CN114464272A - CO and CO2Emission proportionality coefficient estimation method and device, storage medium and terminal - Google Patents
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Abstract
The invention discloses CO and CO2Emission proportionality coefficient estimation method and device, storage medium and terminal, wherein the method comprises: determining a region to be analyzed and a time to be analyzed; acquiring weather reanalysis data and gas data of a to-be-analyzed time of a to-be-analyzed area; acquiring CO assimilation grid flux data set and CO at moment to be analyzed in a preset acquisition mode2Assimilation grid flux data set, CO prediction regional flux and CO2Predicting the regional flux; obtaining CO and CO2A discharge proportionality coefficient; the gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data, CO satellite observation data, CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite. The method can obtain CO of different countries, provinces, cities and regions of the world2A proportionality coefficient with the CO having space-time characteristics; and the obtained proportionality coefficients of different areas are comparable.
Description
Technical Field
The invention relates to the technical field of atmospheric chemistry, in particular to CO and CO2Emission proportionality coefficient estimation method and apparatus, storage medium, and terminal.
Background
During the combustion and conversion of energy, sufficient combustion will produce CO2Emissions, and CO emissions are generated when combustion is inadequate, so CO is2And CO is a homologous exhaust gas. By investigating CO2The proportional relation between the emission and the CO emission is beneficial to researching the development level of regional energy technology and disclosing the artificial carbon emission rule, and data support is provided for the double-carbon target. The current research mainly utilizes the data of ground monitoring to CO2The research on the proportional relation with CO is carried out, and with the development of the remote sensing technology, regional CO is obtained by utilizing satellite observation data2And CO proportionality coefficient provide conditions.
CO is carried out by utilizing observation data of ground station2The main data source is the ground station, and satellite data is not used, and the CO is estimated2The CO proportion coefficient adopts concentration data and is artificial to CO2And the CO proportionality coefficient is directly derived from the inventory data and is not optimized by model calculation, and the factors enable the existing CO to be optimized2The estimation of space-time with CO proportionality coefficient can not be continuous and the accuracy is not high.
Disclosure of Invention
The invention aims to solve the technical problem that the CO is carried out by utilizing the observation data of the ground station2The estimation of space-time by the CO proportional coefficient estimation method cannot be continuous, and the estimation accuracy is not high.
In order to solve the technical problems, the invention provides CO and CO2An emission proportionality coefficient estimation method, comprising:
determining a region to be analyzed and a time to be analyzed;
acquiring weather reanalysis data and gas data of a to-be-analyzed time of a to-be-analyzed area;
acquiring a CO assimilation grid flux data set and CO at the time to be analyzed in a preset acquisition mode based on the meteorological reanalysis data and the gas data at the time to be analyzed of the area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2Predicting the regional flux;
predicting regional flux and the CO based on the CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on the CO assimilation grid flux data set and the CO2Assimilation grid flux data set to obtain CO and CO of each space grid2Grid discharge proportionality coefficient;
wherein the gas data comprises CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2Gas-like data including CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite.
Preferably, the preset acquiring mode is as follows:
inputting weather reanalysis data at the moment to be analyzed into a preset atmospheric chemical model to obtain weather initial boundary field data, and performing Gaussian disturbance on the weather initial boundary field data to obtain a preset number of disturbed weather initial boundary field data sets;
respectively adding preset gas data to all the disturbance meteorological initial boundary field data sets to form disturbance meteorological initial boundary field data sets with preset numbers;
interpolating flux data in class-A gas data in to-be-analyzed time gas data of a to-be-analyzed region into a spatial grid of the preset atmospheric chemical mode model to generate a grid flux data set, and performing Gaussian disturbance on the grid flux data to obtain a disturbed grid flux data set;
processing satellite observation data in class A gas data in to-be-analyzed time gas data of a to-be-analyzed area in a local mode to obtain target observation data;
on the basis of the target observation data, assimilating initial field data and the disturbance grid flux data sets in all the disturbance initial boundary field data sets by adopting an ensemble adaptive Kalman filtering method to obtain a preset number of assimilation initial field data sets and an assimilation grid flux data set of gas A;
