A method, system and device for estimating regional windblown snow amount

Abstract

The present invention discloses a method, system and device for estimating the amount of regional windblown snow, and relates to the technical field of climate change and cryosphere numerical simulation. The method comprises: building a windblown snow physical model PIEKTUKD for simulating the windblown snow process in a target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within 1 km from the ground surface; coupling PIEKTUKD with a Polar WRF model; using the coupled Polar WRF model to estimate the amount of windblown snow in the measured area; and by coupling PIEKTUKD on the basis of Polar WRF, it is possible to accurately simulate the windblown snow phenomenon on a regional scale, and improve the simulation accuracy of polar near-surface climate variables and ice sheet surface material balance.

Description

Technical Field

[0001] The present invention relates to the technical field of numerical simulation of climate change, and in particular to a method, system and device for estimating the amount of regional windblown snow. Background Art

[0002] The Antarctic continent is covered with snow all year round and is affected by strong winds with an average annual rate of more than level 8, so the phenomenon of wind-blown snow is prevalent. As a special form of mass and energy transport, wind-blown snow blows the snow surface on the windward slope into undulating wind erosion, and can even blow away all the local snow to form a blue ice area. The blown snow partially sublimates, and the rest falls back on the leeward slope to form deposits. Wind-blown snow not only exacerbates the uneven distribution of snow space through the transport of snow particles and the sublimation that occurs during the transport process, but also affects the material and energy balance on the surface of the ice sheet and interacts with the lower atmosphere.

[0003] Due to the extremely harsh climatic conditions in Antarctica, wind and snow observations are very scarce and are only distributed in a few Antarctic research stations. Currently, the Polar WRF model, as a regional climate model dedicated to polar climate simulation, has shown great potential in assessing climate change in the Antarctic region.

[0004] However, the development and evaluation activities of the Polar WRF model did not take wind-blown snow processes into account, which greatly restricted the simulation of polar snow accumulation processes, resulting in significant deviations in the simulation of snow accumulation processes in the Antarctic region and their interaction with the atmosphere. In particular, the model was unable to reproduce the surface mass balance of the ice sheet well, resulting in large uncertainties in the estimation of the surface mass balance. Summary of the invention

[0005] In view of the deficiency that the prior art does not take the wind-blown snow process into consideration, which greatly restricts the simulation of the polar snow accumulation process and thus leads to significant deviations in the simulation of the snow accumulation process and the interaction process with the atmosphere in the Antarctic region, the present invention proposes a method, system and device for estimating the amount of regional wind-blown snow, which couples the dual-moment snow blowing physical model PIEKTUKD with Polar WRF to better simulate the wind-blown snow process and ice-air interaction in the Antarctic region, thereby solving the problems existing in the prior art.

[0006] A method for estimating the amount of regional windblown snow comprises the following steps:

[0007] A wind-blown snow physical model PIEKTUKD for simulating the wind-blown snow process is built in the target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within a set value from the ground surface;

[0008] PIEKTUKD is coupled with the Polar WRF model, and the coupled Polar WRF model is used to estimate the amount of wind-blown snow in the target area, which specifically includes the following steps:

[0009] Determine the data transfer between the Polar WRF model and PIEKTUKD, specifically: the logarithmic vertical coordinates of PIEKTUKD are used within the set value above the surface in the Polar WRF model, and the vertical coordinates of the Polar WRF model are used between the set value and the set value of the highest layer of the wind and snow model;

[0010] According to the determined vertical coordinates, it is determined whether the height of each vertical layer of PIEKTUKD is higher than the bottom layer of the Polar WRF model, and the amount of windblown snow in the target area is estimated based on the determination result; wherein, the determination result includes: if it is higher than the bottom layer of the PolarWRF model, the output data generated by each layer in the Polar WRF model is directly transmitted to the vertical layer of PIEKTUKD at the corresponding height for estimation; if it is lower than or equal to the bottom layer of the Polar WRF model, the data of each layer below the bottom layer in PIEKTUKD is initialized by using logarithmic distribution for estimation.

