A regional snow albedo prediction method, system and device

Abstract

The invention discloses a method, system and device for predicting regional snow albedo, and relates to the technical field of albedo prediction. The method comprises: calculating the albedo of snow based on a land process model Noah-MP; stratifying snow according to the snow thickness in a target area, and evolving the snow particle size; calculating the basic albedo according to the evolved snow particle size, and calculating the ice and snow albedo according to the basic albedo and the light-absorbing carbon concentration in ice and snow; establishing a functional relationship between the snow particle size, cloud optical thickness and solar zenith angle according to the basic albedo, the ice and snow albedo and the albedo change value caused by the change of the solar altitude angle; obtaining the surface snow albedo according to the functional relationship; predicting the climate of the region according to the albedo of the snow, and by considering the situation of multiple snow layers and a single snow layer in the region, the surface snow albedo can be more realistically reflected, thereby improving the prediction accuracy of the regional snow albedo.

Description

Technical Field

[0001] The present invention relates to the technical field of albedo prediction, and in particular to a method, system and device for predicting regional snow cover albedo. Background Art

[0002] Snow is an extremely important component of polar regions and can respond sensitively to regional climate change. When snow accumulates and melts on the surface of the ice sheet, the albedo of the snow on the surface will change dramatically. The absorption of shortwave radiation is a major source of energy for melting ice and snow in polar regions. Its magnitude depends on the incident radiation and the albedo of the surface snow. Therefore, the albedo of snow is a key parameter affecting the surface energy budget, which determines the rate of snow melting.

[0003] Studies have shown that over the past few decades, temperatures in parts of the Antarctic ice sheet have risen sharply, and significant melting has occurred on the ice shelves of East Antarctica and the Antarctic Peninsula. Most of the melted snow refreezes in the snow, while a small part freezes on the ice, which greatly changes the structure of the snow. Because the re-frozen ice water increases the size of snow particles, light must travel a longer distance in large particles to scatter from the surface, thereby increasing the surface's absorption of light. In addition to melting and freezing, when the snow temperature rises, small-sized new snow will also turn into large-sized old snow, reducing the albedo of the snow. The reduction in the albedo of the snow increases the surface's absorption of solar radiation, which in turn provides energy for the melting and refreezing of snow, which is a positive feedback. But on the other hand, in addition to accelerating the melting of glaciers, the rise in near-surface air temperature will also convert into greater precipitation by affecting the positive exponential relationship between temperature and atmospheric humidity, and the increase in precipitation may increase the albedo of snow on the surface of the ice sheet. The Antarctic region has a high snow albedo, and even the slightest change in snow albedo will lead to a large change in the net solar radiation balance at the surface, which in turn exacerbates climate change. In addition, in polar regions, the distribution of snow albedo varies over time and space, so a reliable snow albedo solution is needed when simulating the climate in the Antarctic region.

[0004] There are many albedo parameterization schemes, and their physical basis, parameterization variables and targeted problems are different. Greuell and Konzelmann proposed a cloud-related albedo parameterization in SOMARS to explain the increase in cloud cover; Dickinson et al. parameterized the albedo as a function of the solar zenith angle to explain the change of albedo with the solar zenith angle. In addition, many albedo parameterization schemes are based on snow particle size or related variables such as snow temperature or snow density generated by its evolution: Brun et al. parameterized the snow albedo as a function of the effective diameter of particles in three spectral ranges; RACMO2.1 carried out an albedo parameterization scheme based on snow density; the CLM land surface process considered more complex parameterization schemes such as the solar zenith angle, the albedo of the matrix under the snow, the mass concentration of aerosols (black carbon, mineral dust and organic carbon), and the effective particle size of ice and snow. Its simulation effect is good but the calculation is too large.

[0005] However, the various albedo parameterization schemes currently proposed do not take into account the existence of multiple snow layers or a single snow layer when simulating the surface snow albedo in Antarctica, and cannot truly reflect the surface snow albedo, resulting in certain errors in the prediction of snow albedo in this region. Summary of the invention

[0006] In view of the fact that the prior art does not take into account the existence of multiple snow layers or a single snow layer, and cannot truly reflect the albedo of surface snow, the present invention proposes a regional snow albedo prediction method, system and equipment. By stratifying the regional snow depth based on the land process model Noah-MP, the evolution of the snow particle size of the snow layer is performed, and the surface snow albedo in the case of multiple snow layers or a single snow layer can be predicted more accurately, thereby solving the problems existing in the prior art.