respectively updating boundary field data in the corresponding disturbance initial boundary field data sets based on all the assimilation initial field data sets to obtain a preset number of updated boundary field data sets, wherein the assimilation initial field data sets and the updated boundary field data sets correspondingly form a preset number of assimilation initial boundary field data sets;
sequentially inputting all the assimilation initial boundary field data sets into the preset atmospheric chemical mode model to obtain a preset number of predicted gas initial boundary field data sets at the next moment of the to-be-analyzed region;
acquiring the predicted area flux of the gas A at the time of the area to be analyzed based on the area of the area to be analyzed and the assimilation grid flux data set of the gas A;
wherein the preset gas data is:
if the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time A in the area to be analyzed currently exists, performing Gaussian disturbance on the preset mixed gas initial boundary field data in the class A gas data at the time A in the area to be analyzed to obtain a preset number of disturbance preset mixed gas initial boundary field data sets, and taking the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time A in the area to be analyzed and the preset number of the disturbance preset mixed gas initial boundary field data sets as preset gas data; otherwise, respectively carrying out initial analysis on the A gas in the A gas data at the time to be analyzed in the area to be analyzedPerforming Gaussian disturbance on the boundary field data and the preset mixed gas initial boundary field data to obtain a preset number of disturbance gas initial boundary field data sets and a preset number of disturbance preset mixed gas initial boundary field data sets, and taking the disturbance gas initial boundary field data sets and the disturbance preset mixed gas initial boundary field data sets as preset gas data; a is CO or CO2。
Preferably, the step of determining the area to be analyzed and the time to be analyzed further includes:
determining a time resolution and a spatial resolution, wherein the time to be analyzed is different from the previous time of the time to be analyzed and the next time of the time to be analyzed by a time resolution; taking the initial boundary field data of the preset mixed gas, the meteorological reanalysis data, the CO ecological flux data, the CO wildfire flux data and the CO artificial flux data as state quantities in the CO assimilation process, and taking the CO as the state quantity in the CO assimilation process2Initial boundary field data, CO2Flux data and meteorological reanalysis data as CO2State quantities in the assimilation process;
and determining the atmospheric physical parameters and the chemical parameters of the preset atmospheric chemical mode model.
Preferably, the preset mixed gas initial boundary field data includes gas initial boundary field data of all gases affecting CO concentration that can be processed and currently available by the preset atmospheric chemical model.
Preferably, the CO is2Flux data is CO2Artificial flux data, CO2Ecological flux data, CO2Summation of ocean flux data and CO2 wildfire flux data.
Preferably, the preset number is greater than 20.
Preferably, the update formula for updating the boundary field data in the corresponding perturbation initial boundary field data set based on the assimilation initial field data set is as follows:
TENDnew＝BDYold+F(BDYold-INPmean)
wherein TENDnewA new value of the boundary condition is indicated,BDYoldindicating the old boundary condition value, INPmeanRepresenting the initial field data and F the distance weight function.
In order to solve the technical problems, the invention also provides CO and CO2The emission proportionality coefficient estimation device comprises a data to be analyzed determining unit, a data obtaining unit, a prediction region flux obtaining unit and a proportionality coefficient obtaining unit;
the data to be analyzed determining unit is used for determining an area to be analyzed and time to be analyzed;
the data acquisition unit is used for acquiring meteorological reanalysis data and gas data of a to-be-analyzed moment of a to-be-analyzed area;
the prediction area flux acquisition unit is used for acquiring a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed in a preset acquisition mode based on the meteorological reanalysis data and the gas data at the time to be analyzed of the area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2Predicting the regional flux;
the scale factor obtaining unit is used for predicting the regional flux and the CO based on the CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on the CO assimilation grid flux data set and the CO2Assimilating grid flux data sets to obtain CO and CO of each space grid2Grid discharge proportionality coefficient;
wherein the gas data comprises CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2The gas-like data comprises CO2 initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite.
To solve the above technical problem, the present invention further provides a storage medium having a computer program stored thereon, the computer program implementing CO and CO when executed by a processor2An emission proportionality coefficient estimation method.
In order to solve the above technical problem, the present invention further provides a terminal, including: the system comprises a processor and a memory, wherein the memory is in communication connection with the processor;
the memory is used for storing computer programs, and the processor is used for executing the computer programs stored by the memory so as to enable the terminal to execute CO and CO2An emission proportionality coefficient estimation method.
Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:
the CO and the CO provided by the embodiment of the invention are applied2Emission proportionality coefficient estimation method for mixing CO with CO2Combining the initial boundary field data with the meteorological reanalysis data at the relative moment, and introducing satellite observation data to realize data assimilation to obtain CO and CO at the relative moment2The assimilated flux data of the grid, and then the assimilated CO flux data and the assimilated CO of the same grid2The flux data is compared to obtain CO and CO2A discharge proportionality coefficient; wherein flux data is acquired for improving accuracy, and CO initial boundary field data and CO at the next moment are acquired through an atmospheric chemical model2Gas initial boundary field data is predicted as a basis for acquiring the next moment flux data. By setting the region to be analyzed and the time to be analyzed, the method of the invention can obtain CO of different countries, provinces, cities and regions of the global scope2A proportionality coefficient with the CO having space-time characteristics; and the obtained proportionality coefficients of different areas are comparable.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 shows a CO and CO embodiment of the present invention2A flow chart of the emission proportionality coefficient estimation method;
FIG. 2 shows the two COs and CO of the embodiment of the present invention2The structure schematic diagram of the emission proportionality coefficient estimation device;
fig. 3 shows a schematic structural diagram of a four-terminal according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
CO is carried out by utilizing observation data of ground station2The main data source is the ground station, and satellite data is not used, and the CO is estimated2The CO proportion coefficient adopts concentration data and is artificial to CO2And the CO proportionality coefficient is directly derived from the inventory data and is not optimized by model calculation, and the factors enable the existing CO to be optimized2The estimation of space-time with CO proportionality coefficient can not be continuous and the accuracy is not high.
Example one
In order to solve the technical problems in the prior art, the embodiment of the invention provides CO and CO2An emission proportionality coefficient estimation method.
FIG. 1 shows a CO and CO embodiment of the present invention2A flow chart of the emission proportionality coefficient estimation method; referring to FIG. 1, the present invention shows an embodiment of CO and CO2The emission proportionality coefficient estimation method includes the following steps.
Step S101, determining a region to be analyzed, a time to be analyzed, a spatial resolution and a temporal resolution.
To make it convenient forBy design, the area to be analyzed and the moment to be analyzed are synchronously set with the spatial resolution and the time resolution. Specifically, the area to be researched is set as an area to be analyzed, and the maximum longitude and latitude and the minimum longitude and latitude of the area to be analyzed are determined to be used as the acquiring position basis of the meteorological reanalysis data and the meteorological data. The time to be researched is taken as the time to be analyzed, the time to be analyzed can comprise a plurality of target moments, and each target moment is the time to obtain CO and CO2And when the proportional coefficient is discharged, a plurality of target moments can be sequentially used as moments to be analyzed by using the method provided by the embodiment of the invention. Selecting a spatial resolution similar to the satellite spatial resolution as the spatial resolution; preferably, the spatial resolution may be chosen to be similar to the spatial resolution of the satellites. Meanwhile, the time resolution is selected according to the requirement, for example, if 1 hour can be selected as the time resolution for researching the daily variation, 6 hours or days can be selected as the time resolution for researching the seasonal variation, and the like. When the target time is set, a plurality of target times are sequentially different by a time resolution.
Step S102, determining CO assimilation state quantity constitution and CO2And (3) an assimilation state quantity constitution.
Specifically, preset mixed gas initial boundary field data, meteorological reanalysis data, CO ecological flux data, CO wildfire flux data and CO artificial flux data are used as state quantities in the CO assimilation process. Simultaneously adding CO2Initial boundary field data, CO2Flux data and meteorological reanalysis data as CO2Assimilation of system state quantities as CO2The state quantities in the assimilation process. The CO assimilation state quantity composition and CO2The assimilation state quantity can be modified according to actual conditions, and is not fixedly limited.
And step S103, determining the atmospheric physical parameters and the chemical parameters of the preset atmospheric chemical mode model.
Preferably, the atmospheric physical parameters of the preset atmospheric chemical mode model may be set as: selecting mp _ physics ═ 17; meanwhile, the chemical mode parameters can be selected as follows: the CO assimilation selects chem _ opt 112 or 14, and the CO2 assimilation selects chem _ opt 16. It should also be noted that the atmospheric physical parameters and chemical parameters can be modified according to practical situations, and are not limited herein.
And step S104, acquiring weather reanalysis data and gas data of the to-be-analyzed time of the to-be-analyzed area.
Specifically, meteorological data and gas data of the to-be-analyzed time of the to-be-analyzed area need to be acquired respectively. Wherein the meteorological data includes: weather data in the format of NCAR website or other WMO GRIB2 may be downloaded to the weather providing website for the weather reanalysis data at the time of the analysis in the area to be analyzed, such as ds 083.2. The gas data includes: downloading CO initial boundary field data (such as ds313.7) and preset mixed gas initial boundary field data at an NCAR website; downloading CO2 initial boundary field data (e.g., CT2019B _ CO2_ glb) from a CarbonTracker website; downloading CO ecological flux data (e.g., MAGANv2.1), CO wildfire flux data (e.g., Finnv1.5), and CO artifact flux data (e.g., EDGARv4.3.2) from a UCAR website; downloading CO2 flux data (e.g., CT2019_ flux) from the CarbonTracker website; downloading CO satellite Observation data (e.g., MOPITT V8) and CO from NCAR websites2Satellite observations (e.g., OCO-2V 10). The data may be downloaded from another appropriate website, and is not fixedly set here.
