Greenhouse gas passive load atmospheric roof radiation simulation method and device

Abstract

The invention provides a greenhouse gas passive load atmospheric roof radiation simulation method and device. The method provided by the invention comprises the steps of determining a satellite radiation band based on spectral response characteristics of a satellite load and energy distribution characteristics of solar radiation; calculating a first radiation quantity after the initial solar radiation is modulated by the earth surface reflection by using the earth surface reflectance data under the radiation wave band; the total optical thickness of molecules is determined based on the Doppler broadening characteristic and the collision broadening characteristic of molecular absorption spectral lines under the radiation wave band and distribution of different molecular number densities in the atmosphere, and the first radiation attenuation of molecular absorption on the atmospheric roof is calculated; determining the optical thickness of the aerosol based on the scattering coefficient and the extinction coefficient of the aerosol particles under the radiation wave band and the distribution of the density of the aerosol particles in the atmosphere, and calculating the second radiation attenuation of the aerosol to the atmospheric roof; and subtracting the first radiation attenuation and the second radiation attenuation from the first radiation quantity to obtain the atmospheric top radiation intensity.

Description

Technical Field

[0001] The present application relates to the technical field of remote sensing monitoring of atmospheric environment, and in particular to a method and device for simulating top of atmosphere radiation of greenhouse gas passive load. Background Art

[0002] Top of atmosphere radiation simulation plays a vital role in modern remote sensing technology, especially in greenhouse gas monitoring and climate change research. Top of atmosphere radiation simulation can accurately describe the transmission process of electromagnetic waves in the atmosphere, covering the physical processes of atmospheric molecules, aerosols, cloud absorption, scattering effects, and surface reflection. Through these simulations, the radiation balance, energy balance, changes in gas concentrations, and the distribution of earth radiation and solar radiation in the atmosphere can be studied, providing important decision-making basis for meteorology, energy, agriculture and other fields. For remote sensing satellites, accurate radiation transmission simulation is the basis for obtaining reliable remote sensing data.

[0003] At present, the existing top of atmosphere radiation simulation relies on the atmospheric radiation transfer model, such as the 6S model, which can simultaneously consider molecular absorption and aerosol scattering effects. However, when facing high-precision remote sensing tasks such as greenhouse gas monitoring, it has the defect of low spectral resolution and cannot meet the demand for high spectral resolution. This limitation makes it difficult for traditional atmospheric radiation transfer models to accurately simulate the absorption effect of greenhouse gases, affecting the accurate assessment of greenhouse gas concentrations and environmental changes.

[0004] Therefore, a method is urgently needed to solve the problem that the existing atmospheric radiation transfer models cannot simultaneously simulate molecular absorption and aerosol scattering with high spectral resolution, so as to accurately simulate the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, and provide strong support for the evaluation of greenhouse gas passive load performance. Summary of the invention

[0005] In view of this, the present application provides a method and device for simulating top of atmosphere radiation of a greenhouse gas passive load, so as to solve the problem that the existing atmospheric radiation transmission model cannot simultaneously simulate molecular absorption and aerosol scattering with high spectral resolution, thereby achieving accurate simulation of the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, and providing strong support for the performance evaluation of greenhouse gas passive loads.

[0006] Specifically, the present application is implemented through the following technical solutions:

[0007] The first aspect of the present application provides a method for simulating top of atmosphere radiation of a greenhouse gas passive load, the method comprising:

[0008] Based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, determine the radiation band that the satellite can detect;

[0009] Based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type, obtaining surface reflectivity data;

[0010] Using the surface reflectivity data, the modulation of the surface reflection on the initial solar radiation is calculated through a reflection model to generate a first radiation amount of sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation;

[0011] Based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, an absorption model for each molecule is established; based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, the total optical thickness of the molecule is determined, and based on the total optical thickness of the molecule, the first radiation attenuation of the molecular absorption to the top of the atmosphere is calculated; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0012] Calculating the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determining the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculating the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness;

[0013] The first radiation amount is subtracted from the first radiation amount by the second radiation attenuation amount to obtain the top of atmosphere radiation intensity.

[0014] The second aspect of the present application provides a greenhouse gas passive load top of atmosphere radiation simulation device, the device includes a determination module, an acquisition module, a generation module and a calculation module; wherein,

[0015] The determination module is used to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation;

[0016] The acquisition module is used to acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type;

[0017] The generating module is used to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through the reflection model, and generate the first radiation amount of the sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation;

[0018] The calculation module is used to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, and calculate the first radiation attenuation of the molecular absorption to the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0019] The calculation module is further used to calculate the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculate the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness;

[0020] The acquisition module is further used to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0021] The present application provides a method and device for simulating the top of the atmosphere radiation of a greenhouse gas passive payload. In the first aspect, the present application first determines the radiation band that the satellite can detect based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation. The selection of this radiation band not only takes into account the energy distribution of the solar radiation, but also takes into account the capabilities of the satellite sensor, thereby ensuring that detailed and high-spectral data covering multiple bands can be selected. The setting of this step provides band data with high spectral resolution for subsequent molecular absorption and aerosol scattering simulations. In addition, by further utilizing the surface reflectivity data, combining the spatial distribution characteristics of the surface reflectivity and the surface type, the modulation of the surface reflection on the solar radiation is calculated through the reflection model. This process not only provides a more refined initial value for the radiation intensity, but also provides a basis for the calculation of radiation attenuation under high spectral resolution, further enhancing the simulation accuracy of the reflection effect. In the second aspect, in the simulation process of molecular absorption, the present application is based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum under the radiation band, and realizes the simulation of high spectral resolution by establishing an absorption model for each molecule. In order to perform accurate absorption simulation at high spectral resolution, the absorption model can accurately calculate the absorption effect in each band by considering the absorption characteristics in different bands. The absorption characteristics of each band can be distinguished by high spectral resolution data, ensuring that the absorption of radiation by greenhouse gases can be accurately quantified in a more refined band. The absorption effect of each molecule can be reflected in a finer band through high spectral resolution data, avoiding the blurring effect in wide band processing. For example, the absorption characteristics of carbon dioxide and water vapor in different bands are very different. High spectral resolution enables these differences to be accurately captured, improving the accuracy of molecular absorption simulation. In the third aspect, the present application simulates the scattering and extinction characteristics of aerosols by calculating the optical characteristic parameters of aerosol particles, and then using complex mathematical formulas such as the Mie scattering formula and the Riccati-Bessel function. The scattering effect of aerosols on radiation is different in different bands. By calculating the scattering coefficient and extinction coefficient of each aerosol particle, and combining the distribution of aerosol particle density in the atmosphere, the scattering and extinction effects of aerosols can be accurately simulated in each band. High spectral resolution data can provide detailed aerosol optical properties for each band, which is essential for the high-precision calculation of aerosol optical depth. Combined with the scattering and extinction coefficients, the aerosol characteristics at different altitudes are further combined to accurately calculate the aerosol optical depth. High spectral resolution data ensures a detailed simulation of the aerosol scattering and absorption effects at each altitude, thereby providing accurate aerosol radiation attenuation. Finally, after obtaining detailed simulation data of surface reflection, molecular absorption, and aerosol scattering and absorption, the top of the atmosphere radiation intensity is calculated by combining these effects.Since each link is calculated in detail under high spectral resolution data, from surface reflection to atmospheric molecular absorption, and then to aerosol scattering, the final radiation intensity result is of high accuracy, achieving accurate simulation of the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, providing strong support for the evaluation of greenhouse gas passive load performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flow chart of a method for simulating top of atmosphere radiation of greenhouse gas passive load provided in Example 1 of the present application;

[0023] Figure 2 This is a schematic diagram of the structure of the greenhouse gas passive load top of atmosphere radiation simulation device provided in Example 2 of the present application. DETAILED DESCRIPTION

[0024] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0025] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0026] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0027] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0028] Figure 1 This is a flow chart of the greenhouse gas passive load top of atmosphere radiation simulation method provided in Example 1 of the present application. Figure 1 , the method provided in this embodiment may include:

[0029] S101. Determine the radiation band that the satellite can detect based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation.