[0011] Furthermore, the output data generated by each layer in the Polar WRF model include temperature, specific humidity, 10m wind speed U Quantity and V Components and vertical layers U , V Component wind speed and air pressure.

[0012] Furthermore, in the process of estimating the amount of wind-blown snow in the target area using the coupled Polar WRF model, the relative humidity of each vertical layer of water is converted into the relative humidity of ice. The conversion process is expressed as:

[0013] ;

[0014] ;

[0015] ;

[0016] in, represents the relative humidity of ice, Indicates air pressure, and represent the saturated vapor pressure of water and ice respectively. is 273.15K, represents the air temperature at each layer of the model in degrees Kelvin, RH Indicates relative humidity with respect to water.

[0017] Furthermore, estimating the amount of windblown snow in the target area according to the judgment result specifically includes the following steps:

[0018] By calculating the mass ratio of suspended ice particles to dry air , the amount of snow in suspension is estimated, which is expressed as:

[0019] ;

[0020] in, Indicates the ice density value, the value is 900kg / m 3 ; Indicates air density in kg / m 3 ; is the instantaneous radius of the falling snow particles, is the spectral density of suspended particles;

[0021] Turbulent diffusion coefficient through snow particles Kr The proportional relationship between the turbulent diffusion coefficient of momentum and the diffusion degree of snow particles is calculated. The calculation process is expressed as:

[0022] ;

[0023] in, u * is the friction speed, in m / s; Indicates the mixing length; k is the von Kármán constant, which is 0.4; z 0 is the ground roughness length, in m; yes The maximum value is 40m; represents the reduction coefficient of eddy diffusivity, z is the model layer height, in m;

[0024] Estimate the terminal settling velocity of snow particles based on the physical properties of snow particles and the meteorological conditions in the target area v b , which is expressed as:

[0025] ;

[0026] in, is the terminal velocity of the particle obtained by the balance between gravity and drag, n Gamma distribution n order moment;

[0027] Calculate the total sublimation amount of drifting snow by the sublimation rate of windblown snow in local areas and transmission volume ;

[0028] By combining the diffusion degree of snow particles, the terminal settling velocity, the total sublimation and transmission amount of drifting snow during the sublimation process, as well as the changes in ambient temperature and water vapor mixing ratio, the wind-blown snow phenomenon in the target area is simulated and the amount of snow in the target area is estimated.

[0029] Furthermore, the sublimation rate of the blowing snow in the local area is used to calculate the total sublimation amount of the drifting snow. and transmission volume ; Its calculation process is expressed as:

[0030] Calculate the sublimation rate of windblown snow in a local area :

[0031] ;

[0032] in is the Nusselt number; q is It represents the mixing ratio of saturated water vapor relative to ice, in kg / k; F K and F d They represent conduction and diffusion involved in phase change, respectively, and the unit is ms / kg; represents the water vapor mixing ratio; r m represents the average particle radius, in m; r m The calculation formula is:

[0033] ;

[0034] The total amount of sublimation of falling snow It is expressed as:

[0035] ;

[0036] Transmission volume of falling snow It is expressed as:

[0037] ;

[0038] in is the horizontal wind speed, and Respectively represent the height of the upper and lower boundaries of the snow layer, and They represent the shape and scale parameters of the gamma distribution of snow particle clusters, respectively.

[0039] Furthermore, the change process of the ambient temperature and the water vapor mixing ratio is expressed as:

[0040] Ambient air temperature T a The calculation formula is:

[0041] ;

[0042] in, K h is the turbulent diffusion coefficient of heat, which indicates the rate at which heat diffuses in the atmosphere;

[0043] The water vapor mixing ratio calculation formula is expressed as:

[0044] ;

[0045] in, K v is the eddy diffusivity of heat and humidity; Q It indicates the heating rate caused by sublimation, in K / S; Q The calculation formula is:

[0046] ;

[0047] in, LS represents the latent heat flux of sublimation, in J / kg; C p Represents the heat capacity of air, in J / kg.K.

