CN113466181A - Atmospheric visibility data processing method, system and application - Google Patents
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Abstract
The invention belongs to the technical field of meteorological data processing, and discloses a method, a system and an application for processing atmospheric visibility data, wherein the real part of an aerosol complex refractive index under a drying condition and the imaginary part of the aerosol complex refractive index under the drying condition are calculated; calculating the moisture absorption growth factor of the aerosol particle size; and constructing a modeling data set to obtain the atmospheric visibility. The invention respectively simulates n of complex refractive index DACRI of aerosol under the drying condition according to the formulare(dry), the R between the respective simulated value and its measured value is 0.21 and 0.85, respectively, and the MRE is 2.31% and 13.36%, respectively. N of complex refractive index ACRI of aerosol under environmental condition is respectively simulated according to formulare(RH) and ni(RH), the respective analog values and the measured values thereofR between the two parts is 0.51 and 0.83 respectively, MRE is 3.46 percent and 18.84 percent respectively, and the atmospheric visibility simulation effect is obviously improved.
Description
Technical Field
The invention belongs to the technical field of meteorological data processing, and particularly relates to an atmospheric visibility data processing method, system and application.
Background
At present: the evolution mechanism of the extinction coefficient becomes a key technical link for the visibility research. Many studies have shown that aerosol extinction coefficients are the dominant of atmospheric extinction coefficients, which are typically over 90%. Currently, the parameterization scheme for aerosol extinction coefficient or visibility mainly includes two types, a statistical model and a calculation model using Mie theory.
The core of the statistical model is to establish the statistical relationship between the atmospheric extinction coefficient or visibility and the influence factors thereof. As early as 1985, the us impro program led to the establishment of PM 2.5, the quantitative relation between the mass concentration of each chemical component and the extinction coefficient of the aerosol provides an effective calculation scheme for early visibility forecast. Further studies indicate that PM2 is responsible. 5, the scattering extinction capacities of certain chemical components have obvious space-time difference, and the scattering coefficient calculated by using an Imprive relational expression can greatly deviate from actual observed data. The factors influencing visibility are very complex, and research on multiple zones consistently shows that particulate matter concentration, particularly fine particulate matter concentration, dominates in the reduction of visibility in a zone. In addition, the change characteristics of the extinction coefficient of the aerosol along with the relative humidity in the kyford Ji area are researched, and the aerosol scattering moisture absorption growth factor f (RH) is found to be increased slowly when the relative humidity is less than 85 percent, and f (RH) is increased rapidly when the relative humidity is more than 85 percent, which indicates that the moisture absorption growth characteristic of the aerosol is an important cause for rapid reduction of visibility under high-humidity conditions. Visibility in the wuhan region and PM 2. The non-linear relationship between 5 mass concentration and relative humidity indicates that both fine particle concentration and relative humidity limit changes in visibility. The research of HaChi external field observation data shows that compared with an extinction coefficient parameterization scheme constructed by only aerosol volume concentration and relative humidity, the extinction coefficient parameterization scheme with aerosol number concentration is further considered to remarkably improve the simulation effect of low visibility.
In recent years, with the development of environmental monitoring technology, aerosol extinction or visibility models based on Mie scattering models have been intensively researched and applied. The particle size spectrum observed in Yancun, aerosol moisture absorption growth factors of different modes and environmental extinction coefficients are utilized, the aerosol extinction coefficients are calculated based on the Mie theory, and the calculated value is well matched with the measured value. Although the physical significance of the aerosol extinction coefficient calculation model based on the Mie scattering model is clear, the value of the key parameter of the model has great experience and artificial arbitrariness. The actual variation of the optical parameters of the aerosol is very complex, and the empirical values are important sources of uncertainty in the calculation of the atmospheric extinction coefficient. Therefore, an immune evolution algorithm is utilized to successively provide an inversion method of complex refractive index (DACRI) of the aerosol and moisture absorption growth factor gf (RH) of the aerosol particle size under a drying condition, and a parameterization scheme of the DACRI and the gf (RH) suitable for the region is further constructed based on the statistical analysis of DACRI and the main particulate matter mass concentration index in the Chengdu region.
At present, although the WRF-Chem/CMAQ mode is widely used for research and forecast of ambient air quality, the visibility parameterization scheme of the WRF-Chem/CMAQ mode has low applicability, so that the computation accuracy of the mode on visibility is poor.
Through the above analysis, the problems and defects of the prior art are as follows: the existing WRF-Chem/CMAQ mode visibility parameterization scheme has low applicability, so that the mode has poor visibility calculation accuracy.
The difficulty in solving the above problems and defects is:
1. parameters that model visibility lack an effective inversion means.
2. The simulation scheme of visibility lacks accuracy.
The significance of solving the problems and the defects is as follows:
1. a parameterization scheme for accurately calculating optical parameters is provided for atmospheric visibility simulation.