[0030] Specifically, the spectral response characteristics of a satellite payload refer to the satellite payload's ability to respond to radiation of different wavelengths. Each satellite payload has specific spectral response characteristics, which determine which bands of radiation it can effectively detect. For example, some instruments may be particularly sensitive to the visible light region, while having a weaker response to infrared or ultraviolet bands. The energy distribution characteristics of solar radiation refer to the distribution of radiation intensity of a radiation source (such as the sun) within different wavelength ranges, and the energy distribution of solar radiation is different at different wavelengths.

[0031] Furthermore, since the remote sensing mission of satellites is usually to obtain information about the earth's surface or atmosphere, and different objects or atmospheric components have different absorption and scattering characteristics for radiation of different wavelengths. Therefore, it is necessary to determine the appropriate radiation band to maximize the observation effect. For example, the absorption peak of greenhouse gases usually appears in the infrared band, while the reflection peak of vegetation appears in the visible and near-infrared bands. By selecting the appropriate band, satellites can monitor specific objects or atmospheric components more effectively.

[0032] In a specific implementation, the method of determining the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation includes:

[0033] (1) Based on the spectral response characteristics of the satellite payload, a multi-band spectral response function is established.

[0034] Specifically, the multi-band spectral response function is a mathematical description of the satellite payload's ability to respond to radiation in different bands, and characterizes the response characteristics of the satellite payload to radiation at multiple wavelengths (or bands).

[0035] In a specific implementation, the multi-band spectral response function is established based on the spectral response characteristics of the satellite payload, including: determining parameters related to the satellite payload according to the spectral response characteristics of the satellite payload; the parameters characterize the response capability of the satellite payload in different bands and the sensitivity to radiation in different bands; based on the parameters, in combination with the application requirements of the satellite payload and the absorption characteristics of greenhouse gases, determining the appropriate spectral range and sampling interval; based on the selected spectral range and sampling interval, spectrally sampling the Gaussian function to obtain a multi-band spectral response function.

[0036] Specifically, the parameters related to satellite payloads characterize the sensitivity and response capability of satellite payloads to radiation in different wavelength ranges, including the band range, sensitivity, noise level, transmittance, etc. The Gaussian function is usually used to represent the shape of the satellite payload response, which is calculated and determined by mathematical methods to determine the response intensity of each band.

[0037] In specific implementation, the spectral response characteristics of the satellite payload are obtained based on the technical documents or experimental data of the satellite payload provided by the satellite payload manufacturer. Parameters related to the performance of the satellite payload, such as the band range, sensitivity, noise level, transmittance, etc. of the instrument, are extracted from the spectral response characteristics of the satellite payload. According to the spectral response characteristics of the satellite payload, the sensitivity of the satellite payload to radiation in different bands is defined, and the response intensity of the satellite payload in each band is calculated. Further, the mission objectives of the satellite payload are comprehensively considered, including the monitoring targets (such as greenhouse gases. When monitoring greenhouse gases, the satellite payload needs to cover the specific absorption bands of greenhouse gases) and the requirements of high spectral resolution. And combined with the absorption characteristics of greenhouse gases, for different greenhouse gases, the position and intensity of their absorption spectra are studied. For example, the absorption spectrum of CO2 is mainly located in the mid-infrared region (about 15μm), while the absorption spectrum of methane is located near 3.3μm. Determine the absorption characteristics of the target gas in different bands, select those bands with significant absorption characteristics near the absorption band of the target gas, and also consider the absorption intensity of the greenhouse gas, that is, the degree of absorption of radiation by the gas at different concentrations. When determining the spectral range, the spectral range should cover all relevant greenhouse gas absorption characteristics. For example, if you need to monitor CO2 and methane at the same time, you may need to cover the range of 2-15μm. In addition, the selection of the spectral range must also take into account the transmittance characteristics of the atmosphere. Some bands may be affected by the absorption or scattering of other gases in the atmosphere (such as water vapor). Therefore, when selecting bands, you need to avoid selecting these bands to avoid affecting the extraction of greenhouse gas concentrations. When determining the sampling interval, in order to ensure sufficient resolution for the absorption characteristics of greenhouse gases, it is necessary to determine a suitable sampling interval. The sampling interval should be small enough to ensure that the satellite payload can capture the absorption peaks and details of the target gas. This interval should be fine enough to obtain enough data points within the spectral range. Using the selected spectral range and sampling interval, the spectral response is fitted and sampled with a Gaussian function, the spectral response of each band is combined with its corresponding Gaussian curve, and the response data of each band is combined to form a complete multi-band spectral response function.

[0038] (2) Based on the energy distribution characteristics of solar radiation within the spectral range, a high spectral resolution solar light source simulation model is constructed; the simulation model represents the spectral distribution of solar radiation.

[0039] Specifically, the high spectral resolution solar light source simulation model is modeled based on the spectral distribution of solar radiation, and can simulate the intensity or energy density of solar radiation in different wavelength ranges.

[0040] In this embodiment, the high spectral resolution solar light source simulation model is a Kurucz solar spectrum model with a resolution of 0.001nm, and the spectral range covers 200nm-3000nm. Since more than 99% of the solar radiation spectrum of the upper boundary of the earth's atmosphere is between wavelengths of 0.15 and 4.0μm, about 50% of the solar radiation energy is in the visible spectrum (at wavelengths of 0.4 to 0.76μm), 7% in the ultraviolet spectrum region (wavelength <0.4μm), and 43% in the infrared spectrum region (wavelength>0.76μm), with the maximum energy at a wavelength of 0.475μm. Since the wavelength of solar radiation is much smaller than the wavelength of ground and atmospheric radiation (about 3 to 120μm), solar radiation is usually referred to as short-wave radiation, and ground and atmospheric radiation is referred to as long-wave radiation. The Kurucz solar spectrum is observed using a ground Fourier transform spectrometer, which can clearly capture the subtle features in the solar spectrum, and the ultra-high spectral resolution reaches 0.001nm. In terms of data processing, the collected solar FTS spectrum data was processed, including setting continuous levels and drawing spectrum graphs, to ensure the accuracy and reliability of the data; in addition, the total opacity of the spectrum was quickly calculated to improve data processing efficiency. In addition, Kurucz also used a semi-empirical method to fit the central intensity spectrum calculated by the model with the actual observed spectrum to generate a flux spectrum, providing theoretical support for understanding the formation of the spectrum.

[0041] In the specific implementation, based on the spectral distribution characteristics of solar radiation, for each wavelength band, the spectral energy distribution of solar radiation is calculated, and the spectral distribution is modeled according to sufficiently fine wavelength intervals using hyperspectral sampling technology to generate the intensity distribution data of solar radiation in each band, thus obtaining a high spectral resolution solar light source simulation model.

[0042] (3) Combining the multi-band spectral response function with the high spectral resolution solar light source simulation model to determine the radiation bands that can be detected by the satellite.

