Regional automatic station air temperature sequence reconstruction method based on scale separation and meteorological geographic partitioning

Abstract

The invention discloses a regional automatic station air temperature sequence reconstruction method based on scale separation and meteorological geographic partitioning, which comprises the following steps: carrying out scale separation on regional automatic station observation data to obtain a regional automatic station climate state sequence and a regional automatic station distance flat sequence; performing scale separation on observation data of the national standard meteorological station to obtain a climate state sequence of the national station; combining the two climate state sequences to obtain a fitting climate state sequence; performing scale separation on the remote sensing data to obtain a remote sensing data offset sequence; combining the two tako-flat sequences to obtain a fitted tako-flat sequence; and performing sequence synthesis on the fitting climate state sequence and the fitting distance flat sequence to obtain a regional automatic station reconstruction sequence. Through a scale separation method, the original data of the automatic station is divided into a climate state sequence and a barometric sequence, and data with different representativeness is used for fitting data with different scales, so that the fitted data sequence can be closer to the actual distribution condition of large-scale and medium-small-scale air temperatures.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of regional automatic weather station data processing, and particularly relates to a regional automatic station temperature sequence reconstruction method based on scale separation and meteorological geographical zoning. BACKGROUND

[0002] The identification of extreme weather and climate events mainly relies on direct observation data of ground meteorological stations, and the ground meteorological stations include national standard meteorological stations and regional automatic meteorological observation stations. The national standard meteorological stations need real-time manual supervision, and the construction region has strict requirements. The regional automatic stations can continuously observe in many remote areas due to automatic observation, and the observation station distribution density is high, which makes up for the shortcomings of the traditional national standard meteorological station distribution and the observation station is relatively sparse. However, the regional observation station is unattended, and the maintenance frequency is lower than that of the national standard meteorological station, so that when the observation station instrument fails or is damaged, there will be a certain period of data missing; some regional observation stations are built near farmland, cities or highways, and the observation value suddenly changes due to human activities during the observation period, which happens from time to time, so that the data quality in climate analysis and research cannot meet the demand, which affects the use rate of regional automatic station observation data. The missing data sequence reconstruction of the regional automatic station is an effective way to ensure fine meteorological analysis and research.

[0003] Since the temperature changes relatively slowly in time and the spatial gradient is small in most areas, it has certain continuity characteristics in time and space, so that the long time scale and continuous good temperature reconstruction data sequence can be obtained by performing sequence reconstruction on the regional automatic station data. The first problem of the missing data sequence reconstruction of the automatic station is the selection of the reference station. The reference station is the observation station selected for spatial interpolation, which has high observation data quality and good continuity, and is usually a national standard meteorological station. However, since the data amount of the automatic station is much larger than that of the national station, the national station is selected as the reference meteorological station, and fewer national standard meteorological stations are selected as the reference stations of multiple regional automatic stations, which leads to high spatial similarity of the reconstructed data sequence, and the reconstructed sequence has certain limitations in the spatial identification of extreme weather and climate events.

[0004] In addition, the current sequence reconstruction of missing data of regional station air temperature mainly adopts the methods of spatial interpolation and time fitting, such as inverse distance weighting method, trend surface method, spline function method, linear fitting method and the like. The spatial interpolation algorithm without considering time fitting usually considers distance as the main parameter of interpolation, and interpolates the data of the automatic station through the distance weighting method of the reference stations around the automatic station in space to supplement the data of the automatic station. This method greatly weakens the spatial distribution of the spatial terrain change and the local climate characteristics, and the real resolution of the interpolated data is not improved, only the mathematical method is down-scaled, and the limitation is large. Moreover, the spatial interpolation method weakens the influence of the uneven distribution of the terrain and underlying surface on the air temperature, has a certain spatial smoothing effect, and eliminates the local climate effect to a certain extent, which weakens the characteristics of the extreme high temperature and low temperature. The fitting method combined with time change such as linear regression has high dependence on the reference stations, which leads to that the change characteristics of the reconstructed data are highly consistent with the reference stations, and the actual air temperature change characteristics cannot be reflected. The accuracy of different interpolation methods is related to the spatial scale and time scale, and different interpolation methods have advantages and disadvantages in different regions. When the interpolation is performed in a large range, the same method may have large errors in some regions.

