CN116930423A - Automatic verification and evaluation method and system for air quality model simulation effect - Google Patents
Automatic verification and evaluation method and system for air quality model simulation effect Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 129
- 230000000694 effects Effects 0.000 title claims abstract description 57
- 238000012795 verification Methods 0.000 title claims abstract description 40
- 238000011156 evaluation Methods 0.000 title claims abstract description 21
- 238000012544 monitoring process Methods 0.000 claims abstract description 67
- 238000004458 analytical method Methods 0.000 claims abstract description 24
- 238000007619 statistical method Methods 0.000 claims abstract description 12
- 238000000605 extraction Methods 0.000 claims abstract description 11
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- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 claims description 6
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- 238000000700 time series analysis Methods 0.000 claims description 4
- 238000004445 quantitative analysis Methods 0.000 abstract description 5
- 239000004973 liquid crystal related substance Substances 0.000 description 10
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/007—Arrangements to check the analyser
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0006—Calibrating gas analysers
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0062—General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method, e.g. intermittent, or the display, e.g. digital
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
Abstract
The invention discloses an automatic verification and evaluation method and system for an air quality model simulation effect, comprising the following steps: determining a meteorological station and an air quality station in the area based on the simulation area range, and acquiring corresponding formatted monitoring data; based on the determined longitude and latitude of each site, selecting the nearest grid point in the grid file corresponding to the monitored site, and extracting the simulation values in the corresponding grids in batches to obtain formatted simulation data; matching and combining the monitoring data with the simulation data to obtain a corresponding combined file; calculating the combined file to obtain statistical index results of different parameters of each site; and carrying out statistical analysis, time sequence analysis and spatial distribution analysis on the statistical index result. The invention can realize quick extraction, analysis and comparison of simulation results, so as to improve verification efficiency, reduce the requirements of human interference and subjective judgment, provide reliable quantitative analysis and improve the objectivity and repeatability of verification.
Description
Technical Field
The invention relates to the technical field of model evaluation, in particular to an automatic verification evaluation method and system for an air quality model simulation effect.
Background
Due to the needs of industry and economic development, fossil fuels are used on a large scale, and various types of atmospheric pollution events such as soot pollution, photochemical smog pollution, acid precipitation and the like are initiated on a global scale. With the change of the fuel structure and the pollution source, the air pollution problem becomes more complex and various, secondary pollution is aggravated, and compound pollution is more serious. Regional and even global pollution has evolved from urban and localized pollution over a range of pollution. The air quality model is an important tool for atmospheric pollution research, is established on the basis of scientific theory and assumption, simulates the transmission, diffusion, chemical conversion and removal processes of pollutants in the atmosphere by a numerical method, and has wide application prospect. In recent years, the development of air quality models is rapid, various air quality models are widely applied in the field of air pollution research, become one of important means for researching the space-time distribution characteristics of air pollutants and predicting the quality of the air environment, and are widely applied in the field of environmental planning and management.
Although the air quality model is developed based on scientific theory and a large number of field observations, a certain error exists in a numerical mode result due to the reasons of an meteorological field, emission data, a chemical and physical process and the like; therefore, an important step of carrying out air quality simulation work is to verify the air quality model simulation result by using actual observation data, evaluate the accuracy and reliability of the model, know the limitation and improvement space of the model based on the result, localize some key parameters in the model, and effectively rate the key parameters compiled by the list, so that the air quality model can truly reflect the air pollution process of the researched area, and therefore, the simulation result verification is a very important ring in air quality research and environmental management.
In general, the accuracy and reliability of model simulation is verified by comparing the meteorological observation data and the atmospheric contaminant concentration observation data obtained by the ground observation meteorological station and the air quality monitoring station with the simulation values in the model grid. The current common simulation result verification method comprises comparison with observed data, calculation of statistical indexes and sensitivity analysis. The comparison with the observation data is usually carried out by drawing a time sequence diagram, a scatter diagram and the like so as to evaluate the accuracy of the simulation result; however, this approach may suffer from data selection, matching scale and observation errors, resulting in the introduction of subjectivity and uncertainty. The calculation of the statistical index is used for quantitatively evaluating the difference between the simulation result and the observed data, and the common indexes comprise root mean square error, correlation coefficient and deviation. Sensitivity analysis evaluates reliability and stability of the model by changing model input parameters and observing changes in simulation results; however, the results of the sensitivity analysis are subject to limitations in parameter selection and parameter space.