[0043] In specific implementation, the established multi-band spectral response function is combined with the solar radiation spectrum model. For each spectral band, the radiation intensity that the satellite can detect is calculated by convolution or product operation of the multi-band spectral response function and the solar radiation intensity data. According to the radiation intensity data, by comparing the radiation intensity of different bands and the response capability of the satellite, the effective radiation band range is determined, and the band that the satellite payload can effectively detect is determined.

[0044] The method provided in this embodiment first determines the radiation bands that the satellite can detect based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation. The selection of such radiation bands not only takes into account the energy distribution of solar radiation, but also the capabilities of the satellite sensor, thereby ensuring that detailed hyperspectral data covering multiple bands can be selected. This step of setting provides high spectral resolution band data for subsequent molecular absorption and aerosol scattering simulations.

[0045] S102. Acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type.

[0046] Specifically, surface reflectivity refers to the ability of the surface to reflect electromagnetic waves (such as solar radiation) irradiated on its surface, which is usually between 0 and 1. The spatial distribution characteristics of surface reflectivity refer to how the surface reflectivity is distributed in geographic space. The spatial distribution characteristics will greatly affect the surface reflectivity, which reflects the change pattern of surface reflectivity in different locations or regions, including cities, forests, lakes, deserts, etc. Surface type refers to the different physical characteristics and coverage types of the earth's surface. The surface type determines the characteristics of surface reflectivity. Surface types usually include forests, deserts, urban areas, etc. Surface reflectivity data contains various types of information related to surface reflectivity, including reflectivity values ​​(reflectivity values ​​in different bands), spatial distribution (distribution pattern of reflectivity in space, which may be represented by maps or raster data formats, usually reflectivity distribution in a certain region or globally), time information (reflecting reflectivity changes in different periods), surface type, etc.

[0047] In a specific implementation, the obtaining of surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type includes:

[0048] (1) Obtaining overall surface reflectivity data; the overall surface reflectivity data includes surface reflectivity data at multiple bands, multiple surface types, and multiple spatial resolutions.

[0049] Specifically, the overall surface reflectivity data includes surface reflectivity data at multiple bands, multiple surface types, and multiple spatial resolutions. The overall surface reflectivity data can be obtained through sensors carried on satellite payloads.

[0050] For example, in this embodiment, the overall surface reflectivity data comes from the bidirectional reflectance distribution function and albedo (BRDF / Albedo) data measured by MODIS on Terra and Aqua satellites, including two spatial resolutions of 0.05° and 500m, and its temporal resolution is 16 days, including a total of seven detection bands, and the surface types include evergreen coniferous forests, evergreen broad-leaved forests, deciduous coniferous forests, deciduous broad-leaved forests, mixed forests, closed shrubs, open shrubs, forest grasslands, savannas, grasslands, wetlands, cultivated land, cities, Antarctic snowfields, deserts, marine waters, tundra, fresh snow, sea ice, etc.

[0051] (2) Based on the radiation band, first surface reflectivity data of a corresponding band is obtained by screening from the overall surface reflectivity data.

[0052] Specifically, in combination with the above description, the overall surface reflectivity data includes surface reflectivity data of multiple bands. In this embodiment, only the surface reflectivity data of the radiation band needs to be obtained, so screening is required.

[0053] In specific implementation, based on the determined radiation band, by retrieving the corresponding field (such as band) in the overall surface reflectance data, the band in the field is set as the radiation band for extraction, and the first surface reflectance data that meets these bands is screened out. For example, assuming that the reflectance data of a specific band (such as the near-infrared band) is required, all the surface reflectance data of the band are extracted from the overall data set.

[0054] (3) For the second surface reflectivity data other than the first surface reflectivity data in the overall surface reflectivity data, the second surface reflectivity data is converted into third surface reflectivity data of a corresponding band based on a conversion coefficient between different bands; the second surface reflectivity data corresponds to a different band from the first surface reflectivity data.

[0055] Specifically, the second surface reflectance data and the first surface reflectance data have different bands, and other information may be the same or different (such as surface type, spatial resolution). Different bands can be converted by constructing linear equations through a conversion system. The conversion coefficient is usually determined based on a physical model or an empirical formula (such as a conversion coefficient obtained through regression analysis), and the conversion coefficients between different bands are different.

[0056] In specific implementation, for the second surface reflectivity data that is not in the radiation band in the overall surface reflectivity data, the second surface reflectivity data of each band is converted into the third surface reflectivity data of the radiation band based on the second surface reflectivity data of each band and the conversion coefficient between the band and the radiation band.

[0057] For example, in one embodiment, the radiation band is a near-infrared band. For the second surface reflectivity data in the ultraviolet band, the second surface reflectivity data is converted into the third surface reflectivity data in the radiation band based on the conversion coefficient 1; for the second surface reflectivity data in the visible light band, the second surface reflectivity data is converted into the third surface reflectivity data in the radiation band based on the conversion coefficient 2.

[0058] (4) Determine a target surface type based on simulation requirements, and select fourth surface reflectivity data of a corresponding surface type from the first surface reflectivity data and the third surface reflectivity data based on the target surface type.

[0059] In specific implementation, the target surface type (such as city, forest, agriculture, etc.) is selected according to the simulation requirements, and the corresponding fields (surface type) in the first surface reflectivity data and the third surface reflectivity data are retrieved, and matched according to the surface type identifier or classification label to extract the fourth surface reflectivity data of the target surface type.

[0060] (5) Determine a target spatial resolution based on simulation requirements, and select surface reflectance data of a corresponding spatial resolution from the fourth surface reflectance data based on the target spatial resolution.

[0061] In specific implementation, the required target spatial resolution is determined based on the resolution of the satellite image in the simulation requirements (such as 10 meters, 30 meters, etc.). Based on the target spatial resolution, a data resampling technique (such as nearest neighbor interpolation, bilinear interpolation, or nearest neighbor interpolation, etc.) is used to filter out the matching spatial resolution from the fourth surface reflectance data to obtain the surface reflectance data of the corresponding spatial resolution.

[0062] The method provided in this embodiment realizes high-precision and high-efficiency acquisition of surface reflectance data under the combined use of multi-band, multi-surface type and different spatial resolution data, and ensures the accuracy of radiation and greenhouse gas simulation under high spectral resolution. First, by acquiring the overall surface reflectance data, covering reflectance data of multiple bands, different surface types and multiple spatial resolutions, the reflectance characteristics of the surface can be described in an all-round way. The data of different bands support a detailed analysis of the reflectance characteristics of the surface in different bands, which is particularly important for the simulation of high spectral resolution, because high spectral resolution requires accurate capture of small changes in radiation, thereby effectively improving the accuracy of simulation. The diversity of surface types takes into account the different reflectance characteristics of different surfaces (such as forests, deserts, cities, etc.) in different bands. Secondly, by screening and converting the reflectance data of different bands, the problem of mismatch or missing reflectance data of different bands in practice can be handled. In particular, by the conversion coefficient between different bands, the second surface reflectance data is converted into the reflectance data of the corresponding band, avoiding the problem of insufficient data in some bands. This approach ensures that effective surface reflectance data can be obtained in all bands, ensuring the continuity and integrity of the simulation. Furthermore, by screening specific surface types and spatial resolution data according to simulation requirements, the simulation results can be further optimized. For example, after determining the target surface type according to the simulation requirements, the surface reflectance data that matches it is obtained from the screened first surface reflectance data and the third surface reflectance data. This process ensures that only the data most relevant to the simulation target is used, avoiding unnecessary data redundancy and improving the efficiency of the simulation. In terms of spatial resolution, selecting appropriate spatial resolution data according to simulation requirements not only ensures the refinement of the simulation results, but also optimizes the calculation process and avoids the calculation burden caused by excessive resolution.