[0005] An important purpose of the sequence reconstruction of air temperature data is to realize the fine identification of extreme air temperature events such as high temperature, low temperature and variable temperature, so as to extract the spatial and temporal key indicators of extreme events. However, due to the limitation of the above method, the sequence reconstruction of the automatic station air temperature data is easy to weaken the extreme climate signal, the interpolated data is highly consistent with the change characteristics of the national station air temperature, the local air temperature change is greatly eliminated in space, which leads to the reduction of the identification ability of the fine extreme weather and climate events, the significance of using the automatic station air temperature data with high fine degree is lost, and the risk assessment and early warning of the extreme weather and climate events are adversely affected. SUMMARY

[0006] The present application aims to at least solve one of the technical problems existing in the related art.

[0007] The present application aims to at least solve one of the technical problems existing in the related art.

[0008] In order to achieve the above-mentioned purpose, the present application provides a regional automatic station temperature sequence reconstruction method based on scale separation and meteorological geographical zoning in one aspect, comprising the following steps:

[0009] S1, selecting the regional automatic station in the research area whose observation data quality meets the requirements; and performing quality control on the observation data of the selected regional automatic station;

[0010] S2, performing scale separation on the quality-controlled regional automatic station observation data, and respectively obtaining the regional automatic station climatic sequence representing the climate scale temperature change and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatic sequence from the original temperature sequence;

[0011] S3, based on the climatic geographical zoning, selecting a plurality of national standard meteorological stations located in the same geographical zoning as the regional automatic station by the nearest distance method; performing scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatic sequence; then combining the regional automatic station climatic sequence and the national station climatic sequence, constructing a linear multiple regression model, and calculating to obtain the fitted climatic sequence;

[0012] S4, according to the latitude and longitude information of the regional automatic station, finding the nearest grid point from the high-resolution remote sensing inversion surface temperature grid data to the regional automatic station, selecting the grid data within the set range as the center to obtain the remote sensing data by averaging, performing scale separation on the remote sensing data to obtain the remote sensing data anomaly sequence; combining the regional automatic station anomaly sequence and the surface data anomaly sequence, constructing a linear regression model, and calculating to obtain the fitted anomaly sequence;

[0013] S5, performing sequence synthesis on the fitted climatic sequence and the fitted anomaly sequence to obtain the regional automatic station reconstruction sequence.

[0014] The further preferred technical solution of the present application is that the regional automatic station in the research area whose observation data quality meets the requirements is selected in step S1; specifically:

[0015] The station information of the regional automatic station in the research area is extracted, the site position and the surrounding environment are determined, the regional automatic stations used for traffic observation and large water body observation are excluded, and the observation data of the remaining regional automatic stations are counted, and the regional automatic stations with a data length of less than 5 years and a missing data amount exceeding 45% of the total data amount are excluded.

[0016] As preferred, the quality control is performed on the observation data of the selected regional automatic station in step S1; specifically including:

[0017] (1) temperature threshold check, determining the annual temperature upper and lower thresholds according to the annual temperature observation values of the research area, and excluding the data exceeding the annual temperature upper and lower thresholds in the observation data of the regional automatic station;

[0018] (2) Statistics of the observation data of the national standard meteorological stations around the regional automatic station, calculation of the monthly temperature upper and lower threshold values of each month, and elimination of the data of the regional automatic station observation data corresponding to the month exceeding the monthly temperature upper and lower threshold values;

[0019] (3) According to the observation data of the national standard meteorological stations, the 24, 48 and 72 hour temperature change amplitude threshold values of the region where the regional automatic station is located are calculated respectively, and the data of the regional automatic station observation data exceeding the temperature change amplitude threshold value is eliminated;

[0020] (4) Set the standard deviation of temperature change in a specified period, and eliminate the data of the regional automatic station observation data exceeding ±3 times the standard deviation in the period.

[0021] As preferred, the regional automatic station observation data after quality control in step S2 is subjected to scale separation, and the regional automatic station climate state sequence representing the climate scale temperature change is obtained, and the regional automatic station anomaly sequence is obtained by subtracting the regional automatic station climate state sequence from the original temperature sequence; Specifically:

[0022] S21, the regional automatic station has a total of year data, the original temperature sequence of the year is , the daily climate average sequence of the year is calculated:

[0023] ;

[0024] S22, the moving average method is used to perform moving average on the daily climate average sequence, the moving window is 31 days, and each time the window is moved by 1 day, and is connected to perform circular sliding, and the regional automatic station climate state sequence is obtained, which is represented as:

[0025] ;

[0026] is the regional automatic station climate state sequence.

