Scale-adaptive cloud micro-physical conversion rate calculation method and device

Abstract

The invention relates to a scale-adaptive cloud micro-physical conversion rate calculation method and device, and the method comprises the steps: obtaining a cloud micro-physical process conversion rate through employing a current weather forecast parameter, and the cloud micro-physical process conversion rate comprises the cloud and rain automatic conversion efficiency, the rainwater collision cloud water efficiency, and the vaterite melting efficiency; fitting a cloud water content sub-grid fitting formula and a rainwater content sub-grid fitting formula according to the grid distance, the cloud amount value and a range value covered by rainwater in one 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 micro physical process conversion rate by using the correction factor. According to the method, the processes of automatic conversion of cloud and rain, collision of rainwater with cloud water and vaterite melting are improved, and the refined forecasting capability is improved. The invention also relates to a device and a storage medium.

Description

Technical Field

[0001] The present invention relates to the field of meteorological technology, and in particular to a method and device for calculating a scale-adaptive cloud microphysical conversion rate. Background Art

[0002] Cloud microphysical processes are processes that describe the growth and evolution of cloud particles and the formation of precipitation, including a series of complex processes such as nucleation, condensation, collision and growth, freezing, melting, and evaporation. The description of cloud microphysical processes in numerical models is achieved through parameterization schemes, namely cloud microphysical parameterization.

[0003] The study of cloud microphysics parameterization has always been a frontier topic in the study of numerical weather models. There are two different development directions for cloud microphysics parameterization schemes: one is the bulk water scheme, which uses a preset spectral distribution function to characterize the scale distribution of hydrometeors in clouds and predict 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 discrete mass or diameter bins to characterize the scale distribution of hydrometeors and explicitly calculates the particle spectrum distribution and evolution. Compared with the bulk scheme, the bin scheme is not restricted by a given spectral function and is closer to natural images. However, the disadvantage of the bin scheme is that it is computationally intensive and is mostly used in theoretical research. The fact that the current general cloud microphysics scheme does not consider the resolution dependency is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In order to solve the technical problem that general cloud microphysics schemes lack consideration of resolution dependence and there is no scale-adaptive cloud microphysics scheme suitable for high-resolution forecasting and capable of carefully characterizing the grid resolution dependence of cloud physical processes, the present invention provides a method and device for calculating a scale-adaptive cloud microphysics conversion rate.

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

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

[0007] According to the grid distance, cloud amount value and the range of rain coverage in one grid, the sub-grid fitting formula of cloud water content and the sub-grid fitting formula of rain water content are fitted;

[0008] A correction factor is obtained through the cloud water content subgrid fitting formula and the rain water content subgrid fitting formula, and the cloud microphysical process conversion rate is corrected using the correction factor.

[0009] Based on the above technical solution, further, the sub-grid fitting formula of cloud water content and the sub-grid fitting formula of rain water content are fitted according to the grid distance, cloud amount value and the range value of rain water coverage in a grid, specifically including:

[0010] According to the grid distance and cloud amount value, the sub-grid fitting formula of cloud water content is fitted Among them, x is the grid distance, C is the cloud cover value;

[0011] According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

[0012] Based on the above technical solution, further, the correction factor is obtained by using the cloud water content subgrid fitting formula and the rain water content subgrid fitting formula, specifically including:

[0013] The correction factor E:

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

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

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

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

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

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

[0020] The intercept parameter N of the hail particle spectrum distribution is 0g, latent heat of fusion L f , thermal conductivity of air K a , T0=273.15, temperature T, graupel particle spectrum distribution slope parameter λ g , air density-corrected final falling velocity parameter AGN of hail particles, air viscosity coefficient μ k , Schmidt number S c , the final velocity parameter of the falling hail particles BG, the constant pressure specific heat of water C pw The efficiency of raindrops colliding with sleet is P racg Enter the formula

[0021]

[0022] The graupel melting efficiency is obtained.

[0023] In a second aspect, the present invention further provides a device for calculating a scale-adaptive cloud microphysical conversion rate, the device comprising:

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

[0025] The second processing module is used to fit a cloud water content sub-grid fitting formula and a rain water content sub-grid fitting formula according to the grid distance, the cloud amount value and the range value of rain water coverage in a grid;

[0026] The third processing module is used to obtain a correction factor through the cloud water content sub-grid fitting formula and the rain water content sub-grid fitting formula, and use the correction factor to correct the cloud microphysical process conversion rate.

