A method and apparatus for calculating scale-adaptive cloud microphysical conversion rates

Abstract

The application relates to a scale-adaptive cloud microphysical conversion rate calculation method and device, which comprises the following steps: obtaining a cloud microphysical process conversion rate by using current meteorological prediction parameters, wherein the cloud microphysical process conversion rate comprises cloud-rain automatic conversion efficiency, rainwater-cloud water collision efficiency and graupel melting efficiency; fitting a cloud water content sub-grid fitting formula and a rainwater content sub-grid fitting formula according to a grid distance, a cloud amount value and a rainwater coverage range value in a grid; and obtaining a correction factor through the cloud water content sub-grid fitting formula and the rainwater content sub-grid fitting formula, and correcting the cloud microphysical process conversion rate by using the correction factor. The cloud-rain automatic conversion, rainwater-cloud water collision and graupel melting processes are improved, and the fine prediction capability is improved. The application also relates to an equipment and a storage medium.

Description

Technical Field

[0001] This invention relates to the field of meteorological technology, and in particular to a method and apparatus for calculating cloud microphysical conversion rate that is scale-adapted. Background Technology

[0002] Cloud microphysics describes the growth and evolution of particles in clouds and the formation of precipitation, including a series of complex processes such as nucleation, condensation, collision-coalescence, freezing, thawing, and evaporation. The description of cloud microphysics in numerical models is achieved through parameterization schemes, i.e., cloud microphysics parameterization.

[0003] Cloud microphysics parameterization has always been a cutting-edge topic in numerical weather modeling. There are two main directions in cloud microphysics parameterization schemes: one is the bulk scheme, which uses a pre-defined spectral distribution function to characterize the scale distribution of water condensates in clouds, forecasting one or two parameters or moments (usually mixing ratio and number concentration). The other is the bin scheme, which uses dozens or even hundreds of discretized mass or diameter bins to characterize the scale distribution of water condensates, explicitly calculating the particle spectral distribution and evolution. Compared to the bulk scheme, the bin scheme is not limited by a pre-defined spectral function and is closer to natural images. However, the bin scheme has the disadvantage of high computational cost and is mostly used in theoretical research. The current general cloud microphysics schemes do not consider resolution dependence, which is a pressing technical problem that needs to be solved. Summary of the Invention

[0004] To address the lack of consideration for resolution dependence in general cloud microphysics schemes, and the absence of a scale-adaptive cloud microphysics scheme suitable for high-resolution forecasting that can meticulously characterize the grid resolution dependence of cloud physical processes, this invention provides a method and apparatus for calculating scale-adaptive cloud microphysics conversion rates.

[0005] In a first aspect, the present invention provides a method for calculating scale-adaptive cloud microphysics conversion efficiency, the method comprising:

[0006] Using current meteorological forecast parameters, the cloud microphysical process conversion rate is obtained, which includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency.

[0007] Based on the grid spacing, cloud cover value, and the range of rainwater coverage within a grid, fit the subgrid fitting formulas for cloud water content and rainwater content;

[0008] A correction factor is obtained by using the subgrid fitting formulas for cloud water content and rainfall content, and the correction factor is used to correct the conversion rate of cloud microphysical processes.

[0009] Based on the above technical solution, further, the step of fitting the subgrid fitting formula for cloud water content and the subgrid fitting formula for rainwater content according to the grid spacing, cloud cover value, and the range value of rainwater coverage within a grid specifically includes:

[0010] Based on the grid spacing and cloud cover value, a subgrid fitting formula for cloud water content is fitted. Where x is the grid spacing and C is the cloud cover value;

[0011] Based on the grid spacing and the range of rainwater cover within a grid, a subgrid fitting formula for rainwater content is fitted. Where R is the range of rainwater coverage within a grid.

[0012] Based on the above technical solution, further, obtaining the correction factor through the subgrid fitting formula of cloud water content and the subgrid fitting formula of rainfall content specifically includes:

[0013] The correction factor E:

[0014] Where b1 and b2 are constants, and ρ is the correlation value between the cloud water content and the rainwater content.

[0015] Based on the above technical solution, further, the cloud microphysical process conversion rate is obtained by utilizing current meteorological forecast parameters. This cloud microphysical process conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including:

[0016] The cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ were used to determine the cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ. a Enter formula The cloud and rain automatic conversion efficiency was obtained.