The gas data can be divided into CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2Gas-like data including CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite. The preset mixed gas initial boundary field data comprises gas initial boundary field data of all gases which can be processed by the preset atmospheric chemical mode model and can be acquired at present and influence the CO concentration. Because the data of the initial boundary field of the gas influencing the CO concentration which can be processed by the preset atmospheric chemical model are limited, and simultaneously, not all the data of the initial boundary field of the gas influencing the CO concentration can be downloaded, the initial boundary field of the gas influencing the CO concentration which can be processed by the preset atmospheric chemical model is selectedAnd taking the intersection of the field data and the gas initial boundary field data which can be currently acquired and influences the CO concentration as preset mixed gas initial boundary field data. Table 1 shows a tabulated example of a partial impact CO concentration gas.
Table 1 table example of gas affecting CO concentration
Step S105, acquiring a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed in a preset acquisition mode based on meteorological reanalysis data and gas data at the time to be analyzed in the area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2And predicting the regional flux.
Specifically, a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed and CO are acquired in a preset acquisition mode based on meteorological reanalysis data and gas data at the time to be analyzed in an area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2And predicting the regional flux. Wherein the CO is obtained by a preset obtaining mode2Assimilation grid flux data set and CO prediction region flux and obtaining CO prediction region flux and CO2The process of predicting the regional flux is the same, so for simplicity of explanation we will use A to denote CO or CO in the preset acquisition mode2. The specific preset acquisition mode specifically comprises the following steps.
Step S501, weather reanalysis data at the moment to be analyzed is input into a preset atmospheric chemical model to obtain weather initial boundary field data, and Gaussian disturbance is performed on the weather initial boundary field data to obtain a preset number of disturbed weather initial boundary field data sets.
Specifically, since the present embodiment is applied to the atmospheric chemical mode model, the atmospheric chemical mode model is set based on the parameters set in step S101 and step S103, and the set atmospheric chemical mode model is used as the preset atmospheric chemical mode model. And then inputting the acquired weather reanalysis data of the to-be-analyzed time of the to-be-analyzed area into a preset atmospheric chemical model to acquire weather initial boundary field data, wherein the weather initial boundary field data comprises weather initial field data and weather boundary field data.
The process of acquiring the meteorological initial boundary field data based on the preset atmospheric chemical model is substantially as follows: generating spatial grid information through a geogrid program in a preset atmospheric chemical model, wherein the spatial grid information is generated based on static geographic data of a preset area, and the static geographic data is downloaded from a WPS _ GEOG website; and then extracting meteorological data matched with the space grid information from the meteorological reanalysis data by an ungrib program in the preset atmospheric chemical mode model, interpolating the meteorological data into a grid by a metgrid program in the preset atmospheric chemical mode model, and finally generating meteorological initial boundary field data by a real program in the preset atmospheric chemical mode model.
After acquiring meteorological initial boundary field data, gaussian disturbance with an average value of 0 is added to the meteorological initial boundary field data through 3DVAR (three-dimensional variational) and in order to improve data accuracy, a preset number of sets of the disturbed meteorological initial boundary field data generated through gaussian disturbance are set, and preferably, the preset number is 20. Step S502, adding preset gas data into all the disturbance meteorological initial boundary field data sets respectively to form disturbance meteorological initial boundary field data sets with preset number.
Specifically, the selected preset gas data are different according to different situations. Further, if the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time a of the area to be analyzed currently exists, that is, the previous time of the time a of the area to be analyzed is taken as the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time a, the preset mixed gas initial boundary field data in the class a gas data of the area to be analyzed at the time a of the area to be analyzed is subjected to gaussian disturbance to obtain the preset number of disturbance preset mixed gas initial boundary field data sets, and the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time a of the area to be analyzed and the preset number of the disturbance preset mixed gas initial boundary field data sets are taken as the preset gas data.
If the preset number of predicted gas initial boundary field data sets of the gas at the time a to be analyzed does not exist currently, that is, the previous target time of the time a to be analyzed is not taken as the time to be analyzed to obtain the proportionality coefficient, at this time, gaussian disturbance is performed on the initial boundary field data of the gas a and the preset mixed gas initial boundary field data in the class a gas data at the time a to be analyzed to obtain the preset number of disturbed gas initial boundary field data sets and the preset number of disturbed preset mixed gas initial boundary field data sets, and the preset number of disturbed gas initial boundary field data and the preset number of disturbed preset mixed gas initial boundary field data sets are taken as the preset gas data. Wherein the gaussian perturbations in this step are all perturbations with a mean value of 0.
