A piecewise calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation

Abstract

The present invention relates to the fields of meteorology and remote sensing science. It is mainly a segmented calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation, including determining the particle size distribution form of ice crystal particles and the particle size distribution parameters corresponding to the ice cloud according to the cloud microphysics scheme adopted by the numerical model to be simulated; segmenting the ice cloud particles according to the particle size, calculating the weight coefficient of the contribution of ice particles in each particle size range to the overall radiation characteristics of the ice cloud, and making a weight coefficient lookup table; in the rapid radiation transfer model, find the corresponding weight coefficient and bring it into the calculation of the ice cloud scattering characteristics. The present invention establishes a segmented calculation method for ice cloud scattering characteristics, which, on the one hand, ensures the calculation efficiency to meet the needs of business forecasting, and on the other hand, more accurately calculates the ice cloud scattering characteristics to reduce the infrared brightness temperature simulation error.

Description

Technical Field

[0001] The present invention relates to meteorology and remote sensing science, and in particular to the field of a calculation method and system for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation. Background Art

[0002] In recent years, with the use of a new generation of meteorological satellites, they provide infrared brightness temperature observations with high temporal and spatial resolution and high coverage, and have been widely used in data assimilation and model evaluation. Using infrared brightness temperature simulation technology, the meteorological field output by the numerical model can be simulated into infrared brightness temperature that can be directly compared with the corresponding channel of satellite observation. On the one hand, this can better utilize satellite data to evaluate and constrain numerical models and optimize key parameters in the model; on the other hand, it can provide a basis for the assimilation of satellite data in cloud areas, thereby improving the numerical model's ability to predict weather. Therefore, satellite infrared brightness temperature simulation technology is one of the key technologies in the fields of meteorological forecasting, climate prediction, and environmental monitoring.

[0003] In meteorological research and business applications, satellite infrared brightness temperature simulation is usually achieved using the radiation transfer model. The workflow of the radiation transfer model is to first obtain the horizontal and vertical distribution of physical quantities such as atmospheric temperature, humidity and cloud hydrometeors from the results of the numerical forecast model, and then calculate the radiation characteristics of components such as the surface, atmosphere and clouds. According to the principle of radiation transfer, the brightness temperature is calculated in combination with the characteristics of the infrared observation channel corresponding to the meteorological satellite. The infrared brightness temperature generated by the radiation transfer model is an important data for satellite observation assimilation and numerical model evaluation and improvement.

[0004] In this process, how to accurately calculate the scattering characteristics of ice clouds is an important challenge for simulating infrared brightness temperature in the radiation transfer model. Ice clouds are the dominant cloud type that develops and changes rapidly with severe convective weather. They are composed of particles of various sizes and complex shapes, which depend on the temperature, humidity and dynamic conditions of the local environment. In the upper atmosphere, ice clouds usually have a high optical thickness, that is, the clouds absorb and scatter radiation strongly per unit area. At the same time, the scattering effect of ice clouds has a strong dependence on wavelength, especially it will have a significant impact on the calculation of brightness temperature in the infrared band. In the radiation transfer simulation, inaccurate calculation of the scattering effect of ice clouds will lead to simulation deviations of infrared brightness temperature, thereby affecting the availability of satellite observation data in the cloud area.

[0005] Looking back at the existing methods for calculating the scattering characteristics of ice clouds, there are mainly two types: optical methods and table lookup methods. The optical method refers to first theoretically solving the scattering characteristics of a single ice particle, such as the improved optical algorithm IGOM (Yang et al., 1996) and the finite difference time domain method FDTD (Yang et al., 2005). Then, by integrating the particle size distribution of ice cloud particles, the volume scattering characteristics of the ice cloud are obtained. This type of calculation method has a relatively complete physical framework and has the advantage of high calculation accuracy, but its main limitation is that it requires a lot of calculation data and cannot meet the requirements for data assimilation timeliness in the operation of meteorological numerical forecasting operations.