[0048] The present invention also includes a regional wind-blown snow amount estimation system, comprising:

[0049] A model building module is used to build a wind-blown snow physical model PIEKTUKD for simulating the wind-blown snow process in the target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within a set value from the ground surface;

[0050] The estimation module is used to couple PIEKTUKD with the Polar WRF model and use the coupled Polar WRF model to estimate the amount of windblown snow in the target area, including:

[0051] The coordinate selection unit is used to determine the data transfer between the Polar WRF model and PIEKTUKD, specifically including: the logarithmic vertical coordinates of PIEKTUKD are used within the set value above the surface in the Polar WRF model, and the vertical coordinates of the Polar WRF model are used between the set value and the set value of the highest layer of the wind and snow model;

[0052] A judgment unit is used to judge whether the height of each vertical layer of PIEKTUKD is higher than the bottom layer of the Polar WRF model according to the determined vertical coordinates, and estimate the amount of windblown snow in the target area according to the judgment result; wherein the judgment result includes: if it is higher than the bottom layer of the Polar WRF model, the output data generated by each layer in the Polar WRF model is directly transmitted to the vertical layer of PIEKTUKD at the corresponding height for estimation; if it is lower than or equal to the bottom layer of the Polar WRF model, the data of each layer below the bottom layer in PIEKTUKD is initialized by using logarithmic distribution for estimation.

[0053] The present invention also includes a computer device for estimating the amount of snow and wind blowing in a region, comprising: a memory, a processor, and a computer program stored in the memory, wherein the processor implements the steps of the method for estimating the amount of snow and wind blowing in a region when executing the computer program.

[0054] The present invention also includes a readable storage medium, wherein the readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the steps of the regional windblown snow amount estimation method are executed.

[0055] The present invention provides a method, system and device for estimating the amount of windblown snow in a region, which have the following beneficial effects:

[0056] The present invention couples the wind and snow physical model PIEKTUKD on the basis of Polar WRF, sets the bottom layer of the wind and snow model on the snow surface, sets the top layer of the model within a set value from the ground surface, adopts a higher resolution near the ground surface, adopts logarithmic vertical coordinates with finer resolution in the wind and snow model at a position lower than the set value above the ground surface in the Polar WRF model, uses the vertical coordinates of Polar WRF between the set value and the set value of the top layer of the wind and snow model, determines the data transmission between Polar WRF and PIEKTUKD, so as to better simulate the wind and snow process and ice-air interaction in the target area; provides a comprehensive wind and snow simulation solution through the coupled model, can accurately simulate the wind and snow phenomenon, and improve the accuracy and efficiency of wind and snow prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the flow of a method for estimating the amount of regional windblown snow in an embodiment of the present invention;

[0058] Figure 2 Schematic diagram of the coupling between the vertical layer of the Polar WRF model and the vertical layer of the wind and snow module in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0060] The present invention proposes a method for estimating the amount of regional wind-blown snow, by coupling the wind-blown snow physical model PIEKTUKD on the basis of Polar WRF, so as to better simulate the wind-blown snow process and ice-air interaction; Figure 1 As shown, the method specifically comprises the following steps:

[0061] S1. Determine the interaction variables between the Polar WRF model and the PIEKTUKD model, and implement it by placing the snow blowing module in the planetary boundary layer parameterization process in PolarWRF.

[0062] S2, the vertical layer of the Polar WRF model is matched with the vertical layer of the wind-blown snow physical model, e.g. Figure 2 As shown: The PolarWRF model uses terrain-following coordinates, with the ground as the bottom layer. The vertical horizontal plane from the ground surface to 10hPa is divided into 61 layers, of which 7 layers are located at the lowest 20 meters in the atmosphere, and the second layer is located 8m above the ground. There are 11 vertical profile layers in the vertical coordinate interval of the wind and snow module. The bottom layer of the wind and snow model ( ) is set on the snow surface, the top layer of the model ( ) Within 1000 m from the surface, the vertical coordinate system is equidistant on a logarithmic scale and is divided into 14 vertical sections, with a higher resolution near the surface. In order to match the two, the logarithmic vertical coordinates with a finer resolution used in the wind and snow model are used below 8 m, and the vertical coordinate division height is as shown in formula (1), while the vertical coordinates of PolarWRF are used in the case of 8~1000 m.