2. Based on the Mie scattering theory, the atmospheric visibility is accurately simulated.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an atmospheric visibility data processing method, system and application.
The invention is realized in such a way that an atmospheric visibility data processing method comprises the following steps:
calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
calculating the moisture absorption growth factor of the aerosol particle size;
and constructing a modeling data set to obtain the atmospheric visibility.
Further, the formula for calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition is as follows:
in the formula, CBC，CPM1，CPM2。5And CPM10Are BC, PM respectively1，PM2。5And PM10In units of μ g/m3。
Further, the formula for calculating the moisture absorption growth factor of the aerosol particle size is as follows
Further, ambient atmospheric extinction coefficient b at wavelength of 550nmext(RH)(km-1) Relationship to atmospheric visibility v (km):
in the formula, bsp(RH is the coefficient of aerosol scattering at 550nm wavelength under ambient conditions, in Mm-1。
Another object of the present invention is to provide a computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program, which when executed by the processor, causes the processor to perform the steps of:
calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
calculating the moisture absorption growth factor of the aerosol particle size;
and constructing a modeling data set to obtain the atmospheric visibility.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
calculating the moisture absorption growth factor of the aerosol particle size;
and constructing a modeling data set to obtain the atmospheric visibility.
Another objective of the present invention is to provide an information data processing terminal, which is used for implementing the atmospheric visibility data processing method.
Another object of the present invention is to provide an atmospheric visibility data processing system for executing the atmospheric visibility data processing method, the atmospheric visibility data processing system including:
the parameter processing module is used for calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition; calculating the moisture absorption growth factor of the aerosol particle size;
and the atmospheric visibility calculation module is used for constructing a modeling data set to obtain the atmospheric visibility.
The invention also aims to provide a meteorological data processing terminal which is used for realizing the atmospheric visibility data processing method.
Another objective of the present invention is to provide an environment monitoring terminal, which is used for implementing the atmospheric visibility data processing method.
By combining all the technical schemes, the invention has the advantages and positive effects that: the invention couples DACRI and gf (RH) parameterization schemes, provides a visibility improvement algorithm based on a Mie scattering model, and performs example verification in a Chengdu area so as to provide technical support for improving regional visibility forecasting capability and atmospheric environment management level.
The change of optical parameters of the sol is very complex, and the empirical value is an important source of uncertainty in calculation of the atmospheric extinction coefficient. The invention provides a visibility improvement algorithm based on a Mie scattering model by coupling an aerosol complex refractive index (DACRI) and an aerosol particle size hygroscopic growth factor (gf (RH)) parameterization scheme. According to the invention, ground hourly observation data observed by a WS600 integrated meteorological station, an AURORA-3000 integrated turbidimeter, an AE-31 black carbon instrument and a GRIMM180 environment particulate matter monitor in 2017 of metropolis are compared and analyzed with simulation results of two visibility calculation models (a Mie theoretical model and a statistical model of empirical parameters) in different visibility intervals (<2km, 2km to 5km, 5km to 10km and more than 10km), so that the applicability of the improved calculation method is evaluated. The test results of the invention show that: the three visibility calculation methods can well simulate the change characteristics of visibility. The improved algorithm estimates DACRI and gf (RH) more accurately through a localized parameterization scheme, so that the simulation effect in four visibility intervals is remarkably superior, the correlation coefficients R of corresponding simulation values and measured values are respectively 0.62, 0.90, 0.89 and 0.93, and the average relative errors MRE are respectively 9.6%, 10.39% and 9.94% and 14.06%.
Drawings
Fig. 1 is a flowchart of an atmospheric visibility data processing method according to an embodiment of the present invention.
FIG. 2 is a schematic structural diagram of an atmospheric visibility data processing system according to an embodiment of the present invention;
in fig. 2: 1. a parameter processing module; 2. and an atmospheric visibility calculation module.
Fig. 3(a) is a schematic diagram of comparison between a simulated value and a measured value of an atmospheric visibility <2km scheme provided by an embodiment of the present invention.
FIG. 3(b) is a schematic diagram showing the comparison between a simulated value and a measured value of the atmospheric visibility 2-5km scheme provided by the embodiment of the invention.
FIG. 3(c) is a schematic diagram showing the comparison between a simulated value and a measured value of the atmospheric visibility 5-10km scheme provided by the embodiment of the invention.
Fig. 3(d) is a schematic diagram of the comparison between a simulated value and its measured value of the atmospheric visibility >10km scheme provided by the embodiment of the present invention.
Fig. 4 is a schematic view of scatter plots of gf (rh) analog values and measured values thereof according to an embodiment of the present invention.
