Air temperature forecasting method based on dynamic approximate vertical variation rate under complex terrain

Abstract

The invention belongs to air temperature prediction, and provides a dynamic approximate vertical variation rate-based air temperature prediction method under a complex terrain, which comprises the following steps of: dynamically calculating an approximate vertical variation rate by comprehensively considering the coarse resolution mode air temperature prediction of an observation station, the coarse resolution mode terrain height and the relationship between an air temperature live value and a real terrain height; through the terrain downscaling technology, a high-resolution grid air temperature forecasting product and a high-resolution site air temperature forecasting product of an observation site are obtained, the integrating degree of the air temperature forecasting product and the real terrain change is improved, and the accuracy of air temperature forecasting under the complex terrain is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of temperature prediction, and in particular to a temperature prediction method based on dynamic approximate vertical variability under complex terrain. Background Art

[0002] Temperature forecast is an important part of meteorological services, and is of great significance to power dispatching, forest fire prevention, daily life and other fields. Affected by global warming and human activities, extreme high temperatures, low temperatures, rain, snow and ice and other meteorological disasters directly related to temperature have occurred frequently, and society has put forward higher requirements for refined temperature forecasts, from "points" to "surfaces", that is, from site forecasts to grid point forecasts. Especially in areas with complex underlying surfaces, the terrain has a significant impact on the temperature, and it is difficult to accurately forecast the temperature.

[0003] In the prior art, temperature forecasts usually rely on numerical forecast models, which simulate atmospheric physical and chemical processes to predict future weather conditions. However, the application of numerical forecast models in complex terrain areas has certain limitations. First, due to the low resolution of numerical models, it is impossible to accurately capture the impact of terrain on local weather. Secondly, numerical models are usually calculated based on the average terrain height, but in practice, slight differences in terrain height may lead to significant changes in temperature. In addition, existing temperature forecast models also show shortcomings when dealing with complex weather systems, such as cold waves, temperature changes caused by severe convective weather, etc.

[0004] For temperature forecasts in complex terrain, numerical models are difficult to accurately depict such complex underlying surface conditions. The resolution of temperature forecast results is coarse, poorly consistent with terrain changes, and low in accuracy. Therefore, it is particularly urgent to improve the refinement and accuracy of temperature forecasts in complex terrain areas. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a temperature forecasting method based on dynamic approximate vertical variability under complex terrain, so as to improve the accuracy of temperature forecasting under complex terrain.

[0006] A temperature forecasting method based on dynamic approximate vertical variability in complex terrain, comprising:

[0007] The bilinear interpolation method is used to interpolate the coarse-resolution model temperature forecast and model terrain height of sample j in the target area during the training period to the location of the observation station, and the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation station during the training period are obtained;

[0008] According to the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation site during the training period, as well as the real temperature and real terrain height of sample j at the observation site during the training period, the coarse-resolution approximate vertical variability of sample j at the observation site during the training period is calculated;

[0009] According to the coarse-resolution approximate vertical variability of sample j at the observation station during the training period and all samples during the training period, the average value of the coarse-resolution approximate vertical variability of the observation station is calculated;

[0010] The inverse distance weighted interpolation method is used to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability of each grid point on the high-resolution grid;

[0011] The coarse-resolution model temperature forecast and the coarse-resolution model terrain height of the target area are interpolated onto the high-resolution grid using the bilinear interpolation method to obtain the high-resolution model temperature forecast and the high-resolution model terrain height on the high-resolution grid;

[0012] Based on the high-resolution model temperature forecast on the high-resolution grid, the high-resolution model terrain height, the high-resolution approximate vertical variability and the true grid terrain height, the high-resolution grid temperature forecast product for each grid point on the high-resolution grid is calculated;

[0013] The high-resolution site temperature forecast product of the observation site is calculated based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the true terrain height of the observation site at each grid point on the high-resolution grid;

[0014] The high-resolution grid temperature forecast products and the high-resolution station temperature forecast products of the observation sites are determined as the final temperature forecast products of the high-resolution grids and observation sites.