[0063] z ( k )=ln(( k -1)*( / ( n -1))) (1);

[0064] in, k Indicates k layer, n Indicates the number of layers to be divided. This is the highest level of the Snow and Wind module, which is equal to 1000.

[0065] S3. Determine the data transmission between Polar WRF and the wind and snow module, including the following two situations:

[0066] (1) When the vertical coordinates have been determined, the temperature generated by the Polar WRF model ( T ), specific humidity ( q v ), 10m wind speed U Quantity ( U 10) and V Quantity ( V 10) and each vertical layer U , V Component wind speed ( U z , V z )) and air pressure ( p ) is transmitted to the wind and snow module for operation. Before operation, the relative humidity of water needs to be converted to the relative humidity of ice according to the expressions proposed by formulas (2) to (4) ( RH i ).

[0067] (2);

[0068] (3);

[0069] (4);

[0070] (2) When the vertical coordinate height divided in S2 z , at 8m≤ z For the case of ≤1 km, the variables calculated by Polar WRF itself are used for calculation. For the vertical layer below 8 m, both theory and experiment show that the vertical distribution of different layers is similar to that of the vertical distribution of different layers under the condition of fixed surface and close to the neutral layer. U The wind components of each layer can be calculated using a typical logarithmic profile as shown in formula (5). The relative humidity and temperature are calculated following a logarithmic distribution from the measurement height of 2 m to the snow surface, while the air pressure is calculated based on the model height, surface air temperature and surface air pressure.

[0071] (5);

[0072] U ( z ) is the wind speed at each layer of the vertical coordinate; kappa is the von Karman constant, equal to 0.4; z 0 is the height of the bottom layer of the vertical coordinate; z ( k ) is the k The vertical coordinate height of the layer; ustar is the friction velocity, which is calculated based on the wind speed at the bottom layer of the model. = U10*kappa / alog(10 / z 0).

[0073] S4. After the variables required for the operation of the wind and snow module are transferred, it is determined whether wind and snow occur under the climate and environmental conditions. If it is determined that the current climate variables cannot cause the wind and snow process, the calculation of the wind and snow module is exited. Otherwise, step 5 is performed to determine whether wind and snow occur under the current environmental variables, see formula (6).

[0074] (6).

[0075] S5. Implementation of the wind and snow plan includes the following steps:

[0076] S5.1. The calculation process of the wind and snow scheme includes:

[0077] (1) Model design and mixing ratio calculation.

[0078] In order to simplify the calculation, the PIEKTUK-D scheme is used in the model to calculate the wind-blown snow process. In this scheme, the ratio of the mass of suspended ice particles to the mass of dry air, i.e., the blowing snow mixing ratio, is used. To describe the possibility of the amount of snow in suspension. Assuming that the blowing snow is composed of ice balls, we can use formula (7) to obtain:

[0079] (7);

[0080] in, Indicates the ice density value, which is 900kg / m^3. Indicates air density in kg / m 3 .

[0081] (2) Calculation of turbulent diffusion coefficient and terminal settling velocity.

[0082] Next, assuming that the turbulent diffusion coefficient K(r) of snow particles is proportional to the turbulent diffusion coefficient of momentum, it can be obtained using formula (8):

[0083] (8);

[0084] in, u * is the friction speed, in m / s; Indicates the mixing length; k is the von Kármán constant, which is 0.4; z 0 is the ground roughness length, in m; yes The maximum value is 40m; represents the reduction coefficient of eddy diffusivity,z is the model layer height, in m.

