verkrijgen.6. HASM-based , are obtained to simulate data of continuous distribution of atmospheric XCO; obtainable in space-time; and a fusion unit configured to fuse observational data of XCO:, obtained from observations of the TanSat satellite, and simulation data of continuous distribution of atmospheric to obtain merger data of continuous propagation of XCO: in space-time. 7. HASM-gebaseerd XCO;-datafusiesysteem volgens con- clusie 6, met het kenmerk dat de simulatie-eenheid verder geconfigureerd is om de concentratie van atmosferisch CO: volgens de assimilatiedata van meteorologische waarnemin- gen te simuleren die door het Goddard- aardobservatiesysteem, gebaseerd op het GEOS-Chem-model, verkregen worden om een atmosferisch CO:-profiel te ver- krijgen en het berekenen van het atmosferische CO:-profiel volgens de druk-gewicht-functie van atmosferische CO:- concentratie om simulatiedata van continue verspreiding van atmosferisch XCO: in ruimte-tijd te verkrijgen, waar- bij volgens de formule: n= [- Pin 2) + | Dit 2 : Pi Di+1 Pi Di+1 In 1 n= J|Dsurf Pi Di de druk-gewicht-functie van atmosferische CO:- concentratie berekend wordt; waarbij in de formule h de druk-gewicht-functie van atmosferische CO:-concentratie vertegenwoordigt, i het aantal lagen van de gelaagdheid van atmosfeer volgens een oppervlak van gelijke luchtdruk vertegenwoordigt en i een positief geheel getal iS; Psut oppervlakdruk vertegenwoor- digt; p de druk vertegenwoordigt.7. HASM-based based on the GEOS-Chem model, can be obtained to obtain an atmospheric CO: profile and calculate the atmospheric CO: profile according to the pressure-weight function of atmospheric CO: concentration to simulate data of continuous dispersion of atmospheric XCO: can be obtained in space-time, where according to the formula: n= [- Pin 2) + | This 2 : Pi Di+1 Pi Di+1 In 1 n= J|Dsurf Pi Di the pressure-weight function of atmospheric CO: concentration is calculated; where in the formula h represents the pressure-weight function of atmospheric CO: concentration, i represents the number of layers of the stratification of atmosphere along a surface of equal air pressure and i is a positive integer iS; Psut represents surface pressure; p represents the pressure. 3. HASM-gebaseerd XCO:-datafusiesysteem volgens con- clusie 7, met het kenmerk dat de simulatie-eenheid verder geconfigureerd is om assimilatiedata van meteorologische waarnemingen volgens het Goddard-aardobservatiesysteem, gebaseerd op het GEOS-Chem-model, te verkrijgen, en het simuleren van de concentratie van atmosferisch CO: volgens een vooraf ingestelde staplengte om het atmosferische CO:- profiel te verkrijgen.3. HASM-based simulating the concentration of atmospheric CO: according to a preset step length to obtain the atmospheric CO: profile. 9. HASM-gebaseerd XCO:-datafusiesysteem volgens con- clusie 6, met het kenmerk dat de fusie-eenheid verder ge- configureerd is om de gemiddelde waarde van de waarne- mingsdata van XCO: van de TanSat-satelliet in hetzelfde raster met de simulatiedata van continue verspreiding van het atmosferische XCO: in ruimte-tijd, nadat de atmosferi- sche rasterverdeling uitgevoerd is, te fuseren om fusieda- ta van continue verspreiding van XCO:, gebaseerd op de HASM-werkwijze, waarbij de atmosfeer met een ruimteresolu- tie van 0.1 ° x 0.1 ° verdeeld is.9. HASM-based simulation data of continuous distribution of the atmospheric XCO: in space-time, after the atmospheric grid division has been carried out, to fuse fusion data of continuous distribution of is divided by 0.1° x 0.1°. 10. HASM-gebaseerd XCO:-datafusiesysteem volgens één van de conclusies 6-9, met het kenmerk dat het HASM- gebaseerde XCO:-datafusiesysteem verder: een verificatie-eenheid omvat die geconfigureerd is om de fusiedata van continue verspreiding van XCO; in ruimte-tijd volgens grondwaarnemingsdata van het atmosfe- rische XCO: die van de TCCON-grondwaarnemingsstation ver- kregen worden te verifiëren.The HASM-based XCO: data fusion system according to any one of claims 6 to 9, characterized in that the HASM-based in space-time according to ground observation data from the atmospheric XCO: which are obtained from the TCCON ground observation station.
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