Air temperature spatial interpolation method considering altitude and four-season change

Abstract

The invention discloses an air temperature spatial interpolation method considering altitude and four-season change, which comprises the following steps: S1, calculating empirical air temperature direct reduction rates considering altitude and four-season change: calculating air temperature direct reduction rates of all station pairs under each elevation zone in a research area, taking the average value of all the air temperature direct reduction rates under the same elevation band as the empirical air temperature direct reduction rate under the elevation band; and S2, calculation of air temperature raster data considering altitudes and four-season changes: restoring the air temperature data to a unified sea level based on empirical air temperature direct reduction rates under different altitudes and time, and restoring the restored air temperature data of the unified sea level to an original elevation after inverse distance interpolation. The method has the advantages that the accuracy and reliability of air temperature interpolation are improved, the application field of air temperature data is expanded, and more reliable and accurate air temperature data support is provided for research and application of related fields.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing methods, and in particular to a temperature spatial interpolation method that takes altitude and seasonal changes into consideration. Background Art

[0002] The existing temperature interpolation methods are mainly divided into the following categories:

[0003] (1) Distance weighted interpolation method: According to the distance between the target point and the known observation point, the temperature value of the known observation point is weighted averaged. Generally, the closer the observation point is, the higher the weight is, and the farther the observation point is, the lower the weight is.

[0004] (2) Inverse distance weighted interpolation: Similar to distance weighted interpolation, but the weight is the inverse of the distance. This means that the closer the observation point is, the greater the influence it has on the target point.

[0005] (3) Kriging interpolation: This method is based on spatial autocorrelation and assumes that the temperature data has a certain continuity and variation pattern in space. Kriging interpolation simulates the spatial correlation between data by fitting the semivariogram and performs interpolation based on it.

[0006] (4) Spline interpolation: The spatial relationship between data points is regarded as a series of spline curves, and then these curves are used to estimate the values ​​of missing points. Commonly used spline interpolation methods include cubic spline interpolation and natural spline interpolation.

[0007] (5) Nearest neighbor interpolation: Simply set the value of the target point to the value of the closest known observation point.

[0008] However, the above interpolation methods often ignore the impact of terrain height on temperature distribution and the impact of different seasons on temperature changes. They are unable to simultaneously consider the impact of altitude and seasonal changes on temperature interpolation data, resulting in large errors in the final interpolated raster data and limited accuracy. Summary of the invention

[0009] The object of the present invention is to provide a method for spatial interpolation of temperature taking into account changes in altitude and seasons, thereby solving the above-mentioned problems existing in the prior art.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0011] A method for spatial interpolation of temperature considering altitude and seasonal changes includes the following steps:

[0012] S1. Calculation of empirical temperature lapse rate considering altitude and seasonal changes:

[0013] Calculate the temperature lapse rate of all station pairs in each elevation zone in the study area, and take the average of all temperature lapse rates in the same elevation zone as the empirical temperature lapse rate in that elevation zone;

[0014] S2. Calculation of temperature grid data considering altitude and seasonal changes:

[0015] Based on the empirical temperature lapse rate at different altitudes and times, the temperature data are restored to the uniform sea level, and the restored temperature data at the uniform sea level are restored to the original elevation by inverse distance interpolation.

[0016] Preferably, step S1 specifically includes the following contents:

[0017] S11. Divide the study area into elevation zones based on the station locations, ensuring that there are at least three temperature stations in each elevation zone;

[0018] S12, calculate the temperature lapse rate for all station pairs (i, j) under the elevation zone;

[0019] S13. Calculate the average value of all temperature lapse rates in the same elevation zone, and use the average value as the empirical temperature lapse rate for the corresponding elevation zone.

[0020] Preferably, step S11 specifically includes setting the range of the elevation zone, and dividing the study area into multiple elevation zones according to station distribution and elevation interpolation, so that there are at least three temperature stations in each elevation zone.

[0021] Preferably, step S12 specifically includes calculating the temperature lapse rate of each station in the same elevation zone, and calculating the temperature lapse rate for all station pairs (i, j) in the elevation zone; the calculation formula is,

[0022]

[0023] Among them, γ ijt is the temperature lapse rate between station i and station j at time t, T it 、T jt are the temperatures of site i and site j at time t, respectively, i 、h j are the elevations of site i and site j respectively.