[0027] S23, the regional automatic station climate state sequence is subtracted from the original temperature sequence of the regional automatic station, and the regional automatic station anomaly sequence is obtained, which is represented as:

[0028] ;

[0029] is the regional automatic station anomaly sequence.

[0030] As preferred, the scale separation of the observation data of the selected national standard meteorological stations in step S3 and the scale separation of the remote sensing surface data in step S4 are performed by the same method as the scale separation of the observation data of the regional automatic stations in step S2.

[0031] As preferred, the scale separation of the observation data of the selected national standard meteorological stations in step S3 and the scale separation of the remote sensing surface data in step S4 are performed by the same method as the scale separation of the observation data of the regional automatic stations in step S2. Then, a linear multiple regression model is constructed with the climate state sequence of the regional automatic stations, and a fitted climate state sequence is calculated , which is expressed as:

[0032] ;

[0033] wherein, is the climate state sequence of the kth national standard meteorological station; is the regression coefficient.

[0034] As preferred, in step S4, the nearest grid point to the regional automatic station is found from the high-resolution remote sensing inversion surface temperature grid data according to the latitude and longitude information of the regional automatic station, and the grid data within a set range is selected to obtain the remote sensing data by averaging.

[0035] The nearest grid point to the regional automatic station is found from the high-resolution remote sensing inversion surface temperature grid data according to the latitude and longitude information of the regional automatic station , and the remote sensing temperature data value of the grid point is ; 25 grid point data are selected with the grid point as the center , and the daily remote sensing data is obtained by averaging the 25 grid point data, which is expressed as:

[0036] ;

[0037] The daily remote sensing data is grouped to form a remote sensing data sequence , m is the total number of years of the remote sensing data sequence, and l is the total number of days in the mth year.

[0038] As preferred, the scale separation of the remote sensing data sequence in step S3 is performed to obtain a remote sensing data anomaly sequence Then, a regression model is constructed with the regional automatic station anomaly sequence, and a fitted climate state sequence is calculated , which is expressed as:

[0039] ;

[0040] wherein n0 and n1 are fitting coefficients.

[0041] As preferred, step S5 performs sequence synthesis on the fitted climate state sequence and the fitted anomaly sequence to obtain a regional automatic station reconstructed sequence, denoted as:

[0042] ;

[0043] wherein, is the regional automatic station reconstructed sequence, is the fitted anomaly sequence, is the fitted climate state sequence; is the number of days in a year, and is 1-366, and i is the year.

[0044] As preferred, in each step of steps S2-S5, the number of days in a year is 366 in a leap year, and if the number of observations on a certain date is less than 50% of the total number of years, the observation data on the date is recorded as missing, and all the data on the date does not participate in the calculation.

[0045] Another aspect of the present application provides a non-transitory computer readable storage medium having computer instructions stored thereon, the computer instructions causing a computer to execute the above-mentioned regional automatic station temperature sequence reconstruction method based on scale separation and meteorological geographical zoning.

[0046] Still another aspect of the present application provides an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus, and the processor invokes logical instructions in the memory to execute the above-mentioned regional automatic station temperature sequence reconstruction method based on scale separation and meteorological geographical zoning.

[0047] Still another aspect of the present application provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program being executed by a processor to cause a computer to execute the above-mentioned regional automatic station temperature sequence reconstruction method based on scale separation and meteorological geographical zoning.

[0048] Beneficial Effects: The regional automatic weather station temperature sequence reconstruction method based on scale separation and meteorological and geographical zoning of this invention separates the climatological temperature sequence from the short-term scale through scale separation. The climatological sequence is fitted using national standard meteorological station data, while the anomaly sequence is fitted using refined remote sensing inversion surface temperature data. Different representative data are selected for fitting at different scales, which not only ensures the large-scale decadal background characteristics but also preserves the influence of local meteorological and geographical environments. Combining the two for automatic weather station data sequence reconstruction yields results that better reflect the extreme characteristics of temperature changes, avoiding the smoothing effect of using only national stations for fitting extreme values. This method is more suitable for extracting the characteristics of extreme temperature events, making the fitted data sequence closer to the actual distribution of temperature at large and small scales.

