Artificial snow increasing effect evaluation method

Abstract

The invention discloses an artificial snow increasing effect evaluation method, belongs to the technical field of meteorological engineering, combines MOS correction, radar assimilation, micro-physical diagnosis and cold cloud catalysis simulation to construct a simulation-catalysis-verification full-chain artificial snow increasing evaluation system, and is suitable for artificial snow increasing operation optimization and effect verification in a complex terrain area. According to the method, radar data is assimilated through a mesoscale mode to optimize forecast, catalytic simulation and actual operation comparative analysis are combined, the snow increasing effect is obtained through quantification, the artificial snow increasing efficiency is further improved, and the method can be expanded to other artificial influence weather scenes such as rain increasing and hail suppression.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of meteorological engineering, and particularly relates to a design of an artificial snowfall effect evaluation method. BACKGROUND

[0002] Artificial snowfall promotes ice crystal formation by seeding catalyst into cloud layers to increase snowfall. In the prior art, the catalysis scheme depends on experience, and lacks dynamic optimization based on high-precision numerical simulation. In particular, in complex terrain areas, due to the changeable weather conditions, the traditional method has the problem of inaccurate selection of catalysis time and height. Although mesoscale models are used for weather forecasting, a method of combining three-dimensional variational assimilation to optimize the initial field and integrating a cold cloud catalysis module for dynamic simulation and evaluation has not been reported.

[0003] In traditional artificial snowfall operations, the initial field error and the insufficient simulation of microphysical processes under complex terrain result in a large deviation in the selection of catalytic parameters (such as seeding height and dosage). In the prior art, three-dimensional variational assimilation (3D-VAR) has not been effectively combined to optimize the initial field, and there is a lack of double-parameterization modeling of solid water condensate (such as snow and graupel), which limits the prediction accuracy of snowfall. SUMMARY

[0004] The purpose of the present application is to provide an artificial snowfall effect evaluation method, which combines numerical weather prediction, data assimilation and cold cloud catalysis simulation, and is suitable for artificial snowfall operation optimization and effect verification in complex terrain areas.

[0005] The technical scheme of the present application is as follows: an artificial snowfall effect evaluation method, comprising the following steps: S1, selecting snowfall cases in the target area in the past 10 years, using WRF model to simulate multiple parameterization schemes, comparing with the measured snowfall data, and selecting the scheme with the smallest error as the model output.

[0006] S2, MOS correction is performed on the model output to reduce the system bias and obtain the corrected result.

[0007] S3, based on the corrected result, Thompson double-parameter microphysical scheme is used to simulate snowfall to obtain the simulation result before assimilation.

[0008] S4, WRF-3DVAR is used to assimilate the radar data of the target area, and Thompson double-parameter microphysical scheme is used to simulate snowfall to obtain the simulation result after assimilation.

[0009] S5, snowfall potential analysis is performed on the target area to select the appropriate catalysis area.

[0010] S6, AgI cold cloud catalysis coupled with Thompson double-parameter microphysical scheme is used in the appropriate catalysis area to obtain the simulation result after catalysis.

[0011] S7. Based on the simulation results before assimilation, after assimilation, and after catalysis, the snow enhancement effect is quantified through comparative analysis.

[0012] Furthermore, the Thompson two-parameter microphysics scheme in step S3 includes solid particle modeling, ice crystal growth mechanism, graupel particle distribution optimization, cumulus convection scheme, radiation scheme, boundary layer scheme and land surface process scheme.

[0013] Furthermore, the solid particle modeling adopts the raindrop spectrum Gamma particle size distribution function, and the specific formula is: in represents the raindrop spectrum Gamma particle size distribution function, D represents the equivalent diameter of snow particles, represents the intercept parameter, represents the shape parameter, represents the slope parameter.

[0014] Furthermore, the ice crystal growth mechanism is specifically as follows: the sublimation process is triggered when the ice crystal diameter exceeds 200μm, and a variable snow growth efficiency parameter is introduced to match the Doppler radar observation data.

