A method for calculating the uniformity degree of entrainment mixing process in clouds based on remote sensing means

Abstract

The present invention relates to the field of atmospheric science and discloses a method for calculating the uniformity degree of the entrainment mixing process in clouds based on remote sensing means, including: establishing a functional relationship between the parameter b and the uniform mixing degree ψ in the functional relationship (Z-LWC) between the radar reflectivity factor and the water content based on a high-resolution numerical model; establishing a Z-LWC relationship based on remote sensing observations according to the radar reflectivity factor output by the cloud radar and the retrieved water content, and obtaining the value of the parameter b; calculating the uniform mixing degree of the entrainment mixing process according to the value of the parameter b obtained from remote sensing observations and combining the functional relationship between the parameter b and ψ obtained from the numerical model. Through the present invention, the observational study of the entrainment mixing process can be extended from expensive and discontinuous aircraft observational data to economical, continuous, and widely used remote sensing observational data, breaking through the limitations of previous research methods, adding new means for the observational study of the entrainment mixing process, and improving the efficiency of the study of the entrainment mixing process.

Description

Technical Field

[0001] The present invention relates to the field of atmospheric science, and particularly to a method for calculating the uniformity degree of the entrainment mixing process in clouds based on remote sensing means. Background Art

[0002] Clouds are an important part of the Earth system. Changes in the macro and micro physical properties of clouds directly affect the radiation balance, atmospheric motion, and moisture distribution of the Earth system, and thus affect weather and climate. Results of atmospheric numerical models show that a 2-micron decrease in the effective radius of cloud droplets can offset the impact of doubling the carbon dioxide concentration. Among the micro physical processes related to clouds, the turbulent entrainment mixing process between clouds and ambient air has an important impact on the macro and micro physical properties of clouds. Different degrees of entrainment mixing in this process correspond to different cloud micro physical characteristics. In the uniform entrainment mixing mechanism, all cloud droplets in the cloud are in a nearly identical supersaturated / undersaturated environment, and cloud droplets of all scales undergo partial evaporation, resulting in a decrease in the scale of cloud droplets, but the number concentration of cloud droplets remains unchanged. In the non-uniform entrainment mixing mechanism, cloud droplets in contact with dry air will evaporate, and cloud droplets not in contact with ambient air are not affected. In extreme cases, cloud droplets in contact with ambient air will completely evaporate, saturating the ambient air, resulting in a decrease in the number concentration of cloud droplets and an unchanged scale of cloud droplets. At this time, this mechanism is called the extreme non-uniform entrainment mixing mechanism. Grabowski 2006 and Slawinska 2008 respectively found through cloud resolving models (CRM) and large eddy simulations (LES) that in clean and polluted clouds assuming the uniform entrainment mixing mechanism and the non-uniform entrainment mixing mechanism respectively, the solar radiation reaching the ground is almost equal, indicating that the entrainment mixing mechanism has an important impact on the radiation characteristics of clouds.

[0003] Atmospheric numerical models are an important way to study weather and climate. Accurately describing the entrainment mixing mechanism in atmospheric numerical models is crucial for the accuracy of model forecasts and is conducive to improving the model's simulation capabilities for cloud physics, precipitation, and the feedback between clouds and climate. However, the entrainment mixing process occurs at the sub-grid scale, and the resolution of atmospheric numerical models is insufficient to resolve this process. Therefore, it can only be described by parameterization schemes in atmospheric numerical models currently.

[0004] At present, the observational studies on the entrainment mixing mechanism are only limited to using aircraft observational data. Although aircraft sampling is the most direct means of observing cloud microphysical and dynamic characteristics, aircraft observations are restricted by the high cost of detectors, low sampling frequency, small sampling volume, and many uncertainties when aircraft carry out field observation tasks. Therefore, aircraft cannot conduct continuous spatial and temporal observations of clouds, which seriously hinders the further study of the entrainment mixing mechanism. The development of radar detection technology has enabled cloud radars to be widely used in the study of the macroscopic and microscopic physical properties of clouds by remote sensing. Cloud radars can continuously observe clouds with high spatial and temporal resolutions. However, at present, there is no method established to remotely sense the entrainment mixing process in clouds using cloud radar data. Therefore, if a method can be established to study the entrainment mixing process using cloud radar observational data, it will be an important progress in studying the entrainment mixing process by means of observational data. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for calculating the uniformity degree of the entrainment mixing process in clouds based on remote sensing means, including the following steps:

[0006] Step 1, establish a functional relationship between parameters b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model;

[0007] Step 2, establish a Z-LWC relationship based on remote sensing observations according to the radar reflectivity factor output by the cloud radar and the retrieved water content, and obtain the value of parameter b;

[0008] Step 3, according to the value of parameter b obtained from remote sensing observations, combine the functional relationship between parameters b and ψ obtained from the numerical model to obtain the uniform mixing degree of the entrainment mixing process.

[0009] Further, the establishment of the functional relationship between parameters b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model includes:

[0010] Input meteorological sounding observational data into the high-resolution entrainment mixing model to drive the model, and simulate entrainment mixing cases by setting different influencing factors of the entrainment mixing process, and establish the b-ψ functional relationship for the entrainment mixing cases where the cloud has not completely dissipated after experiencing the entrainment mixing process;

[0011] Wherein the influencing factors of the entrainment mixing process include the initial cloud droplet number concentration, the proportion of entrained environmental air, the turbulent kinetic energy dissipation rate, and the height at which the entrainment process occurs; the determination criterion for the cloud not completely dissipating is that the water content is greater than 0.001 g / m³ -3 and the cloud droplet number concentration is greater than 10 cm⁻³ -3 ; ψ is the uniform mixing degree;

[0012] Furthermore, for the entrainment mixing cases where the cloud has not completely dissipated after the entrainment mixing process, the establishment of the b-ψ functional relationship includes:

[0013] According to the cloud droplet spectrum output by the entrainment mixing model, the radar reflectivity factor is calculated using the following formula:

[0014] Z = ∫N(D)D 6 dD

[0015] where Z is the radar reflectivity factor, D is the diameter of the liquid droplet, and N(D) is the concentration of cloud droplets within a unit D interval; according to the cloud droplet spectrum output by the entrainment mixing model, cloud microphysical quantities are calculated, and the cloud microphysical quantities include liquid water content LWC, cloud droplet number concentration n c , volume mean radius r v ;

[0016] According to the Z-LWC functional form, the value of parameter b in the functional form is obtained:

[0017] Z = a * LWC b

[0018] where parameters a and b are constants; the instantaneous Z and LWC values for each case during the entrainment mixing process are fitted using the Z-LWC functional form to obtain the parameter b for the entrainment mixing cases;

[0019] According to the cloud microphysical quantities of the entrainment mixing cases, ψ is obtained:

[0020]

[0021]

[0022] where n a , r va , LWC a are respectively the cloud droplet number concentration, volume mean radius, and liquid water content in the adiabatic cloud. n0 and LWC0 are respectively the cloud droplet number concentration and liquid water content before evaporation after entrainment, and n0 can also be considered as the number concentration under the uniform mixing mechanism; n i is the number concentration under the extremely non-uniform mixing mechanism, n c is the number concentration during the entrainment mixing process, r vh is the volume mean radius after uniform mixing, ρ w is the density of water, LWC f is the liquid water content when the cloud reaches a new equilibrium state during the mixing evaporation process;

[0023] Based on the combination of parameter b and ψ for the entrainment mixing cases, a functional relationship between parameter b and ψ is established:

[0024] ψ = 1.11 * [1 - exp(-2.71 * (b - 1))].

[0025] Further, establishing the Z - LWC relationship based on remote sensing observations according to the basic data output by the cloud radar and the retrieved water content results to obtain the value of parameter b includes:

[0026] Obtaining the radar reflectivity factor according to the basic data output by the cloud radar; obtaining the water content in the cloud according to the method of retrieving water content by the cloud radar.

[0027] If the cloud radar is a single - polarization cloud radar, the water content is:

[0028]

[0029]

[0030] where r0 is the distance from the radar to the cloud base, ri is the i - th segment distance from the cloud base, r is the distance to the cloud top, K is the mass absorption coefficient, b is the parameter b in the Z - LWC functional relationship, Z is the reflectivity factor observed by the radar, and LWP is the cloud water path. i is the distance from the cloud base, r T is the distance to the cloud top, K is the mass absorption coefficient, b is the parameter b in the Z - LWC functional relationship, Z m is the reflectivity factor observed by the radar, and LWP is the cloud water path.