In summary, although these methods are widely used, there are certain limitations, mainly reflected in the following aspects:
1. subjectivity and artifacts: traditional manual verification methods are susceptible to subjective opinion and personal preferences of operators, resulting in less objective and consistent verification results.
2. Time and resource consumption: conventional manual verification methods typically require significant time and human resources to process and analyze large amounts of monitoring and analog data. Such a process can be cumbersome and time consuming.
3. Lack of spatial and temporal analysis: some methods may only focus on statistical analysis of overall performance, ignoring spatial and temporal variations in simulation effects. This may result in the inability to find specific areas and periods of poor simulation, limiting further analysis and improvement of the problem.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides the automatic verification and evaluation method and the system for the air quality model simulation effect, and the computer program and the algorithm are used for realizing the rapid extraction, analysis and comparison of the simulation result so as to improve the verification efficiency, reduce the requirements of human interference and subjective judgment, provide reliable quantitative analysis and improve the objectivity and the repeatability of the verification.
The invention discloses an automatic verification and evaluation method for an air quality model simulation effect, which comprises the following steps:
step 1, determining a meteorological station and an air quality station in an area based on a simulation area range, and acquiring meteorological element observation data and pollutant concentration monitoring data; processing the meteorological element observation data and the pollutant concentration monitoring data to obtain formatted actual monitoring data;
step 2, based on the longitude and latitude of each site determined in the step 1, judging the distance between the longitude and latitude of the monitoring site and the longitude and latitude of the simulation grid point, selecting the nearest grid point in the grid file corresponding to the monitoring site, and extracting the simulation values in the corresponding grids in batches to obtain formatted simulation data;
step 3, matching the formatted actual monitoring data with the formatted analog data, and merging the matched data to obtain a corresponding merging file;
step 4, calculating the combined file to obtain statistical index results of different parameters of each site so as to quantify the difference between the simulation data and the monitoring data;
and 5, carrying out statistical analysis, time sequence analysis and spatial distribution analysis on the statistical index result.
As a further development of the invention, in said step 1,
meteorological elements include temperature, humidity, wind speed and wind direction, and contaminants include particulates and ozone.
As a further development of the invention, in said step 1,
processing meteorological element observation data and pollutant concentration monitoring data through shell scripts, including: traversing each site monitoring data file, and processing the data in the monitoring data file, wherein the data comprises uniform date and time formats; and obtaining formatted actual monitoring data.
As a further development of the invention, in said step 2,
the method for extracting the simulation values in the corresponding grids in batches through the automatic script comprises the following steps: firstly, using ncl command to obtain position information of an analog grid point, extracting specific analog data based on the position information of the analog grid point, and calculating and processing the analog data to obtain formatted analog data of weather and pollutants.
As a further development of the invention, in said step 3,
matching the formatted actual monitoring data with the formatted analog data through the longitude and latitude positions and the time of the site, reading the monitoring data file row by row, searching the matched rows in the analog data file, and merging the matched data to obtain a corresponding merging file.
As a further development of the invention, in said step 4,
the statistical indexes comprise: average deviation MB, normalized average deviation NMB, normalized average error NME, average fractional deviation MFB, average fractional error MFE, and correlation coefficient R.
As a further improvement of the present invention, the calculation formula of each statistical index is:
where Cm represents an analog value, co represents an actual monitored value, and N represents the number of data.
As a further improvement of the present invention,
the average deviation MB reflects the average deviation degree of the analog value and the monitoring value, and the closer to 0 is the better the analog effect;
the normalized mean deviation NMB reflects the deviation degree of the analog value from the monitoring value, and the closer to 0, the better the analog effect is;
the normalized average error NME reflects the average absolute error of the analog value and the monitored value, the closer it is to 0, the better the analog effect;
the average score deviation MFB and the average score error MFE are used to measure the credibility index of the model simulation, and if MFB is less than +/-30% and MFE is less than 50%, the model performs excellent; whereas if MFB is less than ±60% while MFE is less than 75%, the performance of the model is in an acceptable range;
the correlation coefficient R reflects the degree of correlation of the analog value with the monitored value, and the closer to 1 the better the analog effect.
