A method for dynamic evaluation of near-surface icing risk in complex mountainous areas

Abstract

The application relates to the technical field of cryosphere science, and provides a near-surface icing risk dynamic evaluation method for complex mountainous areas, which comprises the following steps: introducing a radiation terrain factor, a snow albedo suppression mechanism and a wind cooling correction for a gridded meteorological and surface temperature input field, and constructing an energy index; obtaining a humidity index by comprehensively considering precipitation, snowmelt, humidity and humidity degree and other elements; and introducing a persistence background index of an annual / seasonal scale as a background enhancement factor, so as to comprehensively consider the energy index, the humidity index, the background enhancement factor and an icing phase indicator, and obtain a corresponding icing risk score. In this way, a systematic process framework from data preprocessing, physical mechanism diagnosis, hydrological process constraint to risk comprehensive evaluation can be provided, so that the near-surface icing risk of the complex mountainous area can be efficiently and accurately evaluated.

Description

Technical Field

[0001] This application relates to the field of cryosphere science and technology, and to, but is not limited to, a method for dynamic assessment of near-surface icing risk in complex mountainous areas. Background Technology

[0002] Near-surface icing is a complex meteorological disaster caused by the combined effects of low temperature, moisture, and persistence. Mountainous areas have large elevation differences and complex terrain. The processes of temperature lapse rate, topographic radiation differences, wind field channel effects, and precipitation phase transitions are intertwined and superimposed, resulting in strong spatiotemporal heterogeneity of icing risk. This limits the applicability of traditional monitoring and early warning methods in high-altitude and complex terrain areas.

[0003] Among related technologies, the assessment of near-surface icing mainly includes the following methods: 1. Using spatial interpolation methods based on ground station observations to assess near-surface icing in mountainous areas. However, this method suffers from limitations such as a limited number of stations in high-altitude areas and uneven spatial distribution, with observations concentrated in valleys and around settlements, making it difficult to represent the microclimate characteristics of key areas such as high altitudes and leeward slopes. Furthermore, pure interpolation methods lack constraints on physical processes such as topographic shading, differences in slope aspect radiation, and redistribution of precipitation, leading to systematic biases in areas with large altitude differences and strong micro-topography. 2. Using a threshold discrimination method based on single-satellite remote sensing land surface temperature (LST) to assess near-surface icing in mountainous areas. When thermal infrared remote sensing cannot penetrate clouds and fog, this method will produce significant underreporting, and it also suffers from limited satellite transit frequency, making it difficult to characterize continuous nighttime cooling and rapid intraday changes. 3. The dynamic simulation method based on mesoscale numerical weather prediction models for evaluating near-surface icing in mountainous areas has the following problems: high-resolution simulation requires high computational resources and high accuracy of boundary conditions, making it difficult to support large-scale, long-term routine operation, and it is difficult to analyze the local icing differences caused by micro-topography such as canyons, mountain passes, and winding mountain roads.

[0004] Therefore, how to efficiently and accurately assess the risk of near-surface icing in complex mountainous areas has become an urgent problem to be solved. Summary of the Invention

[0005] Based on the above problems, this application provides a method for dynamic assessment of near-surface icing risk in complex mountainous areas. It aims to achieve efficient and accurate assessment of near-surface icing risk in complex mountainous areas by leveraging the provided systematic process framework, which includes data preprocessing, physical mechanism diagnosis, hydrological process constraints, and comprehensive risk assessment.

[0006] The technical solution of this application embodiment is implemented as follows:

[0007] This application provides a method for dynamic assessment of near-surface icing risk in complex mountainous areas. The method includes: integrating the digital elevation model (DEM) of the mountainous area to be assessed and the daily meteorological data of the ERA5-Land system for the mountainous area to be assessed to obtain a rasterized meteorological and surface temperature input field for the mountainous area to be assessed; and processing each grid cell in the rasterized meteorological and surface temperature input field... : Get merged grid At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor within the grid; for the raster At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Humidity index within; determination with grid The freeze-thaw index is correlated with freezing days and thawing days, and a background enhancement factor integrating freezing days and freeze-thaw index is constructed; energy index, humidity index, background enhancement factor and raster are then used. At the preset time step The icing phase indicator within the grid is substituted into the daily risk scoring function to obtain the raster. At the preset time step Icing risk assessment within the field; integrating all grids in the rasterized meteorological and surface temperature input field at a preset time step. The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. Ice risk map inside.

[0008] The beneficial effects of the technical solutions provided in this application include at least the following:

[0009] The near-surface icing risk dynamic assessment method for complex mountainous areas provided in this application embodiment first integrates the digital elevation model (DEM) and daily ERA5-Land meteorological data of the mountainous area to be assessed to obtain a rasterized meteorological and surface temperature input field for the mountainous area to be assessed; then, for each grid in the rasterized meteorological and surface temperature input field... : Get merged grid At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor within the grid; for the raster At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Humidity index within; determination with grid The freeze-thaw index is correlated with freezing days and thawing days, and a background enhancement factor integrating freezing days and freeze-thaw index is constructed; energy index, humidity index, background enhancement factor and raster are then used. At the preset time step The icing phase indicator within the grid is substituted into the daily risk scoring function to obtain the raster. At the preset time step Icing risk assessment within the area; finally, integrating all grids in the rasterized meteorological and surface temperature input fields at a preset time step. The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. An icing risk map within the area. Thus, by integrating high-resolution DEM reprojection and daily meteorological data from the ERA5-Land region to be assessed, a rasterized meteorological and surface temperature input field is obtained, mitigating systematic errors caused by the scarcity and location bias of meteorological stations in complex mountainous areas. Based on this, on the one hand, a radiation topography factor is introduced to comprehensively characterize slope, aspect, and topographic shadow (or shortwave radiation correction) to reflect windward / leeward and sunny / shaded differences. This is combined with the snow albedo suppression mechanism, which reflects the net energy reduction and warming inhibition caused by increased snow cover, as well as the superposition... A wind-cooling correction, reflecting the promoting effect of increased wind speed on cooling and icing maintenance, is added to construct an energy index, an energy environment indicator, to characterize the combined impact of surface heat gain and loss. On the other hand, a humidity index is obtained by integrating factors such as precipitation, snowmelt, humidity, and wetness, serving as a moisture constraint for risk assessment. Furthermore, a persistent background indicator at an annual / seasonal scale is introduced as a stability constraint for risk assessment, namely a background enhancement factor. Thus, by integrating the energy index, humidity index, background enhancement factor, and icing phase indicator, a corresponding icing risk score is obtained. In this way, by utilizing the provided systematic process framework from data preprocessing, physical mechanism diagnosis, hydrological process constraints to comprehensive risk assessment, the risk of near-surface icing in complex mountainous areas can be assessed efficiently and accurately.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the technical solutions provided in the embodiments of this application. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein:

[0012] Figure 1A flowchart illustrating a dynamic assessment method for near-surface icing risk in complex mountainous areas, provided as an embodiment of this application;

[0013] Figure 2 A schematic diagram of the digital elevation model of the mountainous area to be measured provided in an embodiment of this application;

[0014] Figure 3 The following is a schematic diagram of the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor of the mountainous area to be tested during a preset time period, provided for the embodiments of this application; Part (a) of the figure is a schematic diagram of the radiation topography factor of the mountainous area to be tested during a preset time period; Part (b) of the figure is a schematic diagram of the snow albedo suppression factor of the mountainous area to be tested during a preset time period; Part (c) of the figure is a schematic diagram of the wind-cooled correction factor of the mountainous area to be tested during a preset time period.