[0031] If the cloud radar is a dual - polarization cloud radar, the water content is:

[0032]

[0033] DFR = dBZ f1 - dBZ f2

[0034] where A l and A g are the absorption coefficients of liquid water and gas respectively, r is the distance, dBZ is the radar echo intensity, and f1, f2 are the frequencies of the dual - wavelength radar respectively.

[0035] Further, obtaining the uniform mixing degree of the entrainment mixing process according to the obtained value of parameter b and combining the functional relationship between the obtained parameter b and ψ includes:

[0036] Taking the parameter b obtained from remote sensing observation data as the independent variable and substituting it into the b - ψ functional relationship to quantitatively calculate ψ, and obtaining the uniform mixing degree of the entrainment mixing process in the cloud based on remote sensing observation data.

[0037] The beneficial effects of the present invention are as follows: The present invention utilizes a high-resolution entrainment mixing model specifically developed for studying the entrainment mixing process to conduct a large number of entrainment mixing case simulations. By observing the changes in microphysical quantities during the entrainment mixing process, a functional relationship between the parameter b and ψ in the Z-LWC functional relationship is first established (i.e., the b-ψ relationship). Subsequently, the parameter b obtained from remote sensing observation data is substituted into the b-ψ relationship to calculate ψ, thereby realizing the quantitative calculation of the uniformity degree of the entrainment mixing process through remote sensing means. This method combines a high-resolution numerical model and remote sensing observation data, enabling the expansion of the observational study of the entrainment mixing process from expensive and discontinuous aircraft observation data to economical, continuous, and widely used remote sensing observation data, breaking through the limitations of previous observational research methods for this process, having strong adaptability, adding a new means to the observational study of the entrainment mixing process, and thus improving the efficiency of the study of the entrainment mixing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of the main steps for implementing this method;

[0039] Figure 2 It is a flow chart of the specific steps for implementing this method;

[0040] Figure 3 It is a schematic diagram of the high-resolution entrainment mixing model;

[0041] Figure 4 It is a graph of the functional relationship (b-ψ) between the parameter b and the degree of uniform mixing based on the high-resolution entrainment mixing model. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the protection scope of the present invention is not limited to the following description.

[0043] The features and performance of the present invention will be further described in detail below with reference to examples.

[0044] As Figure 1 shown, a method for calculating the uniformity degree of the entrainment mixing process in clouds based on remote sensing means includes the following steps:

[0045] Step 1: Establish a functional relationship between the parameter b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model;

[0046] Step 2: Based on the radar reflectivity factor output by the cloud radar and the retrieved water content, establish a Z-LWC relationship based on remote sensing observation and obtain the value of the parameter b;

[0047] Step 3: According to the value of the parameter b obtained from remote sensing observation and in combination with the functional relationship between the parameter b and ψ obtained from the numerical model, obtain the degree of uniform mixing in the entrainment mixing process.

[0048] Establishing the functional relationship between parameters b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model includes:

[0049] Inputting meteorological sounding observation data into the high-resolution entrainment mixing model to drive the model, and simulating entrainment mixing cases by setting different influencing factors of the entrainment mixing process. For the entrainment mixing cases where the cloud has not completely dissipated after experiencing the entrainment mixing process, establish the b-ψ functional relationship;

[0050] Among them, the influencing factors of the entrainment mixing process include the initial cloud droplet number concentration, the proportion of entrained environmental air, the turbulent kinetic energy dissipation rate, and the height at which the entrainment process occurs; the determination criterion for the cloud not completely dissipating is that the water content is greater than 0.001 g / m³ -3 and the cloud droplet number concentration is greater than 10 cm⁻³ -3 ; ψ is the degree of homogeneous mixing;

[0051] Establishing the b-ψ functional relationship for the entrainment mixing cases where the cloud has not completely dissipated after experiencing the entrainment mixing process includes:

[0052] According to the cloud droplet spectrum output by the entrainment mixing model, calculate the radar reflectivity factor using the following formula:

[0053] Z = ∫N(D)D² 6 dD

[0054] where Z is the radar reflectivity factor, D is the diameter of the droplet, and N(D) is the concentration of cloud droplets within the unit D interval; according to the cloud droplet spectrum output by the entrainment mixing model, calculate the cloud microphysical quantities, and the cloud microphysical quantities include the liquid water content LWC, the cloud droplet number concentration n c , the volume mean radius r v ;

[0055] According to the Z-LWC functional form, obtain the value of parameter b in the functional form:

[0056] Z = a*LWCᵇ b

[0057] where the parameters a and b are constants; fit the instantaneous Z and LWC values of each case during the entrainment mixing process using the Z-LWC functional form to obtain the parameter b of the entrainment mixing case;

[0058] According to the cloud microphysical quantities of the entrainment mixing case, obtain ψ:

[0059]

[0060]

[0061] Among them, n a , r va , LWC a are the number concentration, volume mean radius, and water content of cloud droplets in an adiabatic cloud, respectively. n0 and LWC0 are the number concentration and water content of cloud droplets before evaporation after entrainment, and n0 can also be considered as the number concentration under the homogeneous mixing mechanism; n i is the number concentration under the extremely inhomogeneous mixing mechanism, n c is the number concentration during the entrainment mixing process, r vh is the volume mean radius after homogeneous mixing, ρ w is the density of water, LWC f is the water content when the cloud reaches a new equilibrium state during the mixing evaporation process;

[0062] Based on the combination of the parameters b and ψ of the entrainment mixing case, establish the functional relationship between the parameters b and ψ:

[0063] ψ = 1.11 * [1 - exp(-2.71 * (b - 1))]

[0064] The above-mentioned method of establishing the Z - LWC relationship based on remote sensing observations and obtaining the value of parameter b according to the basic data output by the cloud radar and the retrieved water content results includes:

[0065] Based on the basic data output by the cloud radar, obtain the radar reflectivity factor; according to the method of retrieving the water content by the cloud radar, obtain the water content in the cloud;

[0066] If the cloud radar is a single - polarization cloud radar, the water content is:

[0067]

[0068]

[0069] Among them, r0 is the distance from the radar to the cloud base, r i is the i - th distance from the cloud base, r T is the distance to the cloud top, K is the mass absorption coefficient, b is the parameter b in the Z - LWC functional relationship, Z m is the reflectivity factor observed by the radar, and LWP is the cloud water path;

[0070] If the cloud radar is a dual - polarization cloud radar, the water content is:

[0071]

[0072] DFR = dBZ f1 - dBZ f2

[0073] Among them, A l , Ag are the absorption coefficients of liquid water and gas respectively, r is the distance, dBZ is the radar echo intensity, and f1 and f2 are the frequencies of the dual-wavelength radar respectively.

[0074] The degree of uniform mixing in the entrainment mixing process is obtained by combining the value of parameter b obtained from remote sensing observations with the functional relationship between parameter b and ψ obtained from the numerical model, including:

[0075] Taking the parameter b obtained from remote sensing observation data as the independent variable and substituting it into the b-ψ functional relationship to quantitatively calculate ψ, thereby obtaining the degree of uniform mixing in the entrainment mixing process in clouds based on remote sensing observation data.

[0076] Specifically, the technical solution adopted in the present invention is as follows: First, a high-resolution entrainment mixing model is used to conduct a large number of entrainment mixing case simulations. Using the cloud droplet spectrum output by the model, the radar reflectivity factor (Z) and cloud water content (LWC) are calculated, and a Z-LWC functional relationship based on the numerical model is established. Second, the parameter b in the Z-LWC relationship of the model is combined with ψ calculated from the cloud droplet spectrum to establish a functional relationship between b and ψ (i.e., the b-ψ relationship); Third, using cloud radar remote sensing observation means, the radar reflectivity factor and cloud water content are obtained, and a Z-LWC functional relationship based on remote sensing observation is established; Finally, taking the parameter b in the Z-LWC relationship of remote sensing as the independent variable and substituting it into the b-ψ relationship to calculate the value of ψ, so as to realize the calculation of the degree of uniform mixing in the entrainment mixing process through remote sensing observation means, breaking through the limitation that the observational research on the entrainment mixing process is only carried out based on aircraft observation data. The specific steps include:

[0077] (1) Establish a b-ψ functional relationship based on numerical simulation: First, use a high-resolution entrainment mixing model, set a large number of sensitivity experiments, and calculate Z, LWC, and ψ respectively according to the cloud droplet spectrum output by the model; Second, use the calculated Z and LWC to establish a Z-LWC functional relationship; Finally, combine the parameter b in the Z-LWC functional relationship with the calculated ψ to establish a functional relationship between parameter b and ψ (i.e., the b-ψ relationship).

[0078] (2) Establish a Z-LWC functional relationship based on remote sensing observation and obtain the value of parameter b in this functional relationship: First, obtain Z according to the basic data output by the cloud radar; Second, obtain LWC according to the cloud radar water content inversion method; Finally, combine the obtained Z and LWC to establish a Z-LWC functional relationship based on remote sensing observation and obtain the value of parameter b in remote sensing observation.

[0079] (3) Calculate the uniformity degree of the entrainment mixing process: Use the parameter b value based on remote sensing observations in step (2) as the independent variable and substitute it into the b-ψ functional relationship in step (1) to directly calculate ψ, obtaining the uniformity degree of the entrainment mixing process and realizing the study of the entrainment mixing mechanism using remote sensing means.

[0080] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, in conjunction with the attached Figure 2 , the present invention is further described in detail.

[0081] Step 1: Establish a functional relationship (i.e., b-ψ relationship) between parameter b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model.

[0082] To establish the relationship between parameter b and ψ in the Z-LWC relationship, the present invention uses a high-resolution entrainment mixing model (EMPM) to conduct a large number of simulations on the entrainment mixing process in clouds. This model is a high-resolution model specially developed by Krueger et al. for studying the entrainment mixing process, with a resolution of approximately 1 mm. The attached Figure 3 shows a schematic diagram of the high-resolution entrainment mixing model. In this model, the cloud is first adiabatically lifted and stops lifting after reaching the entrainment height. At the entrainment height, environmental air enters the cloud through entrainment and an isobaric mixing process occurs. A part of the cloud at a random position in the cloud is replaced by the same-sized entrained environmental air and is entrained out. Under the action of a finite-rate turbulent dissipation rate, the entrained unsaturated environmental air can undergo turbulent deformation, break into environmental air blocks of different sizes, and be randomly distributed in the cloud. Cloud droplets can grow or evaporate accordingly according to the change of the surrounding supersaturation.

[0083] First, input the meteorological sounding observation data in the EMPM to drive the model, and by setting different influencing factors of the entrainment mixing process (initial cloud droplet number concentration, proportion of entrained environmental air, turbulent kinetic energy dissipation rate, height at which the entrainment process occurs), a total of more than 23,000 entrainment mixing cases are simulated, and among them, 12,218 cases where the cloud does not completely dissipate (water content is greater than 0.001 g / m -3 and the cloud droplet number concentration is greater than 10 cm -3 ) are used to establish the b-ψ functional relationship. To make the simulation results have better statistics, different random seeds are further used for each of these 12,218 cases and looped 10 times. The parameter settings in the EMPM are shown in Table 1.

[0084] Table 1 Parameter settings in the entrainment mixing model

[0085]

[0086] Secondly, according to the cloud droplet spectrum output by the entrainment mixing mode, the radar reflectivity factor is calculated using the following formula:

[0087] Z = ∫N(D)D 6 dD (1)

[0088] where Z is the radar reflectivity factor, D is the diameter of the liquid droplet, and N(D) is the concentration of cloud droplets within a unit D interval.

[0089] Then, again according to the cloud droplet spectrum output by the entrainment mixing mode, the liquid water content LWC, cloud droplet number concentration n c , volume mean radius r v and other cloud microphysical quantities are calculated. Using the following Z-LWC functional form, the value of parameter b in this functional form is obtained:

[0090] Z = a*LWC b (2)

[0091] where the parameters a and b are constants. The Z and LWC values for each instantaneous case during the entrainment mixing process are fitted using the Z-LWC functional form, and the parameter b for 12,218 entrainment mixing cases is obtained.