As a further development of the invention, in said step 5,
the statistical analysis comprises calculating an average value, a standard deviation, a maximum value and a minimum value, and providing an overview for the overall performance of the simulation result based on the statistical result;
the time series analysis is as follows: screening out a part with poor simulation effect according to the simulation effect judgment standard, and carrying out time sequence analysis on the part of data, thereby pertinently judging the simulation problem from the simulation period;
the spatial distribution analysis is to perform spatial distribution analysis on the statistical index result so as to observe the region with poor simulation effect.
The invention also discloses an automatic verification and evaluation system for the air quality model simulation effect, which is used for realizing the automatic verification and evaluation method for the air quality model simulation effect, and comprises the following steps:
the monitoring value acquisition and processing module is used for realizing the step 1;
the analog value extraction and processing module is used for realizing the step 2;
the data matching and combining module is used for realizing the step 3;
the statistical index calculation module is used for realizing the step 4;
and the result analysis module is used for realizing the step 5.
Compared with the prior art, the invention has the beneficial effects that:
1. the verification efficiency is improved: the shell language-based automatic verification process can realize quick extraction, analysis and comparison of the simulation results, so that the bottom layer function of the operating system is fully utilized to improve the verification efficiency, the time and the workload of manual operation are reduced, and the verification efficiency is greatly improved.
2. And the artificial interference is reduced: the automatic verification method reduces the possibility of human interference and provides more objective and reliable verification results through programmed steps and algorithms.
3. Providing reliable quantitative analysis: the invention quantitatively analyzes the simulation result and the observed data by using the statistical indexes, evaluates the simulation effect by using the numerical indexes, ensures that the evaluation result is more reliable and objective, and is convenient for providing accurate evaluation of the simulation result for decision makers and researchers.
4. Complete result analysis: the invention not only provides statistical analysis of overall performance, but also performs spatial distribution analysis and time sequence analysis; through the analysis, the area and the time period with poor simulation effect can be more comprehensively known, the simulation problem can be identified and solved in a targeted manner, and therefore the reliability and the accuracy of the simulation result are further improved.
Drawings
FIG. 1 is a flow chart of an automatic verification and evaluation method for air quality model simulation effect disclosed by the invention;
FIG. 2 is a block diagram of an automated verification and evaluation system for air quality model simulation effects of the present disclosure;
FIG. 3 is a time series diagram of a temperature distribution of a station 54401 according to an embodiment of the present invention
Fig. 4 is a spatial distribution diagram of a correlation coefficient R according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is described in further detail below with reference to the attached drawing figures:
as shown in FIG. 1, the invention provides an automatic verification and evaluation method for the simulation effect of an air quality model, so as to improve the verification efficiency, reduce the human interference and provide reliable quantitative analysis; the method specifically comprises the following steps:
step 1, determining a meteorological station and an air quality station in an area based on a simulation area range, and acquiring meteorological element observation data and pollutant concentration monitoring data; processing meteorological element observation data and pollutant concentration monitoring data to convert the meteorological element observation data and the pollutant concentration monitoring data into standard units to obtain formatted actual monitoring data; wherein, the liquid crystal display device comprises a liquid crystal display device,
meteorological factors include temperature, humidity, wind speed, wind direction, etc., and contaminants include particulate matter (PM 2.5 ) Ozone (O) 3 ) Etc.;
the data processing method comprises the following steps: processing meteorological element observation data and pollutant concentration monitoring data through shell scripts, including: traversing each site monitoring data file, and processing the data in the monitoring data file, wherein the data comprises uniform date and time formats; and obtaining formatted actual monitoring data.
Step 2, based on the longitude and latitude of each site determined in the step 1, judging the distance between the longitude and latitude of the monitoring site and the longitude and latitude of the simulation grid point, selecting the nearest grid point in the grid file corresponding to the monitoring site, and extracting the simulation values in the corresponding grids in batches to obtain formatted simulation data; wherein, the liquid crystal display device comprises a liquid crystal display device,
the method for extracting the simulation values in the corresponding grids in batches through the automatic script comprises the following steps: firstly, obtaining position information of an analog grid point by using a ncl command, extracting specific analog data based on the position information of the analog grid point, and calculating and processing the analog data to obtain formatted analog data of weather and pollutants; the extracted simulation values include the desired weather (e.g., temperature, humidity, wind speed, wind direction, etc.), contaminants (e.g., particulates, ozone, nitrogen oxides, etc.).