[0015] Figure 4 A schematic diagram illustrating the energy index of the mountainous area to be tested during a preset time period, provided in an embodiment of this application;

[0016] Figure 5 A schematic diagram of the air temperature and wet-bulb temperature of the mountainous area to be measured during a preset time period provided in the embodiments of this application; part (a) in the figure is a schematic diagram of the air temperature of the mountainous area to be measured during a preset time period; part (b) in the figure is a schematic diagram of the wet-bulb temperature of the mountainous area to be measured during a preset time period.

[0017] Figure 6 A schematic diagram illustrating the humidity index of the mountainous area to be tested during a preset time period, provided in an embodiment of this application;

[0018] Figure 7 A schematic diagram of the freezing day in the mountainous area to be tested, provided for an embodiment of this application;

[0019] Figure 8 A schematic diagram of the freeze-thaw index of the mountainous area to be tested, provided for an embodiment of this application;

[0020] Figure 9 A schematic diagram of the surface temperature of the mountainous area to be measured during a preset time period, provided as an embodiment of this application;

[0021] Figure 10 A schematic diagram illustrating the icing risk score of the mountainous area to be tested within a preset time period, provided as an embodiment of this application;

[0022] Figure 11 This is a schematic diagram illustrating the overall unreliability of data from the mountainous area to be tested within a preset time period, as provided in an embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0025] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0026] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0027] Example 1:

[0028] See Figure 1 The diagram shown is a flowchart illustrating a dynamic assessment method for near-surface icing risk in complex mountainous areas provided in this application embodiment. This method can be executed by an electronic device, such as a computer or server. Here, in conjunction with... Figure 1 The following explanation is provided:

[0029] Step 101: Integrate the digital elevation model (DEM) of the mountainous area to be evaluated and the daily meteorological data of the ERA5-Land of the mountainous area to be evaluated to obtain the rasterized meteorological and surface temperature input fields of the mountainous area to be evaluated.

[0030] In some embodiments, the mountainous area to be evaluated can refer to an area with high altitude, large topographic relief, steep slope, and complex terrain, which is often dominated by landforms such as mountains, hills, canyons, and valleys. Here, the digital elevation model (DEM) of the mountainous area to be evaluated is topographic elevation data with high spatial resolution of the mountainous area to be evaluated, obtained through existing datasets or remote sensing photogrammetry.

[0031] Here, the daily meteorological data of the ERA5-Land mountainous area to be evaluated is a gridded meteorological dataset with a spatial resolution of approximately 9 kilometers, generated based on the reanalysis model and assimilation system. It provides daily surface variables from 1950 to the present, including: 2-meter temperature, precipitation, wind speed, soil moisture, and snow depth.

[0032] In some embodiments, step 101 described above can be implemented in the manner of steps A1 to A3:

[0033] Step A1: Perform unified projection definition, spatial resolution resampling, and daily temporal aggregation on the daily meteorological data of ERA5-Land to obtain the basic rasterized meteorological input field.

[0034] Here, a unified spatial reference and data alignment are performed, and a unified projection, resolution and daily time step are established to reproject and resample the daily meteorological data of ERA5-Land, so as to obtain a basic rasterized meteorological input field with a consistent spatial reference.

[0035] Step A2: For each basic grid in the basic rasterized meteorological input field: the difference between the elevation value of the basic grid in the DEM and the elevation value of the basic grid in the ERA5-Land daily meteorological data is determined as the elevation residual of the basic grid. Based on the product of the temperature lapse rate and the elevation residual of the basic grid, the ERA5 temperature value of the basic grid in the ERA5-Land daily meteorological data at a preset time step is corrected to obtain the air temperature of the basic grid at the preset time step. Based on the air temperature and dew point temperature of the basic grid at the preset time step, the relative humidity of the basic grid at the preset time step is determined. Using the exposure factor of the basic grid, the daily 10m wind speed in the ERA5-Land daily meteorological data is corrected to obtain the time-varying wind speed of the basic grid.

[0036] Among them, the preset time step The daily time step.

[0037] In some embodiments, for each base grid in the base rasterized meteorological input field That is, the pixel performs the following operations:

[0038] The first step is to perform DEM-constrained temperature topography downscaling (i.e., temperature lapse rate correction). First, the base grid within the DEM... elevation value The base grid in the daily meteorological data of ERA5-Land Representative elevation value Substitute into formula (1) to calculate the basic grid. Elevation residual :

[0039] Formula (1);

[0040] here, The daily meteorological data of ERA5-Land is visually displayed on the base raster. Underestimation ( >0) or overestimated ( <0) represents the degree of actual terrain.

[0041] Then, the temperature lapse rate is used. (℃ / m, supports constant or monthly scale / regional values), use the following formula (2) to process the basic raster in the daily meteorological data of ERA5-Land. At the preset time step ERA5 temperature value Correction is performed to obtain the basic grid. At the preset time step temperature :

[0042] Formula (2);

[0043] in, The typical range of values ​​is Its default value is 0.0065℃ / m.

[0044] The second step is to start with the basic grid. At the preset time step temperature With the preset time step Dew point temperature Calculate relative humidity Here, the saturated vapor pressure can be calculated first using the Magnus formula as shown in formula (3). With actual water vapor pressure :

[0045] Formula (3);

[0046] in, and The unit is hPa.

[0047] Correspondingly, the basic grid At the preset time step relative humidity It can be calculated using the following formula (4):

[0048] Formula (4);

[0049] in, This is the threshold truncation function.

[0050] The third step is to use the basic grid. At the preset time step Wind speed changes Replace the constant wind speed. Here, we directly use formula (5) for the 10m wind speed in the daily meteorological data of ERA5-Land: Secondary corrections were made based on terrain exposure / roughness to obtain the time-varying wind speed. :

[0051] Formula (5);

[0052] in, Basic grid The exposure factor is also the wind speed terrain correction factor.

[0053] It should be noted that the basic grid The precipitation input and phase discrimination preparation can be directly obtained by taking the total precipitation in the daily meteorological data of ERA5-Land as the main moisture input and calculating wet-bulb temperature, etc. without any correction.

[0054] Step A3: Based on the time-varying temperature, relative humidity, and wind speed of all basic grids in the basic gridded meteorological input field at the preset time step, the data in the basic gridded meteorological input field are corrected accordingly to obtain the gridded meteorological and surface temperature input field of the mountainous area to be evaluated.

[0055] In some embodiments, for the mountainous area to be evaluated, firstly, a unified projection, resolution, and daily time step are established to obtain a unified spatiotemporal framework. Then, the daily meteorological data of ERA5-Land, i.e. the daily variables, are reprojected and resampled under this unified spatiotemporal framework to obtain the basic input field. Then, the high-resolution DEM is projected onto the same projection and aggregated into the grid of the basic input field to generate a representative elevation surface corresponding to the ERA5 pixels, thereby obtaining the rasterized meteorological and surface temperature input field of the mountainous area to be evaluated.