[0027] Based on the above technical solution, further, the second processing module is specifically used to fit the cloud water content sub-grid fitting formula according to the grid distance and cloud amount value. Among them, x is the grid distance, C is the cloud cover value;

[0028] According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

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

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

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

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

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

[0034] Based on the above technical solution, further, the first processing module is specifically used to convert the intercept parameter N of the hail particle spectrum distribution into 0g , latent heat of fusion L f , thermal conductivity of air K a , T0=273.15, temperature T, graupel particle spectrum distribution slope parameter λ g , air density-corrected final falling velocity parameter AGN of hail particles, air viscosity coefficient μ k , Schmidt number S c , the final velocity parameter of the falling hail particles BG, the constant pressure specific heat of water C pw The efficiency of raindrops colliding with sleet is P racg Enter the formula

[0035] The graupel melting efficiency is obtained.

[0036] In a third aspect, the present invention further provides a memory storing a plurality of instructions, wherein the instructions are used to implement a method for calculating a scale-adaptive cloud microphysical conversion rate as described in any one of the above technical solutions.

[0037] In a fourth aspect, the present invention also provides an electronic device, comprising a processor and a memory connected to the processor, wherein the memory stores a plurality of instructions, which can be loaded and executed by the processor so that the processor can execute a method for calculating a scale-adaptive cloud microphysical conversion rate as described in any one of the above technical solutions.

[0038] The present invention provides a method for calculating a scale-adaptive cloud microphysical conversion rate, including using current meteorological forecast parameters to obtain a cloud microphysical process conversion rate, wherein the cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency, and graupel melting efficiency; according to the grid distance, cloud amount value, and the range value of rainwater coverage in a grid, a cloud water content subgrid fitting formula and a rainwater content subgrid fitting formula are fitted; through the cloud water content subgrid fitting formula and the rainwater content subgrid fitting formula, a correction factor is obtained, and the cloud microphysical process conversion rate is corrected using the correction factor. The present invention improves the cloud-rain automatic conversion, rainwater collision and cloud water, and graupel melting processes, and enhances the refined forecasting capability. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] In the attached picture:

[0041] Figure 1 It is a schematic flow chart of a method for calculating a scale-adaptive cloud microphysical conversion rate provided by an embodiment of the present invention;

[0042] Figure 2 A module schematic diagram of a scale-adaptive cloud microphysical conversion rate calculation method and device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0044] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0045] In recent years, with the continuous improvement of computing power, the resolution of numerical weather forecast models has developed towards the convection resolvable scale (kilometer scale) and the large eddy scale (hundred-meter scale). Under this resolution condition, deep convection can be partially resolved by the model, and the terrain characteristics are better expressed, but new problems and challenges are raised for the parameterization of model physical processes, such as the "gray area" problem. The "gray area" refers to the fact that some physical processes cannot be fully resolved or fully parameterized within a certain resolution range (Wyngaard, 2004). Typical examples are cumulus convection and boundary layer and intra-cloud turbulent mixing processes. When the model resolution is in the gray zone of cumulus convection or boundary layer processes, the traditional parameterization scheme will no longer be applicable, and it is urgent to develop a scale-adaptive parameterization scheme suitable for the gray zone scale. Corresponding scale-adaptive parameterization schemes have been developed for the gray zone problems of cumulus convection and boundary layer. For example, Sakradzija et al.

[0046] (2016) developed a random scale-adaptive shallow cumulus convection parameterization scheme for simulating shallow convective gray zones. Shin and Hong (2013) modified the traditional boundary layer parameterization scheme based on the variation of turbulent flux with the model grid spacing and established scale perception. However, the study of scale dependence of cloud microphysical processes is still in its infancy. In fact, the scale sensitivity of cloud microphysical processes has been confirmed by numerical experiments. For example, some studies have pointed out that the entrainment and turbulent mixing processes in clouds are scale-dependent. The higher the model resolution, the better the resolution of the entrainment and turbulent mixing processes, resulting in 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, in the study of snowfall microphysics, it was found that fine resolution can resolve stronger vertical motion, simulated ice nucleation and sleet conversion rates are higher, and ice crystal particles tend to be more spherical (Jensen et al., 2020). There have been preliminary attempts to construct scale-adaptive cloud microphysics schemes. For example, Boutle et al. (2014) developed a scale-adaptive microphysics scheme for warm clouds. The design concept is to use a microphysics scheme suitable for large eddy simulation and upscale the microphysics conversion rate simulated by the scheme to the required grid scale. Numerical simulation experiments have shown that compared with traditional schemes, the precipitation simulated by the scale-adaptive microphysics scheme is more convergent and closer 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 of resolution dependence, and there is no scale-adaptive cloud microphysics scheme that is suitable for high-resolution forecasts and can carefully characterize the grid resolution dependence of cloud physical processes.