[0017] Based on the above technical solution, further, the cloud microphysical process conversion rate is obtained by utilizing current meteorological forecast parameters. This cloud microphysical process conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including:

[0018] Mix the rainwater ratio Q R The mixing ratio of cloud and water, Qc, is input Pra = 67 (Q C Q R ) 1.15 , and get rainwater and cloud water.

[0019] Based on the above technical solution, further, the cloud microphysical process conversion rate is obtained by utilizing current meteorological forecast parameters. This cloud microphysical process conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including:

[0020] The intercept parameter N of the graupel spectrum distribution 0gLatent heat of fusion L f Air thermal conductivity K a T0 = ​​273.15, temperature T, graupel spectral distribution slope parameter λ g The terminal velocity parameter AGN of graupel after air density correction, and the air viscosity coefficient μ. k Schmidt number S c The terminal velocity parameter BG of the falling graupel, and the specific heat of water at constant pressure C. pw The efficiency P of colliding with sleet and raindrops racg Enter formula

[0021]

[0022] The graupel melting efficiency was obtained.

[0023] Secondly, the present invention also provides a scale-adaptive cloud microphysics conversion rate calculation device, the device comprising:

[0024] The first processing module is used to obtain the cloud microphysical process conversion rate using the current weather forecast parameters. The cloud microphysical process conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency.

[0025] The second processing module is used to fit the subgrid fitting formulas for cloud water content and rainwater content based on the grid spacing, cloud cover value and the range value of rainwater coverage within a grid.

[0026] The third processing module is used to obtain a correction factor through the subgrid fitting formula of cloud water content and the subgrid fitting formula of rainwater content, and to use the correction factor to correct the conversion rate of cloud microphysical processes.

[0027] Based on the above technical solution, the second processing module is further configured to fit a subgrid fitting formula for cloud water content based on the grid spacing and cloud cover value. Where x is the grid spacing and C is the cloud cover value;

[0028] Based on the grid spacing and the range of rainwater cover within a grid, a subgrid fitting formula for rainwater content is fitted. Where R is the range of rainwater coverage within a grid.

[0029] Based on the above technical solution, the third processing module is further specifically used for the correction factor E:

[0030] Where b1 and b2 are constants, and ρ is the correlation value between the cloud water content and the rainwater content.

[0031] Based on the above technical solution, further, the first processing module is specifically used to process the cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ. a Enter formula The cloud and rain automatic conversion efficiency was obtained.

[0032] Based on the above technical solution, further, the first processing module is used for

[0033] Mix the rainwater ratio Q R The mixing ratio of cloud and water, Qc, is input Pra = 67 (Q C Q R ) 1.15 , and get rainwater and cloud water.

[0034] Based on the above technical solution, further, the first processing module is specifically used to process the graupel spectral distribution intercept parameter N. 0g Latent heat of fusion L f Air thermal conductivity K a T0 = ​​273.15, temperature T, graupel spectral distribution slope parameter λ g The terminal velocity parameter AGN of graupel after air density correction, and the air viscosity coefficient μ. k Schmidt number S c The terminal velocity parameter BG of the falling graupel, and the specific heat of water at constant pressure C. pw The efficiency P of colliding with sleet and raindrops racg Enter formula

[0035] The graupel melting efficiency was obtained.

[0036] Thirdly, the present invention also provides a memory storing multiple instructions for implementing a method for calculating the scale-adaptive cloud microphysics conversion rate as described in any of the above technical solutions.

[0037] Fourthly, the present invention also provides an electronic device, including a processor and a memory connected to the processor, the memory storing a plurality of instructions which can be loaded and executed by the processor to enable the processor to perform a method for calculating the scale-adapted cloud microphysical conversion rate as described in any of the above technical solutions.

[0038] This invention provides a scale-adaptive method for calculating cloud microphysical conversion rates, comprising: obtaining cloud microphysical process conversion rates using current meteorological forecast parameters, wherein the cloud microphysical process conversion rates include automatic cloud-rain conversion efficiency, rainwater collision and cloud water efficiency, and graupel melting efficiency; fitting subgrid fitting formulas for cloud water content and rainwater content based on grid spacing, cloud cover value, and the range of rainwater coverage within a grid; obtaining correction factors through the subgrid fitting formulas for cloud water content and rainwater content, and using the correction factors to correct the cloud microphysical process conversion rates. This invention improves the automatic cloud-rain conversion, rainwater collision and cloud water, and graupel melting processes, thereby enhancing the precision of forecasting. Attached Figure Description

[0039] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0040] In the attached diagram:

[0041] Figure 1 This is a flowchart illustrating a method for calculating scale-adaptive cloud microphysics conversion efficiency provided in an embodiment of the present invention.