[0015] Furthermore, according to the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation site during the training period, as well as the actual temperature and actual terrain height of sample j at the observation site during the training period, the coarse-resolution approximate vertical variability of sample j at the observation site during the training period is calculated, including:

[0016] The coarse resolution approximate vertical variability of sample j at the observation site during the training period is calculated using the method shown in the following formula based on the coarse resolution model temperature forecast and coarse resolution model terrain height of sample j at the observation site during the training period, as well as the actual temperature and actual terrain height of sample j at the observation site during the training period:

[0017]

[0018] In the formula, γi,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period, F i,j , O i,j denote the coarse-resolution model temperature forecast and the real temperature of sample j at observation station i during the training period, respectively, and H i,m , H i,o They represent the coarse-resolution model terrain height and the true terrain height of observation station i respectively.

[0019] Furthermore, according to the coarse resolution approximate vertical variability of sample j at the observation site during the training period and all samples during the training period, the average value of the coarse resolution approximate vertical variability of the observation site is calculated, including:

[0020] The method shown in the following formula is used to calculate the average value of the coarse resolution approximate vertical variability of the observation station based on the coarse resolution approximate vertical variability of sample j at the observation station during the training period and all samples during the training period:

[0021]

[0022] In the formula, is the average of the approximate vertical variability of the coarse-resolution site at observation site i, 3N represents all samples in the training period, and γ i,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period.

[0023] Furthermore, the inverse distance weighted interpolation method is used to interpolate the average value of the coarse-resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability of each grid point on the high-resolution grid, including:

[0024] The inverse distance weighted interpolation method is used as shown in the following formula to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution grid approximate vertical variability at the kth grid point on the high-resolution grid:

[0025]

[0026] In the formula, λ i is the weight coefficient, Y is the number of observation stations involved in the calculation, is the approximate vertical variability of the kth grid point on the high-resolution grid.

[0027] Furthermore, based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true grid terrain height of each grid point on the high-resolution grid, the high-resolution grid temperature forecast products for each grid point on the high-resolution grid are calculated, including:

[0028] The high-resolution grid temperature forecast product for the kth grid point on the high-resolution grid is calculated using the method shown in the following formula based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true grid terrain height of each grid point on the high-resolution grid:

[0029]

[0030] Where, T k ′ is the high-resolution grid temperature forecast product at the kth grid point on the high-resolution grid, F k， is the high-resolution model temperature forecast at the kth grid point on the high-resolution grid, H k,m , H k,o are the high-resolution model terrain height and the real grid terrain height at the kth grid point on the high-resolution grid, respectively.

[0031] Furthermore, based on the high-resolution model temperature forecast of each grid point on the high-resolution grid, the high-resolution model terrain height, the high-resolution approximate vertical variability and the actual terrain height of the observation site, the high-resolution site temperature forecast product of the observation site is calculated, including:

[0032] Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the actual terrain height of the observation site at each grid point on the high-resolution grid, the temperature forecast at the four high-resolution grid points on the plane with the same terrain height around the observation site i is calculated. The temperature forecast values ​​at the four grid points are calculated as follows:

[0033]

[0034] Where g is the g-th grid point among the four high-resolution grid points on the plane with the same terrain height around the observation station i, T g is the high-resolution temperature forecast at the g-th grid point, F g is the high-resolution model temperature forecast for the g-th grid point, is the high-resolution approximate vertical variability at the g-th grid point, H g,m is the high-resolution model terrain height at the g-th grid point, H g,o is the actual terrain height of the g-th grid point corresponding to site i;

[0035] Based on the calculated temperature forecasts at four high-resolution grid points on the plane with the same terrain height around observation station i, the high-resolution station temperature forecast product of the observation station is calculated using the bilinear interpolation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific embodiments of the present invention, the following will briefly introduce the drawings required for the specific embodiments or the prior art description. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0037] Figure 1 A flow chart of a temperature forecasting method based on dynamic approximate vertical variability under complex terrain provided by one embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a bilinear interpolation method provided by an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of an inverse distance weighted interpolation method provided by an embodiment of the present invention;

[0040] In the accompanying figure, (x, y) is the coordinate of the corresponding position of the observation site, (x1, y1), (x2, y1), (x1, y2), (x2, y2) are the coordinates of the corresponding positions of the four grid points at the lower left, lower right, upper left, and upper right near the observation site, respectively, (x, y1) and (x, y2) are the coordinates of the corresponding positions of the two middle grid points whose horizontal coordinates are the same as those of the observation site; k is the kth grid point, R is the interpolation radius, r1 represents the straight-line distance from the first observation site to the kth grid point, r2 represents the straight-line distance from the second observation site to the kth grid point, and r3 represents the straight-line distance from the third observation site to the kth grid point. DETAILED DESCRIPTION