[0056] In this way, using reanalysis meteorological data (ERA5-Land daily meteorological data) and multi-source remote sensing products (DEM) as inputs, a high-resolution rasterized meteorological and surface temperature input field suitable for the mountainous area to be evaluated is generated. The input field is then corrected by characterizing the energy processes under complex terrain. A dynamic filling mechanism of remote sensing observation and model simulation is established to perform spatiotemporal interpolation of the downscaled meteorological field, so as to alleviate the systematic errors caused by the scarcity of stations and location bias in complex mountainous areas in the existing technology.

[0057] Step 102: For each grid cell in the rasterized meteorological and surface temperature input field : Get merged grid At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor within the grid; for the raster At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Humidity index within; determination with grid The freeze-thaw index is correlated with freezing days and thawing days, and a background enhancement factor integrating freezing days and freeze-thaw index is constructed; energy index, humidity index, background enhancement factor and raster are then used. At the preset time step The icing phase indicator within the grid is substituted into the daily risk scoring function to obtain the raster. At the preset time step Ice risk score within the area.

[0058] Here, for each grid cell in the gridded meteorological and surface temperature input field... In step 102, "obtain the merged raster" At the preset time step Before determining the energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor, the method may further perform the following steps B1 to B4:

[0059] Step B1, grid slope Slope aspect and grid At the preset time step solar altitude angle Sun azimuth Substituting into the formula for calculating the cosine of the incident angle, we obtain the grid. At the preset time step Cosine term of solar incidence angle within .

[0060] The formula for calculating the cosine of the incident angle is:

[0061] Formula (6);

[0062] It should be noted that the grid slope Slope aspect These are terrain-derived parameters, which can be calculated from the DEM of the mountainous area to be evaluated. And the raster... At the preset time step Solar altitude angle within Sun azimuth It can be calculated based on the aspect / slope + time / latitude and longitude extracted from the DEM of the mountainous area to be evaluated.

[0063] Step B2, cosine of the solar incidence angle Normalization and terrain shadow indication correction are performed sequentially to obtain the raster. At the preset time step Radiation topography factor within .

[0064] In some embodiments, the cosine term of the solar incidence angle can be achieved through the following formula (7). Normalization processing to obtain a raster At the preset time step Radiation topography factors to be processed within .

[0065] Formula (7);

[0066] in, and They are grids The angle at which the sun's incident angle is most oblique, and the angle at which the sun's incident angle is most positive (closest to the zenith) throughout the entire calculation period; This is the threshold truncation function.

[0067] Correspondingly, the radiation topography factor to be processed can be realized through the following formula (8). Perform terrain shadow indication correction to obtain a raster. At the preset time step Radiation topography factor within .

[0068] Formula (8);

[0069] in, This is the shadow suppression coefficient, with a typical value range of [0.3, 0.7] and a default value of 0.5; For grid At the preset time step The terrain shadow indicator factor within, and ∈{0,1}.

[0070] Step B3: Grid At the preset time step Snow cover indicator and albedo suppression intensity Substituting these values ​​into the albedo suppression calculation formula, we obtain the raster. At the preset time step Snow albedo inhibitor within .

[0071] The formula for calculating albedo suppression is as follows:

[0072] Formula (9);

[0073] here, The default value is 0.4, and the typical value range is [0.2, 0.6].

[0074] Step B4: Grid At the preset time step Wind speed variation within After normalization, the relative intensity index of the wind-cooling effect is obtained. Using the air-cooled conversion formula, the relative intensity index is... Convert to raster At the preset time step Internal air-cooling correction factor .

[0075] The air-cooling conversion formula is as follows:

[0076] Formula (10);

[0077] in, The value is the air-cooling suppression strength, with a default value of 0.4 and a typical value range of [0.2, 0.6].

[0078] Here, the relative intensity index of the wind-cooling effect. As a heat dissipation enhancement, the energy index decreases as wind speed increases.

[0079] Here, the following formula (11) can be used to divide the grid. At the preset time step Wind speed variation within After normalization, the relative intensity index of the wind-cooling effect is obtained. :

[0080] Formula (11);

[0081] in, and These are the predefined minimum and maximum wind speed time variations, applicable to the entire mountainous area under evaluation and the entire study period; This is the threshold truncation function.

[0082] Thus, after calculating the grid At the preset time step Radiation topography factor within Snow albedo inhibitor and air-cooling correction factor Then, a corresponding grid can be constructed. The dimensionless index representing the combined effects of surface energy gain and loss, namely the energy index. and use it as a grid At the preset time step The energy index for risk assessment within. Correspondingly, in step 102 above, "obtaining the fused grid..." At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor can be achieved through the following steps B5 and B6:

[0083] Step B5: Apply a preset energy weighting ratio to the radiation topography factor. Snow albedo inhibitor and air-cooling correction factor Weighted fusion is performed to obtain a raster. At the preset time step Initial constraints within.

[0084] In some embodiments, as shown in formula (12) below, the radiation topography factor can be... Snow albedo inhibitor and air-cooling correction factor Weighted fusion is performed to obtain a raster. At the preset time step Initial constraints within :

[0085] ;Formula (12);

[0086] in, , , These are preset energy weights, and .here, , , The values ​​can be 0.4, 0.3, and 0.3 respectively.

[0087] Step B6, for the grid At the preset time step The initial constraints within the grid are normalized to obtain the raster. At the preset time step Internal energy index .

[0088] Here, the following formula (13) can be used to apply the grid. At the preset time step Initial constraints within Normalization is performed to obtain a raster. At the preset time step Internal energy index :

[0089] Formula (13);

[0090] in, This is the threshold truncation function.

[0091] In this way, by coupling the radiation topography factor with radiation topography correction, the snow feedback factor with snow albedo suppression factor, and the wind chill effect with wind chill correction factor, a multi-factor weighted energy index is constructed. This allows for the creation of an energy environment index to characterize the combined effects of surface heat gain and loss. Specifically, the radiation topography factor is introduced to comprehensively represent slope, aspect, and topographic shadow (or shortwave radiation correction), reflecting the differences between windward / leeward and sunny / shaded areas. Combined with the snow albedo suppression factor, it reflects the net energy reduction and temperature inhibition caused by increased snow cover. The wind chill correction factor is superimposed to reflect the promoting effect of increased wind speed on cooling and icing maintenance. This achieves a calculable expression of energy budget in complex terrain, providing parameter support for subsequent freezing risk assessment.

[0092] In some embodiments, during step 102, "for the grid" At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Before checking the humidity index, you can also perform the following steps C1 to C3:

[0093] Step C1: Integrate the grid At the preset time step Intra-ice phase indication Grid At the preset time step Effective precipitation within and effective precipitation Corresponding effective precipitation triggering indicator , obtain the grid At the preset time step Internal freezing water source triggering amount .

[0094] In some embodiments, grid At the preset time step Intra-ice phase indication The acquisition process includes:

[0095] First, the grid At the preset time step Indoor temperature relative humidity Substituting into the wet-bulb temperature calculation formula, we obtain the grid. At the preset time step wet-bulb temperature .