And, when A is CO, it is noted that2When it is CO2The gas-like data does not include the preset mixed gas initial boundary field data, and the preset mixed gas initial boundary field data can be regarded as no data processing.
And after the preset gas data are selected, adding the preset gas data into the disturbance meteorological initial boundary field data set to form a preset number of disturbance meteorological initial boundary field data sets.
Step S503, interpolating flux data in A-type gas data in the gas data at the moment to be analyzed in the region to be analyzed into a spatial grid of a preset atmosphere chemical mode model to generate grid flux data, and performing Gaussian disturbance on the grid flux data to obtain a disturbed grid flux data set.
Specifically, the flux data in the class-A gas data in the gas data at the moment to be analyzed in the region to be analyzed is interpolated into a spatial grid of a preset atmosphere chemical model to generate grid flux data, and then the grid flux data is subjected to Gaussian disturbance to obtain a disturbed grid flux data set.
Further, when downloading the flux data from the UCAR website, the bio _ emis tool, fire _ emis tool and anti _ emis tool are also downloaded. When A is CO, an anti _ emis tool is required to be utilized, and CO artificial flux data is used as input to be interpolated into a space grid of a preset atmospheric chemical mode model so as to generate CO artificial grid flux data; interpolating the CO ecological flux data into a spatial grid of a preset atmospheric chemical mode model by using a bio _ emis tool as input so as to generate CO ecological grid flux data; and finally, interpolating the CO combustion flux data into a spatial grid of a preset atmosphere chemical mode model by using a fire _ emis tool as input to generate CO combustion grid flux data. And then respectively carrying out Gaussian disturbance with the mean value of 0 on the flux data of the CO artificial grid, the flux data of the CO ecological grid and the flux data of the CO combustion grid, respectively generating a flux data set of the CO disturbance artificial grid, a flux data set of the CO disturbance ecological grid and a flux data set of the CO disturbance combustion grid, and jointly forming a flux data set of the CO disturbance artificial grid, the flux data set of the CO disturbance ecological grid and the flux data set of the CO disturbance combustion grid.
When A is CO2When necessary, CO is added2Interpolation of flux data into a spatial grid of a preset atmospheric chemistry pattern model to generate CO2Grid flux data, and for CO2The grid flux data is subjected to Gaussian disturbance with the mean value of 0 to generate CO2Perturbing the grid flux data set. And, it is noted that CO2Flux data is CO2Artificial flux data, CO2Ecological flux data, CO2Ocean flux data and CO2Summation of wildfire flux data.
Step S504, the satellite observation data in the A-type gas data in the to-be-analyzed time gas data of the to-be-analyzed area is processed in a localized mode to obtain target observation data.
Specifically, the target observation data needs to be acquired based on the satellite observation data in the class a gas data in the gas data at the time to be analyzed. Further, when A is CO, the CO satellite observation data is processed by adopting a localization method to obtain CO target observation data, wherein the linear distance between the observations is required to be less than 0.1 radian. It should be noted that the linear distance between the observations can also be set to other values, and is not limited herein. When A isCO2When it is mixed with CO2The satellite observation data is processed by adopting a localization method to obtain CO2Target observation data, wherein the observation mean value is 10s, and the method for averaging uses a probabilistic expectation method. It should be noted that the observation mean value may also be set to other values, and is not limited herein.
And step S505, assimilating the initial field data and the disturbance grid flux data sets in all the disturbance initial boundary field data sets by adopting an ensemble adaptive Kalman filtering method on the basis of the target observation data so as to obtain assimilation initial field data sets and assimilation grid flux data sets of A gas in preset numbers.
Specifically, based on the target observation data acquired in step S504, the initial field data and the disturbance grid flux data sets in all disturbance initial boundary field data sets are respectively assimilated by using an ensemble adaptive kalman filter method, so as to acquire a preset number of assimilation initial field data sets and an assimilation grid flux data set of gas a. For example, based on CO target observation data, respectively assimilating initial field data and a CO disturbance grid flux data set in all CO disturbance initial boundary field data sets by adopting a set adaptive Kalman filtering method to obtain a preset number of CO assimilation initial field data sets and CO assimilation grid flux data sets; obtaining CO with preset number by the same principle2Assimilating initial field data set and CO2The mesh flux data set was assimilated.
Step S506, based on all the assimilation initial field data sets, respectively updating the boundary field data in the corresponding perturbation initial boundary field data sets to obtain a preset number of updated boundary field data sets, and correspondingly forming the assimilation initial field data sets and the updated boundary field data sets into a preset number of assimilation initial boundary field data sets.