[0006] The table lookup method stores the scattering coefficient per unit mass in a lookup table in advance, including dimensions such as satellite observation channel frequency and ice cloud particle effective radius. When simulating infrared brightness temperature, the corresponding ice cloud scattering characteristics can be quickly found by giving the required satellite infrared channel frequency and inputting the meteorological field simulated by the numerical model. Since there is no need for repeated calculations, this type of method fully meets the timeliness requirements of data assimilation. However, while simplifying the calculation, it adopts some physical assumptions that are different from actual observations and numerical models, which affects the accuracy of the calculation. For example, the scattering characteristics of the unit mass of ice clouds are determined only based on the average characteristic of the effective radius of ice particles. However, in nature, the particle size of ice crystal particles varies greatly, and the scattering characteristics of ice crystals of different particle sizes are also different. Therefore, although this type of method is currently used more in radiation transfer models, it cannot avoid errors caused by unreasonable physical assumptions.

[0007] In summary, the current calculation methods of ice cloud scattering characteristics in satellite infrared brightness temperature simulations have the problem of not being able to balance calculation efficiency and calculation accuracy. The optical method fully considers the influence of the morphology and particle size of ice crystal particles on the scattering characteristics, but the calculation efficiency is low and cannot be applied to high-frequency data assimilation; while the table lookup method ensures calculation efficiency, it ignores the influence of different ice particle sizes on the scattering of ice cloud bodies at the numerical model grid points on the microscopic scale, resulting in large calculation errors. Due to the limitations of the above two types of existing ice cloud scattering characteristics calculation methods, the current utilization rate of satellite infrared observations in ice cloud areas is relatively low. Therefore, at this stage, a method with high calculation efficiency and accuracy is needed to calculate ice cloud scattering characteristics. Summary of the invention

[0008] The present invention aims to solve one of the technical problems existing in the related art at least to a certain extent.

[0009] One object of the present invention is to provide a segmented calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation, and to establish a segmented calculation scheme for ice cloud scattering characteristics. On the one hand, the calculation efficiency is guaranteed to meet the needs of business forecasting, and on the other hand, the ice cloud scattering characteristics are calculated more accurately to reduce the infrared brightness temperature simulation error, thereby providing a guarantee for numerical model data assimilation and simulation evaluation.

[0010] Another object of the present invention is to provide a satellite infrared brightness temperature simulation method, which utilizes the above-mentioned segmented calculation method of ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation, aiming to improve the effect of satellite data in numerical model evaluation and data assimilation applications, and enhance the model prediction capability.

[0011] In order to achieve the above-mentioned object, the present invention provides a piecewise calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation, comprising the following steps:

[0012] S100, determining the particle size distribution form of ice crystal particles and particle size distribution parameters corresponding to the ice cloud according to the cloud microphysics scheme adopted by the numerical model to be simulated;

[0013] S200, segmenting the ice cloud particles according to their particle sizes, calculating the weight coefficients of the ice particles within each particle size range that contribute to the overall radiation characteristics of the ice cloud, and making a weight coefficient lookup table;

[0014] S300. In the rapid radiation transfer model, the weight coefficient table is searched through the particle size distribution parameters of the ice cloud, the corresponding weight coefficient is found, and brought into the calculation of the ice cloud scattering characteristics.

[0015] A further preferred technical solution of the present invention is that the particle size distribution of the ice crystal particles in step S100 is expressed as:

[0016] n(L)=N0L μ e -vL

[0017] Where L represents the diameter of the ice particles; N0, μ and v represent the intercept parameter, slope parameter and shape parameter, respectively; μ is a constant, and the intercept parameter N0 and shape parameter v are determined by the mass concentration and number concentration of ice particles predicted or diagnosed in the cloud microphysics scheme, respectively.

[0018] Preferably, the shape parameter v and the intercept parameter N0 are calculated as follows:

[0019]

[0020] Where q and N are the mass concentration and number concentration of ice crystal particles, respectively, c and d are constants, and Γ is the gamma function.