[0063] S103, using the surface reflectivity data, calculating the modulation of the initial solar radiation by the surface reflection through a reflection model, and generating a first radiation amount of the sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation.

[0064] Specifically, initial solar radiation refers to the original electromagnetic energy distribution radiated from the sun to the earth outside the atmosphere. Initial solar radiation varies with wavelength, mainly concentrated in the visible light and near-infrared bands, and is presented in the form of a solar spectrum. Initial solar radiation is not affected by the earth's atmosphere and has a relatively stable distribution characteristic.

[0065] In specific implementation, a high spectral resolution solar source simulation model based on the energy distribution characteristics of solar radiation is constructed, combined with existing observation data (such as AM0 spectrum) and solar blackbody radiation characteristics, to generate initial solar radiation through interpolation, fitting and other methods. According to the determined radiation band, the initial solar radiation is adjusted to the spectral resolution and unit form of the corresponding band. According to the reflection model, the initial solar radiation and the surface reflectivity are calculated band by band, and the product of the initial solar radiation and the surface reflectivity in each radiation band is determined as the first radiation amount of sunlight after reflection on the surface.

[0066] S104. Establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, and calculate the first radiation attenuation of the molecular absorption to the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics.

[0067] Specifically, molecular absorption lines refer to the absorption characteristics of molecules for radiation of a specific wavelength (or frequency). Doppler broadening characteristics refer to the broadening of absorption lines due to molecular thermal motion. When molecules perform thermal motion in a gas, the molecules moving along the direction of light propagation will shift the absorption frequency due to the Doppler effect. The broadened absorption lines have a Gaussian distribution, and the line width is related to the gas temperature and molecular mass. The higher the temperature or the smaller the molecular mass, the more significant the broadening effect. Collision broadening characteristics refer to the broadening of absorption lines due to collisions between gas molecules. During the collision process, the molecules are disturbed by the outside world, which shortens the lifetime of the energy level, resulting in broadening of the absorption lines. The broadened absorption lines have a Lorentz distribution, and the line width is proportional to the molecular pressure and the collision frequency. The higher the pressure and the more frequent the collisions, the more significant the broadening effect.

[0068] Further, the absorption model characterizes the absorption characteristics of the molecule, which reflects the shape and intensity of the molecular absorption spectrum. The total molecular optical thickness is used to describe the overall effect of a specific molecule on radiation absorption in the atmosphere. The total molecular optical thickness is a measure of the degree of attenuation of radiation energy by absorption and scattering along the light propagation path. The total molecular optical thickness is the integral value of the number density of absorbing molecules and the absorption cross section in each altitude layer. It should be noted that in this embodiment, greenhouse gases are mainly studied, so the molecules are greenhouse gases with optical absorption characteristics.

[0069] In a specific implementation, the absorption model of each molecule is established based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, including: calculating the Gaussian distribution function of the molecular absorption cross section based on the Doppler broadening characteristics of the molecular absorption spectrum in the radiation band; calculating the Lorentz distribution function of the molecular absorption cross section based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band; performing Voigt convolution on the Gaussian distribution function and the Lorentz distribution function to determine the frequency distribution of the absorption cross section of each molecule, and obtaining the absorption model of each molecule.

[0070] Specifically, based on the Doppler broadening characteristics of the molecular absorption spectrum in the radiation band, the Gaussian distribution function of the molecular absorption cross section is calculated. In specific implementation, the Doppler broadening parameters are calculated based on the molecular temperature, molecular mass, and the center frequency of the absorption spectrum, that is:

[0071]

[0072] Among them, the Δv D is the Doppler broadening parameter; v0 is the central frequency of the absorption line; k is the Boltzmann constant; T is the molecular temperature; m is the molecular mass; and c is the speed of light.

[0073] Furthermore, the Gaussian distribution function of the molecular absorption cross section is constructed based on the Doppler broadening parameter, the frequency in the radiation band, and the center frequency of the absorption spectrum, that is:

[0074]

[0075] Wherein, G(v) is the Gaussian distribution function of the molecular absorption cross section; Δv D is the Doppler broadening parameter; v is the frequency in the radiation band; and v0 is the center frequency of the absorption spectrum.

[0076] Based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band, the Lorentz distribution function of the molecular absorption cross section is calculated. In specific implementation, the collision broadening parameters are calculated based on the molecular pressure and molecular temperature, that is:

[0077]

[0078] Among them, γ is the collision broadening parameter; γ0 is the collision broadening parameter under reference conditions; P is the molecular pressure; P0 is the pressure under reference conditions; T is the molecular temperature; T0 is the temperature under reference conditions; and n is the dependence index.

[0079] Furthermore, the Lorentz distribution function of the molecular absorption cross section is constructed based on the collision broadening parameter, the frequency in the radiation band, and the center frequency of the absorption spectrum, that is:

[0080]

[0081] Wherein, L(v) is the Lorentz distribution function of the molecular absorption cross section; γ is the collision broadening parameter; v is the frequency in the radiation band; and v0 is the center frequency of the absorption spectrum line.

[0082] Furthermore, after the Gaussian distribution function and the Lorentzian distribution function of the molecular absorption cross section are calculated, the Gaussian distribution function and the Lorentzian distribution function are used as inputs to perform Voigt convolution, and the convolution is calculated using a numerical integration method (such as fast Fourier transform or partition integration) to determine the frequency distribution of the molecular absorption cross section. The frequency distribution of the molecular absorption cross section can be expressed as:

[0083]

[0084] Among them, V(v) is the frequency distribution of the molecular absorption cross section; G(v′) is the Gaussian distribution function of the molecular absorption cross section; L(vv′) is the Lorentz distribution function of the molecular absorption cross section; v is the frequency in the radiation band; and v′ is the frequency within the integral range.

[0085] After obtaining the frequency distribution of the molecular absorption cross section, based on the molecular absorption spectrum, the frequency distribution of the molecular absorption cross section is combined with the absorption intensity of the molecular absorption cross section (such as the transition intensity of the absorption line) to obtain the absorption cross section of the molecule at different frequencies, that is, the molecular absorption model.

[0086] In a specific implementation, the method of determining the total molecular optical thickness based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere includes: determining the number of similar molecules in each altitude layer based on the distribution of different molecular number densities in the atmosphere; for each altitude layer, calculating the absorption amount of each type of molecule based on the number of similar molecules and the absorption model, and determining the sum of the absorption amounts of different types of molecules as the total molecular absorption amount of each altitude layer; adding the total molecular absorption amounts of all altitude layers to obtain the total molecular absorption amount, and calculating the total molecular optical thickness based on the total molecular absorption amount.

[0087] Specifically, according to the distribution of different molecular number densities in the atmosphere, the atmosphere is divided into multiple altitude layers according to altitude. For each altitude layer, according to the distribution of molecular number density, the same type of molecules in the altitude layer are determined, and the number of similar molecules is obtained. Based on the molecular absorption model, the number of similar molecules in each altitude layer is combined with the absorption model, and the product of the absorption amount of each molecule and the number of similar molecules is determined as the absorption amount of each type of molecule in the altitude layer. For each altitude layer, the total molecular absorption amount of each altitude layer is summarized, and the absorption amounts of different types of molecules are added to obtain the total molecular absorption amount of the altitude layer. The total molecular absorption amounts of all altitude layers are summed to obtain the total molecular absorption amount of the entire atmosphere. Further, after obtaining the total molecular absorption amount, the quotient of the total molecular absorption amount and the initial solar radiation intensity is determined as the total molecular optical thickness.