Specifically, the assimilation formula for assimilating boundary field data in the corresponding perturbation initial boundary field data set based on the assimilation initial field data set is as follows:
TENDnew＝BDYold+F(BDYold-INPmean)
wherein TENDnewIndicating a new boundary condition value, BDYoldIndicating the old boundary condition value, INPmeanRepresenting the initial field data and F the distance weight function.
And step S507, sequentially inputting all assimilation initial boundary field data sets into a preset atmospheric chemical mode model so as to obtain a preset number of predicted gas initial boundary field data sets at the next moment to be analyzed in the region to be analyzed.
Specifically, all assimilation initial boundary field data sets are sequentially input into a preset atmospheric chemical model, and a preset number of predicted gas initial boundary field data sets at the next moment to be analyzed in the to-be-analyzed region can be obtained. Similarly, in step S502, a preset number of predicted gas initial boundary field data sets at the time a to be analyzed are obtained, that is, CO and CO at the time before the time a to be analyzed is obtained2The discharge proportionality coefficient is obtained at this step. Similarly, the preset number of predicted gas initial boundary field data sets of the gas A at the next moment to be analyzed obtained in the step can also be applied to the CO and the CO at the next moment to be analyzed2And a discharge proportionality coefficient obtaining step.
Step S508, the predicted area flux of the gas A at the time of the area to be analyzed is obtained based on the area of the area to be analyzed and the assimilation grid flux data set of the gas A.
Specifically, the area of the area to be analyzed is introduced, based on the assimilation grid flux data set of the gas A, the unit area average flux is firstly solved, and then the total flux is solved through the area of the area, so that the predicted area flux of the gas A at the time to be analyzed of the area to be analyzed is obtained.
A is taken as CO and CO respectively2The CO predicted area flux and the CO2 predicted area flux can be obtained by repeating the above steps S501 to S508.
Step S106, predicting regional flux and CO based on CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on CO assimilation grid flux data set and CO2Obtaining each spatial grid by assimilating the grid flux data setCO and CO2Grid discharge proportionality coefficient.
In particular, since the CO assimilation grid flux data set and CO are being acquired2The same static geographic data is adopted when the grid flux data sets are assimilated, so that the CO assimilation grid flux data sets and the CO2The assimilation mesh flux data sets have the same spatial mesh information and therefore do not need to be registered. The CO assimilation grid flux data set and CO can be used as the basis2And respectively acquiring the grid emission proportionality coefficients of CO and CO2 of each spatial grid by using the assimilation grid flux data set. Predicting regional flux and CO based on CO2Predicting regional flux capture CO and CO2Zone discharge proportionality coefficient.
It should be noted that, when the time to be analyzed includes a plurality of target times, each target sequence may be respectively used as the time to be analyzed according to the time sequence, and the above steps S101 to S106 may be repeated to obtain CO and CO at all target times in the time to be analyzed in the region to be analyzed2And (4) a discharge proportionality coefficient.
The embodiment of the invention provides CO and CO2Method for estimating emission proportionality coefficient by using CO and CO2Combining the initial boundary field data with the meteorological reanalysis data at the relative moment, and introducing satellite observation data to realize data assimilation to obtain CO and CO at the relative moment2The assimilated CO flux data of the same grid is compared with the assimilated CO2 flux data of the same grid to obtain CO and CO2A discharge proportionality coefficient; wherein flux data is acquired for improving accuracy, and CO initial boundary field data and CO at the next moment are acquired through an atmospheric chemical model2Gas initial boundary field data is predicted as a basis for acquiring the next moment flux data. By setting the region to be analyzed and the time to be analyzed, the method of the invention can obtain CO of different countries, provinces, cities and regions of the global scope2A proportionality coefficient with the CO having space-time characteristics; and the obtained proportionality coefficients of different areas are comparable.
Example two
To solve the problems existing in the prior artIn view of the above technical problems, the embodiments of the present invention further provide a CO and CO2And an emission proportionality coefficient estimating device.
FIG. 2 shows the two COs and CO of the embodiment of the present invention2The structure schematic diagram of the emission proportionality coefficient estimation device; referring to FIG. 2, the embodiment of the present invention is shown with respect to CO and CO2The emission scale factor estimation device includes a data to be analyzed determination unit, a data acquisition unit, a prediction area flux acquisition unit, and a scale factor acquisition unit.
The data to be analyzed determining unit is used for determining the area to be analyzed and the time to be analyzed.
The data acquisition unit is used for acquiring meteorological reanalysis data and gas data of a to-be-analyzed moment of the to-be-analyzed area.
The prediction area flux acquisition unit is used for acquiring a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed in a preset acquisition mode based on meteorological reanalysis data and gas data at the time to be analyzed of the area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2And predicting the area flux.