FIG. 5(a) is a simulated value n of complex refractive index ACRI of aerosol provided by an embodiment of the present inventionre(dry) versus measured values.
FIG. 5(b) is a simulated value n of complex refractive index ACRI of aerosol provided by the embodiment of the inventioni(dry) versus measured values.
FIG. 5(c) is a simulated value n of complex refractive index ACRI of aerosol provided by the embodiment of the present inventionreSchematic comparison between (RH) and measured values.
FIG. 5(d) is a simulated value n of complex refractive index ACRI of aerosol provided by an embodiment of the present inventioniSchematic comparison between (RH) and measured values.
Fig. 6(a) is a schematic diagram of comparison between two simulated values and their measured values of the atmospheric visibility <2km scheme provided by the embodiment of the present invention.
FIG. 6(b) is a schematic diagram showing the comparison between the two simulated values and the measured values of the atmospheric visibility 2-5km scheme provided by the embodiment of the invention.
FIG. 6(c) is a schematic diagram showing the comparison between the two simulated values and the measured values of the atmospheric visibility 5-10km scheme provided by the embodiment of the invention.
Fig. 6(d) is a schematic diagram showing the comparison between the two simulated values and their measured values of the atmospheric visibility >10km scheme provided by the embodiment of the present invention.
Fig. 7 is a comparison between the calculated value and the measured value of the hygroscopic growth factor gf (rh) of the aerosol particle size provided by the embodiment of the present invention.
FIG. 8(a) is a simulated value n of complex refractive index ACRI of aerosol provided by an embodiment of the present inventionre(dry) versus measured values.
FIG. 8(b) is a simulated value n of complex refractive index ACRI of aerosol provided by the embodiment of the present inventioni(dry) versus measured values.
FIG. 8(c) is a simulated value n of complex refractive index ACRI of aerosol provided by an embodiment of the present inventionreSchematic comparison between (RH) and measured values.
FIG. 8(d) is a simulated value n of complex refractive index ACRI of aerosol provided by an embodiment of the present inventioniSchematic comparison between (RH) and measured values.
Fig. 9(a) is a schematic diagram of comparison between three simulated values and their measured values of the atmospheric visibility <2km scheme provided by the embodiment of the present invention.
FIG. 9(b) is a schematic diagram showing the comparison between the three simulated values and their measured values of the atmospheric visibility 2-5km scheme provided by the embodiment of the present invention.
FIG. 9(c) is a schematic diagram showing the comparison between the three simulated values and the measured values of the atmospheric visibility 5-10km scheme provided by the embodiment of the invention.
Fig. 9(d) is a schematic diagram of comparison between three simulated values and their measured values of the atmospheric visibility >10km scheme provided by the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a method, a system and an application for processing atmospheric visibility data, and the present invention is described in detail with reference to the accompanying drawings.
As shown in fig. 1, the atmospheric visibility data processing method provided by the present invention includes the following steps:
s101: calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
s102: calculating the moisture absorption growth factor of the aerosol particle size;
s103: and constructing a modeling data set to obtain the atmospheric visibility.
Those skilled in the art can also implement the method for processing atmospheric visibility data provided by the present invention by using other steps, and the method for processing atmospheric visibility data provided by the present invention in fig. 1 is only one specific embodiment.
As shown in fig. 2, the atmospheric visibility data processing system provided by the present invention includes:
the parameter processing module 1 is used for calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition; calculating the moisture absorption growth factor of the aerosol particle size;
and the atmospheric visibility computing module 2 is used for constructing a modeling data set to obtain atmospheric visibility.
The technical scheme of the invention is further described by combining the comparison.
1 materials and methods
1.1 Observation Instrument
The data used by the invention comprises the hourly viewing data acquired by a WS600 integrated weather station, an AURORA-3000 integrated turbidimeter, an AE-31 black carbon instrument and a GRIMM180 environmental particulate matter monitor in 2017 of metropolis in 10-12 months.