[0015] The optimization of hail particle distribution is specifically as follows: real-time calculation of the terminal falling velocity of hail particles and correction of solid precipitation simulation errors based on the vertical wind field.

[0016] The cumulus convection scheme is as follows: the Kain-Fritsch scheme is used for coarse grids, and the cumulus parameterization is turned off for fine grids to directly resolve convection.

[0017] The specific radiation schemes are: RRTM scheme is used for long-wave radiation, and Dudhia scheme is used for short-wave radiation to accurately simulate the surface energy balance.

[0018] The specific boundary layer solution is: using the YSU solution to optimize the calculation of turbulent vertical diffusion.

[0019] The specific land surface process scheme is: using the Noah scheme to dynamically simulate the snow stratification and soil freezing effects.

[0020] Furthermore, the objective function of assimilating the radar data of the target area using WRF-3DVAR in step S4 is: in represents the cost function, Represents a variable, represents the cost function of the background field, represents the cost function of the observation field, background field of the representation mode, background error covariance matrix, observed data or observation, observation operator, observation error covariance matrix, superscript denotes the transpose of a matrix.

[0021] Further, the background error covariance matrix is calculated by the formula: wherein denotes the 24 h forecast field from the same time, denotes the 12 h forecast field from the same time.

[0022] Further, the solid particle modeling of the Thompson double parameter microphysical scheme in step S4 adopts a raindrop spectrum Gamma particle size distribution function, and dynamically calculates the mass concentration and the number concentration : wherein denotes the raindrop spectrum Gamma particle size distribution function, D denotes the equivalent diameter of snow particles, , , and are snow particle distribution constants, is a shape parameter of the snow particle spectrum Gamma distribution, denotes the n th moment of the spectrum distribution, when , it is the number concentration , when , it is the mass concentration , denotes the particle capability, denotes the energy spectrum distribution function, denotes the minimum energy, denotes the maximum energy.

[0023] Further, step S5 includes the following sub-steps: S51, analyze the vertical temperature stratification, water vapor flux, supercooled water content and wind field of the target region in the target period to determine the catalytic potential region.

[0024] S52, the Thompson double parameter microphysical scheme is used to simulate the ice crystal and snowflake concentration distribution in the cloud in the catalytic potential area, and the suitable catalytic area is identified.

[0025] Further, the AgI cold cloud catalysis coupled with the Thompson double parameter microphysical scheme in step S6 is specifically: using the catalytic mode of precise high-altitude sowing by the unmanned aerial vehicle and continuous diffusion by the ground smoke furnace, the influence of different catalytic rates on the snowfall amount is simulated to determine the optimal sowing time, height and dosage.

[0026] Further, the catalytic rate calculation formula is: wherein represents the catalytic rate, and are model parameters, represents the ice core activation number concentration, represents the reference ice core, represents the temperature and humidity adjustment factor, represents the temperature, represents the humidity.

[0027] The beneficial effects of the present application are: (1) The present application combines MOS correction, radar assimilation, microphysical diagnosis and cold cloud catalysis simulation to build a full-chain artificial snowfall evaluation system of "simulation-catalysis-verification", which is suitable for optimization and effect verification of artificial snowfall operation in complex terrain areas.

[0028] (2) The present application first applies WRF-3DVAR to assimilate radar data for artificial snowfall initial field optimization, which improves the accuracy of the initial conditions of catalytic simulation.

[0029] (3) The present application uses AgI cold cloud catalysis coupled with the Thompson double parameter microphysical scheme for multi-scenario simulation, realizes dynamic optimization of catalytic parameters (time, height, dosage), reduces the cost of catalyst dosage, quantifies the catalytic effect through numerical simulation, reduces blind operation, and improves the catalytic accuracy.