Step 3, matching the formatted actual monitoring data with the formatted analog data, and merging the matched data to obtain a corresponding merging file; wherein, the liquid crystal display device comprises a liquid crystal display device,
the specific matching and merging method comprises the following steps: matching the formatted actual monitoring data with the formatted analog data through the longitude and latitude positions and the time of the site, reading the monitoring data file row by row, searching the matched rows in the analog data file, and merging the matched data to obtain a corresponding merging file.
Step 4, calculating the combined file to obtain statistical index results of different parameters of each site so as to quantify the difference between the simulation data and the monitoring data; wherein, the liquid crystal display device comprises a liquid crystal display device,
the statistical indexes comprise: average deviation MB, normalized average deviation NMB, normalized average error NME, average fractional deviation MFB, average fractional error MFE, and correlation coefficient R;
the calculation formula of each statistical index is as follows:
where Cm represents an analog value, co represents an actual monitored value, and N represents the number of data (all analog times at the monitored point). The average deviation MB reflects the average deviation degree of the analog value and the monitoring value, and the closer to 0 is the better the analog effect; the normalized mean deviation NMB reflects the deviation degree of the analog value from the monitoring value, and the closer to 0, the better the analog effect is; the normalized average error NME reflects the average absolute error of the analog value and the monitored value, the closer it is to 0, the better the analog effect; the average score deviation MFB and the average score error MFE are used to measure the credibility index of the model simulation, and if MFB is less than +/-30% and MFE is less than 50%, the model performs excellent; whereas if MFB is less than ±60% while MFE is less than 75%, the performance of the model is in an acceptable range; the correlation coefficient R reflects the degree of correlation of the analog value with the monitored value, and the closer to 1 the better the analog effect.
Step 5, carrying out statistical analysis on the statistical index result to quantitatively evaluate the overall performance of the simulation result; and carrying out spatial distribution analysis on the statistical index result to observe the region with poor simulation effect. In addition, according to the simulation effect judgment standard, screening out the part with poor simulation effect, and carrying out time sequence analysis on the part of data, thereby pertinently judging the simulation problem from the simulation period; wherein, the liquid crystal display device comprises a liquid crystal display device,
the statistical analysis is: calculating an average value, a standard deviation, a maximum value, a minimum value or the like of the statistical indexes in the step 4, and providing an overview for the overall performance of the simulation result based on the statistical results;
the time series analysis is as follows: screening the difference value, determining a threshold value or range of a statistical index according to the simulation effect judgment standard, and screening out partial data with poor simulation effect; and (3) carrying out time sequence analysis on the data, drawing a time sequence chart, observing the trend and deviation of the change of the analog value and the observed value along with time, and determining the abnormal period from the time perspective by comparing the characteristics of the trend, periodicity, abnormal value and the like of the analog value and the observed value, so as to determine the period required to be improved for simulation.
The spatial distribution analysis is as follows: the simulation results for specific indicators (e.g., R) are plotted to see which areas are less effective in simulation as a whole to determine the direction of improvement. By drawing the space distribution diagram, the deviation condition of the simulation result in different areas can be intuitively known, and the area needing model improvement is determined.
As shown in fig. 2, the present invention provides an automatic verification and evaluation system for air quality model simulation effect, comprising:
the monitoring value acquisition and processing module is used for realizing the step 1;
the analog value extraction and processing module is used for realizing the step 2;
the data matching and combining module is used for realizing the step 3;
the statistical index calculation module is used for realizing the step 4;
and the result analysis module is used for realizing the step 5.
Examples:
based on the automatic verification and evaluation method, the eastern coast of China is taken as a target area, 7 months in 2018 are taken as verification time periods, and the WRF-Chem is selected as an air quality model; the method specifically comprises the following steps:
s1, determining a meteorological station and an air quality station in an area based on a simulation area range, and acquiring meteorological element observation data and pollutant concentration monitoring data; processing meteorological element observation data and pollutant concentration monitoring data to convert the meteorological element observation data and the pollutant concentration monitoring data into standard units to obtain formatted actual monitoring data;
specific:
the meteorological site data come from the National Climate Data Center (NCDC), 139 sites are arranged in the simulation range, and the selected meteorological elements comprise temperature, humidity and wind speed; air quality station data come from a national urban air quality real-time release platform of a China environmental monitoring total station, 215 stations are all arranged in the simulation range, and selected pollutants comprise Particulate Matters (PM) 2.5 ) Ozone (O) 3 )；
Preprocessing data by utilizing shell scripts, specifically including: traversing each site monitoring data file, processing the data in the file by using an awk command, unifying the date and time formats, converting the temperature from Fahrenheit to Centigrade, and the like; through these operations, automated acquisition of formatted actual monitoring data may be achieved, in preparation for S3.