[0041] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0042] In one embodiment, if Figure 1 As shown, a temperature forecast method based on dynamic approximate vertical variability under complex terrain is provided, including:

[0043] 1. Use the bilinear interpolation method to interpolate the coarse-resolution model temperature forecast and model terrain height of sample j in the target area during the training period to the location of the observation station, and obtain the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation station during the training period, including:

[0044] (1) Using the bilinear interpolation method, the coarse-resolution model temperature forecast of sample j in the target area during the training period is interpolated to the location of the observation station to obtain the coarse-resolution model temperature forecast of sample j at the observation station during the training period;

[0045] Bilinear interpolation is a commonly used numerical analysis method, which is used to interpolate data from a grid point to another grid point or non-grid point.

[0046] The training period includes multiple samples, and each sample in the training period can be regarded as a forecast day. The numerical forecast model will give a model temperature forecast result for each forecast day in the target area. In order to distinguish it from the high-resolution model temperature forecast later, it is named coarse-resolution model temperature forecast here.

[0047] like Figure 2 As shown, the bilinear interpolation method is used to interpolate the coarse-resolution model temperature forecast of sample j in the target area during the training period from the numerical forecast model to the location of the observation site to obtain the coarse-resolution model temperature forecast of sample j at the observation site during the training period.

[0048]

[0049] where F(x,y) is the coarse-resolution model temperature forecast of sample j at the observation site during the training period, (x,y) are the coordinates of the corresponding position of the observation site; F(x1,y1), F(x2,y1), F(x1,y2), and F(x2,y2) are the coarse-resolution model temperature forecasts of sample j at the four grid points at the lower left, lower right, upper left, and upper right near the observation site during the training period, respectively; (x1,y1), (x2,y1), (x1,y2), and (x2,y2) are the coordinates corresponding to the four grid points; F(x,y1) and F(x,y2) are the coarse-resolution model temperature forecasts of the two middle grid points whose abscissas are the same as those of the observation site.

[0050] Preferably, the numerical prediction models include the European Centre for Medium-Range Weather Forecasts global numerical prediction model, the China Meteorological Administration global numerical prediction model, the China Meteorological Administration regional numerical prediction model and the Chongqing mesoscale regional numerical prediction model.

[0051] (2) Using the bilinear interpolation method, the coarse-resolution model terrain height on the target area is interpolated to the location of the observation station to obtain the coarse-resolution model terrain height of the observation station;

[0052] The interpolation calculation method of the coarse-resolution model terrain height of the observation station is the same as the interpolation calculation method of the coarse-resolution model temperature forecast of the observation station, that is, the bilinear interpolation method is used to interpolate the coarse-resolution model terrain height on the target area to the location of the observation station to obtain the coarse-resolution model terrain height of the observation station. However, the coarse-resolution model terrain height of the observation station does not need to be calculated once for each sample j (i.e., each forecast day) during the training period, because the coarse-resolution model terrain height on the target area is the same on each forecast day, so the coarse-resolution model terrain height of the observation station is calculated once, and the coarse-resolution model terrain height of the observation station on each other forecast day is the same.

[0053] 2. According to the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation site during the training period, as well as the real temperature and real terrain height of sample j at the observation site during the training period, the coarse-resolution approximate vertical variability of sample j at the observation site during the training period is calculated;

[0054] Preferably, the method shown in the following formula is used to calculate the coarse resolution approximate vertical variability of sample j at the observation site during the training period based on the coarse resolution model temperature forecast and the coarse resolution model terrain height of sample j at the observation site during the training period, as well as the actual temperature and the actual terrain height of sample j at the observation site during the training period:

[0055]

[0056] In the formula, γ i,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period, F i,j , O i,j denote the coarse-resolution model temperature forecast and the real temperature of sample j at observation station i during the training period, respectively, and H i,m , H i,o They represent the coarse-resolution model terrain height and the true terrain height of observation station i respectively.