[0085] Terminal settling velocity of snow particles Determined by the physical properties of snow particles and meteorological conditions, the method of Kong and Yau (1997) is used for calculation:

[0086] (9);

[0087] in, is the terminal velocity of the particle obtained by the balance between gravity and drag, n Gamma distribution n The moment of order is obtained through testing n =5.

[0088] (3) Prediction equations for temperature and water vapor mixing ratio.

[0089] Ambient air temperature T a The change in ambient air temperature is described by the following equation:

[0090] (10);

[0091] in K h is the turbulent diffusion coefficient of heat, which indicates the rate at which heat diffuses in the atmosphere. Q is the heat source term, representing the heat change due to sublimation of snow particles.

[0092] The water vapor mixing ratio is described by the following equation:

[0093] (11);

[0094] in K v are the eddy diffusivities of heat and humidity, respectively, and the heating rate due to sublimation (here negative) is expressed as Q (Unit: K / S), the calculation formula is as follows:

[0095] (12);

[0096] In formula (12) L s (Unit: J / kg) represents the latent heat flux of sublimation, c p (J / kg.K) represents the heat capacity of air.

[0097] (4) Calculation of sublimation rate and transmission amount of windblown snow:

[0098] First, calculate the sublimation rate of windblown snow in the local area:

[0099] (13);

[0100] in is the Nusselt number, q is (Unit: kg / k) represents the saturated water vapor mixing ratio relative to ice. F k and F d (unit: ms / kg) represent the conduction and diffusion involved in the phase change, and r m (Unit: m) represents the average particle radius, and the calculation formula is as follows:

[0101] (14);

[0102] Finally, calculate the total sublimation amount of the falling snow and transmission volume for:

[0103] (15);

[0104] (16);

[0105] in (Unit: m / s) is the horizontal wind speed and and They represent the heights of the upper and lower boundaries of the drifting snow layer respectively.

[0106] S5.2. Use the finite difference method or the finite volume method to discretize the continuous partial differential equation and convert it into an algebraic equation. The distribution of snow particles at different heights and times can be obtained by step-by-step iterative solution.

[0107] Select appropriate time step and space step to ensure the stability and convergence of the numerical solution. The selection of time step needs to consider the total simulation time and computing resources, and the selection of space step needs to consider the size and resolution of the study area. The discretized algebraic equation is solved by iterative method to calculate the mixing ratio of snow particles. q b , Ambient temperature T a Mixing ratio of water vapor q v Changes over time.

[0108] S5.3. After the calculation is completed in the wind and snow module, the variables changed by the snow blowing process need to be transmitted back to the Polar WRF model to participate in the calculation of the subsequent physical parameterization process, and the total particle concentration, temperature change trend, snow blowing mixing ratio, snow blowing sublimation rate, snow blowing transport rate, etc. calculated by the model need to be transmitted out for storage.

[0109] The present invention can accurately simulate the phenomenon of blowing snow and improve the accuracy and efficiency of blowing snow prediction. The model provides a comprehensive blowing snow simulation solution by describing the diffusion, sedimentation and sublimation process of snow particles in detail and combining the changes in ambient temperature and water vapor mixing ratio.

[0110] Based on the same inventive concept, the present invention also proposes a regional wind-blown snow amount estimation system, comprising:

[0111] The model building module is used to build a wind and snow physical model PIEKTUKD for simulating the wind and snow process in the target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within a set value from the ground surface.

[0112] The estimation module is used to couple PIEKTUKD with the Polar WRF model and use the coupled Polar WRF model to estimate the amount of windblown snow in the target area, including:

[0113] The coordinate selection unit is used to determine the data transfer between the Polar WRF model and PIEKTUKD, specifically including: the logarithmic vertical coordinates of PIEKTUKD are used within the set value above the surface in the Polar WRF model, and the vertical coordinates of the Polar WRF model are used between the set value and the set value of the highest layer of the wind and snow model.