[0096] The formula for calculating wet-bulb temperature is as follows:

[0097] Formula (14);

[0098] in, It is the square root function; It is the arctangent function.

[0099] Then, grid At the preset time step wet-bulb temperature and temperature Substituting the phase state recognition and attachment icing trigger functions into the calculation, the grid is obtained. At the preset time step Intra-ice phase indication .

[0100] The phase recognition and attachment icing trigger functions are as follows:

[0101] Formula (15);

[0102] in, For indicator functions; , Wet-bulb temperature The minimum and maximum thresholds are used for identifying supercooled water / sleet sensitive areas, with default values ​​of: =-2℃; =1℃.

[0103] Here, formula (15) means when and Only when this occurs is it determined to be an attachment-induced icing trigger.

[0104] In some embodiments, effective precipitation Corresponding effective precipitation triggering indicator That is: .here, The minimum effective precipitation is set to 0.5 mm / day by default, with a typical range of [0.1, 1].

[0105] In some embodiments, the grid can be integrated with reference to formula (16). At the preset time step Intra-ice phase indication Grid At the preset time step Effective precipitation within and effective precipitation Corresponding effective precipitation triggering indicator , obtain the grid At the preset time step Internal freezing water source triggering amount :

[0106] Formula (16);

[0107] Among them, grid At the preset time step Internal freezing water source triggering amount This refers to the amount of water that triggers freezing due to precipitation.

[0108] Step C2, grid At the preset time step Indoor temperature Substituting into the snowmelt water source estimation formula, we obtain the grid. At the preset time step snowmelt within .

[0109] The formula for estimating snowmelt water sources is as follows:

[0110] Formula (17);

[0111] in, Preset time step The day factor is measured in mm / (℃·day), with a default value of 3 mm / (℃·day) and a typical range of [2, 6]. This is the critical temperature threshold for snow melting. For grid At the preset time step The corresponding snow cover factor; This is the function for finding the maximum value.

[0112] here, The value range is [-13.5℃, 0℃].

[0113] In some embodiments, the grid can be directly applied. At the preset time step snowmelt within as a grid At the preset time step Additional water supply .

[0114] Step C3: Grid The ratio of catchment area Grid slope Substituting these values ​​into the terrain wet background calculation formula, the raster is obtained. Topographic moisture .

[0115] The formula for calculating the topographically humid background is as follows:

[0116] Formula (18);

[0117] in, It is the natural logarithm function. This is the terrain wetting correction amount, which typically ranges from [0.0001, 0.01].

[0118] Correspondingly, in step 102, "for the grid" At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step The humidity index inside can be achieved through the following steps C4 to C6:

[0119] Step C4: For each grid At the preset time step Internal freezing water source triggering amount , snowmelt amount relative humidity and topographic moisture After normalization, the freezing water source triggering factor, snow melting factor, relative humidity factor, and topographic wetting factor were obtained.

[0120] In some embodiments, the following formulas (19) to (22) can be used to correspondingly apply to the grid. At the preset time step Internal freezing water source triggering amount , snowmelt amount relative humidity and topographic moisture After normalization, the freezing water source triggering factor was obtained. Snow melting factor Relative humidity factor and topographic wetting factors :

[0121] Formula (19);

[0122] Formula (20);

[0123] Formula (21);

[0124] Formula (22);

[0125] in, This is a reference value for triggering freezing of water sources; This is a reference amount for snow melting. and These are the minimum and maximum threshold values ​​for relative humidity, respectively. and These are the maximum and minimum thresholds for terrain moisture content, respectively; This is the threshold truncation function.

[0126] Step C5: According to the preset humidity weighting ratio, the freezing water source triggering factor, snow melting factor, relative humidity factor, and terrain wetting factor are weighted and fused to obtain a raster. At the preset time step The initial humidity inside.

[0127] In some embodiments, as shown in formula (23) below, the freezing water source triggering factor can be... Snow melting factor Relative humidity factor and topographic wetting factors Weighted fusion is performed to obtain a raster. At the preset time step Initial humidity inside :

[0128] Formula (23);

[0129] in, , , , These are the preset humidity weights, and .here, , , , The values ​​can be 0.4, 0.2, 0.2, and 0.2 in sequence.

[0130] Step C6, for the grid At the preset time step The initial humidity inside is normalized to obtain a grid. At the preset time step indoor humidity index .

[0131] Here, the following formula (24) can be used to apply the grid. At the preset time step Initial humidity inside Normalization is performed to obtain a raster. At the preset time step indoor humidity index :

[0132] Formula (24);

[0133] in, This is the threshold truncation function.

[0134] In this way, precipitation phase discrimination, snowmelt factor, topographic wetness and relative humidity are coupled to construct a humidity index, thereby realizing the calculable expression of surface water and providing parameter support for subsequent freezing risk assessment.

[0135] In some embodiments, the "determine and grid" step 102 above The freeze-thaw index, which is related to the freezing days and thawing days, and the background enhancement factor that integrates the freezing days and freeze-thaw index, can be constructed through the following steps D1 to D3:

[0136] Step D1: Grid Frozen life Living by melting Substituting these values ​​into the freeze-thaw index calculation formula, we obtain the raster. Freeze-thaw index .

[0137] The freeze-thaw index is calculated using the following formula:

[0138] Formula (25);

[0139] in, This is the freeze-thaw correction value, and its range is usually

[10] . -10 10 -6 ].

[0140] It should be noted that Freezing Degree Days (FDD) and Melting Degree Days (MDD) refer to the cumulative intensity of daily average temperature below 0°C over a period of time (T) and the cumulative intensity of daily average temperature above 0°C over a period of time (T), respectively. In this embodiment of the application, T is taken as 1 day. The following formulas (26) and (27) give the freezing degree days. Living by melting :

[0141] Formula (26);

[0142] Formula (27);

[0143] in, It is a function for maximizing the value; For grid At the preset time step The temperature inside.

[0144] Step D2: Analyze the frozen days separately. and freeze-thaw index After normalization, the frozen daily life index is obtained. and freeze-thaw index .

[0145] Here, using formulas (28) and (29), the frozen daily life is respectively... and freeze-thaw index After normalization, the frozen daily life index is obtained. and freeze-thaw index :

[0146] Formula (28);

[0147] Formula (29);

[0148] in, and These are the minimum and maximum thresholds for the degree of freezing, respectively. and These are the maximum and minimum thresholds for the freeze-thaw index, respectively. This is the threshold truncation function.

[0149] Step D3: Use the background enhancement integration formula to freeze the daily life index. and freeze-thaw index The integration process yields the enhancement factor to be processed. and the enhancement factor to be treated Normalization is performed to obtain a raster. Background enhancement factor .

[0150] The background enhancement integration formula is as follows:

[0151] Formula (30);

[0152] in, , These are the background enhancement weights, and α1 + α2 = 1.

[0153] In this way, the freeze-thaw index and freezing days serve as a continuous constraint layer to set the annual background, so as to achieve a continuous and dynamic unity of freezing risk.

[0154] Here, the enhancement factor to be treated can be calculated using the following formula (31). Normalization is performed to obtain a raster. Background enhancement factor :

[0155] Formula (31);

[0156] in, This is the threshold truncation function.