[0021] Preferably, the number concentration is a fixed constant or is linearly related to the mass concentration q, and the distribution form of the ice crystal particle size is determined according to the value of the mass concentration q.

[0022] Preferably, in step S200, the ice cloud particles are segmented according to the particle size, and the weight coefficients of the ice particles in each particle size range contributing to the overall radiation characteristics of the ice cloud are calculated to prepare a weight coefficient lookup table, which specifically includes:

[0023] S201. Establish the overall extinction coefficient k of ice cloud ex The calculation formula is:

[0024]

[0025] Among them, Q ex represents the single particle extinction efficiency at the corresponding particle size and wavelength, S(L) and V(L) are the cross-sectional area and volume of the ice crystal particle with a particle diameter of L, respectively; λ represents the wavelength

[0026] S202. Define the weight coefficient F of the extinction coefficient in the particle size interval (L0, L1) ex The calculation formula for (L0, L1) is:

[0027]

[0028] The particle size distribution of the ice crystal particles obtained in step S100 is brought into play, and after the particle size intervals L0 and L1 are determined, the weight coefficient F in the particle size interval is calculated. ex (L0,L1);

[0029] S203, segment the particle size, determine the particle size range of the segment, and establish the convolution summation formula of the overall extinction coefficient of the ice cloud:

[0030]

[0031] Where N is the number of particle size interval segments;

[0032] In the ice cloud scattering calculation of the rapid radiative transfer model, Q ex ((L i +L i+1 ) / 2,λ) needs to be obtained from the ice cloud scattering factor lookup table provided by the rapid radiative transfer model, and Q ex The first dimension is the discrete effective radius value, such as 2μm, 5μm, 10μm, etc. Therefore, in order to calculate k by table lookup convolution ex (λ), when segmenting, it is necessary to ensure that the number of segments N is consistent with the length of the effective radius dimension given in the ice cloud scattering factor lookup table of the rapid radiative transfer model, and the mean value of each particle size interval (L i +Li+1 ) / 2 is the same as the particle size value given in the ice cloud scattering factor lookup table. After segmentation according to this standard, the particle size range of N groups (L i ,L i+1 ) is brought into the weight coefficient F in S202 ex (L i ,L i+1 ) to obtain its specific form.

[0033] S204, creating a weight coefficient lookup table. Selecting different values ​​of mass concentration q of ice particles, determining the corresponding particle size distribution form and the particle size distribution parameters corresponding to the ice cloud, and generating a weight coefficient lookup table related to the mass concentration q according to the calculation formula given in S202.

[0034] In the ice cloud scattering calculation module of the rapid radiation transfer mode, a set of N weight coefficients for the corresponding particle size distribution can be found based on the mass concentration q and the weight coefficient lookup table, which is consistent with the number of particle size interval segments.

[0035] When the difference between L0 and L1 is small enough, Q ex When (L, λ) changes little in this particle size range (L0, L1), Q ex (L, λ) is approximately equal to Q ex ((L0+L1) / 2,λ).

[0036] Based on this, the calculation formula of the ice cloud overall extinction coefficient in S203 is applied to the rapid radiation transfer model. In the cloud scattering calculation module, according to the mass concentration and wavelength, a corresponding set of N Q values ​​are found from the ice cloud scattering factor lookup table and the weight coefficient lookup table respectively. ex and F ex According to the convolution summation formula given in S203, the overall scattering characteristics of ice clouds required for infrared brightness temperature simulation are calculated, including extinction coefficient, single scattering albedo and asymmetric parameters.