The related instruments are introduced as follows: (1) AURORA-3000 turbidimeter (Ecotech, Australia) with observation wavelength of 525nm, sampling frequency of 5 min/time, TSP cutting head, and detection range>0。25Mm-1Zero-point check every 24h, 24h zero-point drift<And +/-1%, carrying out span calibration by using R134a gas every week, and controlling an internal heating system of the turbidimeter by using an internal temperature and humidity sensor so that the relative humidity of the aerosol in the internal chamber of the instrument is controlled to be below 40%, and taking the aerosol as the dry state. (2) A model AE-31 black carbon detector (Magee Scientific, USA) observes the mass concentration of Black Carbon (BC) and the data acquisition frequency is 5 min/time. The black carbon appearance adopts the TSP cutting head, adds the silicone tube in the middle of sampling head and the instrument is connected and reduces the influence of moisture to black carbon measurement. The monitoring data of the turbidimeter and the black carbon meter are processed into hour average data after quality controlValue data. (3) The GRIMM180 environmental particulate matter monitor (GRIMM corporation, Germany) can measure PM10 and PM2 in the atmosphere in real time. 5 and PM1, and 31 particle size fractions, from which an aerosol particle spectral distribution n [ (dry)]And the data frequency is 5 min/time, wherein the initial value of the particle diameter of each particle diameter section is 0.25, 0.28, 0.3, 0.35, 0.4, 0.45, 0.5, 0.58, 0.65, 0.7, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.0, 3.5, 4.0, 5.0, 6.5, 7.5, 8.0, 10.0, 12.5, 15.0, 17.5, 20.0, 25.0, 30.0 and 32.0 μm. (4) Meteorological elements (atmospheric visibility and relative humidity RH) were monitored by WS600(LUFFT corporation, germany) integrated meteorological station; gaseous pollutant NO2Volume concentration of chemical luminescence NO, NO2-NOx analyzer (Thermo 42i, usa).
1.2 site of observation
The observation point of the GRIMM180 atmospheric particulate monitor is positioned on the top floor of a metropolitan-loop united building (104 degrees 02'E and 30 degrees 39' N) and is 81m away from the ground. The other instrument observation points are positioned on the roof (30 degrees 39'N and 104 degrees 02' E) of a comprehensive building of a metropolis environmental protection scientific research institute, 21m away from the ground, no high buildings are arranged within 2km around the building, the visual field is wide, and the surrounding is a concentrated residential area. No obvious atmospheric pollution source exists around the two monitoring point positions, the straight line distance between the two monitoring point positions is 410m, and the environmental meteorological conditions are basically consistent.
1.3 data processing method
And uniformly processing the monitoring data into hour mean value data. All data of days with precipitation, sand and dust and strong wind are removed; secondly, rejecting abnormal data with relative humidity still greater than 40% after the instrument is dried so as to eliminate the influence of water vapor; and finally, rejecting abnormal data such as data exceeding a threshold value, continuous unchanged data, missing data and inverse hanging of aerosol mass concentration, and thus obtaining 1145 matched samples.
The atmospheric extinction coefficient represents the relative attenuation rate of light propagating in the atmosphere for a unit distance, and the ambient atmospheric extinction coefficient b at a wavelength of 550nmext(RH)(km-1) The relationship with the atmospheric visibility V (km) is shown in formula (1) and formula (2).
bext(RH)＝bsp(RH)+bap+bsg+bag (2)
In the formula, bsp(RH)，bap，bsg，bag，bspRespectively is the scattering coefficient of aerosol, the absorption coefficient of aerosol, the scattering coefficient of dry clean atmosphere and the absorption coefficient of dry clean atmosphere under the environment condition at the wavelength of 550nm, and the unit is Mm-1。
Dry aerosol scattering coefficient b at wavelength 525nm measured by AURORA-3000 integral turbidimeter according to equation (3)sp，525nm correction to obtain the scattering coefficient of dry aerosol at the wavelength of 550nm (b)sp) Wherein α is 1. 36, representative of the metropolitan Angstrom wavelength index.
According to Wu Zu et al [2]Aiming at the comparative observation test principle of the AE-31 type black carbon detector, the mass concentration (C) of Black Carbon (BC) at 880nm wavelength which is not corrected is directly observed by the AE-31 type black carbon detectorBC)/μg·m-3Inverting the absorption coefficient b of the aerosol at a wavelength of 532nm according to the formula (4)ap，532nm(Mm-1) And the absorption coefficient b at a wavelength of 550nm is obtained by correcting the formula (5)ap(Mm-1) See formula (4):
bap，532nm＝8.28CBC+2.23 (4)
corresponding to b at a wavelength of 550nmsgGenerally takes a value of 13Mm-1. Corresponding to b at a wavelength of 550nmagSee formula (6):
bag＝0.33·CNO2 (6)
in the formula, CNO2Is NO2Mass concentration (10)-9g/m3)。
2 design of visibility calculation scheme
As a main body of the atmospheric extinction coefficient, the aerosol scattering coefficient is extremely susceptible to the mass concentration of particles, the chemical components of the particles and the hygroscopicity of the aerosol, and has been an important source of uncertainty of the atmospheric extinction coefficient and visibility prediction. Therefore, the calculation method of the aerosol scattering coefficient is the key for determining the visibility calculation accuracy. The present invention takes the first 2/3 time series of samples (763) as the modeling data set for the following three atmospheric visibility calculation schemes. The post 1/3 time series of samples (382) serve as verification data sets for comparing the suitability of the respective schemes in atmospheric visibility simulations. Atmospheric visibility is first divided into<2km, 2-5km, 5-10km and>the 4 ranges of 10km and corresponding statistics for the conventional meteorological elements for these 4 ranges of atmospheric visibility conditions are given (table 1). As can be seen from Table 1, the lower the atmospheric visibility, the corresponding PM2。5The higher the BC, RH population.