[0057] During the training period, the real temperature and real terrain height of sample j at observation site i are real values ​​measured by instruments.

[0058] 3. According to the coarse-resolution approximate vertical variability of sample j at the observation site during the training period and all samples during the training period, the average value of the coarse-resolution approximate vertical variability of the observation site is calculated;

[0059] Preferably, the method shown in the following formula is used to calculate the average value of the coarse resolution approximate vertical variability of the observation station based on the coarse resolution approximate vertical variability of sample j at the observation station during the training period and all samples during the training period:

[0060]

[0061] In the formula, is the average of the approximate vertical variability of the coarse-resolution site at observation site i, 3N represents all samples in the training period, and γ i,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period.

[0062] Optionally, all samples in the training period are mixed with data from N days before the forecast day, N days before the forecast day of the previous year, the forecast day, and N-1 days after the forecast day, with sliding sampling along the forecast day, so that all samples in the training period have 3N days.

[0063] Preferably, according to the principle of minimizing the mean absolute error, the optimal sliding training period N is determined monthly to obtain the approximate vertical variability at the observation station i that is closest to the real atmosphere. The specific steps are as follows:

[0064] For each month, N ranges from 5 to 70 days with an interval of 5 days. The approximate vertical variability average of all samples in the training period at each N value is calculated daily. Then the coarse-resolution model temperature forecast of the observation station is corrected. The specific correction method is as follows:

[0065]

[0066] In the formula, F i，m 、T i,m are the coarse-resolution model temperature forecast and temperature correction value of observation station i on day m, respectively. is the approximate average vertical variability of observation station i on the mth day.

[0067] Then the daily temperature correction values ​​of the observation stations in this month are tested. The test indicator is the mean absolute error. The specific calculation method is as follows:

[0068]

[0069] Where N1 is the number of days in the month, O i,m The actual temperature value of observation station i on the mth day, MAE i is the mean absolute error of the month, when MAE i When it is minimum, the N value at this time is the optimal sliding training period, and the approximate vertical variability average of all samples at observation station i under the optimal sliding training period is calculated monthly.

[0070] 4. Use the inverse distance weighted interpolation method to interpolate the average value of the coarse-resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability of each grid point on the high-resolution grid;

[0071] Preferably, the inverse distance weighted interpolation method is used by using the method shown in the following formula to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability at the kth grid point on the high-resolution grid:

[0072]

[0073] In the formula, λ i is the weight coefficient, Y is the number of observation stations involved in the calculation, is the approximate vertical variability of the kth grid point on the high-resolution grid.

[0074] Preferably, the weight coefficient λ i The calculation formula is as follows:

[0075]

[0076] in, R is the interpolation radius, which is taken as 0.1° in this paper, r i is the distance from the i-th observation station to the k-th grid point. Figure 3 As shown in the figure, a circle with a radius of R is drawn with the kth grid point on the high-resolution grid as the center, and then the number of observation stations in the circle and the straight-line distance from each observation station in the circle to the center of the circle (i.e., the kth grid point) are calculated. The weight is assigned according to the size of the straight-line distance from each observation station in the circle to the center of the circle, which is the weight coefficient λ. i .

[0077] 5. Use the bilinear interpolation method to interpolate the coarse-resolution model temperature forecast and coarse-resolution model terrain height of the target area onto the high-resolution grid to obtain the high-resolution model temperature forecast and high-resolution model terrain height of each grid point on the high-resolution grid;

[0078] Here, the bilinear interpolation method is also used to interpolate the coarse-resolution model temperature forecast and coarse-resolution model terrain height of the target area to the high-resolution grid, and the high-resolution model temperature forecast and high-resolution model terrain height of each grid point on the high-resolution grid are obtained. The difference here from the previous site interpolation (site interpolation refers to the use of bilinear interpolation method to interpolate the coarse-resolution model temperature forecast and model terrain height of sample j in the target area during the training period to the location of the observation site, and obtain the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation site during the training period) is that the location of the target area is exactly on the high-resolution grid.