[0049] This invention uses climate-geographic zoning to select reference stations instead of relying on the principle of proximity to select national meteorological stations. This ensures the consistency of the surrounding geographical environment between the two types of observation stations being fitted, and minimizes the difference in climate background between the reconstructed sequence and the original sequence caused by differences in the surrounding environment of the stations.

[0050] Because remote sensing data has high spatial resolution, each grid point has a certain spatial difference from the surrounding grid points. By averaging the remote sensing data over a small area to obtain surface data to replace the single grid point data for data reconstruction, the uniformity of the reconstructed data in terms of spatial range is ensured. Attached Figure Description

[0051] Figure 1 This is a flowchart of the method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to the present invention;

[0052] Figure 2 This is the original temperature sequence from the regional automatic weather station in Example 1;

[0053] Figure 3 Reconstructing the temperature sequence from the regional automatic weather stations in Example 1;

[0054] Figure 4 The frequency distribution of the raw temperature data from the regional automatic weather stations in Example 1;

[0055] Figure 5 The frequency distribution of reconstructed temperature data from regional automatic weather stations in Example 1;

[0056] Figure 6 The spatial distribution map of daily maximum temperature in summer is plotted using regional automatic weather stations to reconstruct temperature sequences, as shown in Example 1. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, embodiments of this invention, and should not be construed as limiting the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention. In the description of this invention, it should be understood that the terminology used is for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0058] The following is combined Figures 1-6 This invention describes a method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning.

[0059] Example 1: This example provides a method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning, such as... Figure 1 As shown, it includes the following steps:

[0060] S1. Select regional automatic stations within the study area whose observation data quality meets the requirements; and perform quality control on the observation data of the selected regional automatic stations.

[0061] In this embodiment, Jiangsu Province in eastern my country is selected as the study area. In each step of the calculation, each year is counted as 366 days as a leap year. If the number of observations on a certain date is less than 50% of the total number of years, the observation data on that date is recorded as missing, and all data for that date are not included in the calculation.

[0062] S11. Selection of Regional Automatic Stations. This step is to select suitable regional automatic stations for data sequence reconstruction. The station construction information is extracted, the station locations and surrounding environment are determined, and regional automatic stations specifically used for traffic observation or large water body observation are excluded, as their data is not suitable for identifying extreme high-temperature disasters. The number of observations from the selected automatic stations is statistically analyzed, and regional automatic stations with data durations less than 5 years or missing data exceeding 45% of the total data volume are excluded to avoid poor fitting results due to insufficient data.

[0063] S12. Perform quality control on the acquired regional automatic weather station observation data, removing outliers that may affect the reconstruction of the data sequence. Since most regional automatic weather station data is unattended and relies on automatic instrument observation, the stations may be affected by nearby human activities or natural phenomena (such as spontaneous combustion, anthropogenic heat sources, or artificial watering), leading to abnormal observations that do not match the actual atmospheric conditions. These outliers will affect the fitting model parameters and adversely impact the accuracy of the reconstructed data sequence. Outlier removal needs to be performed according to the following procedure:

[0064] (1) Temperature threshold check: my country has a vast territory and significant differences between the north and south. Based on historical temperature observations, temperature thresholds are set for different regions. The temperature range in eastern my country is [-50, 50]. Values ​​exceeding this range are considered outliers and are therefore removed. , If a certain observation data or Then the data will be removed. The threshold can be adjusted for different regions based on historical statistics.

[0065] (2) Monthly threshold check: Since temperature observation data have obvious annual cycle characteristics, monthly threshold removal can compress the extreme value range and make it easier to find outliers. Data from national stations surrounding the statistical area's automatic weather stations are used to calculate the maximum monthly temperature. and minimum value .