TABLE 14 statistical results of conventional meteorological elements in atmospheric visibility of various ranges
Note that R (V, PM)2。5) R (V, BC), R (V, RH) respectively represent V and PM2。5Correlation coefficient between mass concentration, BC, RH
2.1 statistical model of visibility calculation (scheme one)
The aerosol scattering hygroscopic growth factor f (rh) is the ratio of the aerosol scattering coefficient under ambient conditions to the corresponding aerosol scattering coefficient under dry conditions. The formula for calculating f (RH) in autumn and winter in the adult areas is shown as formula (7):
according to the formulae (2), (7) and bspAnd PM2。5Statistical relationship between bext(RH) is represented by the formula (8):
in the formula, a, b, c, d and e are undetermined parameters; cPM2.5Is PM2。5Mass concentration of (d/. mu.g/m)3)。
According to the formula (8), each undetermined parameter is solved by using the modeling data to obtain the autumn and winter b of the Chengdu areaext(RH) as a statistical model, see formula (9):
b is calculated by equation (9)ext(RH), calculating according to the formula (1) to obtain the atmospheric visibility, and modeling calculation results show that the calculated value of the atmospheric visibility is slightly lower than the measured value by 3.81 percent in total, and R and MRE between the two are 0.94 and 18.01 percent respectively. Wherein, R between the 4 atmospheric visibility analog values and the measured values is 0.66, 0.75, 0.59 and 0.83 respectively, and MRE is 12.42%, 18.56%, 17.21% and 19.64% respectively.
2.2 empirical parameter Mie theoretical model for visibility calculation (scheme two)
Based on Mie scattering theory, bext(RH) is given by the following formula (10):
bext(RH)＝bsp(RH)+bap+bsg+bag＝∫πr2Qsp[a(RH),m(RH)]N[r(RH)]dr(RH)+bap+bsg+bag(10)
m(RH)＝nre(RH)+ni(RH)(12)
wherein a (RH) is a dimensional parameter of the aerosol at ambient conditions; n isre(RH) andni(RH) is the real and imaginary parts of the complex refractive index of the aerosol (DACRI) m (RH) at ambient conditions; qsp[a(RH)，m(RH)]Is an aerosol scattering efficiency factor under the environmental condition calculated by a Mie scattering model and is determined by a (RH) and m (RH); r (RH) is the aerosol particle size at ambient conditions; n [ R (RH)]Is aerosol number concentration particle size distribution under environmental conditions; r (RH) is the particle size of the aerosol at ambient conditions. The aerosol scale parameters a (RH) and complex refractive index m (RH) at ambient conditions can be further decomposed into the formulae (13-17):
m(dry)＝nre(dry)+ni(dry)(16)
m(water)＝nre(water)+ni(water)＝1.33+0×i (17)
wherein gf (RH) is an aerosol particle size hygroscopic growth factor; r (dry) is the particle size of the aerosol under dry conditions; n isre(dry) and ni(dry) is the real and imaginary parts of the complex refractive index m (dry) of the aerosol under dry conditions; n isre(water) and ni(water) is the real and imaginary parts of the complex refractive index m (water).
Due to the complexity of DACRI measurement and inversion, its part nre(dry) and imaginary part ni(dry) was generally empirically assumed to be 1.55 and-0.005, respectively, in the past studies. In addition, based on the gf (rh) parameterization scheme proposed by seqijing et al, see formula (18):
DACRI and gf (RH) are key optical parameters necessary for simulating atmospheric visibility based on Mie scattering model, but are limited by the complexity of measurement and inversion of DACRI and gf (RH), and n in previous researchesre(dry)、niThe parameters (μ) of the (dry) and gf (rh) parameterization schemes are typically empirically derived. For this purpose, the invention first calculates gf (rh) using the parameterized scheme of equation (18), assuming the parameter μ in equation (22) to be 4.4, since aerosol types in the metropolitan area are of the contaminating type.
In the above nre(dry)、niUnder the condition of empirical values of (dry) and gf (RH), atmospheric visibility can be obtained according to the formula (1) based on a modeling data set, and a modeling calculation result shows that nre(dry) and niThere is no correlation between the empirical values of (dry) and the measured values (due to the empirical determination of DACRI), the corresponding MREs are 3.37% and 77.80%, respectively, the R and MRE between gf (rh) and the measured values, calculated according to equation (23), are 0.92 and 18.19%, respectively, and n, calculated according to equation (18), is 0.92 and 18.19%, respectivelyre(RH) and niThe R between (RH) and its measured values is 0.58 and 0.47, respectively, and the corresponding MREs are 6.23% and 82.15%, respectively, the calculated atmospheric visibility is on average 23.80% lower than the measured values, and the corresponding R and MREs are 0.96 and 24.53%, respectively. Wherein, R between the 4 atmospheric visibility analog values and the measured values is 0.71, 0.81, 0.68 and 0.91 respectively, and MRE is 29.63%, 26.01%, 26.81% and 18.69% respectively. As can be seen from the results of the modeling calculation of scheme two, nre(dry) the error between the empirical value and its measured value is small, but ni(dry), gf (rh) and the error between atmospheric visibility and its measurement are large.