[0037] Another aspect of the present invention provides an infrared brightness temperature simulation method, comprising the steps of:

[0038] The above-mentioned piecewise calculation method for ice cloud scattering characteristics applicable to satellite infrared brightness temperature simulation is used to generate a weight coefficient lookup table;

[0039] According to the rapid radiation transfer model, meteorological conditions and hydrometeoric distribution profiles are read to obtain the particle size distribution form of ice crystal particles and the particle size distribution parameters corresponding to ice clouds;

[0040] The corresponding weight coefficient is found from the weight coefficient lookup table and brought into the calculation of ice cloud scattering characteristics, and the ice cloud scattering characteristics are applied to the calculation of the brightness temperature of the satellite infrared observation channel.

[0041] Yet another aspect of the present invention provides a non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions enabling a computer to execute the above-mentioned infrared brightness temperature simulation method.

[0042] Yet another aspect of the present invention provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls logic instructions in the memory to execute the above-mentioned infrared brightness temperature simulation method.

[0043] Another aspect of the present invention provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-mentioned infrared brightness temperature simulation method.

[0044] Beneficial effects: Based on the problem of insufficient efficiency and accuracy in the current calculation of ice cloud scattering characteristics, the present invention introduces a cloud microphysics scheme to parameterize the ice cloud particle size distribution, makes improvements on the table lookup method, segments the ice cloud particles according to the particle size distribution, and provides a segmented calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation.

[0045] The present invention establishes an applicable ice cloud scattering calculation method for brightness temperature simulation of radiation transfer model, combines the ice crystal size distribution of numerical model cloud microphysics scheme, reduces the calculation error of ice cloud scattering characteristics, further improves the accuracy of brightness temperature simulation of all infrared channels of satellite, and provides guarantee for the rationality of numerical model data assimilation. Through the method of the present invention, on the one hand, it will improve the utilization rate of cloud area observation data of geostationary satellite imagers and improve satellite data assimilation analysis; on the other hand, it will reduce the systematic error of radiation transfer model and improve the interpretability of cloud area satellite observation data in model evaluation, thereby improving the forecast level of numerical model.

[0046] Compared with traditional algorithms, the segmented algorithm of the present invention does not rely on the grid average ice particle effective radius, and defines a number concentration distribution function in the entire particle size range. Its form and parameter values ​​are all derived from the cloud microphysics scheme of the numerical model, and are consistent in physical assumptions. In order to meet the requirement of fast algorithm operation, the weight coefficients required for ice cloud scattering calculations are calculated in advance and saved as a weight coefficient lookup table, and only one table lookup task is added on the basis of the operation of the radiation transmission mode lookup table method. Because the ice crystal size distribution form is usually determined by the mass concentration of ice particles, there is no need to add other physical quantity fields in data input and preprocessing, which further saves calculation time. Before performing the table lookup operation, a screening condition is added to determine whether the grid has an ice phase cloud, thereby avoiding the waste of computing resources.

[0047] The segmented calculation method of the present invention provides a highly versatile calculation framework, which is not limited to a single geostationary satellite, infrared channel and numerical model, and has good portability. When establishing a lookup table, the coefficients of the calculation equation can be calculated and adjusted according to the segmented calculation method provided by this scheme, based on the numerical model cloud microphysics scheme used for simulation, and is not limited to a specific cloud microphysics scheme; in the calculation process, there is no need to introduce empirical assumptions about particle size distribution, and it has good interpretability. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the infrared brightness temperature simulation method of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0050] In order to illustrate all the embodiments of the present application, the problems to be solved by the present application and the related technologies are first described.

[0051] The existing method of calculating the scattering characteristics of ice clouds based on the Mie scattering theory requires the calculation of each cloud profile output by the model, and its computational efficiency cannot meet the needs of business operation update assimilation. In addition, the calculation scheme of satellite infrared brightness temperature simulation of ice cloud scattering based on the table lookup method can only determine the overall scattering properties of ice clouds according to the effective radius of ice clouds. It has two defects: (1) It ignores the influence of ice cloud particle size distribution on scattering properties at model grid points; (2) It is inconsistent with the physical assumptions of ice cloud particle size distribution in numerical prediction models. These defects will bring errors to satellite infrared brightness temperature simulation and reduce the availability of satellite observation data in forecasting, warning and model evaluation. Therefore, they need to be corrected.