[0007] A regional snow albedo prediction method comprises the following steps:

[0008] Get the snow depth of the area to be measured;

[0009] stratifying the snow in the area to be tested according to the snow depth;

[0010] When the accumulated snow is divided into multiple snow layers, the snow albedo of each snow layer is determined respectively, and the surface snow albedo of the area to be measured is obtained according to the snow albedo of each snow layer; wherein the snow albedo determination process of each snow layer includes: evolving the snow particle size of the snow layer; the evolution of the snow particle size includes the evolution of the dry snow particle size, the evolution of the snow particle size under wet snow conditions and the evolution of the re-frozen snow particle size; determining the basic albedo of the snow layer by weighted averaging the evolved dry snow particle size, the snow particle size under wet snow conditions and the re-frozen snow particle size; determining the albedo change value caused by the change of the solar altitude angle according to the change value of the solar altitude angle of the area to be measured, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; obtaining the snow albedo of the snow layer by summing the basic albedo of the snow layer and the albedo change value;

[0011] When the snow is divided into a single snow layer and there is bare ice under the single snow layer, the surface snow albedo of the area to be measured is obtained by interpolating between the snow albedo of the single snow layer and the albedo of the bare ice.

[0012] Furthermore, the dry snow particle size The evolution process is expressed as:

[0013] ,

[0014] in, is the initial value of the snow particle size, , , All are empirical parameters t is the evolution time, r is the snow particle size;

[0015] Snow particle size under wet snow conditions The evolution process is expressed as:

[0016] ,

[0017] in, is the liquid water content, in kg·m -2 ; B is the liquid crystal grain growth constant, and its value is ;

[0018] The evolution of the re-frozen snow particle size, the evolution of the re-frozen snow particle size is 1000 .

[0019] Furthermore, the evolved dry snow particle size, the snow particle size under wet snow conditions, and the re-frozen snow particle size are weighted averaged and expressed as:

[0020] ,

[0021] in, represents the weighted average snow particle size; represents the evolved dry snow particle size; represents the snow particle size after evolution under wet snow conditions; represents the evolved re-frozen snow particle size, r 0 is the new snow particle size.

[0022] Furthermore, the basic albedo of the snow layer The calculation process is expressed as:

[0023] .

[0024] Furthermore, the albedo of ice and snow is determined based on the basic albedo and the concentration of light-absorbing carbon in ice and snow. It is expressed as:

[0025] ,

[0026] in, is the surface area of ​​snow particles per unit mass, Represents the concentration of light-absorbing carbon in ice and snow.

[0027] Furthermore, the albedo change value caused by the change in the solar altitude angle is determined according to the change in the solar altitude angle of the measured area, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow. , expressed as:

[0028] ,

[0029] in, , is the solar zenith angle, is the cloud optical thickness, Represents the albedo of ice and snow; the albedo of ice and snow is determined by the basic albedo and the concentration of light-absorbing carbon in ice and snow. It is expressed as:

[0030] ,

[0031] in, is the surface area of ​​snow particles per unit mass, Represents the concentration of light-absorbing carbon in ice and snow.

[0032] Furthermore, the snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value, which is expressed as:

[0033] ,

[0034] in, For the i The snow albedo of the layer, represents the broadband albedo of ice and snow, the broadband albedo of ice and snow It is expressed as:

[0035]

[0036] Furthermore, by obtaining the snow albedo of each snow layer, the surface snow albedo when there are multiple snow layers in the test area is obtained, which is expressed as:

[0037] ,

[0038] in, is the albedo of the snow on the surface, is the snow layer thickness, For the i The snow albedo of the layer, For the i -1 layer of snow albedo, For the j Snow layer thickness, is the thickness of the snow layer 0.