[0114] The judgment unit is used to judge whether the height of each vertical layer of PIEKTUKD is higher than the bottom layer of the Polar WRF model according to the determined vertical coordinates, and estimate the amount of windblown snow in the target area according to the judgment result; wherein the judgment result includes: if it is higher than the bottom layer of the Polar WRF model, the output data generated by each layer in the Polar WRF model is directly transmitted to the vertical layer of PIEKTUKD with the corresponding height for estimation; if it is lower than or equal to the bottom layer of the Polar WRF model, the data of each layer below the bottom layer in PIEKTUKD is initialized by using logarithmic distribution for estimation.

[0115] The present invention also proposes a computer device for estimating the amount of windblown snow in a region, comprising: a memory, a processor, and a computer program stored in the memory, and the processor implements the steps of a method for estimating the amount of snow in a region when executing the computer program.

[0116] The present invention also proposes a readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, they are used to execute the steps of a method for estimating the amount of windblown snow in a region.

[0117] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for estimating the amount of windblown snow in a region, characterized in that: The following steps are involved: A wind-blown snow physical model PIEKTUKD for simulating the wind-blown snow process is built in the target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within a set value from the ground surface; PIEKTUKD is coupled with the Polar WRF model, and the coupled Polar WRF model is used to estimate the amount of wind-blown snow in the target area, which specifically includes the following steps: Determine the data transfer between the Polar WRF model and PIEKTUKD, specifically: the logarithmic vertical coordinates of PIEKTUKD are used within the set value above the surface in the Polar WRF model, and the vertical coordinates of the Polar WRF model are used between the set value and the set value of the highest layer of the wind and snow model; According to the determined vertical coordinates, it is determined whether the height of each vertical layer of PIEKTUKD is higher than the bottom layer of the Polar WRF model, and the amount of windblown snow in the target area is estimated based on the determination result; wherein, the determination result includes: if it is higher than the bottom layer of the Polar WRF model, the output data generated by each layer in the Polar WRF model is directly transmitted to the vertical layer of PIEKTUKD at the corresponding height for estimation; if it is lower than or equal to the bottom layer of the Polar WRF model, the data of each layer below the bottom layer in PIEKTUKD is initialized by using logarithmic distribution for estimation.

2. A method for estimating the amount of windblown snow in a region according to claim 1, characterized in that: The output data generated by each layer in the Polar WRF model include temperature, specific humidity, 10m wind speed U Quantity and V Components and vertical layers U , V Component wind speed and air pressure.

3. A method for estimating the amount of windblown snow in a region according to claim 1, characterized in that: It also includes the process of converting the relative humidity of water in each vertical layer into the relative humidity of ice when estimating the amount of wind-blown snow in the target area using the coupled Polar WRF model. The conversion process is expressed as: ； ； ； in, represents the relative humidity of ice, Indicates air pressure, and represent the saturated vapor pressure of water and ice respectively. is 273.15K, represents the air temperature at each layer of the model in degrees Kelvin, RH Indicates relative humidity with respect to water.

4. A method for estimating the amount of windblown snow in a region according to claim 1, characterized in that: The method of estimating the amount of windblown snow in the target area according to the judgment result specifically includes the following steps: Calculate the mass ratio of suspended ice particles to dry air , the amount of snow in suspension is estimated, which is expressed as: ； in, Indicates the ice density value, the value is 900kg / m 3 ; Indicates air density in kg / m 3 ; is the instantaneous radius of the falling snow particles, is the spectral density of suspended particles; Turbulent diffusion coefficient through snow particles Kr The proportional relationship between the turbulent diffusion coefficient of momentum and the diffusion degree of snow particles is calculated. The calculation process is expressed as: ； in, u * is the friction speed, in m / s; Indicates the mixing length; k is the von Kármán constant, with a value of 0.4; z0 is the ground roughness length, in meters; yes The maximum value is 40m; represents the reduction coefficient of eddy diffusivity, z is the model layer height, in m; Estimate the terminal settling velocity of snow particles based on the physical properties of snow particles and the meteorological conditions in the target area v b , which is expressed as: ； in, is the terminal velocity of the particle obtained by the balance between gravity and drag, n Gamma distribution n order moment; Calculate the total sublimation amount of drifting snow by the sublimation rate of windblown snow in local areas and transmission volume ; By combining the diffusion degree of snow particles, the terminal settling velocity, the total sublimation and transmission amount of drifting snow during the sublimation process, as well as the changes in ambient temperature and water vapor mixing ratio, the wind-blown snow phenomenon in the target area is simulated and the amount of snow in the target area is estimated.