[0079] 6. Calculate the high-resolution grid temperature forecast product for each grid point on the high-resolution grid based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true grid terrain height for each grid point on the high-resolution grid;

[0080] Preferably, the high-resolution grid temperature forecast product of the kth grid point on the high-resolution grid is calculated according to the high-resolution model temperature forecast, the high-resolution model terrain height, the high-resolution approximate vertical variability and the true grid terrain height of each grid point on the high-resolution grid using the method shown in the following formula:

[0081]

[0082] Where, T k ′ is the high-resolution grid temperature forecast product at the kth grid point on the high-resolution grid, F k， is the high-resolution model temperature forecast at the kth grid point on the high-resolution grid, H k,m , H k,o are the high-resolution model terrain height and the real grid terrain height at the kth grid point on the high-resolution grid, respectively.

[0083] 7. Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true terrain height of the observation site at each grid point on the high-resolution grid, the high-resolution site temperature forecast product of the observation site is calculated;

[0084] Preferably, the high-resolution site temperature forecast product of the observation site is calculated based on the high-resolution model temperature forecast of each grid point on the high-resolution grid, the high-resolution model terrain height, the high-resolution approximate vertical variability and the actual terrain height of the observation site, and the bilinear interpolation method is also adopted.

[0085] Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the actual terrain height of the observation site at each grid point on the high-resolution grid, the temperature forecast at the four high-resolution grid points on the plane with the same terrain height around the observation site i is calculated. The temperature forecast values ​​at the four grid points are calculated as follows:

[0086]

[0087] Where g is the g-th grid point among the four high-resolution grid points on the plane with the same terrain height around the observation station i, T g is the high-resolution temperature forecast at the g-th grid point, F g is the high-resolution model temperature forecast for the g-th grid point, is the high-resolution approximate vertical variability at the g-th grid point, H g,m is the high-resolution model terrain height at the g-th grid point, H g,o is the actual terrain height of the g-th grid point corresponding to site i;

[0088] Then, based on the calculated temperature forecasts at four high-resolution grid points on the plane with the same terrain height around observation station i, the following is used: Figure 2 The bilinear interpolation method shown is used to calculate the high-resolution station temperature forecast product for the observation station.

[0089] 8. The high-resolution grid temperature forecast products and the high-resolution site temperature forecast products of the observation sites are determined as the final temperature forecast products of the high-resolution grid and observation sites.

[0090] The present invention comprehensively considers the relationship between the coarse-resolution model temperature forecast of the observation site, the coarse-resolution model terrain height, the actual temperature and the actual terrain height, dynamically calculates the approximate vertical variability, and obtains high-resolution temperature forecast products for high-resolution grids and observation sites through terrain downscaling technology, thereby improving the fit between the temperature forecast products and the actual terrain changes and further improving the accuracy of temperature forecasts in complex terrain.

[0091] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0092] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A temperature forecasting method based on dynamic approximate vertical variability in complex terrain, characterized in that: include: The bilinear interpolation method is used to interpolate the coarse-resolution model temperature forecast and model terrain height of sample j in the target area during the training period to the location of the observation station, and the coarse-resolution model temperature forecast and coarse-resolution model terrain height of sample j at the observation station during the training period are obtained; The coarse resolution approximate vertical variability of sample j at the observation site during the training period is calculated based on the coarse resolution model temperature forecast and the coarse resolution model terrain height of sample j at the observation site during the training period, as well as the actual temperature and the actual terrain height of sample j at the observation site during the training period; According to the coarse-resolution approximate vertical variability of sample j at the observation station during the training period and all samples during the training period, the average value of the coarse-resolution approximate vertical variability of the observation station is calculated; The inverse distance weighted interpolation method is used to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability of each grid point on the high-resolution grid; The coarse-resolution model temperature forecast and the coarse-resolution model terrain height of the target area are interpolated onto the high-resolution grid using the bilinear interpolation method to obtain the high-resolution model temperature forecast and the high-resolution model terrain height on the high-resolution grid; Based on the high-resolution model temperature forecast on the high-resolution grid, the high-resolution model terrain height, the high-resolution approximate vertical variability and the true grid terrain height, the high-resolution grid temperature forecast product for each grid point on the high-resolution grid is calculated; The high-resolution site temperature forecast product of the observation site is calculated based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the true terrain height of the observation site at each grid point on the high-resolution grid; The high-resolution grid temperature forecast product and the high-resolution site temperature forecast product of the observation site are determined as the final temperature forecast products of the high-resolution grid and the observation site.