[0030] (4) The present application designs the catalytic mode of precise high-altitude sowing by the unmanned aerial vehicle and continuous diffusion by the ground smoke furnace for complex mountainous terrain, solves the problem of low catalytic diffusion efficiency, and can be extended to other artificial weather modification scenarios (such as rain enhancement and hail prevention). DETAILED DESCRIPTION

[0031] Figure 1 The present application provides an artificial snowfall effect evaluation method flow chart.

[0032] ​Exemplary embodiments of the present application will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments illustrated and described herein are only exemplary and are not intended to limit the scope of the present application, which is defined by the appended claims.

[0033] The embodiment of the present application provides a method for evaluating artificial snow increasing effect, as shown in the figure, comprising the following steps S1-S7: Figure 1 S1, select snowfall cases in the target area in the past 10 years, use WRF mode to simulate multi-parameterization scheme combination (such as microphysics and cumulus convection scheme), compare with measured snowfall data, and select the scheme with the smallest error as the mode output.

[0034] The WRF mode (Weather Research and Forecasting Model) is a new generation of mesoscale numerical weather prediction system jointly developed by the U.S. Atmospheric Research Center (NCAR) and other institutions, which has high resolution, non-static balance and modular expansion capability, and is widely used in meteorological research, regional climate simulation and environmental science.

[0035] S2, MOS (Model Output Statistics, MOS) correction is performed on the mode output to reduce system bias and obtain a corrected result.

[0036] MOS correction is a technology for improving weather prediction accuracy by combining numerical mode prediction and statistical methods, which is widely used in the prediction of precipitation, temperature, wind and other elements, and can effectively improve the prediction accuracy.

[0037] S3, based on the corrected result, a Thompson double-parameter microphysics scheme is used to simulate snowfall to obtain a simulation result before assimilation.

[0038] In the embodiment of the present application, the Thompson double-parameter microphysics scheme in step S3 includes solid particle modeling, ice crystal growth mechanism, graupel particle distribution optimization, cumulus convection scheme, radiation scheme, boundary layer scheme and land surface process scheme.

[0039] The solid particle modeling adopts a raindrop spectrum Gamma particle size distribution function, and the specific formula is: wherein represents the raindrop spectrum Gamma particle size distribution function, D represents the equivalent diameter (unit: mm) of snow particles, which is usually defined as the volume equivalent diameter or the maximum projection size; represents the intercept parameter (unit: ), reflecting the particle number concentration reference value,​ denotes the shape parameter, controlling the degree of the distribution curve, degenerates into an exponential distribution (Marshall-Palmer type) when reduces the concentration of small particles, and is more consistent with the actual observation; denotes the slope parameter (unit: mm -1 ), which is inversely proportional to the average size of the particles .

[0040] The ice crystal growth mechanism is specifically set as follows: the sublimation process is triggered when the ice crystal diameter exceeds 200 μm, and a variable snow growth efficiency parameter is introduced to match the Doppler radar observation data.

[0041] The graupel particle distribution optimization is specifically set as follows: the terminal falling velocity of graupel particles is calculated in real time, and the vertical wind field is combined to correct the solid precipitation simulation error.

[0042] The cumulus convection scheme is specifically set as follows: the Kain-Fritsch scheme is adopted for coarse grids, and the cumulus parameterization is turned off for fine grids, and the convection is directly analyzed. The upward and downward motions in the cumulus can be directly solved, the Kain-Fritsch scheme is a mass flux type, which can be tested and adjusted in the model, a simple cloud model containing water vapor rising and falling processes is adopted, including entrainment and outflow, and relatively rough microphysical processes.

[0043] The radiation scheme is specifically set as follows: the RRTM scheme is selected for long-wave radiation, and the Dudhia scheme is selected for short-wave radiation, to accurately simulate the surface energy balance. The long-wave radiation is used to calculate the upward and downward long-wave radiation fluxes under clear sky and cloudy conditions, the RRTM scheme considers molecular species including carbon dioxide, nitrogen dioxide, methane, water vapor, ozone, etc., and details the long-wave processes caused by these molecules. The short-wave radiation is an important part of the surface energy balance, and the Dudhia scheme considers the scattering, reflection and absorption processes of the sun, water vapor and clouds.