5. A method for estimating the amount of windblown snow in a region according to claim 4, characterized in that: The sublimation amount of the overall drifting snow is calculated by the sublimation rate of the wind-blown snow in the local area. and transmission volume ; The calculation process is expressed as: Calculate the sublimation rate of windblown snow in a local area : ； in is the Nusselt number; q is It represents the mixing ratio of saturated water vapor relative to ice, in kg / k; F K and F d They represent conduction and diffusion involved in phase change, respectively, and the unit is ms / kg; represents the water vapor mixing ratio; r m represents the average particle radius, in m; r m The calculation formula is: ； The total amount of sublimation of falling snow It is expressed as: ； Transmission volume of falling snow It is expressed as: ； in is the horizontal wind speed, and Respectively represent the height of the upper and lower boundaries of the snow layer, and They represent the shape and scale parameters of the gamma distribution of snow particle clusters, respectively.

6. A method for estimating the amount of windblown snow in a region according to claim 5, characterized in that: The variation process of the ambient temperature and water vapor mixing ratio is expressed as: Ambient air temperature T a The calculation formula is: ； in, K h is the turbulent diffusion coefficient of heat, which indicates the rate at which heat diffuses in the atmosphere; The water vapor mixing ratio calculation formula is expressed as: ； in, K v is the eddy diffusivity of heat and humidity; Q It indicates the heating rate caused by sublimation, in K / S; Q The calculation formula is: ； in, LS represents the latent heat flux of sublimation, in J / kg; C p Represents the heat capacity of air, in J / kg.K.

7. A regional wind-blown snow amount estimation system, characterized in that: include: A model building module is used to build a wind-blown snow physical model PIEKTUKD for simulating the wind-blown snow process in the target area; wherein the bottom layer of PIEKTUKD is set on the snow surface, and the top layer is set within a set value from the ground surface; The estimation module is used to couple PIEKTUKD with the Polar WRF model and use the coupled Polar WRF model to estimate the amount of windblown snow in the target area, including: The coordinate selection unit is used to determine the data transfer between the Polar WRF model and PIEKTUKD, specifically including: the logarithmic vertical coordinates of PIEKTUKD are used within the set value above the surface in the Polar WRF model, and the vertical coordinates of the Polar WRF model are used between the set value and the set value of the highest layer of the wind and snow model; A judgment unit is used to judge whether the height of each vertical layer of PIEKTUKD is higher than the bottom layer of the PolarWRF model according to the determined vertical coordinates, and estimate the amount of windblown snow in the target area according to the judgment result; wherein the judgment result includes: if it is higher than the bottom layer of the Polar WRF model, the output data generated by each layer in the Polar WRF model is directly transmitted to the vertical layer of PIEKTUKD at the corresponding height for estimation; if it is lower than or equal to the bottom layer of the Polar WRF model, the data of each layer below the bottom layer in PIEKTUKD is initialized by using logarithmic distribution for estimation.

8. A computer device for estimating the amount of windblown snow in a region, characterized in that: include: A memory, a processor and a computer program stored in the memory, wherein when the processor executes the computer program, the steps of the method for estimating the amount of windblown snow in a region as described in any one of claims 1 to 6 are implemented.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, which includes program instructions. When the program instructions are executed by a processor, they are used to execute the steps of the method for estimating the amount of windblown snow in a region as described in any one of claims 1-6.

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