[0088] In a specific implementation, the total molecular absorption of all height layers is added to obtain the total molecular absorption, and the total molecular optical thickness is calculated based on the total molecular absorption, including: adding the total molecular absorption of each height layer at the same horizontal coordinate to obtain the total molecular absorption at each horizontal coordinate; calculating the total molecular optical thickness at each horizontal coordinate based on the total molecular absorption and the equivalent absorption efficiency; correcting the abnormal thickness area based on the change in the distribution trend of the total molecular optical thickness at each horizontal coordinate to obtain the corrected total molecular optical thickness.

[0089] Specifically, for each altitude layer, the total molecular absorption of the layer is calculated based on the molecular absorption model and the molecular number density of the altitude layer. At the same horizontal coordinate (latitude and longitude position), the total molecular absorption of all altitude layers is accumulated to obtain the total molecular absorption at the horizontal coordinate. Based on the relationship between the total molecular absorption and the equivalent absorption efficiency, the corresponding formula (through the relationship formula between the absorption efficiency and the optical thickness) is used to calculate the total molecular optical thickness at each horizontal coordinate. The total molecular optical thickness data under all horizontal coordinates are analyzed to identify abnormal areas, which may represent data errors or special atmospheric phenomena. For the identified abnormal thickness areas, interpolation, smoothing algorithms or setting reasonable thresholds can be used for correction, so that the optical thickness data of these areas are more in line with physical laws and conventional trends. After completing the correction of the abnormal areas, the final corrected total molecular optical thickness is obtained.

[0090] The method provided in this embodiment ensures the vertical resolution of the molecular absorption by gradually accumulating the total molecular absorption of each altitude layer under the same horizontal coordinate, and can accurately reflect the contribution of different altitude layers to the optical properties. At the same time, the introduction of equivalent absorption efficiency converts complex spectral absorption characteristics into simplified parameters, which not only retains the physical meaning of the calculation, but also improves the calculation efficiency and applicability. By using the distribution trend change of the total optical thickness of the molecule to correct the abnormal area, the deviation caused by noise, sensor error or special environmental conditions can be effectively identified and corrected, ensuring the authenticity and consistency of the results. This trend analysis combined with correction method enhances the robustness of the model and makes it have a strong fault tolerance for local anomalies. In addition, through the combination of layered accumulation and correction, it can adapt to a variety of complex simulation needs, such as atmospheric radiation analysis of specific areas, different climatic conditions or spectral ranges, and improves the flexibility and adaptability of the overall calculation. While ensuring the accuracy of the results, this setting optimizes the calculation process and provides a method for calculating the total optical thickness of molecules that is both efficient, reliable and adaptable, laying a solid foundation for subsequent radiation simulation.

[0091] Furthermore, after determining the total optical thickness of the molecule, the first radiation attenuation of the top of the atmosphere due to the absorption of the molecule is determined based on the exponential relationship between the optical thickness and the radiation attenuation:

[0092] I(v)=I0exp(-τ)

[0093] Wherein, I(v) is the first radiation attenuation; I0 is the initial solar radiation; and τ is the total molecular optical thickness.

[0094] S105. Calculate the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculate the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness.

[0095] Specifically, the optical property parameters of aerosol particles are used to describe the interaction between aerosol particles and electromagnetic radiation (such as sunlight) during optical or radiation transmission. Common optical property parameters include scattering cross section (describing how aerosol particles scatter radiation), absorption cross section (describing how aerosol particles absorb radiation), aerosol radius (the size of aerosol particles, which directly affects the scattering and absorption characteristics of aerosol particles), and refractive index (describing the optical properties of the interaction between aerosol particles and light). The scattering coefficient refers to the intensity of light scattered by aerosols in the medium, which represents the intensity of scattered radiation caused by aerosol particles in unit volume. The scattering coefficient also includes the backscattering coefficient. The extinction coefficient refers to the attenuation of radiation intensity when radiation passes through aerosols. Aerosol optical thickness is used to describe the overall impact of aerosols in the atmosphere on radiation. It is the integral of the extinction coefficient along the observation path, which represents the attenuation of radiation caused by aerosol scattering and absorption when passing through the atmosphere.

[0096] In a specific implementation, the scattering coefficient and the extinction coefficient of each aerosol particle are calculated based on the optical characteristic parameters of the aerosol particles in the radiation band, including:

[0097] (1) Determine the optical characteristic parameters of aerosol particles in the radiation band.

[0098] Specifically, the optical characteristic parameters include the radius of aerosol particles, the working wavelength of the laser, the negative refractive index of aerosol particles, etc. Among them, the radius of aerosol particles is obtained through experiments or through an aerosol distribution model, the working wavelength of the laser is known, and the negative refractive index of aerosol particles is obtained through experiments.

[0099] (2) Calculating the scale parameter based on the optical characteristic parameters.

[0100] Specifically, the scale parameter is calculated from the aerosol particle radius and the laser working step size, namely:

[0101]

[0102] Wherein, x is the scale parameter; r is the radius of the aerosol particle; and λ is the working step of the laser.

[0103] (3) Initializing the Riccati-Bessel function based on the recursive relationship of the Riccati-Bessel function, and using the logarithmic inverse recursive method to solve the first Riccati-Bessel function and the second Riccati-Bessel function of each order; the first Riccati-Bessel function represents the propagation of light waves after being scattered by aerosol particles; the second Riccati-Bessel function represents the reflection phenomenon of light waves on the interface.

[0104] Specifically, the Riccati-Bessel function is often used to describe the scattering and diffraction phenomena in wave propagation, especially the propagation of electromagnetic waves around round or spherical objects. The first Riccati-Bessel function characterizes the propagation characteristics of light waves on aerosol particles, mainly describing the propagation of light waves from particles, and the second Riccati-Bessel function characterizes the reflection phenomenon of light waves on the interface, describing the reflection of light waves on the boundary of particles.

[0105] The first Riccati-Bessel function and the second Riccati-Bessel function are usually calculated by iteration, that is:

[0106]

[0107] ξ n (x) = ψ n (x)-iχ n (x)

[0108] Wherein, x is the scale parameter; ψ n (x) is the value of the second Riccati-Bessel function at the iteration number n; n is the iteration number; ξ n (x) is the value of the first Riccati-Bessel function at the iteration number n.

[0109] It should be noted that when performing iterative calculations, there will be problems such as slow iteration convergence and long operation time. Therefore, in this embodiment, the logarithmic reciprocal recursion method is used to calculate the Riccati-Bessel function:

[0110]

[0111] Among them, the D n (x) is the logarithmic inverse of the second Riccati-Bessel function; the ψ n (x) is the value of the second Riccati-Bessel function at the iteration number n; x is the scale parameter; n is the iteration number.

[0112] (4) Calculating a first Mie scattering coefficient and a second Mie scattering coefficient based on the negative refractive index of the particle, the scale parameter, the first Riccati-Bessel function, and the second Riccati-Bessel function using the Mie scattering formula.

[0113] Specifically, when calculating the first Mie scattering coefficient, in the numerator, the negative refractive index of the aerosol particle is multiplied by the value of the second Riccati-Bessel function at the first target position, and then multiplied by the derivative of the second Riccati-Bessel function at the second target position, and then the value of the second Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted; in the denominator, the negative refractive index of the aerosol particle is multiplied by the value of the first Riccati-Bessel function at the first target position, and then multiplied by the derivative of the first Riccati-Bessel function at the second target position, and then the value of the first Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted. Among them, the first target position is the product of the negative refractive index of the aerosol particle and the scale parameter, and the second target position is the scale parameter.