2.3 double-parameter scheme Mie theoretical model for visibility calculation (scheme III)
Aiming at uncertainty of DACRI and Gf (RH) experience values in a scheme II, a multivariate stepwise linear regression method is utilized to provide a parameterization scheme of DACRI, and the parameterization scheme is shown as an expression (19) and an expression (20):
in the formula, CBC，CPM1，CPM2。5And CPM10Are BC, PM respectively1，PM2。5And PM10In units of μ g/m3。
The different types of aerosols have great difference in particle size hygroscopicity, and even if the same type of aerosols are affected by the emission source and the physical and chemical processes of the aerosols, the aerosol particle size hygroscopicity is also different. Therefore, by combining an immune evolution algorithm and Mie scattering theory to invert gf (RH), the relation between the Gf (RH) and the RH is analyzed, and a parameterization scheme of the Gf (RH) suitable for autumn and winter in the Chengdu region is provided based on an expression (18), which is shown as an expression (21):
based on the modeling dataset, first n is calculated according to equation (19) and equation (20), respectivelyre(dry) and ni(dry), equation (21) calculates gf (RH). On the basis, the calculation flow of the atmospheric visibility is the same as the scheme two. The final calculation results show that n is calculated according to the equations (19) and (20)re(dry) and niR between (dry) and measured values is 0.55 and 0.85, respectively, corresponding MRE is 2.27% and 14.72%, respectively, R and MRE between gf (RH) and measured values is 5.03% and 0.92, respectively, calculated according to equation (21), and n is calculated according to equation (18)re(RH) and niThe R between (RH) and its measured values is 0.70 and 0.85, respectively, and the corresponding MREs are 1.46% and 18.98%, respectively, with calculated atmospheric visibility being on average slightly lower than the measured values by 4.92%, and the corresponding R and MREs being 0.93 and 14.26%, respectively. Wherein, R between the 4 atmospheric visibility analog values and the measured values is 0.66, 0.79, 0.65 and 0.91 respectively, and MRE is 10.45%, 13.72%, 16.59% and 13.21% respectively. The result of modeling calculation aiming at complex refractive index and particle size moisture absorption growth factor of the third scheme is obviously superior to that of the second scheme, and aiming at atmospheric energyThe visibility modeling calculation effect is obviously better than that of the first scheme and the second scheme.
3 applicability analysis of three visibility calculation schemes
3.1 schemes one
Based on the verification data set, atmospheric visibility was simulated according to the first scheme, and scatter plots between the above-described atmospheric visibility simulation values and their measured values in different ranges were given, respectively (fig. 3(a) -fig. 3 (d)). The simulation results show that there are 4 ranges: (<2km, 2-5km, 5-10km and>10km) and the measured values thereof are respectively 0.72, 0.80, 0.64 and 0.84, and the MREs are respectively 21.63%, 13.91%, 16.00% and 14.83%. And as the atmospheric visibility range is increased, the simulation effect of the first scheme is gradually reduced, and the simulation error is maximum (21.63%) for the low visibility less than 2km (figure 3 (a)). Combining the analysis of table 1 and fig. 3(a) -3 (d), it can be seen that the R (V, RH) between the low visibility and RH of less than 2km is-0.35, which is significantly greater than the atmospheric visibility and PM2。5And R (V, PM) between BC2。5) And R (V, BC), therefore, uncertainty in predicting the increase in aerosol moisture absorption under high relative humidity conditions may be an important factor in causing statistical model-simulated atmospheric visibility errors.
3.2 scheme two
Based on the validation dataset, n according to scheme twore(dry)、niThe empirical values of (dry) and μ are first obtained by simulating the empirical values of equation (18) and μ ═ 4.4 to obtain the corresponding gf (rh), with R and MRE between the simulated and measured values of 0.91 and 7.81%, respectively. Although the parameter mu of the aerosol particle size moisture absorption growth model is an empirical value of 4.4, the dispersion point distribution (fig. 4) between the gf (RH) simulation value and the measured value thereof can better reflect the moisture absorption growth characteristics of the aerosol particle size under different RH conditions, and the error is still within an acceptable range.