[0039] Furthermore, the surface snow albedo of the area under bare ice conditions is obtained by interpolating the snow albedo of the single snow layer with the bare ice albedo. , expressed as

[0040] ,

[0041] in, H is the snow height in m ; is the albedo of bare ice, which is 0.55.

[0042] The present invention also includes a regional snow albedo prediction system, comprising:

[0043] An acquisition module is used to obtain the snow depth in the area to be measured;

[0044] A stratification module, used for stratifying the snow in the area to be tested according to the snow depth;

[0045] The surface snow albedo prediction module is used to determine the snow albedo of each snow layer when the snow is divided into multiple snow layers, and obtain the surface snow albedo of the area to be measured according to the snow albedo of each snow layer; wherein the snow albedo determination process of each snow layer includes: evolving the snow particle size of the snow layer; the evolution of the snow particle size includes the evolution of the dry snow particle size, the evolution of the snow particle size under wet snow conditions and the evolution of the re-frozen snow particle size; by performing the evolution of the dry snow particle size, the snow particle size under wet snow conditions and the re-frozen snow particle size The basic albedo of the snow layer is determined by weighted averaging; the albedo change value caused by the change of the solar altitude angle is determined according to the change value of the solar altitude angle of the measured area, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; the snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value; when the accumulated snow is divided into a single snow layer and there is bare ice under the single snow layer, the surface snow albedo of the measured area is obtained by interpolating between the snow albedo of the single snow layer and the albedo of the bare ice.

[0046] The present invention also includes a computer device for predicting regional snow albedo, including: a memory, a processor, and a computer program stored in the memory, and the processor implements the steps of the regional snow albedo prediction method when executing the computer program.

[0047] The present invention provides a method, system and device for predicting regional snow albedo, which have the following beneficial effects:

[0048] The present invention stratifies regional snow accumulation, thereby evolving the snow particle size of the snow layer. According to the evolved snow particle size, the surface snow albedo in the case where there are multiple snow layers can be more accurately calculated; the albedo change value caused by the change in the solar altitude angle is determined according to the change in the solar altitude angle of the measured area, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; the influence of the change in the solar altitude angle, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow on the snow albedo is taken into account, from which the influence of black carbon generated by weather and climate change or human activities on the albedo can be obtained, and at the same time, the surface snow albedo in the case where there is bare ice under a single layer in the region is taken into account. By considering the situation of multiple snow layers and a single snow layer in the region, the surface snow albedo can be more realistically reflected, thereby improving the prediction accuracy of the regional snow albedo. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 Schematic diagram of the process of regional snow albedo prediction method in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] 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.

[0051] The present invention proposes a regional snow albedo prediction method, such as Figure 1 As shown, the method specifically comprises the following steps:

[0052] S1. Obtain the snow thickness in the target area and divide the snow layers. In the land process model Noah-MP, the snow layer is stratified and can be divided into up to 3 layers according to the total snow depth. The bottom snow layer close to the soil surface is the 0th layer, and the top snow layer is the number of snow layers + 1. The minimum values ​​of the three snow layers from top to bottom are 0.045m, 0.05m and 0.2m respectively. If the thickness of a layer is less than its minimum value due to sublimation or melting, the layer is merged with the adjacent layer below it. Therefore, in the integration of a time step, it is difficult for new snow to produce an independent snow layer. Therefore, when the snow depth is greater than the minimum snow layer thickness, a new snow layer is added on top. This new snow layer is only used for the calculation of albedo, and does not involve the calculation of other physical processes inside other snow layers.

[0053] Polar WRF mainly includes two land surface schemes, Noah-LSM and Noah-MP. The Noah-MP used in this paper is an enhanced multi-layer model with improved physical fields and multi-parameterization options added on the basis of Noah-LSM (Niu et al, 2011a). Noah-MP includes two albedo parameterization schemes, one is the biosphere-atmosphere transfer scheme (BATS), and the other is the Class scheme, which is the default scheme of Noah-MP. The polar meteorology team has made slight adjustments to the original parameterization scheme.