[0052] Based on these problems, the present invention provides a series of embodiments to specifically solve them.

[0053] A piecewise calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation

[0054] Firstly, a piecewise calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation is provided, which includes the following steps:

[0055] S100, determining the particle size distribution form of ice crystal particles and particle size distribution parameters corresponding to the ice cloud according to the cloud microphysics scheme adopted by the numerical model to be simulated;

[0056] The distribution of ice crystal particle size is expressed as:

[0057] n(L)=N0L μ e -vL

[0058] Where L represents the diameter of the ice particles; N0, μ and v represent the intercept parameter, slope parameter and shape parameter, respectively; μ is a constant, and the intercept parameter N0 and shape parameter v are determined by the mass concentration and number concentration of ice particles predicted or diagnosed in the cloud microphysics scheme, respectively.

[0059] This embodiment takes the Morrison dual-parameter cloud microphysics scheme in the WRF mode as an example. In the Morrison scheme, μ = 0, and n(L) is mainly determined by two parameters, N0 and v. The calculation method of the shape parameter v and the intercept parameter N0 is:

[0060]

[0061] Among them, q and N are the mass concentration and number concentration of ice crystal particles, respectively, c and d are constants, and Γ is the gamma function. Therefore, according to the mass concentration and number concentration of ice crystal particles output by the model, the specific form of the particle size distribution n(L) of the ice crystal particles can be calculated.

[0062] In practical applications, the number concentration N is set as a constant or linearly related to the mass concentration q. Therefore, the above parameters can be understood as being determined by the value of the mass concentration q. In numerical model simulations, they change with the change of mass concentration.

[0063] S200, segmenting the ice cloud particles according to their particle sizes, calculating the weight coefficients of the ice particles in each particle size range that contribute to the overall radiation characteristics of the ice cloud, and making a weight coefficient lookup table.

[0064] First, the ice particle spectrum distribution n(L) assumed by the microphysical scheme is segmented and calculated in different particle size ranges (L i ,L i+1 ), the weight coefficient F of the contribution of ice particles to the overall radiation characteristics of ice clouds ex (L i ,L i+1 ).

[0065] In this embodiment, the ice cloud extinction coefficient k ex Taking the calculation process of as an example, the process of establishing the segmented calculation model is explained in detail.

[0066] S201. Establish the overall extinction coefficient k of ice cloud ex The calculation formula is:

[0067]

[0068] Among them, Q ex represents the single particle extinction efficiency at the corresponding particle size and wavelength, S(L) and V(L) are the cross-sectional area and volume of the ice crystal particle with a particle diameter of L, respectively; λ represents the wavelength.

[0069] S202. Define the weight coefficient F of the extinction coefficient in the particle size interval (L0, L1) ex The calculation formula for (L0, L1) is:

[0070]

[0071] The particle size distribution of the ice crystal particles obtained in step S100 is brought into play, and after the particle size intervals L0 and L1 are determined, the weight coefficient F in the particle size interval is calculated. ex (L0,L1);

[0072] S203, segment the particle size, determine the particle size range of the segment, and establish the convolution summation formula of the overall extinction coefficient of the ice cloud:

[0073]

[0074] Where N is the number of particle size interval segments;

[0075] When the difference between L0 and L1 is small enough, Q ex When (L, λ) changes little in this particle size range (L0, L1), Q ex (L, λ) is approximately equal to Q ex ((L0+L1) / 2,λ).