Secondly, nre(dry) and ni(dry) R between the empirical and measured values is 0, and MRE is 2.79% and 61.19%, respectively. Based on the simulation results of gf (RH), n of ACRI (FIG. 3) was simulated according to the equations (13-18)re(RH) (FIG. 5(c)) and ni(RH) (FIG. 5(d)) and simulation results TableMing, nre(RH) and niR between the simulated value of (RH) and its measured value was 0.46 and 0.39, respectively, and MRE was 3.04% and 78.78%, respectively. From the above simulation results, even nre(dry) and ni(dry) is an empirical value, and has great uncertainty, but the uncertainty of the corresponding optical parameter change is further increased after the moisture absorption of the aerosol is increased. In addition, the greater the ambient relative humidity, nre(RH) and niThe higher the correlation coefficient between (RH) and its measured value, indicating that an increase in relative humidity will enhance the relative humidity determination of refractive index.
Gf (RH), n obtained by the above simulationre(RH) and ni(RH), atmospheric visibility is obtained according to the simulation of the second scheme, and accordingly scatter plots are respectively given between the above-mentioned atmospheric visibility simulation values and their measured values in different ranges (fig. 6(a) -fig. 6 (d)). 4 ranges of (<2km, 2-5km, 5-10km and>10km) and the measured values thereof are 0.68, 0.91, 0.90, 0.92, respectively, and 22.79%, 25.06%, 22.85%, 12.01%, respectively, for MRE. Thus, gf (RH), nre(RH) and niEmpirical values of (RH) can lead to significant errors in atmospheric visibility simulations. In addition, the simulation result of the comparison scheme I and the scheme II can be speculated that the parameterization scheme of the calculation of ACRI and gf (RH) with relative humidity as a dependent variable can more comprehensively characterize the nonlinear evolution characteristic of atmospheric visibility after further combining with a Mie scattering model, and the relative humidity plays a decisive role in the nonlinear evolution of the atmospheric visibility under the condition of the invention without mainly considering the mass concentration of particulate matters, so that the R in the scheme II is obviously higher than that in the scheme I. Of course, due to gf (RH), nre(RH) and ni(RH) is derived from the relevant empirical values, which inevitably causes large simulation errors.
3.3 scheme three
Based on the verification data set, firstly, according to the project III Gf (RH) achievement autumn and winter localization parameterization project (formula (21)), the verification set Gf (RH) is obtained through simulation, and R and MRE between a simulation value and a measured value are respectively 0.91 and 4.24%. Compared with the empirical parameterization scheme in the second scheme, the Gf (RH) precision obtained by adopting the localized parameterization scheme for simulation is higher.
Based on the calculation results of gf (RH), n of complex refractive index DACRI of the aerosol under dry conditions was simulated according to the following equations (19) and (20), respectivelyre(dry) (FIG. 8(a)) and ni(dry) (FIG. 8(b)), R between the respective simulated values and their measured values was 0.21 and 0.85, respectively, and MRE was 2.31% and 13.36%, respectively. On the basis of DACRI and gf (RH) simulation results, n of complex refractive index ACRI of the aerosol under the environmental condition is simulated according to the formulas (13-18)re(RH) (FIG. 8(c)) and ni(RH) (FIG. 8(d)), R between the respective simulated values and their measured values is 0.51 and 0.83, respectively, and MRE is 3.46% and 18.84%, respectively, it can be seen that the complex refractive index simulation effect using the localized parameterization scheme is significantly improved.
nre(dry) and ni(dry) the difference in the sensitivity to the independent variable factor in the parameterization scheme is an important factor that causes the difference in R and MRE between the two analog and measured values. In addition, the DACRI parameterization scheme of the invention can effectively simulate nreAnd niBut may still be insufficiently fine in the characterization of aerosol chemical composition information, which is likely to be the main reason for uncertainty in ACRI simulation results. As can be seen by comparing FIG. 8(a) with FIG. 8(c) and FIGS. 8(b) and 8(d), n isre(dry) to nreThe simulation error of (RH) increased from 2.31% to 3.46%, corresponding to ni(dry) to niThe calculation error of (RH) is improved from 13.36% to 18.84%, which indicates that the moisture absorption growth process of the aerosol can increase the simulation error of key aerosol optical radiation parameters such as ACRI, gf (RH), and the like, and the inference is better embodied for the simulation situation of atmospheric visibility in the second scheme,
the obtained gf (rh) and ACRI are simulated according to the parameterization scheme, and the atmospheric visibility is obtained according to the simulation of the scheme three, and scatter diagrams between the atmospheric visibility simulation values and the measured values in different ranges are respectively given (fig. 9(a) -fig. 9 (d)). The R between the atmospheric visibility simulated values and their measured values for 4 ranges (<2km, 2-5km, 5-10km and >10km) are 0.62, 0.90, 0.89, 0.93, respectively, and the MRE is 9.86%, 10.39%, 9.94%, 14.06%, respectively. Therefore, compared with the first scheme and the second scheme, the simulation effect of the third scheme is remarkably superior in all aspects. In addition, on the premise of the invention that the mass concentration of the particles is not mainly considered, the R of the third scheme is basically consistent with that of the second scheme, but the simulation precision is remarkably better, which also shows that the atmospheric visibility nonlinear evolution characteristic is more dominated by relative humidity, and the accuracy of the optical parameters dominates the simulation precision of the atmospheric visibility.