[0049] like Figure 1 As shown, the present application proposes a method for calculating a scale-adaptive cloud microphysical conversion rate, the method comprising:

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

[0051] 120. According to the grid distance, cloud amount value and the range of rain coverage in a grid, fit the cloud water content sub-grid fitting formula and the rain water content sub-grid fitting formula;

[0052] 130. Obtain a correction factor through the cloud water content subgrid fitting formula and the rain water content subgrid fitting formula, and use the correction factor to correct the cloud microphysical process conversion rate.

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

[0054] According to the grid distance and cloud amount value, the sub-grid fitting formula of cloud water content is fitted Among them, x is the grid distance, C is the cloud cover value;

[0055] According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

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

[0057] The correction factor E:

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

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

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

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

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

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

[0064] The intercept parameter N of the hail particle spectrum distribution is 0g , latent heat of fusion L f , thermal conductivity of air K a , T0=273.15, temperature T, graupel particle spectrum distribution slope parameter λ g , air density-corrected final falling velocity parameter AGN of hail particles, air viscosity coefficient μ k , Schmidt number S c , the final velocity parameter of the falling hail particles BG, the constant pressure specific heat of water C pw The efficiency of raindrops colliding with sleet is P racg Enter the formula

[0065]

[0066] The graupel melting efficiency is obtained.

[0067] The present embodiment proposes a method for calculating a scale-adaptive cloud microphysical conversion rate, including using current meteorological forecast parameters to obtain a cloud microphysical process conversion rate, wherein the cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency, and hail melting efficiency; according to the grid distance, cloud amount value, and the range value of rainwater coverage in a grid, a cloud water content subgrid fitting formula and a rainwater content subgrid fitting formula are fitted; through the cloud water content subgrid fitting formula and the rainwater content subgrid fitting formula, a correction factor is obtained, and the cloud microphysical process conversion rate is corrected using the correction factor. The present invention improves the cloud-rain automatic conversion, rainwater collision and cloud water, and hail melting processes, and enhances the refined forecasting capability.

[0068] At present, general cloud microphysics schemes lack consideration of resolution dependence. There is no scale-adaptive cloud microphysics scheme that is suitable for high-resolution forecasting and can carefully characterize the grid resolution dependence of cloud physical processes.

[0069] In view of the shortcomings of the above cloud microphysics parameterization scheme, the present invention provides a scale-adaptive cloud microphysics scheme to improve the model's refined forecasting capabilities. Based on the Morrison microphysics scheme in the WRF model, improvements are made to the automatic conversion of clouds and rain, the collision of rainwater with cloud water, and the melting of graupel.

[0070] 1. Introduction to Morrison Microphysics Program

[0071] In addition to forecasting the mixing ratio of water vapor and five water-forming substances, namely, cloud water, rain, cloud ice, snow, and graupel, the Morrison dual-parameter scheme also forecasts the number concentrations of rain, cloud ice, snow, and graupel (Morrison et al., 2009). In this scheme, the particle spectrum distribution of hydrometeors satisfies the Gamma distribution. For raindrops, snow, graupel, and cloud ice, the spectrum shape parameter is 0, and the particle spectrum distribution satisfies the Marshall-Palmer exponential distribution. For cloud droplets, the spectrum shape parameter is related to the predicted cloud droplet number concentration.

[0072] To simplify the calculation, each type of particle is assumed to be spherical and the density is given by Reisner et al. (1998). In the calculation, the minimum value of the mixing ratio of rainwater and snow is set to 0.1 g kg -1 To ensure that rain and snow collide and generate graupel, the minimum value of the cloud water and snow mixing ratio is set to 0.5 g kg -1 and 0.1 g kg -1 , to ensure that cloud water and snow collide and form hail.

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

[0074]

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

[0076] (2) Rainwater meets cloud water (Pra)

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

[0078] Where Pra is the rainwater collision and cloud water efficiency, Q R is the rainwater mixing ratio.