[0114] In specific implementation, the first Mie scattering coefficient can be calculated according to the following formula:

[0115]

[0116] Wherein, an is the first Mie scattering coefficient; m is the negative refractive index of the aerosol particles; X is the scale parameter; ψ n (mx) is the value of the second Riccati-Bessel function at the first target position; n (x) is the derivative of the second Riccati-Bessel function at the second target position; n (x) is the value of the second Riccati-Bessel function at the second target position; n (mx) is the derivative of the second Riccati-Bessel function at the first target position; the ξ′ n (x) is the derivative of the first Riccati-Bessel function at the second target position; n (x) is the value of the first Riccati-Bessel function at the second target position.

[0117] Further, when calculating the second Mie scattering coefficient, in the numerator, the value of the second Riccati-Bessel function at the first target position is multiplied by the derivative of the second Riccati-Bessel function at the second target position, and then the negative refractive index of the aerosol particle and the value of the second Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted; in the denominator, the value of the first Riccati-Bessel function at the first target position is multiplied by the derivative of the first Riccati-Bessel function at the second target position, and then the negative refractive index of the aerosol particle and the value of the first Riccati-Bessel function at the second target position and the derivative of the second Riccati-Bessel function at the first target position are subtracted.

[0118] In specific implementation, the second Mie scattering coefficient can be calculated according to the following formula:

[0119]

[0120] Among them, the b n is the second Mie scattering coefficient; m is the negative refractive index of the aerosol particles; X is the scale parameter; ψ n (mx) is the value of the second Riccati-Bessel function at the first target position; n (x) is the derivative of the second Riccati-Bessel function at the second target position; n (x) is the value of the second Riccati-Bessel function at the second target position; n (mx) is the derivative of the second Riccati-Bessel function at the first target position; the ξ′ n (x) is the derivative of the first Riccati-Bessel function at the second target position; n (x) is the value of the first Riccati-Bessel function at the second target position.

[0121] (5) Calculating a scattering coefficient and an extinction coefficient of each aerosol particle based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter.

[0122] In specific implementation, the scattering coefficient is calculated based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0123]

[0124] Among them, the Q scais the scattering coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; n is the second Mie scattering coefficient; and n is the order.

[0125] Calculate the backscattering coefficient based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0126]

[0127] Among them, the Q back is the backscattering coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; n is the second Mie scattering coefficient; and n is the order.

[0128] In specific implementation, the small light coefficient is calculated based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter:

[0129]

[0130] Among them, the Q ext is the extinction coefficient; X is the scale parameter; a n is the first Mie scattering coefficient; n is the second Mie scattering coefficient; and n is the order.

[0131] In a specific implementation, the aerosol optical thickness is determined based on the scattering coefficient, the extinction coefficient and the distribution of the aerosol particle density in the atmosphere, including: obtaining the total scattering coefficient of the aerosol at each altitude layer based on the scattering coefficient of each aerosol particle and the distribution of the aerosol particle density in the atmosphere; obtaining the total extinction coefficient of the aerosol at each altitude layer based on the extinction coefficient of each aerosol particle and the distribution of the aerosol particle density in the atmosphere; and determining the aerosol optical thickness by integration, using the sum of the total scattering coefficient and the total extinction coefficient as the integrand, the thickness of each altitude layer as the integration step, and the height range of the aerosol distribution as the integration interval.

[0132] Specifically, based on the distribution of aerosol particle density in the atmosphere, the atmosphere is divided into multiple altitude layers according to altitude. For each altitude layer, the scattering coefficients of all aerosol particles in the altitude layer are calculated by integration according to the aerosol particle density distribution and the scattering coefficient of each aerosol particle, and the scattering coefficients of all aerosol particles in all altitude layers are added together to obtain the total scattering coefficient of aerosols in each altitude layer.

[0133] In specific implementation, the total scattering coefficient can be calculated according to the following formula:

[0134]

[0135] Wherein, β is the total scattering coefficient; N0 is the aerosol particle number density; σ is r m The standard deviation of r max is the maximum radius of aerosol particles; min is the minimum radius of aerosol particles; back is the backscattering coefficient; λ is the operating wavelength of the laser; r m is the median radius of the aerosol number concentration spectrum distribution.

[0136] Furthermore, based on the distribution of aerosol particle density in the atmosphere, the atmosphere is divided into multiple altitude layers according to altitude. For each altitude layer, the extinction coefficient of all aerosol particles in the altitude layer is calculated by integration according to the aerosol particle density distribution and the extinction coefficient of each aerosol particle, and the extinction coefficients of all aerosol particles in all altitude layers are added together to obtain the total extinction coefficient of aerosols in each altitude layer.

[0137] In specific implementation, the total extinction coefficient can be calculated according to the following formula:

[0138]

[0139] Wherein, α is the total extinction coefficient; N0 is the aerosol particle number density; σ is r m The standard deviation of r max is the maximum radius of aerosol particles; min is the minimum radius of aerosol particles; back is the backscattering coefficient; λ is the operating wavelength of the laser; r m is the median radius of the aerosol number concentration spectrum distribution.

[0140] After the total scattering coefficient and the total extinction coefficient are calculated, the sum of the total scattering coefficient and the total extinction coefficient is calculated for each altitude layer. The integration interval (i.e., the altitude range from low to high layers) and the thickness of each altitude layer (i.e., the altitude difference of each layer) are determined according to the distribution of aerosol particle density. The sum of the total scattering coefficient and the total extinction coefficient of all altitude layers is taken as the integrand, and the numerical integration method (such as the trapezoidal method or the Simpson method) is used for integration:

[0141]

[0142] Among them, the τ aerosol is the aerosol optical thickness; the σ total (h i ) is the sum of the total scattering coefficient and the total extinction coefficient at different altitudes; the Δh iis the thickness of each altitude layer; i is the altitude range of aerosol distribution.

[0143] Furthermore, after obtaining the aerosol optical thickness, the second transmittance of the radiation when passing through the aerosol is calculated based on the aerosol optical thickness, and the product of the initial solar radiation intensity and the second attenuation rate is determined as the second radiation attenuation of the aerosol to the top of the atmosphere. The sum of the second transmittance and the second attenuation rate is 1.

[0144] S106. Subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0145] Specifically, in combination with the above description, the first radiation refers to the radiation intensity of solar radiation before it reaches the top of the atmosphere after being reflected from the ground surface and scattered and absorbed by various layers of the atmosphere. The first radiation attenuation refers to the radiation attenuation calculated based on the total optical thickness of molecular absorption and scattering. The second radiation attenuation refers to the radiation attenuation calculated based on the scattering and absorption characteristics of aerosols.

[0146] Since surface reflection directly affects the ground radiation intensity, molecular absorption determines the heat distribution of radiation in the atmosphere, and aerosol scattering and absorption further affect the propagation path and intensity of radiation in the atmosphere. If any one factor is ignored, it may lead to misjudgment of the radiation intensity and the radiation attenuation at the top of the atmosphere, thereby affecting the analysis and prediction of issues such as atmospheric composition, surface characteristics and climate change. Therefore, it is necessary to comprehensively consider the effects of these three. In this step, the first radiation amount is subtracted from the two radiation attenuations to obtain the final top of the atmosphere radiation intensity, and the greenhouse gas passive load performance is evaluated based on the size of the top of the atmosphere radiation intensity.