[0044] The boundary layer scheme is specifically set as follows: the YSU scheme is adopted to optimize the calculation of turbulent vertical diffusion. The purpose of the boundary layer parameterization scheme is to distribute the surface fluxes by using the eddy fluxes, and to consider the influence of the entrainment process on the growth of the boundary layer, to calculate the vertical diffusion caused by turbulence, and the YSU scheme is widely used because it can simulate reasonable results under general atmospheric conditions.

[0045] The land surface process scheme is specifically set as follows: the Noah scheme is adopted to dynamically simulate the stratification of snow and the freezing effect of soil. The land surface process is driven by the surface energy and water vapor fluxes, and can predict the snow water equivalent and stratification of snow on the ground, and the Noah scheme can predict the freezing of soil and the influence of snow.

[0046] S4, adopt WRF-3DVAR to assimilate radar data of a target region, and adopt a Thompson two-parameter microphysical scheme to simulate snowfall, to obtain a simulated result after assimilation.

[0047] In the embodiment of the application, a target function for adopting WRF-3DVAR to assimilate radar data of a target region is as follows: Wherein represents a cost function, represents a variable, is defined as an optimal estimation obtained by assimilation, and is an analysis field closest to an actual atmospheric state; represents a cost function of a background field, represents a cost function of an observation field, represents a background field of a model, represents a background error covariance matrix, represents observation data or an observation value, represents an observation operator, which is a key function, and is used to bridge a model space and an observation space, represents an observation error covariance matrix, which describes uncertainty of observation data, and is denoted by a superscript represents a transpose of a matrix.

[0048] In the embodiment of the application, an NMC method is used to estimate a form of the background error covariance matrix to optimize an initial field, and to improve accuracy of an initial condition of subsequent catalytic simulation: Wherein represents a 24 h prediction field reported from the same time, represents a 12 h prediction field reported from the same time. A control variable transformation is used to reduce a calculation dimension and a calculation complexity.

[0049] In the embodiment of the application, in a data assimilation process, a multi-level nested grid (for example, 3 levels) is defined, a highest resolution reaches 1 km, and an influence of a terrain on snowfall is finely described.

[0050] In the embodiment of the application, in step S4, a raindrop spectrum Gamma particle size distribution function is used for solid particle modeling of the Thompson two-parameter microphysical scheme, and a mass concentration is dynamically calculated and a number concentration : Wherein represents a raindrop spectrum Gamma particle size distribution function, D represents an equivalent diameter of a snow particle, , 、 and are all snow particle distribution constants. In the embodiment of the present invention, , , , , is the shape parameter of the Gamma distribution of snow particle spectrum, The spectral distribution of n moment, when Number concentration ,when Mass concentration , Indicates particle capacity, represents the energy spectrum distribution function (i.e. the number of particles in a unit energy interval), represents the minimum energy, Indicates the maximum energy.

[0051] In addition, in the Thompson two-parameter microphysics scheme, when the ice crystal diameter grows beyond 200 μm, water vapor condenses into cloud ice particles, and the snow growth efficiency is a variable parameter.

[0052] S5. Analyze the snow-increasing potential of the target area and select suitable catalytic areas.

[0053] Step S5 includes the following sub-steps S51-S52: S51. Analyze the vertical temperature stratification, water vapor flux, supercooled water content, and wind field in the target area during the target period to determine the catalytic potential area.

[0054] S52. In the catalytic potential area, the Thompson two-parameter microphysics scheme is used to simulate the concentration distribution of ice crystals and snowflakes in the cloud to identify the suitable catalytic area.