S2, judging the distance between the longitude and latitude of the monitoring station and the longitude and latitude of the simulation grid point based on the longitude and latitude of each station determined in the S1, selecting the nearest grid point in the grid file corresponding to the monitoring station, and extracting the simulation values in the corresponding grids in batches to obtain formatted simulation data;
specific:
batch extraction is achieved by an automated script comprising: firstly, acquiring longitude and latitude information of each monitoring point, and using the longitude and latitude information to replace corresponding placeholders in a script file; then, obtaining corresponding simulated grid points through ncl processing, and extracting grid position information from the generated temporary file; replacing placeholders in the model file by using the extracted grid position information to generate a new simulation value extraction file; finally, automatic batch extraction of the simulation values in the grids where each monitoring point is located is realized. In addition, the simulation values of each parameter are calculated and processed in the extraction process, so that the final required accurate and formatted meteorological and pollutant element data are obtained.
The extracted simulation data of each element are consistent with the monitoring information in the step S1, and the simulation data comprise meteorological elements (temperature, humidity and wind speed) and pollutant elements (particulate matters and ozone) of the required hour scale.
S3, matching the formatted actual monitoring data with the formatted analog data, and merging the matched data to obtain a corresponding merging file;
specific:
and when the site position information is consistent, reading the corresponding monitoring value file line by line, searching the matched line in the corresponding simulation value file, and rewriting the successfully matched information into a new file. And finally, classifying and combining the generated new file according to the meteorological elements and the pollutant elements to generate the meteorological element file and the pollutant element file.
S4, merging the files for calculation to obtain statistical index results of different parameters of each site;
specific:
and (3) respectively calculating the weather element file and the pollutant element file which are finally generated in the step (S3) to obtain index results of different parameters of each site so as to quantify the difference between the simulation result and the observation data. The statistical indexes selected are MB, NMB, NME, MFB, MFB and R.
And obtaining the statistical index summarization result of each site through calculation, as shown in table 1.
TABLE 1
S5, analyzing a verification result. And (3) carrying out statistical analysis on the calculation result of the step (S4) to quantitatively evaluate the overall performance of the simulation result. And carrying out spatial distribution analysis on the statistical index result to observe the region with poor simulation effect. In addition, according to the simulation effect judgment standard, a part with poor simulation effect is screened out, and time series analysis is carried out on the part of data, so that the simulation problem is judged in a targeted manner from the simulation period.
And (3) carrying out statistical analysis based on the statistical indexes obtained by calculation in the step (S4), screening out partial data with poor simulation effect, and drawing a time sequence chart, as shown in fig. 2, so as to find out a period with poor simulation effect.
In addition, the simulation result is plotted in a spatial distribution so as to find a region where the simulation effect is poor, and the result R is taken as an example, as shown in fig. 3.
The invention has the advantages that:
1. the verification efficiency is improved: the shell language-based automatic verification process can realize quick extraction, analysis and comparison of the simulation results, so that the bottom layer function of the operating system is fully utilized to improve the verification efficiency, the time and the workload of manual operation are reduced, and the verification efficiency is greatly improved. 2. And the artificial interference is reduced: the automatic verification method reduces the possibility of human interference and provides more objective and reliable verification results through programmed steps and algorithms.
3. Providing reliable quantitative analysis: the invention quantitatively analyzes the simulation result and the observed data by using the statistical indexes, evaluates the simulation effect by using the numerical indexes, ensures that the evaluation result is more reliable and objective, and is convenient for providing accurate evaluation of the simulation result for decision makers and researchers.