[0147] The greenhouse gas passive load top of atmosphere radiation simulation method provided in this embodiment, firstly, the present application first determines the radiation band that the satellite can detect based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation. The selection of this radiation band not only takes into account the energy distribution of the solar radiation, but also takes into account the capability of the satellite sensor, thereby ensuring that detailed and high-spectral data covering multiple bands can be selected. The setting of this step provides band data with high spectral resolution for subsequent molecular absorption and aerosol scattering simulation. In addition, by further utilizing the surface reflectivity data, combining the spatial distribution characteristics of the surface reflectivity and the surface type, the modulation of the surface reflection on the solar radiation is calculated by the reflection model. This process not only provides a more refined initial value for the radiation intensity, but also provides a basis for the calculation of radiation attenuation under high spectral resolution, further enhancing the simulation accuracy of the reflection effect. Secondly, in the simulation process of molecular absorption, the present application realizes the simulation of high spectral resolution by establishing an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum under the radiation band. In order to perform accurate absorption simulation at high spectral resolution, the absorption model can accurately calculate the absorption effect in each band by considering the absorption characteristics in different bands. The absorption characteristics of each band can be distinguished by high spectral resolution data, ensuring that the absorption of radiation by greenhouse gases can be accurately quantified in a more refined band. The absorption effect of each molecule can be reflected in a finer band through high spectral resolution data, avoiding the blurring effect in wide band processing. For example, the absorption characteristics of carbon dioxide and water vapor in different bands are very different. High spectral resolution enables these differences to be accurately captured, improving the accuracy of molecular absorption simulation. In the third aspect, the present application simulates the scattering and extinction characteristics of aerosols by calculating the optical characteristic parameters of aerosol particles, and then using complex mathematical formulas such as the Mie scattering formula and the Riccati-Bessel function. The scattering effect of aerosols on radiation is different in different bands. By calculating the scattering coefficient and extinction coefficient of each aerosol particle, and combining the distribution of aerosol particle density in the atmosphere, the scattering and extinction effects of aerosols can be accurately simulated in each band. High spectral resolution data can provide detailed aerosol optical properties for each band, which is essential for the high-precision calculation of aerosol optical depth. Combined with the scattering and extinction coefficients, the aerosol characteristics at different altitudes are further combined to accurately calculate the aerosol optical depth. High spectral resolution data ensures a detailed simulation of the aerosol scattering and absorption effects at each altitude, thereby providing accurate aerosol radiation attenuation. Finally, after obtaining detailed simulation data of surface reflection, molecular absorption, and aerosol scattering and absorption, the top of the atmosphere radiation intensity is calculated by combining these effects.Since each link is carefully calculated under high spectral resolution data, from surface reflection to atmospheric molecular absorption, and then to aerosol scattering, the final radiation intensity result has a high degree of accuracy, which realizes the accurate simulation of the absorption effect of greenhouse gases on radiation and the scattering effect of particulate matter on radiation, and provides strong support for the evaluation of greenhouse gas passive load performance. Fourthly, with the combined use of multi-band, multi-surface type and different spatial resolution data, high-precision and high-efficiency surface reflectance data acquisition is achieved, ensuring the accuracy of radiation and greenhouse gas simulation under high spectral resolution. First, by obtaining the overall surface reflectance data, covering reflectance data of multiple bands, different surface types and multiple spatial resolutions, the reflectance characteristics of the surface can be described in an all-round way. Data in different bands support detailed analysis of the reflectance characteristics of the surface in different bands, which is especially important for high spectral resolution simulation, because high spectral resolution requires accurate capture of small changes in radiation, thereby effectively improving the accuracy of simulation. The diversity of surface types takes into account the different reflectance characteristics of different surfaces (such as forests, deserts, cities, etc.) in different bands. Secondly, by screening and converting the reflectivity data of different bands, the problem of mismatch or missing reflectivity data of different bands in practice can be handled. In particular, the second surface reflectivity data is converted into the reflectivity data of the corresponding band through the conversion coefficient between different bands, avoiding the problem of insufficient data in some bands. This approach ensures that effective surface reflectivity data can be obtained in all bands, ensuring the continuity and integrity of the simulation. Furthermore, by screening specific surface types and spatial resolution data according to simulation requirements, the simulation results can be further optimized. For example, after determining the target surface type according to the simulation requirements, the surface reflectivity data that matches it is obtained from the screened first surface reflectivity data and the third surface reflectivity data. This process can ensure that only the data most relevant to the simulation target is used, avoiding unnecessary data redundancy and improving the efficiency of the simulation. In terms of spatial resolution, selecting appropriate spatial resolution data according to simulation requirements not only ensures the refinement of simulation results, but also optimizes the calculation process and avoids the calculation burden caused by excessive resolution. Fifthly, this application significantly improves the accuracy and reliability of the top of the atmosphere radiation simulation results by introducing the high-resolution Kurucz solar spectrum model and Voigt convolution technology. First, by adopting the high-resolution Kurucz solar spectrum model to characterize the characteristics of solar radiation, its fine spectral resolution can more accurately describe the energy distribution characteristics of solar radiation. Traditional solar radiation models may ignore subtle changes in radiation intensity in certain bands, while the Kurucz model can characterize the radiation characteristics of different bands and different spectral details in detail, especially in specific bands related to greenhouse gas absorption. Accurate solar radiation data is the basis for top of the atmosphere radiation simulation and greenhouse gas absorption simulation.Secondly, the introduction of Voigt convolution technology plays a vital role in the modeling of the molecular absorption line shape of greenhouse gases. The molecular absorption spectrum of greenhouse gases has Doppler effect and collision effect, which will cause the shape of the absorption spectrum to change, thereby affecting the absorption intensity and wavelength distribution. Voigt convolution technology combines Gaussian distribution and Lorentz distribution, which can more accurately simulate the actual absorption characteristics of greenhouse gas molecules, especially at high spectral resolution, this technology can capture small changes in molecular absorption spectrum. This kind of sophisticated simulation helps to improve the prediction of the radiation transfer characteristics of greenhouse gases, especially when the concentration of greenhouse gases is low or changes rapidly. Through the combination of these two technologies, the present invention significantly improves the accuracy and reliability of the top of the atmosphere radiation simulation model, which can not only more accurately predict the radiation absorption characteristics of greenhouse gases, but also provide more accurate radiation data support for greenhouse gas concentration monitoring, air quality assessment and optimization of climate models.

[0148] Corresponding to the aforementioned embodiment of a greenhouse gas passive load top of atmosphere radiation simulation method, the present application also provides an embodiment of a greenhouse gas passive load top of atmosphere radiation simulation device.

[0149] Figure 2 This is a schematic diagram of the structure of the greenhouse gas passive load top of atmosphere radiation simulation device provided in Example 2 of this application. Figure 2 The device provided in this embodiment includes a determination module 210, an acquisition module 220, a generation module 230 and a calculation module 240; wherein,

[0150] The determination module 210 is used to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation;

[0151] The acquisition module 220 is used to acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type;

[0152] The generating module 230 is used to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through the reflection model, and generate the first radiation amount of the sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation;

[0153] The calculation module 240 is used to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, and calculate the first radiation attenuation of the molecular absorption to the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics;

[0154] The calculation module 240 is further used to calculate the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculate the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness;

[0155] The acquisition module 220 is further configured to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

[0156] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0157] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0158] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0159] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for simulating top of atmosphere radiation of greenhouse gas passive load, characterized in that: The method comprises: Based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of solar radiation, determine the radiation band that the satellite can detect; Based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type, obtaining surface reflectivity data; Using the surface reflectivity data, the modulation of the surface reflection on the initial solar radiation is calculated through a reflection model to generate a first radiation amount of sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation; Based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, an absorption model for each molecule is established; based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, the total optical thickness of the molecule is determined, and based on the total optical thickness of the molecule, the first radiation attenuation of the molecular absorption to the top of the atmosphere is calculated; wherein the molecule is a greenhouse gas with optical absorption characteristics; Calculating the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determining the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculating the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness; The first radiation amount is subtracted from the first radiation amount by the second radiation attenuation amount to obtain the top of atmosphere radiation intensity.