[0054] S2. Evolution of snow particle size according to the snow layers divided in S1. Snow particle size is the most useful factor for radiation transmission in snow accumulation, but Noah-MP does not include this variable. Therefore, the effective snow particle size ( r The snow particle evolution scheme is derived from the SNICAR model (Flanner and Zender, 2006). The model simulates the aging of dry snow in a microscopic way and divides the evolution of effective snow particle size into three parts: dry snow metamorphism, wet snow metamorphism, and refreezing of liquid water in snow.

[0055] Dry snow particle size Evolution: The difference in particle curvature will cause a slight water vapor density gradient through Kelvin's law, and this process plays a dominant role in the growth of new snow particle size in the isothermal snow layer (Arons and Colbeck, 1995). The vertical temperature gradient in the snow layer will cause the vapor density gradient between particles to increase rapidly and cause vapor diffusion through ice crystals, making the snow particles grow rapidly (Sturm and Benson, 1997). Therefore, it can be observed that under the vertical temperature gradient, the growth of snow particles will increase with the increase of the vertical temperature gradient and reach a certain limit, but at low temperatures, the dependence on the vertical temperature gradient is very small. In addition, the snow particle size will increase as the snow density decreases (Flanner and Zender, 2006). The SNICAR model parameterizes the size of dry snow particles as follows:

[0056] .

[0057] in, is the initial value of the snow particle size, which is 54.5 ,and , , are empirical parameters, which are obtained by matching the corresponding snow density, snow temperature and snow layer temperature gradient according to the lookup table. The parameterization curve fits the SNICAR results very accurately. t is the evolution time, r The snow particle size.

[0058] Snow particle size under wet snow conditions Calculation: Existing studies have shown that the presence of liquid water can significantly increase the growth rate of snow particle size. The metamorphism of wet snow is parameterized according to the equation of Brun et al (1989), r ( m ) changes with the liquid water content In connection, the specific formula is:

[0059] .

[0060] in, is the liquid water content, in kg·m -2 , B is the liquid crystal grain growth constant, and its value is .

[0061] Refreeze snow particle size setting: Set the refreeze snow particles ( ) is assumed to have a particle size of 1000 .

[0062] S3. Calculate snow particle size by weighted average: Take the weighted average of the effective radius of dry snow, wet snow and re-frozen snow, and the calculation formula is:

[0063] .

[0064] in, represents the weighted average snow particle size; represents the evolved dry snow particle size; represents the snow particle size after evolution under wet snow conditions; represents the evolved refrozen snow particle size, r 0 is the new snow particle size.

[0065] S4. Calculation of albedo: Broadband albedo refers to the ratio of solar radiation reflected by the snow surface to the incident solar radiation, which depends on factors such as the spectral albedo of the snow, the spectral distribution of solar radiation, the solar altitude angle, and the thickness of the cloud layer. In the present invention, the albedo is parameterized as the effective radius of the snow particle ( r )、Cloud Optical Depth( τ )、Effective solar altitude angle( ), the specific formula is:

[0066] .

[0067] (1) Calculate the basic albedo based on the size of snow particles (Albedo is caused only by the size of snow particles, without considering the albedo calculated under the solar altitude angle and the optical thickness of the cloud layer). The formula is:

[0068] .

[0069] (2) Albedo of ice and snow will change as the concentration of light-absorbing carbon in ice and snow increases:

[0070] .

[0071] in, is the surface area of ​​snow particles per unit mass, Represents the concentration of light-absorbing carbon in ice and snow.

[0072] However, there are currently no prediction equations for black carbon and dust in Noah-MP, and the content of these substances in the Antarctic is very low and can be almost ignored. 0.

[0073] (3) Calculate the weighting of albedo caused by the change of solar altitude angle: effective solar altitude angle ( ), which refers to the angle between the direction of solar radiation incident on the snow surface and the vertical direction. When the cloud thickness and the solar altitude angle increase, the solar radiation incident on the snow surface becomes more scattered, and it is closer to diffuse radiation. The present invention adopts a linear weighting function, and according to the changes in cloud thickness and solar altitude angle, the increase of the diffuse part of the incident radiation within the cloud optical thickness between 0 and 30 is approximated by a linear weighting function:

[0074] .