[0042] Figure 2 This is a schematic diagram of a module of a scale-adaptive cloud microphysics conversion rate calculation method device provided in an embodiment of the present invention. Detailed Implementation

[0043] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0044] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0045] In recent years, with the continuous improvement of computing power, the resolution of numerical weather prediction models has developed towards convection-resolvable scales (kilometer scale) and large eddy scales (hundred-meter scale). At this resolution, deep convection can be partially resolved by the model, and topographic features are better represented. However, this presents new problems and challenges for the parameterization of model physical processes, such as the "grey region" problem. The "grey region" refers to a range of physical processes within a certain resolution that cannot be fully resolved or fully parameterized (Wyngaard, 2004). Typical examples include cumulus convection and the mixing of boundary layer and intra-cloud turbulent processes. When the model resolution falls within the gray region of cumulus convection or boundary layer processes, traditional parameterization schemes become inapplicable, necessitating the development of scale-adaptive parameterization schemes suitable for gray region scales. For example, Sakradzija et al. have developed corresponding scale-adaptive parameterization schemes for the gray region problem of cumulus convection and boundary layer processes.

[0046] (2016) A stochastic scale adaptive shallow cumulus convection parameterization scheme was developed for simulating shallow convection gray regions. Shin and Hong (2013) modified the traditional boundary layer parameterization scheme by referring to the variation of turbulent flux with model grid spacing, and established scale awareness. However, the scale dependence research of cloud microphysical processes is still in its early stages. In fact, the scale sensitivity of cloud microphysical processes has been confirmed by numerical experiments. For example, some studies have shown that cloud entrainment and turbulent mixing processes are scale-dependent. The higher the model resolution, the better the analytical resolution of entrainment and turbulent mixing processes, leading to a broadening of the simulated cloud droplet particle spectrum, enhanced cloud water evaporation, and reduced ground precipitation (Bryan and Morrison, 2012; Kumar et al., 2018).

[0047] (Jeevanjee and Zhou, 2022). In addition, studies on snowfall microphysics have found that finer resolution can resolve stronger vertical motions, simulate higher ice crystal nucleation and pine-water conversion rates, and make ice crystal particles more spherical (Jensen et al., 2020). Preliminary attempts have been made in constructing scale-adapted cloud microphysics schemes. For example, Boutle et al. (2014) developed a scale-adapted microphysics scheme for warm clouds. Its design concept is to use a microphysics scheme suitable for large eddy simulations and scale up the simulated microphysics conversion rate to the required grid scale. Numerical simulation experiments have shown that, compared with traditional schemes, the scale-adapted microphysics scheme simulates precipitation with greater convergence and closer accuracy to observations.

[0048] The above work has undoubtedly laid a solid foundation for the development of cloud microphysics parameterization, but there are still obvious shortcomings: at present, general cloud microphysics schemes lack consideration for resolution dependence, and there is no scale-adapted cloud microphysics scheme suitable for high-resolution forecasting that can meticulously characterize the grid resolution dependence of cloud physical processes.

[0049] like Figure 1 As shown, this application proposes a scale-adaptive method for calculating cloud microphysics conversion efficiency, the method comprising:

[0050] 110. Using current weather forecast parameters, obtain the cloud microphysical process conversion rate, which includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency;

[0051] 120. Based on the grid spacing, cloud cover value, and the range of rainwater coverage within a grid, fit the subgrid fitting formulas for cloud water content and rainwater content;

[0052] 130. A correction factor is obtained through the subgrid fitting formula of cloud water content and the subgrid fitting formula of rainwater content, and the correction factor is used to correct the conversion rate of cloud microphysical processes.

[0053] Based on the above embodiments, step 120 further includes:

[0054] Based on the grid spacing and cloud cover value, a subgrid fitting formula for cloud water content is fitted. Where x is the grid spacing and C is the cloud cover value;

[0055] Based on the grid spacing and the range of rainwater cover within a grid, a subgrid fitting formula for rainwater content is fitted. Where R is the range of rainwater coverage within a grid.

[0056] Based on the above embodiments, step 130 further includes:

[0057] The correction factor E:

[0058] Where b1 and b2 are constants, and ρ is the correlation value between the cloud water content and the rainwater content.