[0066] ;

[0067] ;

[0068] Where the subscript mon represents the month, i represents a day within that month (values ​​1-31), and j represents the year of observation (1-5 if there are 5 years of observations). This calculation method extracts the same month from all years and performs extreme value statistics, ensuring that the extreme value is the maximum or minimum value for that month over many years. Monthly temperature extreme values ​​are amplified by a factor of 1.5 to serve as a monthly threshold; values ​​exceeding this threshold are considered outliers and are removed. and The observation data from national standard meteorological stations surrounding the regional automatic weather stations were statistically analyzed, and the upper and lower thresholds for monthly temperatures were calculated. Data from the regional automatic weather stations that exceeded the upper and lower thresholds for the corresponding month were then removed.

[0069] (3) Temperature fluctuation threshold check: Since temperature changes have a certain continuity within a set time period, the range of temperature changes is checked based on this characteristic. Using data from national stations across the province, the maximum 24, 48, and 72-hour temperature fluctuation values ​​are calculated:

[0070] ;

[0071] ;

[0072] ;

[0073] Considering that the temperature fluctuation range of automatic weather stations is greater than that of national weather stations, the temperature change threshold of automatic weather stations is increased. , or At that time, remove Temperature value at any given time.

[0074] (4) Sliding standard deviation check: The sliding standard deviation method considers that within a set time range, the temperature threshold range does not exceed ±3 times the standard deviation within that time period. Within the sliding window range, the temperature value T should satisfy:

[0075] ;

[0076] ;

[0077] Data will be removed if this condition is not met. The sliding window length is set to 10-30 days, with each slide lasting one day. This represents the average value of the data within the sliding window range.

[0078] The frequency distribution of the raw temperature sequence and raw temperature data from the regional automatic weather stations in this embodiment is as follows: Figure 2 and Figure 4 As shown.

[0079] S2. Scale separation is performed on the quality-controlled regional automatic station observation data to obtain the regional automatic station climatological sequence representing climatological scale temperature changes, and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatological sequence from the original temperature sequence.

[0080] S21. When calculating the climate background sequence, a data continuity check is required first, mainly to ensure the reliability of annual cycle data when separating time scales. If the monthly observation data is less than 50% or the annual data is less than 50%, then the monthly and annual data will not be included in the climate background sequence calculation.

[0081] S22. Climatological sequence calculation. The climatological sequence represents the temperature change sequence at the climatological scale, mainly showing the annual temperature cycle, while retaining temperature change information beyond monthly variations.

[0082] Assume there are a total of regional automatic stations Year data, number The original temperature sequence for the year is ,calculate Annual daily climate average series:

[0083] ;

[0084] S23. To filter out temporal variation signals below the monthly scale, a moving average method is used to perform a moving average on the daily climate average series. The moving window is 31 days, and each moving average is 1 day. and By performing a cyclic sliding process, the regional automatic weather station climatological sequence is obtained, as follows:

[0085] ;

[0086] This is a regional automatic weather station climatological sequence.

[0087] S24. Anomaly Series Calculation. The regional automatic weather station anomaly series is obtained by subtracting the regional automatic weather station climatological series from the original temperature series. , represented as:

[0088] ;

[0089] This is an automatic station distance sequence for the region.

[0090] S3. Based on climate geographic zoning, select 1-5 national standard meteorological stations located in the same geographic zone as the regional automatic weather stations using the nearest distance method; perform scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatological series. Then, a linear multiple regression model was constructed using it and the climatological sequences from regional automatic weather stations to calculate the fitted climatological sequences. , represented as:

[0091] ;

[0092] in, This is the national station climatological sequence of the k-th national standard meteorological station; The regression coefficients are estimated using the least squares method:

[0093] ;

[0094] Where X = F= ;

[0095] Existing regional automatic weather station climatological sequences The f-value is substituted into the coefficient formula to obtain the coefficients of each term. Then, the climatological sequence data from the national station is substituted into the equations that determine the coefficients. Calculate the fitted regional automatic weather station climatological sequence f. The fitted climatological sequence does not contain any missing data.

[0096] S4. Based on the latitude and longitude information of the regional automatic weather stations, find the grid points closest to the regional automatic weather stations from the high-resolution remote sensing inversion surface temperature grid data. The remote sensing temperature data value extracted from this grid point is Select 25 grid points centered on this grid point. The daily remote sensing data is obtained by averaging the data from 25 grid points, and is represented as follows:

[0097] ;

[0098] The remote sensing data sequence is composed of daily remote sensing data. , where m is the total number of years in the remote sensing data sequence, and l is the total number of days in the m-th year.