[0079] (3) Graupel melting (Pgmlt)

[0080]

[0081] Where Pgmlt is the graupel melting efficiency, Lf is the latent heat of fusion, N 0g is the intercept parameter of the hail particle spectrum distribution, K a is the thermal conductivity of air, T0 is 273.15, T is the temperature, λ g is the slope parameter of the hail particle spectrum distribution, AGN is the final falling velocity parameter of the hail particles after correction of air density, μ k is the air viscosity coefficient, S c is the Schmidt number, BG is the final velocity parameter of the falling hail particles, C pw is the constant pressure specific heat of water, and Pracg is the efficiency of graupel collision and raindrops.

[0082] 2. Parameterization of sub-grid variations of cloud water and rain water content

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

[0084]

[0085] where σ is the standard deviation of the hydrometeor q, is the grid average.

[0086] f is obtained by fitting the observation data. For cloud water, f c It can be expressed as:

[0087]

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

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

[0090]

[0091] Among them, R is the range of rain coverage within a grid.

[0092] 3. Correction of cloud microphysical conversion rate

[0093] The conversion rate of related cloud microphysical processes is corrected using the fitting formula for the subgrid changes in cloud water, rain water, etc. content obtained in (2).

[0094] Assume that the conversion rate of cloud microphysical processes can be simplified as M = aq b , 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 that the cloud microphysical process conversion rate depends on two variables and there is a correlation (ρ) between the two variables, the cloud microphysical process conversion rate can be simplified as M = aq1 b1 q2 b2 , 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 time, the correction factor E can be expressed as:

[0101]

[0102] Since f is a function of the grid resolution, the correction factor E changes with the resolution, 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, and the present invention is not limited thereto.

[0104] The results of numerical experiments show that the simulation effect of the scale-adaptive cloud microphysics scheme is better than that of the traditional cloud microphysics scheme. As for the simulation results of ground precipitation, the traditional cloud microphysics scheme is very sensitive to the model resolution, while the precipitation rate simulated by the scale-adaptive cloud microphysics scheme is more convergent and closer to observation.

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

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

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

[0108] The second processing module is used to fit a cloud water content sub-grid fitting formula and a rain water content sub-grid fitting formula according to the grid distance, the cloud amount value and the range value of rain water coverage in a grid;

[0109] The third processing module is used to obtain a correction factor through the cloud water content sub-grid fitting formula and the rain water content sub-grid fitting formula, and use the correction factor to correct the cloud microphysical process conversion rate.

[0110] Based on the above embodiment, further, the second processing module is specifically used to fit the cloud water content sub-grid fitting formula according to the grid distance and the cloud amount value. Among them, x is the grid distance, C is the cloud cover value;

[0111] According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

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

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

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

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

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

[0117] Based on the above embodiment, further, the first processing module is specifically used to convert the intercept parameter N of the hail particle spectrum distribution into 0g , latent heat of fusion L f , thermal conductivity of air K a , T0=273.15, temperature T, graupel particle spectrum distribution slope parameter λ g , air density-corrected final falling velocity parameter AGN of hail particles, air viscosity coefficient μ k , Schmidt number S c , the final velocity parameter of the falling hail particles BG, the constant pressure specific heat of water C pw The efficiency of raindrops colliding with sleet is P racg Enter the formula

[0118] The graupel melting efficiency is obtained.

[0119] The present embodiment proposes a scale-adaptive cloud microphysical conversion rate calculation device, including using current meteorological forecast parameters to obtain cloud microphysical process conversion rate, the cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency and hail melting efficiency; according to the grid distance, cloud amount value and the range value of rainwater coverage in a grid, the cloud water content subgrid fitting formula and the rainwater content subgrid fitting formula are fitted; through the cloud water content subgrid fitting formula and the rainwater content subgrid fitting formula, a correction factor is obtained, and the cloud microphysical process conversion rate is corrected using the correction factor. The present invention improves the cloud-rain automatic conversion, rainwater collision and cloud water and hail melting processes, and enhances the refined forecasting capability.

[0120] In a third aspect, the present invention further provides a memory storing a plurality of instructions, wherein the instructions are used to implement a method for calculating a scale-adaptive cloud microphysical conversion rate as described in any one of the above technical solutions.