[0075] In the formula, is the change in albedo caused by the change in the solar altitude angle. is the effective solar altitude angle, This function increases the cosine of the effective solar zenith angle ( ) is adjusted to be close to the cosine of the diffuse radiance (about 50°), which simplifies the equation while maintaining a certain degree of accuracy.

[0076] When the optical thickness is between 0 and 30, the broadband albedo of snow and ice increases with the amount of cloud cover:

[0077] .

[0078] The albedo of a thin layer of snow covering old snow is affected by the albedo of the underlying surface, and the effect of the underlying snow on the albedo decreases exponentially with depth. When there are two snow layers on the surface, the resulting albedo is:

[0079] .

[0080] in, is the albedo of the snow on the surface, is the snow albedo of the second layer, is the snow layer thickness, is the thickness of the snow layer 0, set here z 0=1cm.

[0081] S6. Determine whether it is bare ice; if so, interpolate between the ice albedo and the surface snow albedo to obtain; otherwise, calculate the overall albedo using the albedo of different layers of snow according to the calculation formula.

[0082] .

[0083] in, For the i The snow albedo of the layer, For the i -1 layer of snow albedo, For thej Snow layer thickness.

[0084] If there is snow less than 10cm thick covering the ice surface (density greater than 830kgm −3 ), the albedo is interpolated between the bare ice albedo and the surface snow albedo as a linear function of snow thickness to produce a “combined” surface albedo of snow, ice, and water :

[0085] .

[0086] in, H is the snow height in m , The albedo of bare ice is equal to 0.55.

[0087] Clear sky adjustment: The GS albedo parameterization scheme was developed for the Air Force Geophysical Laboratory (AFGL) and is suitable for low-altitude snow surfaces under subarctic summer atmospheric conditions. However, the atmospheric conditions in the Antarctic are quite different from those in the Arctic, with significantly lower temperatures and, therefore, significantly lower water vapor content. The optical thickness of clouds in the Arctic is about twice that in the Antarctic, and the optical thickness of the atmosphere has an important effect on surface albedo: the optically thin atmosphere transmits more near-infrared radiation (wavelengths > 800nm) to the surface of the snow with low spectral albedo, thereby reducing broadband albedo. Therefore, the actual clear-sky snow albedo in Antarctica will be lower than the clear-sky albedo value calculated using the GS scheme. As the surface elevation in the Antarctic increases, the atmosphere becomes thinner and the decrease will increase accordingly; the present invention uses an existing albedo scheme to calculate the difference between the simulated value and the actual albedo at surface altitudes between 0 and 3000 meters (982 to 668hPa), and the resulting correction function. As a simple solution, the scheme is based on the surface pressure p Adjust its parameterization down by a factor , the formula is:

[0088] .

[0089] in, p and The relationship between u , r and τ In general, this equation captures the expected effect of decreasing albedo as the optical thickness of the atmosphere decreases.

[0090] Based on the same inventive concept, the present invention also proposes a regional snow albedo prediction system, comprising:

[0091] The acquisition module is used to obtain the snow depth in the area to be measured.

[0092] A stratification module, used for stratifying the snow in the area to be tested according to the snow depth;

[0093] The surface snow albedo prediction module is used to determine the snow albedo of each snow layer when the snow is divided into multiple snow layers, and obtain the surface snow albedo of the area to be measured according to the snow albedo of each snow layer; wherein the snow albedo determination process of each snow layer includes: evolving the snow particle size of the snow layer; the evolution of the snow particle size includes the evolution of the dry snow particle size, the evolution of the snow particle size under wet snow conditions and the evolution of the re-frozen snow particle size; by performing the evolution of the dry snow particle size, the snow particle size under wet snow conditions and the re-frozen snow particle size The basic albedo of the snow layer is determined by weighted averaging; the albedo change value caused by the change of the solar altitude angle is determined according to the change value of the solar altitude angle of the measured area, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; the snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value; when the accumulated snow is divided into a single snow layer and there is bare ice under the single snow layer, the surface snow albedo of the measured area is obtained by interpolating between the snow albedo of the single snow layer and the albedo of the bare ice.

[0094] The present invention also proposes a computer device for predicting regional snow albedo, comprising: a memory, a processor, and a computer program stored in the memory, and the processor implements the steps of a method for predicting regional snow albedo when executing the computer program.