[0024] Preferably, step S13 specifically includes calculating the average temperature lapse rate of different elevation zones, and for each elevation zone, calculating the average value of all temperature lapse rates at different times, and using the value as the empirical temperature lapse rate of the corresponding elevation zone; the calculation formula is,

[0025]

[0026] Among them, γ ktis the temperature lapse rate of the kth elevation zone at time t, where N is the number of station pairs in the kth elevation zone.

[0027] Preferably, step S2 specifically includes the following contents:

[0028] S21. Write code for batch calculation, clean the collected DEM data and temperature data of the study area, remove outliers and fill in missing values, and determine the number of meteorological stations involved in temperature interpolation;

[0029] S22, based on the empirical temperature lapse rate at different altitudes and times, restore the pre-processed temperature data to a uniform sea level;

[0030] S23, performing inverse distance interpolation on the temperature data restored to a uniform sea level;

[0031] S24. Based on the DEM data of the study area, the empirical temperature lapse rate is used to restore the temperature data after inverse distance interpolation to the original elevation, generating hourly temperature raster data.

[0032] Preferably, step S22 specifically includes restoring the long-term series measured data preprocessed at different altitudes to a uniform sea level based on the acquired empirical temperature lapse rate; the calculation formula is:

[0033]

[0034] Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

[0035] Preferably, S23 specifically includes, after restoring the original temperature values ​​in all time periods in the study area, using the inverse distance interpolation method to perform spatial interpolation calculation of the temperature in the study area for the temperature restoration values ​​affected by the invisible landforms and seasonal changes.

[0036] Preferably, step S24 is specifically to restore the temperature interpolation value at the actual altitude from the spatially interpolated temperature grid data at the uniform sea level according to the acquired empirical temperature lapse rate; the calculation formula is:

[0037]

[0038] Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

[0039] Preferably, before step S1, the method also includes collecting digital elevation data, EDM data and temperature data of the study area.

[0040] The beneficial effects of the present invention are: 1. The method of the present invention has achieved a significant improvement in the accuracy of temperature interpolation. By considering the impact of terrain height and seasonal changes on temperature, the method of the present invention can more accurately obtain the temperature change trend in different regions and seasons, thereby improving the accuracy and reliability of the obtained temperature data. 2. The method of the present invention can provide more detailed and comprehensive information on the spatial distribution of temperature. Traditional methods often ignore the impact of terrain height on temperature, resulting in large errors in the interpolation results in mountainous areas or high-altitude areas. The method of the present invention can better reflect the impact of terrain on temperature, provide more accurate temperature data for mountainous areas and high-altitude areas, and fill the blank areas of existing temperature data. 3. The method of the present invention has strong versatility and scalability. It is suitable for temperature interpolation in different regions, different seasons and different altitudes, and provides a simple and effective temperature data processing tool for meteorology, environmental science, agricultural production and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flow chart of an interpolation method in an embodiment of the present invention;

[0042] Figure 2 It is a four-season line graph of the temperature lapse rate in an embodiment of the present invention;

[0043] Figure 3 is a weather station map in an embodiment of the present invention;

[0044] Figure 4 is a temperature interpolation grid file diagram in an embodiment of the present invention;

[0045] Figure 5 It is a MAE and RMSE line comparison chart of the observation station in the embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] like Figure 1As shown, in this embodiment, in order to solve the limitations of existing temperature interpolation methods, especially the problem that the influence of altitude and seasonal changes has not been fully considered, a temperature spatial interpolation method that considers altitude and seasonal changes is provided. The inverse distance interpolation method can well reflect the temperature changes in flat areas, but it is difficult to accurately restore the temperature data by directly using the inverse distance interpolation method. Therefore, the present invention adds a parameter to the inverse distance weight method to consider the influence of altitude and time, and proposes an inverse distance weight method that considers time and space. Because when considering the influence of different altitude gradients on temperature, the altitude temperature drop rate is affected by the topography and seasonal changes, and is not a constant value. Therefore, when using the inverse distance weight interpolation considering altitude, the influence of time changes should be added. Starting from the perspective of considering the effects of space and time on temperature interpolation, the present invention analyzes and improves the original temperature interpolation method, integrates the influence of topography and seasonal changes on temperature, and proposes an inverse distance weight interpolation method that considers altitude and time changes, which greatly improves the interpolation effect of temperature data in areas with obvious altitude changes and seasonal changes. The method of the present invention includes the following two parts:

[0048] 1. Calculation of empirical temperature lapse rate considering altitude and seasonal changes:

[0049] Calculate the temperature lapse rate of all station pairs in each elevation zone in the study area, and take the average of all temperature lapse rates in the same elevation zone as the empirical temperature lapse rate in that elevation zone. This includes the following steps:

[0050] 1.1. Divide the study area into elevation zones based on the station locations, ensuring that there are at least three temperature stations in each elevation zone.

[0051] Specifically: the range of the elevation zone is set, and multiple (k) elevation zones are divided in the study area according to station distribution and elevation interpolation, so that there are at least three temperature stations in each elevation zone.

[0052] 1.2. Calculate the temperature lapse rate for all station pairs (i, j) under the elevation zone.

[0053] Specifically: calculate the temperature lapse rate of each station in the same elevation zone, and calculate the temperature lapse rate for all station pairs (i, j) in the elevation zone; the calculation formula is,

[0054]

[0055] Among them, γ ijt is the temperature lapse rate between station i and station j at time t, T it 、T jt are the temperatures of site i and site j at time t, respectively, i 、h j are the elevations of site i and site j respectively.

[0056] 1.3. Calculate the average value of all temperature lapse rates in the same elevation zone, and use the average value as the empirical temperature lapse rate for the corresponding elevation zone.

[0057] Specifically: calculate the average temperature lapse rate of different elevation zones. For each elevation zone, calculate the average value of all temperature lapse rates at different times, and use this value as the empirical temperature lapse rate of the corresponding elevation zone. The calculation formula is:

[0058]

[0059] Among them, γ kt is the temperature lapse rate of the kth elevation zone at time t, where N is the number of station pairs in the kth elevation zone.

[0060] 2. Calculation of temperature grid data considering altitude and seasonal changes:

[0061] Based on the empirical temperature lapse rate at different altitudes and times, the temperature data is restored to the uniform sea level, and the restored uniform sea level temperature data is restored to the original elevation after inverse distance interpolation. The following steps are included:

[0062] 2.1. By writing code for batch calculation, the collected digital elevation data, DEM data and temperature data of the study area are cleaned, outliers are removed, missing values ​​are filled, and the number of meteorological stations involved in temperature interpolation is determined.

[0063] 2.2. Based on the empirical temperature lapse rates at different altitudes and times derived in step 1, restore the preprocessed temperature data to a uniform sea level.

[0064] Specifically: Based on the empirical temperature lapse rate, the long-term series measured data preprocessed at different altitudes are restored to a uniform sea level; the restoration formula is:

[0065]

[0066] Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

[0067] 2.3. Perform inverse distance interpolation on the temperature data restored to the uniform sea level.

[0068] Specifically: after restoring the original temperature values ​​in all time periods in the study area, the inverse distance interpolation method is used to perform spatial interpolation calculations of the temperature in the study area for the temperature restoration values ​​affected by the invisible landforms and seasonal changes.

[0069] 2.4. Based on the DEM data of the study area, the empirical temperature lapse rate is used to restore the temperature data after inverse distance interpolation to the original elevation, generating hourly temperature raster data.

[0070] Specifically: the spatially interpolated temperature grid data at a uniform sea level is restored to the temperature interpolation at the actual altitude according to the obtained empirical temperature lapse rate; the calculation formula is,

[0071]

[0072] Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period, where 6 is taken; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

[0073] Embodiment 2

[0074] In this embodiment, the execution process of the method of the present invention is described by taking the temperature data interpolation method of the Yalong River Basin as an example.