A scale factor obtaining unit for predicting a regional flux and the CO based on the CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on the CO assimilation grid flux data set and the CO2Assimilation grid flux data set to obtain CO and CO of each space grid2Grid discharge proportionality coefficient.
Wherein the gas data comprises CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2Gas-like data including CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite.
The embodiment of the invention provides CO and CO2An emission ratio coefficient estimating device for estimating the ratio of CO to CO2Combining initial boundary field data with meteorological reanalysis data at relative timeAnd introducing satellite observation data to realize data assimilation to obtain CO and CO at relative time2The assimilated CO flux data and the assimilated CO flux data of the same grid are then compared2The flux data is compared to obtain CO and CO2A discharge proportionality coefficient; wherein flux data is acquired for improving accuracy, and CO initial boundary field data and CO at the next moment are acquired through an atmospheric chemical model2Gas initial boundary field data is predicted as a basis for acquiring the next moment flux data. By setting the region to be analyzed and the time to be analyzed, the device of the invention can obtain CO of different countries, provinces, cities and regions in the global scope2A proportionality coefficient with the CO having space-time characteristics; and the obtained proportionality coefficients of different areas are comparable.
EXAMPLE III
To solve the above technical problems in the prior art, an embodiment of the present invention further provides a storage medium storing a computer program, and the computer program can implement CO and CO in the first embodiment when executed by a processor2All steps in the emission proportionality coefficient estimation method.
CO and CO2The specific steps of the method for estimating the discharge proportionality coefficient and the beneficial effects obtained by applying the readable storage medium provided by the embodiment of the invention are the same as those of the first embodiment, and are not described herein again.
It should be noted that: the storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Example four
In order to solve the technical problems in the prior art, the embodiment of the invention also provides a terminal.
Fig. 3 is a schematic structural diagram of a four-terminal according to an embodiment of the present invention, and referring to fig. 3, the terminal according to this embodiment includes a processor and a memory, which are connected to each other; the memory is used for storing computer programs, and the processor is used for executing the computer programs stored in the memory, so that the CO and the CO in the embodiment can be realized when the terminal executes2All steps in the emission proportionality coefficient estimation method.
CO and CO2The specific steps of the method for estimating the emission proportionality coefficient and the beneficial effects obtained by the terminal applying the embodiment of the present invention are the same as those of the first embodiment, and are not described herein again.
It should be noted that the Memory may include a Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The Processor may also be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. CO and CO2An emission proportionality coefficient estimation method, comprising:
determining a region to be analyzed and a time to be analyzed;
acquiring weather reanalysis data and gas data of a to-be-analyzed time of a to-be-analyzed area;
acquiring a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed in a preset acquisition mode based on the weather reanalysis data and the gas data at the time to be analyzed in the area to be analyzed2Number of flux of assimilation gridPredicting regional flux and CO from the set, CO2Predicting the regional flux;
predicting regional flux and the CO based on the CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on the CO assimilation grid flux data set and the CO2Assimilation grid flux data set to obtain CO and CO of each space grid2Grid discharge proportionality coefficient;
wherein the gas data comprises CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2Gas-like data including CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite.
2. The estimation method according to claim 1, wherein the predetermined obtaining manner is:
inputting weather reanalysis data at the moment to be analyzed into a preset atmospheric chemical model to obtain weather initial boundary field data, and performing Gaussian disturbance on the weather initial boundary field data to obtain a preset number of disturbed weather initial boundary field data sets;
respectively adding preset gas data to all the disturbance meteorological initial boundary field data sets to form disturbance meteorological initial boundary field data sets with preset numbers;
interpolating flux data in class-A gas data in to-be-analyzed time gas data of a to-be-analyzed region into a spatial grid of the preset atmospheric chemical mode model to generate a grid flux data set, and performing Gaussian disturbance on the grid flux data to obtain a disturbed grid flux data set;
processing satellite observation data in class A gas data in to-be-analyzed time gas data of a to-be-analyzed area in a local mode to obtain target observation data;
on the basis of the target observation data, assimilating initial field data and the disturbance grid flux data sets in all the disturbance initial boundary field data sets by adopting an ensemble adaptive Kalman filtering method to obtain a preset number of assimilation initial field data sets and an assimilation grid flux data set of gas A;
respectively updating boundary field data in the corresponding disturbance initial boundary field data sets based on all the assimilation initial field data sets to obtain a preset number of updated boundary field data sets, wherein the assimilation initial field data sets and the updated boundary field data sets correspondingly form a preset number of assimilation initial boundary field data sets;