[0076] In the ice cloud scattering calculation of the rapid radiative transfer model, Q ex ((L i +L i+1 ) / 2,λ) needs to be obtained from the ice cloud scattering factor lookup table provided by the rapid radiative transfer model, and Q ex The first dimension is the discrete effective radius value, such as 2μm, 5μm, 10μm, etc. Therefore, in order to calculate k by table lookup convolution ex (λ), when segmenting, it is necessary to ensure that the number of segments N is consistent with the length of the effective radius dimension given in the ice cloud scattering factor lookup table of the rapid radiative transfer model, and the mean value of each particle size interval (L i +L i+1) / 2 is the same as the particle size value given in the ice cloud scattering factor lookup table. After segmentation according to this standard, the particle size range of N groups (L i ,L i+1 ) is brought into the weight coefficient F in S202 ex (L i ,L i+1 ) to obtain its specific form. In this embodiment, N=10, which is consistent with the dimension of the ice particle effective radius Re given in the ice cloud scattering factor lookup table CloudCoeff.bin of the rapid radiative transfer model CRTM. Ten ice particle size intervals are linearly selected within the particle size range of 1.5um to 125um (consistent with the ice particle size range given by CRTM).

[0077] S204, creating a weight coefficient lookup table. Selecting different values ​​of mass concentration q of ice particles, determining the corresponding particle size distribution form and the particle size distribution parameters corresponding to the ice cloud, and generating a weight coefficient lookup table related to the mass concentration q according to the calculation formula given in S202.

[0078] Based on this, the calculation formula of the ice cloud overall extinction coefficient in S203 is applied to the rapid radiation transfer model. In the cloud scattering calculation module, according to the mass concentration and wavelength, a corresponding set of N Q values ​​are found from the ice cloud scattering factor lookup table and the weight coefficient lookup table respectively. ex and F ex According to the convolution summation formula given in S203, the overall scattering characteristics of ice clouds required for infrared brightness temperature simulation are calculated, including extinction coefficient, single scattering albedo and asymmetric parameters.

[0079] It should be noted that the effective radius R e The 10 values ​​in the dimension represent 10 different effective radii in the particle size range of 1.5um to 125um. Therefore, when calculating the weight coefficient, it is necessary to first calculate the effective radius R e The corresponding mass concentration q is obtained to determine the parameters such as λ and N0 in the weight coefficient calculation formula of the 10 intervals in the particle size distribution n(L). When calculating the weight coefficient lookup table SplitCoeff.bin, the mass concentration q dimension is added to reflect the actual changes in the simulated ice crystal size distribution. The value range and resolution of the mass concentration q dimension can be set according to actual needs. For example, if the q dimension length N is set q =100, then the size of the generated weight coefficient lookup table SplitCoeff.bin is N q ×N=100×10, that is, there are 100 sets of weight coefficients F ex, each group of weight coefficients is an array of 1×10. Each time the calculation is performed, the corresponding group is read according to the value of q. This group of weights is applied to the 10 effective radius segments of the wavelength corresponding to the ice cloud scattering, and the weighted summation can be performed to calculate the ice cloud scattering characteristics.

[0080] S300. In the rapid radiation transfer model, the weight coefficient table is searched through the particle size distribution parameters of the ice cloud, the corresponding weight coefficient is found, and brought into the calculation of the ice cloud scattering characteristics.

[0081] Infrared brightness temperature simulation method

[0082] The present invention provides an infrared brightness temperature simulation method in some embodiments, such as Figure 1 As shown, the steps include:

[0083] The above-mentioned piecewise calculation method for ice cloud scattering characteristics applicable to satellite infrared brightness temperature simulation is used to generate a weight coefficient lookup table;

[0084] According to the rapid radiation transfer model, meteorological conditions and hydrometeoric distribution profiles are read to obtain the particle size distribution form of ice crystal particles and the particle size distribution parameters corresponding to ice clouds;

[0085] The corresponding weight coefficient is found from the weight coefficient lookup table and brought into the calculation of ice cloud scattering characteristics, and the ice cloud scattering characteristics are applied to the calculation of the brightness temperature of the satellite infrared observation channel.

[0086] Non-transitory computer readable storage medium

[0087] This embodiment provides a non-transitory computer-readable storage medium on which computer instructions are stored. The computer instructions enable a computer to execute the above-mentioned infrared brightness temperature simulation method.