[0095] 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 regional snow albedo prediction method, characterized in that: The following methods are included: Get the snow depth of the area to be measured; stratifying the snow in the area to be tested according to the snow depth; When the accumulated snow is divided into multiple snow layers, the snow albedo of each snow layer is determined respectively, and the surface snow albedo of the area to be measured is obtained according to the snow albedo of each snow layer; wherein the snow albedo determination process of each snow layer comprises: evolving the snow particle size of the snow layer; the evolution of the snow particle size comprises the evolution of the dry snow particle size, the evolution of the snow particle size under wet snow conditions and the evolution of the re-frozen snow particle size; determining the basic albedo of the snow layer by weighted averaging the evolved dry snow particle size, the snow particle size under wet snow conditions and the re-frozen snow particle size; determining the albedo change value caused by the change of the solar altitude angle according to the change value of the solar altitude angle of the area to be measured, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; obtaining the snow albedo of the snow layer by summing the basic albedo of the snow layer and the albedo change value; wherein the dry snow particle size r dry The evolution process is expressed as: Among them, r i is the initial value of the snow particle size, η and κ are empirical parameters, t is the evolution time, and r is the snow particle size; The snow particle size r under wet snow conditions wet The evolution process is expressed as: Among them, C liq is the liquid water content, in kg·m -2 ; B is the liquid crystal grain growth constant, and its value is 4.22×10 - 13 m 3 / s; The evolution of the re-frozen snow particle size, the re-frozen snow particle size r frz 1000μm; The weighted average of the evolved dry snow particle size, the snow particle size under wet snow conditions, and the re-frozen snow particle size is expressed as: r(t)=(r(t-1)+dr dry +dr wet )*frc old +r0*frc new +r frz *frc_re, Among them, r(t) represents the weighted average snow particle size; frc old represents the dry snow particle size after evolution; frc new represents the snow particle size after evolution under wet snow conditions; frc_re represents the re-frozen snow particle size after evolution, and r0 is the new snow particle size; The base albedo α of the snow layer s The calculation process is expressed as: α s ＝1.48-1.27043r(t) 0.07 ； The albedo change value dα caused by the change in the solar altitude angle is determined according to the change in the solar altitude angle of the measured area, the cloud optical thickness, and the light-absorbing carbon concentration in the ice and snow. θ , expressed as: dα θ =0.53α s (1-dα c )(1-0.64x-(1-x)cosθ) 1.2 , in, θ is the effective solar altitude angle, τ is the optical thickness of the cloud layer, dα c Represents the albedo of ice and snow; the albedo of ice and snow is determined by the basic albedo and the concentration of light-absorbing carbon in ice and snow. The albedo of ice and snow dα c It is expressed as: Where S is the surface area of ​​snow particles per unit mass, and c represents the concentration of light-absorbing carbon in ice and snow; The snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value, which is expressed as: a i =a s +dα c +dα θ +dα cloud , Among them, α i is the snow albedo of the i-th layer, dα cloud represents the broadband albedo of ice and snow, the broadband albedo of ice and snow dα cloud It is expressed as: According to the snow albedo α of each snow layer i , the surface snow albedo α of the measured area is obtained, and the calculation process is expressed as: Among them, α1 is the albedo of the snow on the surface, z is the thickness of the snow layer, and α i is the snow albedo of the i-th layer, α i-1 is the snow albedo of the i-1th layer, z j is the thickness of the jth snow layer, z0 is the thickness of the 0th snow layer; When the snow is divided into a single snow layer, and there is bare ice under the single snow layer, the surface snow albedo of the area to be measured is obtained by interpolating between the snow albedo of the single snow layer and the albedo of bare ice; wherein the surface snow albedo of the area to be measured is obtained by interpolating between the snow albedo of the single snow layer and the albedo of bare ice. * , expressed as: α * =α I +α i (Hs / 0.1), Where Hs is the snow height in meters; α I is the albedo of bare ice, which is 0.

55.