[0075] 1. Download 30m ASTER GDEM 30M resolution digital elevation data from the Geospatial Data Cloud (www.gscloud.cn), and crop the DEM data within the Yalong River Basin using Argis software. The temperature data uses the hourly temperature data from 2017 to 2022 from 41 meteorological bureaus in the Yalong River Basin provided by the Sichuan Provincial Meteorological Bureau.

[0076] 2. Different elevation zones were divided in the study area. Five elevation zones were divided according to the distribution of temperature stations in the Yalong River Basin, ensuring that there were at least three temperature stations in each elevation zone. The temperature data extracted from each elevation zone were batch calculated by writing code to obtain the empirical temperature lapse rate of the study area considering time and altitude, the empirical temperature lapse rate of the Yalong River Basin, and the temperature lapse rate of the four seasons as shown in Tables 1 and Figure 2 As shown:

[0077] Table 1 Empirical temperature lapse rate in the Yalong River Basin

[0078]

[0079]

[0080] 3. Divide the station data into observation group and interpolation group. Three stations (Qiwu, Hekou Town, and Wulaxi) were selected from the 41 meteorological stations in the Yalong River Basin for comparison. Figure 3 As shown in Figure 2, the long-term measured data of the interpolation group at different altitudes are restored to a uniform sea level based on the inferred temperature lapse rate.

[0081] 4. After restoring the original temperature values ​​of all time periods in the study area, the inverse distance interpolation method is used to calculate the temperature spatial interpolation in the study area for the temperature restoration values ​​without the influence of topography and seasonal changes. The spatial interpolation grid data at the uniform sea level is restored according to the calculated empirical temperature lapse rate to the temperature interpolation at the actual altitude, and finally the hourly temperature grid data is generated. The grid data of a certain hour is as follows: Figure 4 shown.

[0082] 5. Use the same observation group and interpolation group to calculate the interpolation effect of the common inverse distance interpolation in the Yalong River Basin for comparison. By calculating the MAE and RMSE values ​​of the inverse distance temperature interpolation method considering altitude and seasonal changes and the common inverse distance interpolation method at Qiwu, Hekou Town, and Wulaxi stations, the data accuracy of the interpolation method considering altitude in the Yalong River Basin is analyzed. The monthly comparison results are as follows: Figure 5 The year comparison results are shown in Table 2.

[0083] Table 2 Annual average values ​​of MAE and RMSE of the compared sites

[0084]

[0085]

[0086] 6. Refer to Table 2, and analyze the MAE and RMSE values ​​of the interpolation data generated by the three comparison stations of Qiwu, Hekou Town and Wula Creek under the two interpolation methods. It can be seen that the temperature error of the inverse distance interpolation method considering the changes in altitude and seasons is smaller than that of the ordinary inverse distance interpolation method, which can meet the project accuracy requirements.

[0087] In general, the method of the present invention takes into account the impact of altitude and seasonal changes on temperature, and effectively improves the accuracy and precision of temperature interpolation by combining information on terrain height and seasonal changes. By considering these factors in the interpolation process, the method of the present invention can better reflect the real climate conditions, making the interpolation results closer to the actual observation data, thereby providing a more reliable temperature data foundation for research and application in meteorology, geographic information systems and related fields. The method of the present invention can provide more accurate temperature data support for climate research, agricultural production, water resources management and other fields, which helps to better understand and respond to the impact of climate change on human society and ecosystems.

[0088] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0089] The present invention provides a method for spatial interpolation of temperature that takes into account changes in altitude and seasons, and has achieved a significant improvement in the accuracy of temperature interpolation. By considering the impact of terrain height and seasonal changes on temperature, the method of the present invention can more accurately obtain the temperature change trend in different regions and seasons, thereby improving the accuracy and reliability of the obtained temperature data. The method of the present invention can provide more detailed and comprehensive information on the spatial distribution of temperature. Traditional methods often ignore the impact of terrain height on temperature, resulting in large errors in the interpolation results in mountainous areas or high-altitude areas. The method of the present invention can better reflect the impact of terrain on temperature, provide more accurate temperature data for mountainous areas and high-altitude areas, and fill the blank areas of existing temperature data. The method of the present invention has strong versatility and scalability. It is suitable for temperature interpolation in different regions, different seasons and different altitudes, and provides a simple and effective temperature data processing tool for meteorology, environmental science, agricultural production and other fields.