[0121] In a fourth aspect, the present invention also provides an electronic device, comprising a processor and a memory connected to the processor, wherein the memory stores a plurality of instructions, which can be loaded and executed by the processor so that the processor can execute a method for calculating a scale-adaptive cloud microphysical conversion rate as described in any one of the above technical solutions.

[0122] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of protection of the claims of the present invention.

Claims

1. A scale-adaptive cloud microphysical conversion rate calculation method, characterized in that: The method comprises: Using the current meteorological forecast parameters, the cloud microphysical process conversion rate is obtained, wherein the cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater-cloud water collision efficiency and graupel melting efficiency; According to the grid distance, cloud amount value and the range of rain coverage in one grid, the sub-grid fitting formula of cloud water content and the sub-grid fitting formula of rain water content are fitted; A correction factor is obtained through the cloud water content subgrid fitting formula and the rain water content subgrid fitting formula, and the cloud microphysical process conversion rate is corrected using the correction factor.

2. The method according to claim 1, characterized in that: The step of fitting a sub-grid fitting formula of cloud water content and a sub-grid fitting formula of rain water content according to the grid distance, cloud amount value and the range value of rain water coverage in a grid specifically includes: According to the grid distance and cloud amount value, the sub-grid fitting formula of cloud water content is fitted Where x is the grid distance, C is the cloud cover value; According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

3. The method according to claim 2, characterized in that The correction factor is obtained by using the cloud water content subgrid fitting formula and the rain water content subgrid fitting formula, specifically including: The correction factor E: Wherein, b1 and b2 are constants, and ρ is the correlation value between the cloud water content and the rain water content.

4. The method according to claim 1, characterized in that: The cloud microphysical process conversion rate is obtained by using the current meteorological forecast parameters. The cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency, and graupel melting efficiency. Specifically, it includes: The cloud water mixing ratio Qc, cloud droplet number concentration Nc and air density ρ a Enter the formula The cloud-rain automatic conversion efficiency is obtained.

5. The method according to claim 1, characterized in that: The cloud microphysical process conversion rate is obtained by using the current meteorological forecast parameters. The cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency, and graupel melting efficiency. Specifically, it includes: The rainwater mixing ratio Q R and cloud water mixing ratio Qc input Pra=67(Q C Q R ) 1.15 , and get rain water and cloud water.

6. The method according to claim 1, characterized in that The cloud microphysical process conversion rate is obtained by using the current meteorological forecast parameters. The cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater collision and cloud water efficiency, and graupel melting efficiency. Specifically, it includes: The intercept parameter N of the hail particle spectrum distribution is 0g , latent heat of fusion L f , thermal conductivity of air K a , T0=273.15, temperature T, graupel particle spectrum distribution slope parameter λ g , air density-corrected final falling velocity parameter AGN of hail particles, air viscosity coefficient μ k , Schmidt number S c , the final velocity parameter of the falling hail particles BG, the constant pressure specific heat of water C pw The efficiency of raindrops colliding with sleet is P racg Enter the formula The haiku melting efficiency is obtained.

7. A scale-adaptive cloud microphysical conversion rate calculation device, characterized in that: The device comprises: The first processing module is used to obtain a cloud microphysical process conversion rate using current meteorological forecast parameters, wherein the cloud microphysical process conversion rate includes cloud-rain automatic conversion efficiency, rainwater-cloud water collision efficiency, and graupel melting efficiency; The second processing module is used to fit a cloud water content sub-grid fitting formula and a rain water content sub-grid fitting formula according to the grid distance, the cloud amount value and the range value of rain water coverage in a grid; The third processing module is used to obtain a correction factor through the cloud water content sub-grid fitting formula and the rain water content sub-grid fitting formula, and use the correction factor to correct the cloud microphysical process conversion rate.

8. The device according to claim 7, characterized in that The second processing module is specifically used to fit the cloud water content sub-grid fitting formula according to the grid distance and cloud amount value. Where x is the grid distance, C is the cloud cover value; According to the grid distance and the range of rain coverage within a grid, the sub-grid fitting formula of rain content is fitted. Among them, R is the range value of rain coverage within a grid.

9. A memory, characterized in that: A plurality of instructions are stored, and the instructions are used to implement the method for calculating the scale-adaptive cloud microphysical conversion rate according to any one of claims 1 to 6.

10. An electronic device, characterized in that: It includes a processor and a memory connected to the processor, wherein the memory stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the method for calculating the scale-adapted cloud microphysical conversion rate as described in any one of claims 1 to 6.

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