2. A regional snow albedo prediction system, characterized in that: include: An acquisition module is used to obtain the snow depth in the area to be measured; A stratification module, used for stratifying the snow in the area to be tested according to the snow depth; The surface snow albedo prediction module is used to determine the snow albedo of each snow layer when the snow is divided into multiple snow layers, and obtain the surface snow albedo of the area to be measured according to the snow albedo of each snow layer; wherein the snow albedo determination process of each snow layer includes: evolving the snow particle size of the snow layer; the evolution of the snow particle size includes the evolution of the dry snow particle size, the evolution of the snow particle size under wet snow conditions and the evolution of the re-frozen snow particle size; by weighted averaging the evolved dry snow particle size, the snow particle size under wet snow conditions and the re-frozen snow particle size, The basic albedo of the snow layer is determined; the albedo change value caused by the change of the solar altitude angle is determined according to the change value of the solar altitude angle of the area to be measured, the cloud optical thickness and the light-absorbing carbon concentration in the ice and snow; the snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value; when the accumulated snow is divided into a single snow layer and there is bare ice under the single snow layer, the surface snow albedo of the area to be measured is obtained by interpolating between the snow albedo of the single snow layer and the albedo of the bare ice; wherein the dry snow particle size r dry The evolution process is expressed as: Among them, r i is the initial value of the snow particle size, η and κ are empirical parameters, t is the evolution time, and r is the snow particle size; The snow particle size r under wet snow conditions wet The evolution process is expressed as: Among them, C liq is the liquid water content, in kg·m -2 ; B is the liquid crystal grain growth constant, and its value is 4.22×10 - 13 m 3 / s; The evolution of the re-frozen snow particle size, the re-frozen snow particle size r frz 1000μm; The weighted average of the evolved dry snow particle size, the snow particle size under wet snow conditions, and the re-frozen snow particle size is expressed as: r(t)=(r(t-1_+dr dry +dr wet )*frc old +r0*frc new +r frz *frc_re, Among them, r(t) represents the weighted average snow particle size; frc old represents the dry snow particle size after evolution; frc new represents the snow particle size after evolution under wet snow conditions; frc_re represents the re-frozen snow particle size after evolution, and r0 is the new snow particle size; The base albedo α of the snow layer s The calculation process is expressed as: α s ＝1.48-1.27043r(t) 0.07 ； The albedo change value dα caused by the change in the solar altitude angle is determined according to the change in the solar altitude angle of the measured area, the cloud optical thickness, and the light-absorbing carbon concentration in the ice and snow. θ , expressed as: dα θ =0.53α s (1-dα c )(1-0.64x-(1-x)cosθ) 1.2 , in, θ is the effective solar altitude angle, τ is the optical thickness of the cloud layer, dα c Represents the albedo of ice and snow; the albedo of ice and snow is determined by the basic albedo and the concentration of light-absorbing carbon in ice and snow. The albedo of ice and snow dα c It is expressed as: Where S is the surface area of ​​snow particles per unit mass, and c represents the concentration of light-absorbing carbon in ice and snow; The snow albedo of the snow layer is obtained by summing the basic albedo of the snow layer and the albedo change value, which is expressed as: a i =a s +dα c +dα θ +dα cloud , Among them, α i is the snow albedo of the i-th layer, dα cloud represents the broadband albedo of ice and snow, the broadband albedo of ice and snow dα cloud It is expressed as: According to the snow albedo α of each snow layer i , the surface snow albedo α of the measured area is obtained, and the calculation process is expressed as: Among them, α1 is the albedo of the snow on the surface, z is the thickness of the snow layer, and α i is the snow albedo of the i-th layer, α i-1 is the snow albedo of the i-1th layer, z j is the thickness of the jth snow layer, z0 is the thickness of the 0th snow layer; The surface snow albedo α of the area to be measured is obtained by interpolating between the snow albedo of the single snow layer and the bare ice albedo * , expressed as: α * =α I +α i (Hs / 0.1) Where Hs is the snow height in meters; α I is the albedo of bare ice, which is 0.

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3. A computer device for predicting regional snow albedo, characterized in that: include: A memory, a processor and a computer program stored in the memory, wherein the processor implements the steps of the regional snow albedo prediction method according to any one of claim 1 when executing the computer program.

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