[0157] In some embodiments, the daily risk scoring function in step 102 is represented by the following formula (32):

[0158] Formula (32);

[0159] in, For grid At the preset time step Ice risk score within; For grid At the preset time step Indicator of the icing phase within; For grid At the preset time step The humidity index inside; For grid At the preset time step The energy index within; For grid Background enhancement factor; For grid At the preset time step The surface temperature within; For grid At the preset time step The temperature inside; For grid The freeze-thaw index; For grid At the preset time step The influence of wind speed within the area; For grid At the preset time step Internal output heat flux; For grid At the preset time step Total dissipation within; , , , , , , , , , All are risk score weights; and ; This is a threshold truncation function; This is an indicator function.

[0160] in, For grid At the preset time step The sum of the evaporation and sublimation within the body is the total dissipation.

[0161] It should be noted that, , The data can be obtained from ground observations or remote sensing inversion of the mountainous area to be evaluated. The method of acquisition can be determined according to actual needs, and this application does not impose any restrictions on it. Meanwhile, It can be calculated from meteorological data of the mountainous area to be evaluated, and the calculation method can be determined according to actual needs; It can be calculated from surface evapotranspiration observations, remote sensing evapotranspiration products, reanalysis data, or land surface process models of the mountainous area to be evaluated, and this application does not impose any limitations on this.

[0162] In some embodiments, , , , , , , , , The determination can be based on historical disaster samples, expert experience weighting, analytic hierarchy process (AHP), entropy weighting, or machine learning training results, and all of these must meet the normalization constraint conditions.

[0163] Here, the calculations obtained above are used: , , , Substituting these values ​​into formula (32) yields the grid. At the preset time step The icing risk score is set within the range of [0, 1].

[0164] Step 103: Integrate all grids in the rasterized meteorological and surface temperature input field at a preset time step. The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. Ice risk map inside.

[0165] In some embodiments, all grids in the rasterized meteorological and surface temperature input field can be integrated at a preset time step according to the positional relationship between all grids. The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. Ice risk map inside.

[0166] Here, according to preset risk level classification rules, all grids in the rasterized meteorological and surface temperature input field can be classified at preset time steps. The icing risk score within the grid is used to classify the risk levels, thereby determining the risk level of each grid at a preset time step. The icing risk level within the area; among which, the preset risk level classification rules divide it into four levels, as follows:

[0167] 1. Level 0: This means there is no or very low risk of icing.

[0168] 2. Level 1: This means low risk of icing.

[0169] 3. Level 2: This means extremely low risk of icing.

[0170] 4. Level 3: This means a high risk of icing.

[0171] in, , , Possible values ​​are 0.25, 0.50, and 0.75. Furthermore, these values ​​can be optimized in real-time using historical samples.

[0172] Thus, after obtaining all grid cells at the preset time step... After determining the icing risk score and icing risk level within the area, the corresponding mountainous region to be assessed will be assessed at a predetermined time step. The icing risk map within is updated by filling in the icing risk level of each grid cell.

[0173] In some embodiments, after performing step 103, this application may further perform the following steps E1 to E3:

[0174] Step E1: Obtain the raster At the preset time step The data availability index, phase threshold neighborhood sensitivity, terrain complexity representativeness error, and downscaling error indication are included.

[0175] In some embodiments, a key input effective ratio can be set regarding data availability. ∈[0,1], corresponding to the raster At the preset time step Data availability metrics within .

[0176] Correspondingly, regarding grids At the preset time step Phase threshold neighborhood sensitivity can be used. This is represented by the following formula (33):

[0177] Formula (33);

[0178] in, The wet-bulb temperature threshold, grid At the preset time step wet-bulb temperature The closer to the threshold center, The more unstable; It is an absolute value function; It is a natural exponential function; This is the smoothing coefficient for wet-bulb temperature.

[0179] The grid can be processed using formula (34). slope Calculations are performed to obtain the grid. Representative error of terrain complexity :

[0180] Formula (34);

[0181] in, and These are the minimum and maximum slope thresholds, respectively. This is the threshold truncation function.

[0182] Meanwhile, grid Downscaling error indication It can be obtained through the following formula (35):

[0183] Formula (35);

[0184] in, For grid The slope difference, i.e., the elevation residual; This is a reference value for the slope difference; It is an absolute value function; This is the threshold truncation function.

[0185] Step E2: Using a confidence weight ratio, the data availability index, phase threshold neighborhood sensitivity, terrain complexity representativeness error, and downscaling error indication are weighted and fused to obtain the confidence level to be processed.

[0186] In some embodiments, as shown in formula (36) below, the data availability metric is... Phase threshold neighborhood sensitivity Representative error of terrain complexity and downscaling error indication Weighted fusion is performed to obtain a raster. At the preset time step The credibility of the pending processing :

[0187] Formula (36);

[0188] in, , , , These are the credibility weights, and Here, it should , , , The values ​​can be 0.35, 0.25, 0.20, and 0.20, respectively.

[0189] Step E3: Normalize the confidence level to be processed to obtain the raster. At the preset time step The overall credibility of the data within.

[0190] Here, the following formula (37) can be used to apply the grid. At the preset time step The credibility of the pending processing Normalization is performed to obtain a raster. At the preset time step Overall credibility of the data :

[0191] Formula (37);

[0192] in, This is the threshold truncation function. Here, the raster can be further calculated. At the preset time step Data integration unreliability within :

[0193] Formula (38);

[0194] Thus, after obtaining all grid cells at the preset time step... After assessing the overall reliability of the data, the mountainous area to be evaluated will be evaluated at a predetermined time step. The icing risk map within the raster is filled with: the overall reliability / unreliability of the data for each raster. Here, it is assumed that each raster already has an icing risk score and icing risk level.

[0195] The near-surface icing risk dynamic assessment method for complex mountainous areas provided in this application embodiment first integrates the digital elevation model (DEM) and daily ERA5-Land meteorological data of the mountainous area to be assessed to obtain a rasterized meteorological and surface temperature input field for the mountainous area to be assessed; then, for each grid in the rasterized meteorological and surface temperature input field... : Get merged grid At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor within the grid; for the raster At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Humidity index within; determination with grid The freeze-thaw index is correlated with freezing days and thawing days, and a background enhancement factor integrating freezing days and freeze-thaw index is constructed; energy index, humidity index, background enhancement factor and raster are then used. At the preset time step The icing phase indicator within the grid is substituted into the daily risk scoring function to obtain the raster. At the preset time step Icing risk assessment within the area; finally, integrating all grids in the rasterized meteorological and surface temperature input fields at a preset time step. The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. An icing risk map within the area. Thus, by integrating high-resolution DEM reprojection and daily meteorological data from the ERA5-Land region to be assessed, a rasterized meteorological and surface temperature input field is obtained, mitigating systematic errors caused by the scarcity and location bias of meteorological stations in complex mountainous areas. Based on this, on the one hand, a radiation topography factor is introduced to comprehensively characterize slope, aspect, and topographic shadow (or shortwave radiation correction) to reflect windward / leeward and sunny / shaded differences. This is combined with the snow albedo suppression mechanism, which reflects the net energy reduction and warming inhibition caused by increased snow cover, as well as the superposition... A wind-cooling correction, reflecting the promoting effect of increased wind speed on cooling and icing maintenance, is added to construct an energy index, an energy environment indicator, to characterize the combined impact of surface heat gain and loss. On the other hand, a humidity index is obtained by integrating factors such as precipitation, snowmelt, humidity, and wetness, serving as a moisture constraint for risk assessment. Furthermore, a persistent background indicator at an annual / seasonal scale is introduced as a stability constraint for risk assessment, namely a background enhancement factor. Thus, by integrating the energy index, humidity index, background enhancement factor, and icing phase indicator, a corresponding icing risk score is obtained. In this way, by utilizing the provided systematic process framework from data preprocessing, physical mechanism diagnosis, hydrological process constraints to comprehensive risk assessment, the risk of near-surface icing in complex mountainous areas can be assessed efficiently and accurately.