2. The method according to claim 1, characterized in that The absorption model of each molecule is established based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band, including: Calculating the Gaussian distribution function of the molecular absorption cross section based on the Doppler broadening characteristics of the molecular absorption spectrum in the radiation band; Calculating the Lorentz distribution function of the molecular absorption cross section based on the collision broadening characteristics of the molecular absorption spectrum in the radiation band; The Gaussian distribution function is subjected to Voigt convolution with the Lorentzian distribution function to determine the frequency distribution of the absorption cross section of each molecule and obtain the absorption model of each molecule.

3. The method according to claim 1, characterized in that The method of determining the total molecular optical thickness based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere includes: Based on the distribution of number densities of different molecules in the atmosphere, determine the number of molecules of the same type in each altitude layer; For each altitude layer, based on the number of molecules of the same type and the absorption model, the absorption amount of each type of molecule is calculated, and the sum of the absorption amounts of different types of molecules is determined as the total absorption amount of molecules at each altitude layer; The total molecular absorption of all height layers is added together to obtain the total molecular absorption, and the total molecular optical thickness is calculated based on the total molecular absorption.

4. The method according to claim 1, characterized in that: The calculating of the scattering coefficient and the extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band includes: Determining optical characteristic parameters of aerosol particles in the radiation band; Calculating a scale parameter based on the optical characteristic parameter; Initialize the Riccati-Bessel function based on the recursive relationship of the Riccati-Bessel function, and use the logarithmic inverse recursive method to solve the first Riccati-Bessel function and the second Riccati-Bessel function of each order; the first Riccati-Bessel function represents the propagation of the light wave after being scattered by the aerosol particles; the second Riccati-Bessel function represents the reflection phenomenon of the light wave on the interface; Calculating a first Mie scattering coefficient and a second Mie scattering coefficient based on the negative refractive index of the particle, the scale parameter, the first Riccati-Bessel function, and the second Riccati-Bessel function by using a Mie scattering formula; Based on the first Mie scattering coefficient, the second Mie scattering coefficient, and the scale parameter, a scattering coefficient and an extinction coefficient of each aerosol particle are calculated.

5. The method according to claim 1, characterized in that Determining the aerosol optical thickness based on the scattering coefficient, the extinction coefficient and the distribution of aerosol particle density in the atmosphere includes: Based on the scattering coefficient of each aerosol particle and the distribution of aerosol particle density in the atmosphere, the total scattering coefficient of aerosol at each altitude layer is obtained; Based on the extinction coefficient of each aerosol particle and the distribution of aerosol particle density in the atmosphere, the total extinction coefficient of aerosol at each altitude layer is obtained; The aerosol optical thickness is determined by integration, with the sum of the total scattering coefficient and the total extinction coefficient as the integrand, the thickness of each height layer as the integration step, and the height range of aerosol distribution as the integration interval.

6. The method according to claim 1, characterized in that The obtaining of surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type includes: Acquire overall surface reflectivity data; the overall surface reflectivity data includes surface reflectivity data at multiple bands, multiple surface types, and multiple spatial resolutions; Filtering the overall surface reflectivity data based on the radiation band to obtain first surface reflectivity data of a corresponding band; For second surface reflectivity data other than the first surface reflectivity data in the overall surface reflectivity data, converting the second surface reflectivity data into third surface reflectivity data of a corresponding band based on a conversion coefficient between different bands; the second surface reflectivity data and the first surface reflectivity data have corresponding bands different from each other; Determine a target surface type based on simulation requirements, and select fourth surface reflectivity data corresponding to the surface type from the first surface reflectivity data and the third surface reflectivity data based on the target surface type; The target spatial resolution is determined based on the simulation requirements, and the surface reflectivity data of the corresponding spatial resolution is obtained by screening from the fourth surface reflectivity data based on the target spatial resolution.

7. The method according to claim 1, characterized in that The step of determining the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation includes: Based on the spectral response characteristics of satellite payload, a multi-band spectral response function is established; Based on the energy distribution characteristics of solar radiation within the spectral range, a high spectral resolution solar light source simulation model is constructed; the simulation model represents the spectral distribution of solar radiation; The multi-band spectral response function is combined with the high spectral resolution solar light source simulation model to determine the radiation bands that can be detected by the satellite.

8. The method according to claim 7, characterized in that The multi-band spectral response function is established based on the spectral response characteristics of the satellite payload, including: Determine parameters related to the satellite payload according to the spectral response characteristics of the satellite payload; the parameters characterize the response capability of the satellite payload in different bands and the sensitivity to radiation in different bands; Based on the parameters, combined with the application requirements of the satellite payload and the absorption characteristics of greenhouse gases, determine the appropriate spectral range and sampling interval; Based on the selected spectral range and sampling interval, the Gaussian function is spectrally sampled to obtain a multi-band spectral response function.

9. The method according to claim 1, characterized in that: The method of adding the total molecular absorption of all height layers to obtain the total molecular absorption, and calculating the total molecular optical thickness based on the total molecular absorption, comprises: Add the total molecular absorption at each height layer at the same horizontal coordinate to obtain the total molecular absorption at each horizontal coordinate; Based on the total molecular absorption and equivalent absorption efficiency, the total molecular optical thickness at each horizontal coordinate is calculated; Based on the change in the distribution trend of the total molecular optical thickness at each horizontal coordinate, the abnormal thickness area is corrected to obtain the corrected total molecular optical thickness.

10. A greenhouse gas passive load top of atmosphere radiation simulation device, characterized in that: The device includes a determination module, an acquisition module, a generation module and a calculation module; wherein, The determination module is used to determine the radiation band that can be detected by the satellite based on the spectral response characteristics of the satellite payload and the energy distribution characteristics of the solar radiation; The acquisition module is used to acquire surface reflectivity data based on the spatial distribution characteristics of the surface reflectivity in the radiation band and the surface type; The generating module is used to use the surface reflectivity data to calculate the modulation of the initial solar radiation by the surface reflection through the reflection model, and generate the first radiation amount of the sunlight after the sunlight is reflected from the surface; the initial solar radiation is generated based on the energy distribution characteristics of the solar radiation; The calculation module is used to establish an absorption model for each molecule based on the Doppler broadening characteristics and collision broadening characteristics of the molecular absorption spectrum in the radiation band; determine the total optical thickness of the molecule based on the absorption model of each molecule and the distribution of different molecular number densities in the atmosphere, and calculate the first radiation attenuation of the molecular absorption to the top of the atmosphere based on the total optical thickness of the molecule; wherein the molecule is a greenhouse gas with optical absorption characteristics; The calculation module is further used to calculate the scattering coefficient and extinction coefficient of each aerosol particle based on the optical characteristic parameters of the aerosol particles in the radiation band, determine the aerosol optical thickness based on the scattering coefficient, extinction coefficient and the distribution of the aerosol particle density in the atmosphere; calculate the second radiation attenuation of the aerosol to the top of the atmosphere based on the aerosol optical thickness; The acquisition module is further used to subtract the first radiation attenuation amount and the second radiation attenuation amount from the first radiation amount to obtain the top of atmosphere radiation intensity.

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