[0059] Based on the above embodiments, step 110 further includes:

[0060] The cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ were used to determine the cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ. a Enter formula The cloud and rain automatic conversion efficiency was obtained.

[0061] Based on the above embodiments, step 110 further includes:

[0062] Mix the rainwater ratio Q R The mixing ratio of cloud and water, Qc, is input Pra = 67 (Q C Q R ) 1.15 , and get rainwater and cloud water.

[0063] Based on the above embodiments, step 110 further includes:

[0064] The intercept parameter N of the graupel spectrum distribution 0g Latent heat of fusion L f Air thermal conductivity K a T0 = ​​273.15, temperature T, graupel spectral distribution slope parameter λ g The terminal velocity parameter AGN of graupel after air density correction, and the air viscosity coefficient μ. k Schmidt number S c The terminal velocity parameter BG of the falling graupel, and the specific heat of water at constant pressure C. pw The efficiency P of colliding with sleet and raindrops racg Enter formula

[0065]

[0066] The graupel melting efficiency was obtained.

[0067] This embodiment proposes a scale-adaptive method for calculating cloud microphysical conversion rates. The method includes obtaining cloud microphysical process conversion rates using current weather forecast parameters. These conversion rates include the automatic cloud-rain conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency. Based on grid spacing, cloud cover value, and the range of rain cover within a grid, a subgrid fitting formula for cloud water content and a subgrid fitting formula for rainwater content are fitted. A correction factor is obtained from these two formulas and used to correct the cloud microphysical process conversion rates. This invention improves the automatic cloud-rain conversion, rain-water collision and cloud water, and graupel melting processes, enhancing the precision of forecasts.

[0068] Currently, general cloud microphysics schemes lack consideration for resolution dependence, and there is no scale-adapted cloud microphysics scheme suitable for high-resolution forecasting that can meticulously characterize the resolution-dependent nature of cloud physical processes.

[0069] To address the shortcomings of the aforementioned cloud microphysics parameterization schemes, this invention provides a scale-adaptive cloud microphysics scheme to enhance the model's refined forecasting capabilities. Based on the Morrison microphysics scheme in the WRF model, improvements are made to the automatic cloud-rain conversion, rainwater collision with cloud water, and graupel melting processes.

[0070] 1. Introduction to the Morrison microphysics scheme

[0071] In addition to forecasting the mixing ratios of water vapor and five types of aqueous substances—cloud water, rainwater, cloud ice, snow, and graupel—the Morrison dual-parameter scheme also forecasts the number concentrations of rainwater, cloud ice, snow, and graupel (Morrison et al., 2009). In this scheme, the particle spectral distribution of condensates follows a Gamma distribution. For raindrops, snow, graupel, and cloud ice, the spectral shape parameter is 0, and the particle spectral distribution follows a Marshall-Palmer exponential distribution. For cloud droplets, the spectral shape parameter is related to the predicted cloud droplet number concentration.

[0072] To simplify calculations, each type of particle is assumed to be spherical, with densities given by Reisner et al. (1998). In the calculations, the minimum mixing ratio of rainwater and snow is set to 0.1 g kg. -1 To ensure that rainwater and snow collide and form graupel, the minimum mixing ratio of cloud water and snow is set at 0.5 g / kg. -1 and 0.1g kg -1 This is to ensure that the clouds and snow collide and form graupel.

[0073] (1) Automatic cloud and rain conversion (Prc)

[0074]

[0075] Where Prc is the automatic cloud-rain conversion efficiency, Qc is the cloud-water mixing ratio, Nc is the cloud droplet number concentration, and ρ is the cloud-rain automatic conversion efficiency. a This refers to air density.

[0076] (2) Rainwater collides with cloud water (Pra)

[0077] Pra = 67(Q) C Q R ) 1.15

[0078] Where Pra is the rainwater collision and coalescence efficiency of cloud water, and Q is the cloud water collision efficiency. R This refers to the rainwater mixing ratio.

[0079] (3)Graupel melting (Pgmlt)

[0080]

[0081] Where Pgmlt is the graupel melting efficiency, Lf To achieve latent heat of melting, N 0g K is the intercept parameter of the graupel spectral distribution. a Where λ is the thermal conductivity of air, T0 is 273.15, T is the temperature, and λ is the thermal conductivity of air. g Here, μ represents the slope parameter of the graupel spectrum distribution, AGN represents the terminal velocity parameter of the graupel after air density correction, and μ represents the velocity parameter of the graupel. k S is the air viscosity coefficient. c Where is the Schmitt number, BG is the terminal velocity parameter of the graupel particles, and C is the final velocity parameter. pw Let be the specific heat at constant pressure of water, and Pracg be the efficiency of graupel colliding with raindrops.