[0196] The following describes the above-mentioned dynamic assessment method for near-surface icing risk in complex mountainous areas with a specific embodiment. However, it is worth noting that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application.

[0197] This embodiment selects the mountainous area to be tested as the study area. The high altitude and undulating terrain of this mountainous area result in a significant temperature lapse rate, strong differences in slope aspect radiation, and a prominent wind channel effect. Frequent rain-snow transitions in winter and spring make it prone to freeze-thaw cycles and icing conditions. The implementation focuses on the near-surface icing complex risk process in the mountainous area, providing a unified risk product for application scenarios such as road icing, slope icing, frost, and line icing. The study period for this embodiment is 2023. The data preparation stage involves:

[0198] 1. Basic data acquisition:

[0199] Study scope: The study area vector is the mountain boundary / natural geographical division of the mountainous area to be measured. An elevation threshold is used to form a mountain mask to highlight the sensitive area of ​​mountain icing and reduce the interference from plains.

[0200] DEM: Copernicus DEM GLO-30, used for calculating and extracting elevation, slope, aspect, and shadow / radiative topographic factors, for downscaling, radiometric correction, and uncertainty characterization, such as... Figure 2 The image shown is a schematic diagram of the digital elevation model of the mountainous area to be measured.

[0201] Meteorological reanalysis data: ERA5-Land HOURLY, 2m air temperature, dew point temperature, precipitation, 10m wind (u / v), etc., are aggregated hourly into daily averages / totals.

[0202] Remote sensing images: MODIS MOD11A1 day / night surface temperature (Ts_day / Ts_night), used to constrain surface thermal state and supplement near-surface information; MOD10A1 day-scale snow cover ratio and albedo, used to reflect the impact of snow cover on energy balance and snowmelt water supply.

[0203] 2. Data assimilation and consistency:

[0204] Multi-source data undergoes assimilation and standardization processes, unifying the coordinate reference system and spatial resolution to form a computable and comparable standardized input raster system. Refined business products employ a resolution of approximately 100m to characterize micro-topographical differences in mountainous areas; to reduce the computational overhead of daily traversal throughout the year, the event screening or coarse evaluation phase uses a resolution of approximately 1km to control memory and task size. The temporal dimension of multi-source data is aligned daily: ERA5-Land aggregates daily meteorological elements, and MODIS-related products are stitched daily with a missing data tolerance strategy to ensure continuous availability of daily sequences throughout the year.

[0205] Step Two: Case Implementation Process

[0206] 1. Construction of high-resolution meteorological and surface temperature input fields:

[0207] Within a unified scale framework, topographic downscaling of ERA5-Land air temperature is performed using DEM as the core constraint. High-resolution reconstruction of mountain air temperature is achieved through direct lapse rate correction, and relative humidity (RH) is calculated using 2m air temperature and dew point temperature to obtain the near-surface humidity field. Daily wind speed in ERA5-Land is used to replace the constant wind speed assumption to improve the time-varying nature and operational consistency of wind cooling effect. Total precipitation in ERA5-Land is incorporated into the moisture module and coupled with wet-bulb temperature for precipitation phase discrimination, providing necessary physical constraints for subsequent icing risk identification.

[0208] 2. Energy Process Characterization: Constructing energy environment indicators to characterize the combined impact of surface heat gain and loss. Introducing, for example... Figure 3The radiative topographic factors shown in section (a) comprehensively characterize slope, aspect, and topographic shading (or shortwave radiation correction), reflecting the differences between windward / leeward and sunny / shaded areas; combined with Figure 3 The snow albedo suppression factor shown in section (b) reflects the net energy reduction and temperature inhibition caused by increased snow cover; superimposed Figure 3 The wind-cooling correction factor shown in section (c) reflects the promoting effect of increased wind speed on cooling and icing maintenance; the above components are normalized and weighted, and the output is as follows: Figure 4 The energy index shown is used for risk assessment and its value ranges from 0 to 1.

[0209] 3. Determining the conditions for water supply and water presence: (e.g.) Figure 5 Part (a) shows And relative humidity calculation Figure 5 As shown in section (b): Wet-bulb temperature As an important criterion for precipitation phase transition and icing tendency; a more stringent precipitation phase identification rule is adopted: in addition to satisfying In addition to ≤0℃, it is also required Located in the [-2℃, 1℃] range, to enhance the ability to identify the process of "liquid water attaching and freezing at sub-zero temperatures"; the degree-day factor method is used to estimate snowmelt as an additional source of moisture supply, and the topographic wetness index TWI or its proxy is introduced to characterize the catchment and local wet background; a normalized humidity index is obtained by integrating precipitation, snowmelt, humidity, and wetness, such as... Figure 6 As shown, it is used as a moisture constraint in risk assessment.

[0210] 4. Persistence and Background Constraints: Annual / seasonal scale persistence background indicators are introduced as stability constraints for risk assessment. (See reference here.) Figure 7 The freezing days shown are used to characterize the accumulation of negative accumulated temperature and the intensity of the freezing background in the mountainous area under test, such as... Figure 8 The freeze-thaw index shown is used to reflect the intensity of freeze-thaw cycles in the mountainous area under test and its tendency to affect the maintenance of ice. The daily freezing degree and freeze-thaw index are used as background terms and coupled with daily triggering conditions to achieve the unity from daily-scale risk monitoring to seasonal-scale risk zoning, thereby improving the physical consistency and interpretability of the assessment results.

[0211] 5. Risk Integration and Uncertainty Output: This includes freezing conditions, persistence indicators (freezing days and freeze-thaw index), and other factors such as... Figure 9 The surface temperature and other factors shown are weighted or fused according to rules to generate daily data. Figure 10The icing risk score (0–1) and corresponding icing level (0–3) shown meet the dual requirements of continuous and graded quantities for early warning and management decisions. Addressing the issues of missing data and significant process uncertainty in the mountainous areas under testing, the system simultaneously outputs the overall data unreliability / overall data credibility, comprehensively reflecting factors such as terrain complexity, LST / snow cover missing ratio, phase threshold neighborhood sensitivity, and downscaling error. This achieves a synergistic expression of "risk results and credibility," improving usability and controllability in operational applications. (See reference here.) Figure 11 The figure shown is a schematic diagram of the overall unreliability of data from the mountainous area to be tested within a preset time period.