[0055] S6. AgI cold cloud catalysis coupled with Thompson's dual-parameter microphysics scheme was used in the suitable catalytic region to obtain the simulation results after catalysis.

[0056] In an embodiment of the present invention, a catalytic method of coordinated operation of precise high-altitude spreading by drones and continuous diffusion by ground smoke stoves is adopted to adapt to complex mountainous terrain, and the effects of different catalytic rates (for example, 1×10^14 particles / g and 5×10^14 particles / g) on ​​snowfall are simulated to determine the optimal spreading time (for example, the peak period of supercooled water in the cloud layer), altitude (for example, near the -10℃ layer) and dosage.

[0057] In the embodiment of the present invention, the catalytic efficiency calculation formula is: in represents a catalytic rate, and are both model parameters (need to be calibrated, usually ), represents the ice nuclei activation number concentration, represents the reference ice nuclei (usually taken ), represents the temperature and humidity adjustment factor, represents the temperature, represents the humidity.

[0058] S7, based on the simulation results before assimilation, simulation results after assimilation and simulation results after catalysis, the snow increasing effect is quantified by comparative analysis.

[0059] In the embodiments of the present application, based on the simulation results before assimilation, simulation results after assimilation and simulation results after catalysis, the radar reflectivity factor and echo area change of the target area before and after assimilation and before and after catalysis are compared, and the snow increasing effect is quantified (such as snow amount increasing by 15%-30%) combined with the ground snow amount monitoring results of the target area.

[0060] Taking the snow disaster emergency snow increasing of Liuchun Lake in Zhejiang in February 2024 as an example, the artificial snow increasing effect evaluation method provided by the embodiments of the present application is adopted: (1) After assimilating the radar data, the Thompson double parameter microphysics + MYNN boundary layer scheme combination is selected, and the simulation error is reduced by 22%.

[0061] (2) The water vapor flux front zone of the process on February 29 is located at 700 hPa, the best catalytic time period is UTC 03:00-06:00, and the scattering height is 3.5 km (-8 ℃ layer).

[0062] (3) The simulation shows that when the catalytic rate is 5×10^14 particles / g, the snow amount increases by the largest (28%).

[0063] (4) After the actual operation, the radar echo area is expanded by 15%, and the ground monitored snow amount is increased by 25%, which is consistent with the simulation results.

[0064] Those skilled in the art will appreciate that the embodiments described herein are intended to help the reader understand the principles of the present application and should be understood as not limiting the scope of protection of the present application to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations according to the technical inspirations disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the scope of protection of the present application.

Claims

1. A method for evaluating the effect of artificial snowmaking, characterized in that: The following steps are involved: S1. Select snowfall cases in the target area over the past 10 years, use the WRF model to simulate a combination of multi-parameter schemes, compare them with the measured snowfall data, and select the scheme with the smallest error as the model output; S2. Perform MOS correction on the model output to reduce the system deviation and obtain the correction result; S3. Based on the revised results, the Thompson two-parameter microphysics scheme is used to simulate snowfall and obtain the simulation results before assimilation. S4. Use WRF-3DVAR to assimilate radar data of the target area and use the Thompson two-parameter microphysics scheme to simulate snowfall and obtain the assimilated simulation results; S5. Analyze the snow-increasing potential of the target area and select suitable catalytic areas; S6. AgI cold cloud catalysis coupled with Thompson's two-parameter microphysics scheme was used in the suitable catalytic region to obtain the simulation results after catalysis. S7. Based on the simulation results before assimilation, after assimilation, and after catalysis, the snow enhancement effect is quantified through comparative analysis.

2. The artificial snowmaking effect evaluation method according to claim 1, characterized in that: The Thompson two-parameter microphysics scheme in step S3 includes solid particle modeling, ice crystal growth mechanism, graupel particle distribution optimization, cumulus convection scheme, radiation scheme, boundary layer scheme and land surface process scheme.