[0082] 2. Parameterization of subgrid variations in cloud water and rainwater content

[0083] The fractional standard deviation (f) is defined to reflect the non-uniformity of the distribution of microphysical parameters within the subgrid.

[0084]

[0085] Where σ is the standard deviation of the hydrogel q. This is the average value across the grid.

[0086] f is obtained by fitting observational data; for cloud water, f c It can be represented as:

[0087]

[0088] Where x is the grid spacing and C is the cloud cover.

[0089] For rainwater, fr can be represented as:

[0090]

[0091] Where R is the extent of rainwater coverage within a grid.

[0092] 3. Correction of cloud micro-physical conversion rate

[0093] Using the fitting formulas for the subgrid variations of cloud water and rainwater content obtained in (2), the conversion rates of relevant cloud microphysical processes are corrected.

[0094] Assume that the conversion rate of cloud microphysical processes can be simplified as M = aq b If a and b are constants, then the corrected microphysical conversion rate can be expressed as:

[0095] M=E(f,b)aq -b

[0096] E is the correction factor, which can be expressed as:

[0097]

[0098] Assuming the cloud microphysical process conversion rate depends on two variables, and these two variables are correlated (ρ), the cloud microphysical process conversion rate can be simplified as M = aq1 b1 q2 b2 If a1, b1, and b2 are constants, then the corrected microphysical conversion rate can be expressed as:

[0099] M=E(f1,b1,f2,b2,ρ)aq1 -b1 q2 -b2

[0100] At this point, the correction factor E can be expressed as:

[0101]

[0102] Since f is a function of grid resolution, the correction factor E changes with the resolution, thus achieving scale dependence.

[0103] It should be understood that the above-mentioned scale-adaptive cloud microphysics scheme construction technology can be applied not only to the Morrison dual-parameter scheme, but also to other schemes such as Thompson and WDM6. This invention does not limit the application of these schemes.

[0104] Numerical experiments show that the scale-adapted cloud microphysics scheme outperforms the traditional cloud microphysics scheme in simulation. Regarding surface precipitation simulations, the traditional cloud microphysics scheme is highly sensitive to model resolution, while the scale-adapted cloud microphysics scheme simulates precipitation rates that are more convergent and closer to observations.

[0105] The key technical point and protection point of this application is the construction method of the scale-adaptive cloud microphysics scheme. Based on the correction factor proposed in this application, the conversion rate of the relevant cloud microphysics process is corrected.

[0106] like Figure 2 As shown, the present invention also provides a scale-adaptive cloud microphysics conversion rate calculation device, the device comprising:

[0107] The first processing module is used to obtain the cloud microphysical process conversion rate using the current weather forecast parameters. The cloud microphysical process conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency.

[0108] The second processing module is used to fit the subgrid fitting formulas for cloud water content and rainwater content based on the grid spacing, cloud cover value and the range value of rainwater coverage within a grid.

[0109] The third processing module is used to obtain a correction factor through the subgrid fitting formula of cloud water content and the subgrid fitting formula of rainwater content, and to use the correction factor to correct the conversion rate of cloud microphysical processes.

[0110] Based on the above embodiments, the second processing module is further configured to fit a subgrid fitting formula for cloud water content based on the grid spacing and cloud cover value. Where x is the grid spacing and C is the cloud cover value;

[0111] Based on the grid spacing and the range of rainwater cover within a grid, a subgrid fitting formula for rainwater content is fitted. Where R is the range of rainwater coverage within a grid.

[0112] Based on the above technical solution, the third processing module is further specifically used for the correction factor E:

[0113] Where b1 and b2 are constants, and ρ is the correlation value between the cloud water content and the rainwater content.

[0114] Based on the above embodiments, the first processing module is further configured to process the cloud-water mixing ratio Qc, cloud droplet number concentration Nc, and air density ρ. a Enter formula The cloud and rain automatic conversion efficiency was obtained.

[0115] Based on the above technical solution, further, the first processing module is used for

[0116] Mix the rainwater ratio Q R The mixing ratio of cloud and water, Qc, is input Pra = 67 (Q C Q R ) 1.15 , and get rainwater and cloud water.