[0099] Scale separation is performed on remote sensing data to obtain remote sensing data anomaly sequences. Then, a regression model was constructed by combining it with the regional automatic station anomaly sequence, and the fitted climatological sequence was calculated. , represented as:

[0100] ;

[0101] Where n0 and n1 are the fitting coefficients; the total length of the automatic station anomaly sequence is l, then the fitting coefficients are:

[0102] ;

[0103] ;

[0104] The existing automatic station anomaly sequences (which contain missing data and have a relatively short time frame) are used as... Substitute the values ​​into the fitting coefficient formula to calculate the fitting coefficient. Based on the fitting equation, substitute the long-sequence remote sensing anomaly values ​​to calculate the fitted long-sequence anomaly sequence. The time length of this fitted sequence is longer than that of the original regional automatic station anomaly sequence, and there are no missing measurements.

[0105] The scale separation of the observation data from the selected national standard meteorological stations described in step S3, and the scale separation of the remote sensing surface data described in step S4, adopt the same method as the scale separation of the regional automatic station observation data after quality control in step S2.

[0106] S5. Synthesize the fitted climatological sequence and the fitted anomaly sequence to obtain the reconstructed temperature sequence from the regional automatic weather stations, as follows:

[0107] ;

[0108] in, For the reconstruction sequence of regional automatic stations, To fit the anomaly sequence, To fit the climatological sequence; The number of days in a year, with a value from 1 to 366, where i represents the year.

[0109] The frequency distribution of the temperature sequence reconstructed from regional automatic weather stations and the temperature data reconstructed from regional automatic weather stations in this embodiment is as follows: Figure 3 and Figure 5As shown in the figure. The spatial distribution map of daily maximum temperatures in Jiangsu Province during summer, plotted using temperature series reconstructed from regional automatic weather stations, is shown below. Figure 6 As shown.

[0110] Example 2: This example provides a non-transitory computer-readable storage medium storing computer instructions that cause a computer to execute a method for reconstructing regional automatic station temperature sequences based on scale separation and meteorological geographic zoning. The method includes the following steps:

[0111] S1. Select regional automatic weather stations within the study area whose observation data quality meets the requirements; and perform quality control on the observation data of the selected regional automatic weather stations.

[0112] S2. Scale separation is performed on the quality-controlled regional automatic station observation data to obtain the regional automatic station climatological sequence representing climatological scale temperature change, and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatological sequence from the original temperature sequence.

[0113] S3. Based on climate-geographic zoning, select several national standard meteorological stations located in the same geographic zone as the regional automatic weather stations using the nearest distance method; perform scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatological sequence; then combine the regional automatic weather station climatological sequence and the national station climatological sequence to construct a linear multiple regression model and calculate the fitted climatological sequence.

[0114] S4. Based on the latitude and longitude information of the regional automatic weather station, find the grid point closest to the regional automatic weather station from the high-resolution remote sensing inversion surface temperature grid data. Using this grid point as the center, select grid data within a set range and average them to obtain remote sensing data. Perform scale separation on the remote sensing data to obtain the remote sensing data anomaly sequence. Combine the regional automatic weather station anomaly sequence and the surface data anomaly sequence to construct a linear regression model and calculate the fitted anomaly sequence.

[0115] S5. The fitted climatological sequence and the fitted anomaly sequence are combined to obtain the regional automatic station reconstruction sequence.

[0116] Example 3: This example provides an electronic device that may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The processor can call logical instructions from the memory to execute a regional automatic weather station temperature sequence reconstruction method based on scale separation and meteorological geographic zoning. This method includes the following steps:

[0117] S1. Select regional automatic weather stations within the study area whose observation data quality meets the requirements; and perform quality control on the observation data of the selected regional automatic weather stations.

[0118] S2. Scale separation is performed on the quality-controlled regional automatic station observation data to obtain the regional automatic station climatological sequence representing climatological scale temperature change, and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatological sequence from the original temperature sequence.

[0119] S3. Based on climate-geographic zoning, select several national standard meteorological stations located in the same geographic zone as the regional automatic weather stations using the nearest distance method; perform scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatological sequence; then combine the regional automatic weather station climatological sequence and the national station climatological sequence to construct a linear multiple regression model and calculate the fitted climatological sequence.