[0212] The corresponding real-time effects and experimental results are explained below:

[0213] Through the above implementation steps, this embodiment achieves the following results in the mountainous area to be tested:

[0214] 1. Without introducing additional dense observations from ground stations, this embodiment can achieve operational representation and stable output of near-surface icing risk in the mountainous area under test, reflecting the significant spatial heterogeneity caused by topographic radiation differences, wind cooling effects, and the superposition of low-temperature background. Compared with methods that only use surface temperature thresholds for discrimination, the results are less affected by the timing of a single remote sensing observation and missing data, and the spatial pattern is more stable; compared with meteorological field extrapolation based on station interpolation, it can better characterize key processes such as slope aspect differences and topographic shading.

[0215] 2. Through Compared to stricter precipitation phase discrimination rules, this embodiment can enhance the identification of wet freezing icing risk during the rain-snow transition period, avoiding reliance solely on... Risk of inflated prices due to dry and cold conditions ≤0℃.

[0216] 3. An organizational approach combining monthly stacks, annual statistics, and TopN multi-stage statistical results with refined products is adopted to ensure that daily risk products are operational, reportable, and readily available for emergency use across a wide range of regions throughout the year.

[0217] The above implementation results show that this application proposes a systematic process framework that can be used for comprehensive risk assessment, from data preprocessing, physical mechanism diagnosis, hydrological process constraints to risk assessment, and forms an integrated risk assessment system for near-surface icing in mountainous areas that has been upgraded from single threshold discrimination to low temperature triggering + energy modulation + moisture constraints + continuous environmental background.

[0218] In other words, this application aims to address the technical challenges in near-surface icing risk assessment in mountainous areas, including the mismatch in meteorological element scales due to station scarcity and topographic effects, missing remote sensing data, lack of moisture supply and phase discrimination mechanisms, difficulty in quantifying energy processes in complex terrain, difficulty in unifying triggering and persistence indicators, and difficulties in operational implementation. To this end, this application achieves the following technical effects:

[0219] ①. Based on the reanalysis / forecast meteorological field, the topographic downscaling and spatial constraints driven by DEM are integrated to construct a high-resolution near-surface meteorological input field (temperature, humidity, wind, etc.) suitable for the micro-topographic differences in mountainous areas, thereby mitigating the systematic errors caused by the scarcity and location bias of stations.

[0220] ②. Transform remote sensing LST from a single discriminant source to a constrained / supplementary source, and make it complementary to the meteorological field; under cloud cover or missing data conditions, introduce meteorological field and time series substitution mechanism to realize daily risk assessment with missing data completion capability, and reduce the risk of missed reporting under cloudy, rainy and snowy conditions.

[0221] ③. Construct a method to distinguish between water supply and water availability conditions, explicitly introduce information such as precipitation, relative humidity, and snowmelt, form an interpretable moisture / water supply index, and differentiate the risk difference between low temperature but water shortage and low temperature with water.

[0222] ④. A more stringent rule for determining the freezing phase is proposed, not only using air temperature. ≤0℃, also introducing wet-bulb temperature Threshold (e.g.) (It is more likely to adhere and form ice when located at [-2℃, 1℃]), thereby enabling the identification and enhanced constraint of freezing rain / rime tendency.

[0223] ⑤. By introducing radiation topographic correction (slope / aspect / shading), snow albedo suppression effect and local wind cooling correction, an energy environment index is constructed to supplement the differences in surface energy conditions that cannot be reflected by temperature alone, making the spatial pattern of risk more consistent with mountain physical processes.

[0224] ⑥. Couple daily triggering conditions (low temperature, moisture, energy) with annual / seasonal persistence indicators (such as FTI freeze-thaw index, FDD freezing degree days) to achieve a consistent expression of daily-scale risk and cumulative background, which can simultaneously support early warning (short-term) and risk zoning (long-term).

[0225] ⑦. Propose a low-memory product organization and export strategy oriented towards business delivery, such as monthly stack export, annual statistical layers, and two-stage screening and refined export of high-risk typical events, to achieve dynamic evaluation on a daily basis throughout the year.

[0226] In other words, the core idea of ​​this application is to establish a framework of physical downscaling + dynamic data fusion + process mechanism model. On the one hand, it can significantly reduce monitoring blind spots and decrease false alarms and missed alarms. It upgrades from point-based judgment to daily products of the entire grid, covering mountain roads / slopes / route corridors. It strengthens the discrimination of freezing rain / rime, and can better distinguish between dry cold and wet cold adhesion. The constructed risk assessment scheme ensures that the corresponding risk weights are only activated and superimposed on the final risk score when specific physical conditions (such as air temperature or ground temperature below freezing point) are met. On the other hand, it can form a business product system that can be reported and scheduled. Here, because this application involves a risk assessment method for near-surface icing (road icing, slope icing, crop frost, line icing, etc.) under complex mountainous terrain conditions, which integrates reanalysis meteorology, remote sensing surface temperature / snow cover and topographic radiation correction, the near-surface icing risk will be summarized into the judgment and assessment of freezing conditions (low temperature), water supply (liquid / supercooled water), persistence (freeze-thaw background), energy environment (radiation / snow albedo / wind cooling), etc., and output the corresponding uncertainty / confidence level. The annual statistics are suitable for cross-year comparison and management decision-making, the high-risk event list supports emergency review and resource allocation, and the monthly stack supports data archiving and subsequent model training.

[0227] It should be noted that, in the embodiments of this application, if the above-mentioned dynamic assessment method for near-surface icing risk in complex mountainous areas is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0228] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0229] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0230] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. The methods disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0231] The features disclosed in the several method embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments.

[0232] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic assessment method for near-surface icing risk in complex mountainous areas, characterized in that, The method includes: By integrating the digital elevation model (DEM) of the mountainous area to be evaluated and the daily meteorological data of ERA5-Land of the mountainous area to be evaluated, the rasterized meteorological and surface temperature input fields of the mountainous area to be evaluated are obtained. For each grid in the gridded meteorological and surface temperature input field : Get merged grid At the preset time step The energy index corresponding to the radiation topography factor, snow albedo suppression factor, and wind-cooled correction factor within the grid; for the raster At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step Humidity index within; determination with grid The freeze-thaw index is correlated with the freezing days and thawing days, and a background enhancement factor integrating the freezing days and freeze-thaw index is constructed; the energy index, the humidity index, the background enhancement factor, and the raster are then combined. At the preset time step The icing phase indicator within the grid is substituted into the daily risk scoring function to obtain the raster. At the preset time step Ice risk score within; Integrating all grids in the rasterized meteorological and surface temperature input field at a preset time step The aforementioned icing risk score is used to obtain the icing risk score for the mountainous area to be assessed within a preset time step. Ice risk map inside.