[0117] Based on the above embodiments, the first processing module is further configured to process the graupel spectral distribution intercept parameter N. 0g Latent heat of fusion L f Air thermal conductivity K a T0 = ​​273.15, temperature T, graupel spectral distribution slope parameter λ g The terminal velocity parameter AGN of graupel after air density correction, and the air viscosity coefficient μ. k Schmidt number S c The terminal velocity parameter BG of the falling graupel, and the specific heat of water at constant pressure C. pw The efficiency P of colliding with sleet and raindrops racg Enter formula

[0118] The graupel melting efficiency was obtained.

[0119] This embodiment proposes a scale-adaptive cloud microphysical conversion rate calculation device, which includes obtaining the cloud microphysical process conversion rate using current meteorological forecast parameters. The cloud microphysical process conversion rate includes the automatic cloud-rain conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency. Based on the grid spacing, cloud cover value, and the range of rain cover within a grid, a subgrid fitting formula for cloud water content and a subgrid fitting formula for rainwater content are fitted. A correction factor is obtained from the subgrid fitting formulas for cloud water content and rainwater content, and the correction factor is used to correct the cloud microphysical process conversion rate. This invention improves the automatic cloud-rain conversion, rain-water collision and cloud water, and graupel melting processes, enhancing the precision of forecasting capabilities.

[0120] Thirdly, the present invention also provides a memory storing multiple instructions for implementing a method for calculating the scale-adaptive cloud microphysics conversion rate as described in any of the above technical solutions.

[0121] Fourthly, the present invention also provides an electronic device, including a processor and a memory connected to the processor, the memory storing a plurality of instructions which can be loaded and executed by the processor to enable the processor to perform a method for calculating the scale-adapted cloud microphysical conversion rate as described in any of the above technical solutions.

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for calculating scale-adaptive cloud microphysical conversion efficiency, characterized in that, The method includes: Using current meteorological forecast parameters, the cloud microphysical process conversion rate is obtained, which includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency. Based on the grid spacing, cloud cover value, and the range of rainwater coverage within a grid, fit the subgrid fitting formulas for cloud water content and rainwater content; A correction factor is obtained by using the subgrid fitting formulas for cloud water content and rainfall content, and the correction factor is used to correct the conversion rate of cloud microphysical processes. The process of fitting subgrid fitting formulas for cloud water content and rainwater content based on grid spacing, cloud cover value, and the range of rainwater coverage within a grid specifically includes: Based on the grid spacing and cloud cover values, a subgrid fitting formula for cloud water content is fitted. Where x is the grid spacing and C is the cloud cover value; Based on the grid spacing and the range of rainwater cover within a grid, a subgrid fitting formula for rainwater content is fitted. Where R is a value representing the extent of rainwater coverage within a grid; The correction factor obtained by fitting the cloud water content subgrid and the rainfall water content subgrid specifically includes: The correction factor E: in, b 1 and b 2 It is a constant. ρ It is the correlation value between the cloud water content and the rainfall content; The cloud microphysical process conversion rate is obtained by using current weather forecast parameters. This conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including: Cloud-water mixing ratio Q c. Cloud droplet number concentration N c and air density ρ a Enter formula The cloud-rain automatic conversion efficiency was obtained; The cloud microphysical process conversion rate is obtained by using current weather forecast parameters. This conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including: Rainwater mixing ratio Q R Mixing ratio with clouds and water Q c input It receives rainwater and cloud water; The cloud microphysical process conversion rate is obtained by using current weather forecast parameters. This conversion rate includes the cloud-rain automatic conversion efficiency, the rain-water collision and cloud water efficiency, and the graupel melting efficiency, specifically including: The intercept parameter of the graupel spectrum distribution N 0g Latent heat of fusion L f thermal conductivity of air K a , temperature T Spectral distribution slope parameter of graupel λ g The terminal velocity parameter AGN of graupel after air density correction, and the air viscosity coefficient. μ k Schmidt number S c The terminal velocity parameter BG of the falling graupel is the isobaric specific heat of water. C pw The efficiency of sleet colliding with raindrops P racg Enter formula The graupel melting efficiency was obtained.

2. A memory, characterized in that, The system stores multiple instructions for implementing the scale-adaptive cloud microphysics conversion rate calculation method as described in claim 1.

3. An electronic device, characterized in that, It includes a processor and a memory connected to the processor, the memory storing multiple instructions that can be loaded and executed by the processor to enable the processor to perform the scale-adapted cloud microphysical conversion rate calculation method as described in claim 1.

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