4. Complete result analysis: the invention not only provides statistical analysis of overall performance, but also performs spatial distribution analysis and time sequence analysis; through the analysis, the area and the time period with poor simulation effect can be more comprehensively known, the simulation problem can be identified and solved in a targeted manner, and therefore the reliability and the accuracy of the simulation result are further improved.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An automatic verification and evaluation method for air quality model simulation effect is characterized by comprising the following steps:
step 1, determining a meteorological station and an air quality station in an area based on a simulation area range, and acquiring meteorological element observation data and pollutant concentration monitoring data; processing the meteorological element observation data and the pollutant concentration monitoring data to obtain formatted actual monitoring data;
step 2, based on the longitude and latitude of each site determined in the step 1, judging the distance between the longitude and latitude of the monitoring site and the longitude and latitude of the simulation grid point, selecting the nearest grid point in the grid file corresponding to the monitoring site, and extracting the simulation values in the corresponding grids in batches to obtain formatted simulation data;
step 3, matching the formatted actual monitoring data with the formatted analog data, and merging the matched data to obtain a corresponding merging file;
step 4, calculating the combined file to obtain statistical index results of different parameters of each site so as to quantify the difference between the simulation data and the monitoring data;
and 5, carrying out statistical analysis, time sequence analysis and spatial distribution analysis on the statistical index result.
2. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein in the step 1,
meteorological elements include temperature, humidity, wind speed and wind direction, and contaminants include particulates and ozone.
3. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein in the step 1,
processing meteorological element observation data and pollutant concentration monitoring data through shell scripts, including: traversing each site monitoring data file, and processing the data in the monitoring data file, wherein the data comprises uniform date and time formats; and obtaining formatted actual monitoring data.
4. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein, in the step 2,
the method for extracting the simulation values in the corresponding grids in batches through the automatic script comprises the following steps: and obtaining the position information of the simulation grid point by using the ncl command, extracting specific simulation data based on the position information of the simulation grid point, and calculating and processing the simulation data to obtain formatted simulation data of weather and pollutants.
5. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein, in the step 3,
matching the formatted actual monitoring data with the formatted analog data through the longitude and latitude positions and the time of the site, reading the monitoring data file row by row, searching the matched rows in the analog data file, and merging the matched data to obtain a corresponding merging file.
6. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein, in the step 4,
the statistical indexes comprise: average deviation MB, normalized average deviation NMB, normalized average error NME, average fractional deviation MFB, average fractional error MFE, and correlation coefficient R.
7. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 6, wherein the calculation formula of each statistical index is:
where Cm represents an analog value, co represents an actual monitored value, and N represents the number of data.
8. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 7,
the average deviation MB reflects the average deviation degree of the analog value and the monitoring value, and the closer to 0 is the better the analog effect;
the normalized mean deviation NMB reflects the deviation degree of the analog value from the monitoring value, and the closer to 0, the better the analog effect is;
the normalized average error NME reflects the average absolute error of the analog value and the monitored value, the closer it is to 0, the better the analog effect;
the average score deviation MFB and the average score error MFE are used to measure the credibility index of the model simulation, and if MFB is less than +/-30% and MFE is less than 50%, the model performs excellent; whereas if MFB is less than ±60% while MFE is less than 75%, the performance of the model is in an acceptable range;
the correlation coefficient R reflects the degree of correlation of the analog value with the monitored value, and the closer to 1 the better the analog effect.
9. The method for automatically verifying and evaluating an air quality model simulation effect according to claim 1, wherein, in the step 5,
the statistical analysis comprises calculating an average value, a standard deviation, a maximum value and a minimum value, and providing an overview for the overall performance of the simulation result based on the statistical result;
the time series analysis is as follows: screening out a part with poor simulation effect according to the simulation effect judgment standard, and carrying out time sequence analysis on the part of data, thereby pertinently judging the simulation problem from the simulation period;
the spatial distribution analysis is to perform spatial distribution analysis on the statistical index result so as to observe the region with poor simulation effect.
10. An automatic verification and evaluation system for an air quality model simulation effect, for realizing the automatic verification and evaluation method for an air quality model simulation effect according to any one of claims 1 to 9, comprising:
the monitoring value acquisition and processing module is used for realizing the step 1;
the analog value extraction and processing module is used for realizing the step 2;
the data matching and combining module is used for realizing the step 3;
the statistical index calculation module is used for realizing the step 4;
and the result analysis module is used for realizing the step 5.
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