sequentially inputting all the assimilation initial boundary field data sets into the preset atmospheric chemical mode model to obtain a preset number of predicted gas initial boundary field data sets at the next moment to be analyzed of the area to be analyzed;
acquiring the predicted area flux of the gas A at the time of the area to be analyzed based on the area of the area to be analyzed and the assimilation grid flux data set of the gas A;
wherein the preset gas data is:
if the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time A in the area to be analyzed currently exists, performing Gaussian disturbance on the preset mixed gas initial boundary field data in the class A gas data at the time A in the area to be analyzed to obtain a preset number of disturbance preset mixed gas initial boundary field data sets, and taking the preset number of the predicted gas initial boundary field data sets of the gas to be analyzed at the time A in the area to be analyzed and the preset number of the disturbance preset mixed gas initial boundary field data sets as preset gas data; otherwise, respectively carrying out Gaussian disturbance on the initial boundary field data of the gas A and the initial boundary field data of the preset mixed gas in the class-A gas data at the time of the to-be-analyzed area to obtain a preset number of disturbance gas initial boundary field data sets and a preset number of disturbance mixed gas initial boundary field data sets, and collecting the disturbance gas initial boundary field data sets and the disturbance preset mixed gas initial boundary field data setsTaking a mixed gas initial boundary field data set as preset gas data; a is CO or CO2。
3. The estimation method according to claim 1, wherein the step of determining the area to be analyzed and the moment to be analyzed further comprises, before:
determining a time resolution and a spatial resolution, wherein the time to be analyzed is different from the previous time of the time to be analyzed and the next time of the time to be analyzed by a time resolution; taking the initial boundary field data of the preset mixed gas, the meteorological reanalysis data, the CO ecological flux data, the CO wildfire flux data and the CO artificial flux data as state quantities in the CO assimilation process, and taking the CO as the state quantity in the CO assimilation process2Initial boundary field data, CO2Flux data and meteorological reanalysis data as CO2State quantities in the assimilation process;
and determining the atmospheric physical parameters and the chemical parameters of the preset atmospheric chemical mode model.
4. The estimation method according to claim 3, wherein the preset mixed gas initial boundary field data includes gas initial boundary field data of all gases that affect the CO concentration that can be processed and currently available by the preset atmospheric chemical pattern model.
5. The estimation method according to claim 1, wherein the CO is2Flux data is CO2Artificial flux data, CO2Ecological flux data, CO2Ocean flux data and CO2Summation of wildfire flux data.
6. The estimation method according to claim 1, characterized in that said preset number is greater than 20.
7. The estimation method according to claim 1, wherein the update formula for updating the boundary field data in the corresponding perturbed initial boundary field data set based on the assimilation initial field data set is:
TENDnew＝BDYold+F(BDYold-INPmean)
wherein TENDnewIndicating a new boundary condition value, BDYoldIndicating the old boundary condition value, INPmeanRepresenting the initial field data and F the distance weight function.
8. CO and CO2The emission proportionality coefficient estimation device comprises a data to be analyzed determining unit, a data obtaining unit, a prediction region flux obtaining unit and a proportionality coefficient obtaining unit;
the data to be analyzed determining unit is used for determining an area to be analyzed and time to be analyzed;
the data acquisition unit is used for acquiring meteorological reanalysis data and gas data of a to-be-analyzed moment of a to-be-analyzed area;
the prediction area flux acquisition unit is used for acquiring a CO assimilation grid flux data set and CO assimilation grid flux data set at the time to be analyzed in a preset acquisition mode based on the meteorological reanalysis data and the gas data at the time to be analyzed of the area to be analyzed2Assimilation grid flux data set, CO prediction regional flux and CO2Predicting the regional flux;
the scale factor obtaining unit is used for predicting the regional flux and the CO based on the CO2Predicting regional flux capture CO and CO2Regional emission scaling factor and based on the CO assimilation grid flux data set and the CO2Assimilation grid flux data set to obtain CO and CO of each space grid2Grid discharge proportionality coefficient;
wherein the gas data comprises CO gas data and CO2The CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data, and the CO gas data comprises CO initial boundary field data, preset mixed gas initial boundary field data, CO ecological flux data, CO wildfire flux data, CO artificial flux data and CO satellite observation data2Gas-like data including CO2Initial boundary field data, CO2Flux data and CO2And (5) observing data by the satellite.
9. Storage medium on which a computer program is stored, characterized in that the program, when being executed by a processor, implements CO and CO according to any one of claims 1 to 72An emission proportionality coefficient estimation method.
10. A terminal, comprising: the system comprises a processor and a memory, wherein the memory is in communication connection with the processor;
the memory for storing a computer program, the processor for executing the computer program stored by the memory to cause the terminal to perform the CO and CO of any of claims 1 to 72An emission proportionality coefficient estimation method.
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