[0090] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for spatial interpolation of temperature taking into account changes in altitude and seasons, characterized in that: The following steps are included: S1. Calculation of empirical temperature lapse rate considering altitude and seasonal changes: Calculate the temperature lapse rate of all station pairs in each elevation zone in the study area, and take the average of all temperature lapse rates in the same elevation zone as the empirical temperature lapse rate in that elevation zone; S2. Calculation of temperature grid data considering altitude and seasonal changes: Based on the empirical temperature lapse rate at different altitudes and times, the temperature data are restored to the uniform sea level, and the restored temperature data at the uniform sea level are restored to the original elevation by inverse distance interpolation.

2. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 1, characterized in that: Step S1 specifically includes the following contents: S11. Divide the study area into elevation zones based on the station locations, ensuring that there are at least three temperature stations in each elevation zone; S12, calculate the temperature lapse rate for all station pairs (i, j) under the elevation zone; S13. Calculate the average value of all temperature lapse rates in the same elevation zone, and use the average value as the empirical temperature lapse rate for the corresponding elevation zone.

3. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 2, characterized in that: Step S11 specifically sets the range of the elevation zone, and divides the study area into multiple elevation zones according to station distribution and elevation interpolation, so that there are at least three temperature stations in each elevation zone.

4. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 2, characterized in that: Step S12 specifically calculates the temperature lapse rate of each station in the same elevation zone, and calculates the temperature lapse rate for all station pairs (i, j) in the elevation zone; the calculation formula is: Among them, γ ijt is the temperature lapse rate between station i and station j at time t, T it , T jt are the temperatures of site i and site j at time t, respectively, i 、h j are the elevations of site i and site j respectively.

5. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 2, characterized in that: Step S13 specifically calculates the average temperature lapse rate of different elevation zones. For each elevation zone, the average value of all temperature lapse rates at different times is calculated, and the value is used as the empirical temperature lapse rate of the corresponding elevation zone. The calculation formula is: Among them, γ kt is the temperature lapse rate of the kth elevation zone at time t, where N is the number of station pairs in the kth elevation zone.

6. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 1, characterized in that: Step S2 specifically includes the following contents: S21. Write code for batch calculation, clean the collected DEM data and temperature data of the study area, remove outliers and fill in missing values, and determine the number of meteorological stations involved in temperature interpolation; S22, based on the empirical temperature lapse rate at different altitudes and times, restore the pre-processed temperature data to a uniform sea level; S23, performing inverse distance interpolation on the temperature data restored to a uniform sea level; S24. Based on the DEM data of the study area, the empirical temperature lapse rate is used to restore the temperature data after inverse distance interpolation to the original elevation, generating hourly temperature raster data.

7. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 6, characterized in that: Step S22 specifically includes restoring the long-term series measured data preprocessed at different altitudes to a uniform sea level based on the acquired empirical temperature lapse rate; the calculation formula is: Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

8. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 6, characterized in that: S23 specifically involves restoring the original temperature values ​​in all time periods in the study area, and then using the inverse distance interpolation method to perform spatial interpolation calculations of the temperature in the study area for the temperature restoration values ​​affected by the invisible landforms and seasonal changes.

9. The method for spatial interpolation of temperature considering altitude and seasonal changes according to claim 6, characterized in that: Step S24 is specifically to restore the temperature interpolation value at the actual altitude from the spatially interpolated temperature grid data at the uniform sea level according to the obtained empirical temperature lapse rate; the calculation formula is: Among them, Z hi is the temperature restoration value of site i; Z pi is the original temperature value of station i; n is the number of elevation zones in the current time period; is the temperature lapse rate in the kth elevation zone during the period t0; H k is the elevation of site i in the kth elevation zone.

10. The method for spatial interpolation of temperature considering altitude and seasonal changes according to any one of claims 1 to 9, characterized in that: Before step S1, the process also includes collecting digital elevation data, EDM data and temperature data of the study area.

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