2. The method of claim 1, wherein, By integrating the digital elevation model (DEM) of the mountainous area to be evaluated and the daily meteorological data of the ERA5-Land system for the mountainous area, a rasterized meteorological and surface temperature input field for the mountainous area to be evaluated is obtained, including: A basic rasterized meteorological input field is obtained by performing unified projection definition, spatial resolution resampling, and daily temporal aggregation on the daily meteorological data of ERA5-Land. For each base grid in the base rasterized meteorological input field: the difference between the elevation value of the base grid in the DEM and the elevation value of the base grid in the ERA5-Land daily meteorological data is determined as the elevation residual of the base grid. Based on the product of the temperature lapse rate and the elevation residual of the base grid, the ERA5 temperature value of the base grid in the ERA5-Land daily meteorological data at a preset time step is corrected to obtain the air temperature of the base grid at the preset time step. Based on the air temperature and dew point temperature of the base grid at the preset time step, the relative humidity of the base grid at the preset time step is determined. The exposure factor of the base grid is used to correct the daily 10m wind speed in the ERA5-Land daily meteorological data to obtain the time-varying wind speed of the base grid. Based on the time-varying temperature, relative humidity, and wind speed of all basic grids in the basic gridded meteorological input field at the preset time step, the data in the basic gridded meteorological input field are corrected accordingly to obtain the gridded meteorological and surface temperature input field of the mountainous area to be evaluated.

3. The method according to claim 1, characterized in that, Acquisition of a fused grid At a predetermined time step Before the energy index corresponding to the radiation topographic factor, the snow albedo suppression factor and the wind cooling correction factor in the preset time step, the method further comprises: grid slope Slope aspect and grid At the preset time step solar altitude angle Sun azimuth Substituting into the formula for calculating the cosine of the incident angle, we obtain the grid. At the preset time step Cosine term of solar incidence angle within The formula for calculating the cosine of the incident angle is: ； Cosine of the angle of incidence of the sun Normalization and terrain shadow indication correction are performed sequentially to obtain the raster. At the preset time step Radiation topography factor within ; grid At the preset time step Snow cover indicator and albedo suppression intensity Substituting these values ​​into the albedo suppression calculation formula, we obtain the raster. At the preset time step Snow albedo inhibitor within The formula for calculating albedo suppression is as follows: ； grid At the preset time step Wind speed variation within After normalization, the relative intensity index of the wind-cooling effect is obtained. Using the air-cooled conversion formula, the relative intensity index is... Convert to raster At the preset time step Internal air-cooling correction factor The air-cooling conversion formula is as follows: ； wherein, is the air cooling suppression strength.

4. The method according to claim 3, characterized in that, Acquisition of fused grid Radiation terrain factor, snow albedo suppression factor and wind cooling correction factor corresponding to energy index in preset time step , including: Using a preset energy weighting ratio, the radiation topography factor is... Snow albedo inhibitor and air-cooling correction factor Weighted fusion is performed to obtain a raster. At the preset time step Initial constraints within; For grid At the preset time step The initial constraints within the grid are normalized to obtain the raster. At the preset time step Internal energy index .

5. The method according to claim 1, characterized in that, grid frozen water source trigger amount, snowmelt amount, relative humidity, and terrain wetness amount within a preset time step grid humidity index within a preset time step The method further comprises, before quantitatively analyzing the humidity index within a preset time step Integrated grid At the preset time step Intra-ice phase indication Grid At the preset time step Effective precipitation within and effective precipitation Corresponding effective precipitation triggering indicator , obtain the grid At the preset time step Internal freezing water source triggering amount ; grid temperature in preset time steps temperature in preset time steps , into the snowmelt water source estimation formula, to obtain the grid snowmelt amount in preset time steps snowmelt amount in preset time steps ; wherein the snowmelt water source estimation formula is: ； wherein, is a degree-day factor for a predetermined time step , is a critical temperature threshold for snowmelt occurrence; is a grid is a corresponding snow cover factor for a predetermined time step , is a max function; The grid of specific catchment area , the slope of the grid , is substituted into a terrain wetness background calculation formula to calculate the terrain wetness amount of the grid ; wherein the terrain wetness background calculation formula is: ； in, It is the natural logarithm function. This is the correction amount for terrain humidity.

6. The method according to claim 5, characterized in that, For grid At the preset time step The data on ice-freezing water source triggering, snowmelt, relative humidity, and terrain wetting were quantitatively analyzed to obtain a grid. At the preset time step The humidity index inside includes: For each grid At the preset time step Internal freezing water source triggering amount , snowmelt amount relative humidity and topographic moisture After normalization, the freezing water source triggering factor, snow melting factor, relative humidity factor, and topographic wetting factor were obtained; According to a preset humidity weighting ratio, the freezing water source triggering factor, snow melting factor, relative humidity factor, and topographic wetting factor are weighted and fused to obtain a raster. At the preset time step Initial humidity inside; For grid At the preset time step The initial humidity inside is normalized to obtain a grid. At the preset time step indoor humidity index .

7. The method according to claim 5, characterized in that, Grid At the preset time step Intra-ice phase indication The acquisition process includes: grid At the preset time step Indoor temperature relative humidity Substituting into the wet-bulb temperature calculation formula, we obtain the grid. At the preset time step wet-bulb temperature The formula for calculating wet-bulb temperature is: ； in, It is the square root function; It is the arctangent function; grid At the preset time step wet-bulb temperature and temperature Substituting the phase state recognition and attachment icing trigger functions into the calculation, the grid is obtained. At the preset time step Intra-ice phase indication The phase recognition and attachment icing trigger functions are as follows: ； in, For indicator functions; , Wet-bulb temperature The minimum and maximum thresholds.

8. The method according to claim 1, characterized in that, Determine and grid The freeze-thaw index is correlated with the freezing days and thawing days, and a background enhancement factor integrating the freezing days and freeze-thaw index is constructed, including: grid Frozen life Living by melting Substituting these values ​​into the freeze-thaw index calculation formula, we obtain the raster. Freeze-thaw index The freeze-thaw index is calculated using the following formula: ； in, This is the freeze-thaw correction amount; Frozen days and freeze-thaw index After normalization, the frozen daily life index is obtained. and freeze-thaw index ; Using a background enhancement integration formula, the frozen daily indicators are... and freeze-thaw index The integration process yields the enhancement factor to be processed. and the enhancement factor to be treated Normalization is performed to obtain a raster. Background enhancement factor The background enhancement integration formula is as follows: ； in, , These are the background enhancement weights, and α1 + α2 = 1.

9. The method according to claim 1, characterized in that, The daily risk scoring function is: ； in, For grid At the preset time step Ice risk score within; For grid At the preset time step Indicator of the icing phase within; For grid At the preset time step The humidity index inside; For grid At the preset time step The energy index within; For grid Background enhancement factor; For grid At the preset time step The surface temperature within; For grid At the preset time step The temperature inside; For grid The freeze-thaw index; For grid At the preset time step The influence of wind speed within the area; For grid At the preset time step Internal output heat flux; For grid At the preset time step Total dissipation within; , , , , , , , , All are risk score weights; and ; This is a threshold truncation function; This is an indicator function.

10. The method according to claim 1, characterized in that, Integrate all grids in the rasterized meteorological and surface temperature input field at a preset time step The icing risk score within the area is used to determine the icing risk of the mountainous region to be assessed within a preset time step. Following the icing risk map within the map, the method further includes: Get Raster At the preset time step Data availability metrics, phase threshold neighborhood sensitivity, terrain complexity representativeness error, and downscaling error indication; The credibility of the data availability index, the sensitivity of the phase threshold neighborhood, the representative error of the terrain complexity, and the downscaling error indication are weighted and fused by the credibility weight ratio to obtain the credibility to be processed. The reliability of the data to be processed is normalized to obtain a raster. At the preset time step The overall credibility of the data within.

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