[0092] Again, based on the cloud microphysical quantities of the entrainment mixing cases, ψ is calculated for 12,218 cases:

[0093]

[0094]

[0095] where n a , r va , LWC a are respectively the cloud droplet number concentration, volume mean radius, and liquid water content in the adiabatic cloud. n0 and LWC0 are respectively the cloud droplet number concentration and liquid water content before evaporation after entrainment, and n0 can also be considered as the number concentration under the uniform mixing mechanism; n i is the number concentration under the extremely non-uniform mixing mechanism, n c is the number concentration during the entrainment mixing process, r vh is the volume mean radius after uniform mixing, ρ w is the density of water, LWC f is the liquid water content when the cloud reaches a new equilibrium state during the mixing evaporation process;

[0096] Finally, the parameters b and ψ for 12,218 entrainment mixing cases are combined to establish a functional relationship between parameter b and ψ (i.e., the b-ψ relationship), as shown in the appendix Figure 4 as follows:

[0097] ψ = 1.11 * [1 - exp(-2.71 * (b - 1))] (4)

[0098] There are a total of 12,218 cases in this figure. Each case is looped 10 times using different random seeds. The contour lines represent the joint probability density function (PDF) of ψ and the parameter b. The black dots and error bars represent the mean and standard deviation of ψ in each parameter b bin, respectively. The weighted least squares method is used to fit the mean values, where the number of data points in each parameter b bin is used as the weight, and the fitting equation, coefficient of determination R 2 and p-value are given.

[0099] Step 2: Based on the output base data of the cloud radar and the retrieved water content results, establish the Z-LWC relationship based on remote sensing observations and obtain the parameter b value.

[0100] Among the base data directly output by the cloud radar, there are radar reflectivity factor, radial velocity, and spectral width of velocity. Therefore, first, the radar reflectivity factor can be directly obtained according to the base data directly output by the cloud radar; second, according to the method of retrieving water content by the cloud radar, the water content in the cloud is obtained. For a single-polarization cloud radar, the parameter adaptive method proposed by Ge et al. in 2023 based on the mass absorption characteristics of a single-wavelength radar can be used to retrieve the water content:

[0101]

[0102]

[0103] where r0 is the distance from the radar to the cloud base, r i is the i-th distance from the cloud base, r T is the distance to the cloud top, K is the mass absorption coefficient, b is the parameter b in the Z-LWC functional relationship, Z m is the reflectivity factor observed by the radar, and LWP is the cloud water path.

[0104] For a dual-polarization cloud radar, the method proposed by Socuellamos et al. in 2024 based on the difference in radar reflectivity factors (DFR) of two wavelengths can be used to retrieve the water content:

[0105]

[0106] DFR = dBZ f1 -dBZ f2 (6b)

[0107] where A l ,A gare the absorption coefficients of liquid water and gas respectively, r is the distance, dBZ is the radar echo intensity, and f1 and f2 are the frequencies of the dual-wavelength radar respectively. Finally, the data is fitted using the Z-LWC functional form, and the value of the parameter b in the functional form is obtained.

[0108] Step 3: Use the b-ψ functional relationship in formula (4) to quantitatively calculate the degree of uniform mixing in the entrainment mixing process.

[0109] Substitute the parameter b obtained using remote sensing observation data in Step 2 as the independent variable into the b-ψ functional relationship (i.e., formula 4) in Step 1 to quantitatively calculate ψ, thereby realizing the quantitative calculation of the degree of uniform mixing in the entrainment mixing process in the cloud using remote sensing observation data.