3. The artificial snowmaking effect evaluation method according to claim 2, characterized in that: The solid particle modeling adopts the raindrop spectrum Gamma particle size distribution function, and the specific formula is: in represents the raindrop spectrum Gamma particle size distribution function, D represents the equivalent diameter of snow particles, represents the intercept parameter, represents the shape parameter, represents the slope parameter.

4. The artificial snow enhancement effect evaluation method according to claim 2, characterized in that: The ice crystal growth mechanism is specifically as follows: setting the ice crystal diameter to exceed 200 μm to trigger the sublimation process, and introducing a variable snow growth efficiency parameter to match Doppler radar observation data; The graupel particle distribution optimization specifically includes: calculating the terminal falling velocity of graupel particles in real time, and correcting the solid precipitation simulation error in combination with the vertical wind field; The cumulus convection scheme is specifically as follows: the Kain-Fritsch scheme is used for the coarse grid, the cumulus parameterization is turned off for the fine grid, and the convection is directly resolved; The radiation scheme is specifically: the RRTM scheme is used for long-wave radiation, and the Dudhia scheme is used for short-wave radiation to accurately simulate the surface energy balance; The boundary layer scheme specifically includes: using the YSU scheme to optimize the turbulent vertical diffusion calculation; The land surface process scheme is specifically as follows: using the Noah scheme to dynamically simulate snow stratification and soil freezing effects.

5. The artificial snowmaking effect evaluation method according to claim 1, characterized in that: The objective function of using WRF-3DVAR to assimilate radar data of the target area in step S4 is: in represents the cost function, Represents a variable, represents the cost function of the background field, represents the cost function of the observation field, A background field representing the pattern, represents the background error covariance matrix, represents observation data or observation values, represents the observation operator, denotes the observation error covariance matrix, the superscript Represents the transpose of a matrix.

6. The method for evaluating the effect of artificial snowmaking according to claim 5, wherein: The background error covariance matrix The calculation formula is: in It indicates the 24-hour forecast field reported from the same time. Indicates the 12-hour forecast field reported from the same time.

7. The artificial snowmaking effect evaluation method according to claim 1, characterized in that: The solid particle modeling of the Thompson dual-parameter microphysics scheme in step S4 adopts the raindrop spectrum Gamma particle size distribution function and dynamically calculates the mass concentration and number concentration : in represents the raindrop spectrum Gamma particle size distribution function, D represents the equivalent diameter of snow particles, 、 、 and are the snow particle distribution constants, is the shape parameter of the Gamma distribution of snow particle spectrum, The spectral distribution of n moment, when Number concentration ,when Mass concentration , Indicates particle capacity, represents the energy spectrum distribution function, represents the minimum energy, Indicates the maximum energy.

8. The artificial snowmaking effect evaluation method according to claim 1, characterized in that: The step S5 comprises the following sub-steps: S51. Analyze the vertical temperature stratification, water vapor flux, supercooled water content, and wind field of the target area during the target period to determine the catalytic potential area; S52. In the catalytic potential area, the Thompson two-parameter microphysics scheme is used to simulate the concentration distribution of ice crystals and snowflakes in the cloud to identify the suitable catalytic area.

9. The artificial snowmaking effect evaluation method according to claim 1, characterized in that: The AgI cold cloud catalysis coupled with the Thompson dual-parameter microphysics scheme in step S6 is specifically: using a catalytic method of coordinated operation of drone precise high-altitude seeding and ground smoke stove continuous diffusion to simulate the effect of different catalytic rates on snowfall to determine the optimal seeding time, height and dosage.

10. The artificial snow enhancement effect evaluation method according to claim 9, characterized in that: The catalytic rate calculation formula is: in represents the catalytic rate, and are all mode parameters, represents the ice nucleation activation number concentration, represents the reference ice core, represents the temperature and humidity adjustment factor, Indicates temperature, Indicates humidity.

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