Aerosol optical radiation parameters are estimated by coupling a regional localization parameterization scheme of DACRI and gf (RH), atmospheric visibility is simulated based on a Mie scattering model, and finally a better simulation effect than that of the traditional method is obtained, which reflects the applicability of the parameterization scheme of DACRI and gf (RH) in aerosol optical radiation forced effect simulation. With the continuous development of atmospheric chemistry models (GEOS-Chem/WRF-CMAQ and the like) and big data machine learning algorithms, atmospheric pollutants (BC, PM) are generated at present1，PM2。5，PM10 andNO2) The predictability of the relative humidity RH and the aerosol number concentration particle size distribution n (r) is also increasing, which provides a reference for the improvement of atmospheric chemistry model, for example, a new parameterization scheme is directly applied to estimate aerosol parameters in the output data of the model, and then the simulation of the optical radiation effect of the aerosol is performed.
According to the method, ground hourly observation data observed by a WS600 integrated meteorological station, an AURORA-3000 integrated turbidimeter, an AE-31 black carbon instrument and a GRIMM180 environment particulate matter monitor in 2017 of metropolis are compared and analyzed with simulation results of two visibility calculation models (Mie scattering models and statistical models of empirical parameters) in different visibility intervals (<2km, 2km to 5km, 5km to 10km and more than 10km), so that the applicability of the improved algorithm is evaluated. The main conclusions are as follows:
(1) the three visibility calculation methods can well simulate the change characteristics of visibility.
(2) The localized parameterization scheme can more accurately estimate the complex refractive index ACRI of the aerosol and the moisture absorption growth factor Gf (RH) of the particle size of the aerosol, so that the simulation effect of the aerosol in four visibility ranges is remarkably superior, the correlation coefficients R of corresponding simulation values and measured values are respectively 0.62, 0.90, 0.89 and 0.93, and the average relative errors MRE are respectively 9.86%, 10.39%, 9.94% and 14.06%.
(3) The parameterization scheme of localized DACRI and gf (RH) and the coupling application of the parameterization scheme and the Mie scattering model more comprehensively consider complex nonlinear mechanisms in the ash haze evolution process.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. An atmospheric visibility data processing method is characterized by comprising the following steps:
calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
calculating the moisture absorption growth factor of the aerosol particle size;
and constructing a modeling data set to obtain the atmospheric visibility.
2. The atmospheric visibility data processing method according to claim 1, wherein the formula for calculating the real part of the complex refractive index of the aerosol under dry conditions and the imaginary part of the complex refractive index of the aerosol under dry conditions is:
in the formula, CBC，CPM1，CPM2.5And CPM10Are BC, PM respectively1，PM2.5And PM10In units of μ g/m3。
3. The atmospheric visibility data processing method of claim 1, wherein the formula for calculating the hygroscopic growth factor of the aerosol particle size is
4. Atmospheric visibility data processing method as defined in claim 1, characterised in that the ambient atmospheric extinction coefficient b is at a wavelength of 550nmext(RH)(km-1) Relationship to atmospheric visibility v (km):
in the formula, bsp(RH is the coefficient of aerosol scattering at 550nm wavelength under ambient conditions, in Mm-1。
5. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition;
calculating the moisture absorption growth factor of the aerosol particle size;
and constructing a modeling data set to obtain the atmospheric visibility.
6. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the atmospheric visibility data processing method as claimed in any one of claims 1 to 4.
7. An atmospheric visibility data processing system for executing the atmospheric visibility data processing method according to any one of claims 1 to 4, wherein the atmospheric visibility data processing system comprises:
the parameter processing module is used for calculating the real part of the complex refractive index of the aerosol under the drying condition and the imaginary part of the complex refractive index of the aerosol under the drying condition; calculating the moisture absorption growth factor of the aerosol particle size;
and the atmospheric visibility calculation module is used for constructing a modeling data set to obtain the atmospheric visibility.
8. A meteorological data processing terminal, characterized in that, the meteorological data processing terminal is used for realizing the atmospheric visibility data processing method of any claim 1-4.
9. An environment monitoring terminal, which is used for realizing the atmospheric visibility data processing method as claimed in any one of claims 1 to 4.
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