Claims

1. A method for calculating the uniformity degree of entrainment mixing process in clouds based on remote sensing means, comprising the following steps: Step 1, establish a functional relationship between parameter b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model; Step 2, establish a Z-LWC relationship based on remote sensing observations according to the radar reflectivity factor output by the cloud radar and the retrieved water content, and obtain the value of parameter b; Step 3, calculate the uniform mixing degree of the entrainment mixing process according to the value of parameter b obtained from remote sensing observations and the functional relationship between parameter b and ψ obtained from the numerical model; The establishment of the functional relationship between parameter b and ψ in the Z-LWC functional relationship based on a high-resolution numerical model includes: Input meteorological sounding data into a high-resolution entrainment mixing model to drive the model, and simulate entrainment mixing cases by setting different influencing factors of the entrainment mixing process. For entrainment mixing cases where the cloud has not completely dissipated after experiencing the entrainment mixing process, establish the b-ψ functional relationship; Among them, the influencing factors of the entrainment mixing process include the initial cloud droplet number concentration, the proportion of entrained ambient air, the turbulent kinetic energy dissipation rate, and the height at which the entrainment process occurs; the determination criterion for the cloud not being completely dissipated is that the water content is greater than 0.001 g / m -3 and the cloud droplet number concentration is greater than 10 cm -3 ; the ψ is the degree of uniform mixing; The establishment of the b-ψ functional relationship for entrainment mixing cases where the cloud has not completely dissipated after experiencing the entrainment mixing process includes: According to the cloud droplet spectrum output by the entrainment mixing model, calculate the radar reflectivity factor using the following formula: Z = ∫N(D)D 6 dD Where Z is the radar reflectivity factor, D is the diameter of the droplet, and N(D) is the concentration of cloud droplets within a unit D interval; cloud microphysical quantities are calculated based on the cloud droplet spectrum output by the entrainment mixing model, and the cloud microphysical quantities include the liquid water content LWC, the cloud droplet number concentration n c , the volume mean radius r v ; Obtain the value of parameter b in the functional form according to the Z-LWC functional form; Z = a * LWC b Among them, parameters a and b are constants; fit the instantaneous Z and LWC values of each case during the entrainment mixing process using the Z-LWC functional form to obtain the parameter b of the entrainment mixing case; Obtain ψ according to the cloud microphysical quantities of the entrainment mixing case; where n a , r va , and LWC a are the number concentration, volume mean radius, and water content of cloud droplets in an adiabatic cloud, respectively; n0 and LWC0 are the number concentration and water content of cloud droplets before evaporation after entrainment, and n0 is the number concentration of cloud droplets under a homogeneous mixing mechanism; n i is the number concentration under an extremely inhomogeneous mixing mechanism, n c is the number concentration during the entrainment mixing process, r vh is the volume mean radius after homogeneous mixing, ρ w is the density of water, and LWC f is the water content when the cloud reaches a new equilibrium state during the mixing evaporation process; Establish a functional relationship between parameter b and ψ according to the combination of parameter b and ψ of the entrainment mixing case; ψ = 1.11 * [1 - exp(-2.71 * (b - 1))].

2. The method for calculating the uniformity degree of the entrainment mixing process in clouds based on remote sensing means according to claim 1, wherein The establishment of the Z-LWC relationship based on remote sensing observations according to the radar reflectivity factor output by the cloud radar and the retrieved water content, and obtaining the value of parameter b includes: Obtain the radar reflectivity factor according to the basic data output by the cloud radar; obtain the water content in the cloud according to the method of retrieving the water content by the cloud radar; If the cloud radar is a single-polarization cloud radar, the water content is: Among them, r0 is the distance from the radar to the cloud base, r i is the i-th segment distance from the cloud base, r T is the distance to the cloud top, K is the mass absorption coefficient, b is the parameter b in the Z-LWC functional relationship, Z m is the reflectivity factor observed by the radar, and LWP is the cloud water path; If the cloud radar is a dual-polarization cloud radar, the water content is: DFR = dBZ f1 -dBZ f2 Among them, A l , A g are the absorption coefficients of liquid water and gas respectively, r is the distance, dBZ is the radar echo intensity, and f1 and f2 are the frequencies of the dual-wavelength radar respectively.

3. A method for calculating the uniformity degree of entrainment mixing process in clouds based on remote sensing means according to claim 1, characterized in that The obtaining of the uniform mixing degree of the entrainment mixing process according to the value of parameter b obtained from remote sensing observations and the functional relationship between parameter b and ψ obtained from the numerical model includes: Take the parameter b obtained from remote sensing observation data as the independent variable, substitute it into the b-ψ functional relationship, quantitatively calculate ψ, and obtain the uniform mixing degree of the entrainment mixing process in the cloud based on remote sensing observation data.

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