[0088] Electronic devices

[0089] This embodiment provides an electronic device, which may include: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. The processor may call logic instructions in the memory to execute the above-mentioned infrared brightness temperature simulation method.

[0090] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0091] Computer program product

[0092] This embodiment provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the above-mentioned infrared brightness temperature simulation method.

[0093] The device embodiments described above are merely illustrative, 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 scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0094] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A piecewise calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation, characterized in that: The steps include: S100, according to the cloud microphysics scheme adopted by the numerical model to be simulated, determine the particle size distribution form of ice crystal particles and the particle size distribution parameters corresponding to the ice cloud; the particle size distribution form of ice crystal particles is expressed as: n(L)=N0L μ yes -vL Where L is the diameter of the ice particles; N0, μ and v represent the intercept parameter, slope parameter and shape parameter, respectively; μ is a constant, the intercept parameter N0 and the shape parameter v are determined by the mass concentration and number concentration of ice particles predicted or diagnosed in the cloud microphysics scheme, respectively; the shape parameter v and the intercept parameter N0 are calculated as follows: Where q and N are the mass concentration and number concentration of ice crystal particles, respectively; c and d are constants; Γ is a gamma function; the number concentration is a fixed constant or is linearly related to the mass concentration q; the particle size distribution of ice crystal particles is determined by the value of the mass concentration q; S200, segmenting the ice cloud particles according to their particle sizes, calculating the weight coefficients of the ice particles within each particle size range that contribute to the overall radiation characteristics of the ice cloud, and making a weight coefficient lookup table; specifically, including: S201. Establish the overall extinction coefficient k of ice cloud ex The calculation formula is: Among them, Q ex represents the single particle extinction efficiency at the corresponding particle size and wavelength, S(L) and V(L) are the cross-sectional area and volume of the ice crystal particle with a particle diameter of L, respectively; λ represents the wavelength; S202. Define the weight coefficient F of the extinction coefficient in the particle size interval (L0, L1) ex The calculation formula for (L0, L1) is: The particle size distribution of the ice crystal particles obtained in step S100 is brought into play, and after the particle size intervals L0 and L1 are determined, the weight coefficient F in the particle size interval is calculated. ex (L0,L1); S203, segment the particle size, determine the particle size range of the segment, and establish the convolution summation formula of the overall extinction coefficient of the ice cloud: Where N is the number of particle size interval segments; S204, making a weight coefficient lookup table, selecting mass concentration q of ice particles with different values, determining the corresponding particle size distribution form, and the particle size distribution parameters corresponding to the ice cloud, and generating a weight coefficient lookup table related to the mass concentration q according to the calculation formula given in S202; S300. In the rapid radiation transfer model, the weight coefficient table is searched through the particle size distribution parameters of the ice cloud, the corresponding weight coefficient is found, and brought into the calculation of the ice cloud scattering characteristics.

2. An infrared brightness temperature simulation method, characterized in that: Includes steps: The weight coefficient lookup table is generated by using the segmented calculation method for ice cloud scattering characteristics suitable for satellite infrared brightness temperature simulation as described in claim 1; According to the rapid radiation transfer model, meteorological conditions and hydrometeoric distribution profiles are read to obtain the particle size distribution form of ice crystal particles and the particle size distribution parameters corresponding to ice clouds; The corresponding weight coefficient is found from the weight coefficient lookup table and brought into the calculation of ice cloud scattering characteristics, and the ice cloud scattering characteristics are applied to the calculation of the brightness temperature of the satellite infrared observation channel.

3. A non-transitory computer-readable storage medium having computer instructions stored thereon, the computer instructions enabling a computer to execute the infrared brightness temperature simulation method according to claim 2.

4. An electronic device comprising: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus, and the processor calls the logic instructions in the memory to execute the infrared brightness temperature simulation method described in claim 2.

5. A computer program product, comprising a computer program, which can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, the computer can execute the infrared brightness temperature simulation method described in claim 2.

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