2. A temperature forecasting method based on dynamic approximate vertical variability under complex terrain as claimed in claim 1, characterized in that: According to the coarse-resolution model temperature forecast and the coarse-resolution model terrain height of the sample j at the observation site during the training period, and the actual temperature and the actual terrain height of the sample j at the observation site during the training period, the coarse-resolution approximate vertical variability of the sample j at the observation site during the training period is calculated, including: The coarse resolution approximate vertical variability of sample j at the observation site during the training period is calculated using the method shown in the following formula based on the coarse resolution model temperature forecast and coarse resolution model terrain height of sample j at the observation site during the training period, as well as the actual temperature and actual terrain height of sample j at the observation site during the training period: In the formula, γ i,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period, F i,j , O i,j denote the coarse-resolution model temperature forecast and the real temperature of sample j at observation station i during the training period, respectively, and H i,m , H i,o They represent the coarse-resolution model terrain height and the true terrain height of observation station i respectively.

3. A temperature forecasting method based on dynamic approximate vertical variability under complex terrain as claimed in any one of claims 1 or 2, characterized in that: According to the coarse-resolution approximate vertical variability of sample j at the observation site during the training period and all samples during the training period, the average value of the coarse-resolution approximate vertical variability of the observation site is calculated, including: The method shown in the following formula is used to calculate the average value of the coarse resolution approximate vertical variability of the observation station based on the coarse resolution approximate vertical variability of sample j at the observation station during the training period and all samples during the training period: In the formula, is the average of the approximate vertical variability of the coarse-resolution site at observation site i, 3N represents all samples in the training period, and γ i,j is the coarse-resolution approximate vertical variability of sample j at observation site i during the training period.

4. A temperature forecasting method based on dynamic approximate vertical variability under complex terrain as claimed in claim 3, characterized in that: The inverse distance weighted interpolation method is used to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution approximate vertical variability of each grid point on the high-resolution grid, including: The inverse distance weighted interpolation method is used as shown in the following formula to interpolate the average value of the coarse resolution approximate vertical variability of each observation station onto the high-resolution grid to obtain the high-resolution grid approximate vertical variability of the kth grid point on the high-resolution grid: In the formula, λ i is the weight coefficient, Y is the number of observation stations involved in the calculation, is the approximate vertical variability of the kth grid point on the high-resolution grid.

5. The temperature forecasting method based on dynamic approximate vertical variability under complex terrain as claimed in claim 4, characterized in that: Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true grid terrain height of each grid point on the high-resolution grid, the high-resolution grid temperature forecast products for each grid point on the high-resolution grid are calculated, including: The high-resolution grid temperature forecast product for the kth grid point on the high-resolution grid is calculated using the method shown in the following formula based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and true grid terrain height of each grid point on the high-resolution grid: Where, T k ′ is the high-resolution grid temperature forecast product at the kth grid point on the high-resolution grid, F k is the high-resolution model temperature forecast at the kth grid point on the high-resolution grid, H k,m , H k,o are the high-resolution model terrain height and the real grid terrain height of the kth grid point on the high-resolution grid, respectively.

6. The temperature forecasting method based on dynamic approximate vertical variability under complex terrain according to claim 1, characterized in that: Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the actual terrain height of the observation site at each grid point on the high-resolution grid, the high-resolution site temperature forecast product of the observation site is calculated, including: Based on the high-resolution model temperature forecast, high-resolution model terrain height, high-resolution approximate vertical variability and the actual terrain height of the observation site at each grid point on the high-resolution grid, the temperature forecast at the four high-resolution grid points on the plane with the same terrain height around the observation site i is calculated. The temperature forecast values ​​at the four grid points are calculated as follows: Where g is the g-th grid point among the four high-resolution grid points on the plane with the same terrain height around the observation station i, T g is the high-resolution temperature forecast at the g-th grid point, F g is the high-resolution model temperature forecast for the g-th grid point, is the high-resolution approximate vertical variability at the g-th grid point, H g,m is the high-resolution model terrain height at the g-th grid point, H g,o is the actual terrain height of the g-th grid point corresponding to site i; Based on the calculated temperature forecasts at four high-resolution grid points on the plane with the same terrain height around observation station i, the high-resolution station temperature forecast product of the observation station is calculated using the bilinear interpolation method.

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