[0120] S4. Based on the latitude and longitude information of the regional automatic weather station, find the grid point closest to the regional automatic weather station from the high-resolution remote sensing inversion surface temperature grid data. Using this grid point as the center, select grid data within a set range and average them to obtain remote sensing data. Perform scale separation on the remote sensing data to obtain the remote sensing data anomaly sequence. Combine the regional automatic weather station anomaly sequence and the surface data anomaly sequence to construct a linear regression model and calculate the fitted anomaly sequence.

[0121] S5. The fitted climatological sequence and the fitted anomaly sequence are combined to obtain the regional automatic station reconstruction sequence.

[0122] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, 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 a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] Example 4: This example provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform a method for reconstructing regional automatic station temperature sequences based on scale separation and meteorological geographic zoning. This method includes the following steps:

[0124] S1. Select regional automatic weather stations within the study area whose observation data quality meets the requirements; and perform quality control on the observation data of the selected regional automatic weather stations.

[0125] S2. Scale separation is performed on the quality-controlled regional automatic station observation data to obtain the regional automatic station climatological sequence representing climatological scale temperature change, and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatological sequence from the original temperature sequence.

[0126] S3. Based on climate-geographic zoning, select several national standard meteorological stations located in the same geographic zone as the regional automatic weather stations using the nearest distance method; perform scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatological sequence; then combine the regional automatic weather station climatological sequence and the national station climatological sequence to construct a linear multiple regression model and calculate the fitted climatological sequence.

[0127] S4. Based on the latitude and longitude information of the regional automatic weather station, find the grid point closest to the regional automatic weather station from the high-resolution remote sensing inversion surface temperature grid data. Using this grid point as the center, select grid data within a set range and average them to obtain remote sensing data. Perform scale separation on the remote sensing data to obtain the remote sensing data anomaly sequence. Combine the regional automatic weather station anomaly sequence and the surface data anomaly sequence to construct a linear regression model and calculate the fitted anomaly sequence.

[0128] S5. The fitted climatological sequence and the fitted anomaly sequence are combined to obtain the regional automatic station reconstruction sequence.

[0129] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0130] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning, characterized in that, Includes the following steps: S1. Select regional automatic weather stations within the study area whose observation data quality meets the requirements; and perform quality control on the observation data of the selected regional automatic weather stations. S2. Scale separation is performed on the quality-controlled regional automatic station observation data to obtain the regional automatic station climatological sequence representing climatological scale temperature change, and the regional automatic station anomaly sequence obtained by subtracting the regional automatic station climatological sequence from the original temperature sequence. S3. Based on climate-geographic zoning, select several national standard meteorological stations located in the same geographic zone as the regional automatic weather stations using the nearest distance method; perform scale separation on the observation data of the selected national standard meteorological stations to obtain the national station climatological sequence; then combine the regional automatic weather station climatological sequence and the national station climatological sequence to construct a linear multiple regression model and calculate the fitted climatological sequence. S4. Based on the latitude and longitude information of the regional automatic weather station, find the grid point closest to the regional automatic weather station from the high-resolution remote sensing inversion surface temperature grid data. Using this grid point as the center, select grid data within a set range and average them to obtain remote sensing data. Perform scale separation on the remote sensing data to obtain the remote sensing data anomaly sequence. Combine the regional automatic weather station anomaly sequence and the surface data anomaly sequence to construct a linear regression model and calculate the fitted anomaly sequence. S5. The fitted climatological sequence and the fitted anomaly sequence are combined to obtain the regional automatic station reconstruction sequence.

2. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 1, characterized in that, Step S1 involves selecting regional automatic weather stations within the study area whose observation data quality meets the requirements; specifically: Extract the construction information of regional automatic weather stations within the study area, determine the station locations and surrounding environment, and remove regional automatic weather stations used for traffic observation and large water body observation; and perform observation quantity statistics on the observation data of the remaining regional automatic weather stations, and remove regional automatic weather stations with data duration of less than 5 years and missing data exceeding 45% of the total data volume.

3. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 1, characterized in that, Step S1 involves quality control of the selected area's automatic weather station observation data; specifically, this includes: (1) Temperature threshold check: Determine the upper and lower thresholds of annual temperature based on the historical temperature observations of the study area, and remove data from the automatic station observation data of the area that exceed the upper and lower thresholds of annual temperature; (2) Statistically analyze the observation data of the national standard meteorological stations around the regional automatic stations, calculate the monthly temperature upper and lower thresholds, and remove the data in the regional automatic station observation data that exceed the monthly temperature upper and lower thresholds for the corresponding month; (3) Based on the observation data of the national standard meteorological station, calculate the temperature change threshold for 24, 48 and 72 hours in the area where the regional automatic station is located, and remove the data in the regional automatic station observation data that exceed the temperature change threshold; (4) Set the standard deviation of temperature change within a specified time period and remove data from the automatic station observation data of the area that exceed ±3 times the standard deviation within that time period.

4. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 1, characterized in that, Step S2 describes the scale separation of the quality-controlled regional automatic weather station observation data to obtain the regional automatic weather station climatological sequence representing climatological-scale temperature changes, and the regional automatic weather station anomaly sequence obtained by subtracting the regional automatic weather station climatological sequence from the original temperature sequence; specifically: S21, Assume there are a total of regional automatic stations. Year data, number The original temperature sequence for the year is ,calculate Annual daily climate average series: ； S22. Using the moving average method, a moving average is calculated on the daily climate average series, with a moving window of 31 days and a moving average of 1 day at a time. and By performing a cyclic sliding process, the regional automatic weather station climatological sequence is obtained, as follows: ； This is a climatological sequence from regional automatic weather stations; S23. Subtract the regional automatic station climatological sequence from the original temperature sequence of the regional automatic stations to obtain the regional automatic station anomaly sequence. , represented as: ； This is an automatic station distance sequence for the region.

5. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 4, characterized in that, The scale separation of the observation data from the selected national standard meteorological stations described in step S3, and the scale separation of the remote sensing surface data described in step S4, adopt the same method as the scale separation of the regional automatic station observation data after quality control in step S2.

6. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 5, characterized in that, In step S3, scale separation is performed on the observation data of the selected national standard meteorological stations to obtain the national station climatological series. Then, a linear multiple regression model was constructed using it and the climatological sequences from regional automatic weather stations to calculate the fitted climatological sequences. , represented as: ； in, This is the national station climatological sequence of the k-th national standard meteorological station; is the regression coefficient.

7. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 5, characterized in that, Step S4 involves finding the grid point closest to the regional automatic weather station from the high-resolution remote sensing inversion surface temperature grid data based on the latitude and longitude information of the regional automatic weather station. Using this grid point as the center, the grid data within a set range is averaged to obtain the remote sensing data. Specifically: Based on the latitude and longitude information of the regional automatic weather stations, the grid points closest to the regional automatic weather stations are found from the high-resolution remote sensing inversion grid data of land surface temperature. The remote sensing temperature data value extracted from this grid point is ; Select 25 grid points centered on this grid point. The daily remote sensing data is obtained by averaging the data from 25 grid points, and is represented as follows: ； The remote sensing data sequence is composed of daily remote sensing data. , where m is the total number of years in the remote sensing data sequence, and l is the total number of days in the m-th year.

8. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 7, characterized in that, In step S3, scale separation is performed on the remote sensing data sequence to obtain the remote sensing data anomaly sequence. Then, a regression model was constructed by combining it with the regional automatic station anomaly sequence, and the fitted climatological sequence was calculated. , represented as: ； Where n0 and n1 are the fitting coefficients.

9. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to claim 7, characterized in that, Step S5 involves synthesizing the fitted climatological sequence and the fitted anomaly sequence to obtain the regional automatic weather station reconstruction sequence, as follows: ； in, For the reconstruction sequence of regional automatic stations, To fit the anomaly sequence, To fit the climatological sequence; The number of days in a year, with a value from 1 to 366, where i represents the year.

10. The method for reconstructing regional automatic weather station temperature sequences based on scale separation and meteorological geographic zoning according to any one of claims 1-9, characterized in that, In the calculations of steps S2 to S5, each year is counted as 366 days as a leap year. If the number of observations on a certain date is less than 50% of the total number of observations for the year, the observation data for that date is recorded as missing, and all data for that date are not included in the calculation.

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