Meteorological station data quality inspection method and device based on spatial correlation

Abstract

The invention provides a meteorological station data quality inspection method and device based on spatial correlation. The method comprises the following steps: acquiring a first data set comprising observation data and a station position; calculating the minimum spatial correlation of the first data set according to the interval distance between the first data set and each observation station; performing spatial interpolation calculation by taking the critical spacing distance as the spacing distance of the observation stations according to the calculation result to obtain a second data set; according to the calculation result of the minimum spatial correlation, performing circular space query by using any data in the first data set, and selecting all observation data in the second data set within the query range as a proofreading data set of the data; any data in the first data set and the second data set and the proofreading data set corresponding to the data are selected respectively, the absolute value of the difference between the data and the proofreading data set is calculated and compared with the second preset value, missing data are calculated and supplemented, and the accuracy of meteorological observation data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data processing, and in particular to a meteorological site data quality inspection method and device based on spatial correlation. Background Art

[0002] The existing quality control methods for meteorological station observation data mainly use mathematical statistical theories and methods for quality control, and discover, eliminate and process missing values, negative values ​​and obvious outliers in the observation values. It only achieves a certain quality control from the meteorological observation values ​​themselves, and does not fully consider that meteorological observation data is closely related to geographical location, terrain, environment and other factors. It is difficult to truly identify and discover the spatial consistency, compliance and accuracy of meteorological observation data. Summary of the invention

[0003] (I) Purpose of the invention

[0004] The purpose of the present invention is to provide a meteorological site data quality inspection method and device based on spatial correlation, which can identify unreasonable observation data according to factors such as the geographical location, terrain and environment of the observation site, and can greatly improve the rationality and accuracy of the observation data.

[0005] (II) Technical solution

[0006] To solve the above problems, the first aspect of the present invention provides a method for verifying meteorological station data quality based on spatial correlation, comprising the following steps:

[0007] Data acquisition step: acquiring a first data set, the first data set comprising: observation data and location information of each observation site, wherein the observation data consists of observation data of each observation site;

[0008] Minimum spatial correlation calculation step: calculating the minimum spatial correlation of the first data set according to the interval distance between the first data set and each of the observation sites, the interval distance corresponding to the minimum spatial correlation is a critical interval distance, if the minimum spatial correlation is greater than a first preset value, the first data set does not have spatial correlation, and the data acquisition step is entered to acquire data again;

[0009] Data expansion step: according to the calculation result of the minimum spatial correlation, taking the critical interval distance as the interval distance of the observation site, using the Kriging algorithm to perform spatial interpolation calculation on the first data set to obtain the second data set;

[0010] The step of obtaining a proofreading data set is as follows: according to the calculation result of the minimum spatial correlation, a circular spatial query is performed with the longitude and latitude of any meteorological observation station in the first data set as the center and the critical interval distance as the radius, and the observation data in the query range of the second data set is selected as the proofreading data set of the observation data of the meteorological observation station;

[0011] Data quality control steps: set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

[0012] Furthermore, the data supplementation step in the above-mentioned meteorological station data quality inspection method based on spatial correlation further includes:

[0013] Data checking step: obtain and compare the observation data of each observation site with the observation data of the adjacent observation site according to the time range and meteorological elements, and determine whether they meet the statistical characteristics. If not, the observation data of the current observation site is abnormal data and the abnormal data is eliminated.

[0014] Furthermore, the calculation formula of the spatial correlation coefficient in the minimum spatial correlation calculation step in the above-mentioned meteorological station data quality inspection method based on spatial correlation is:

[0015]

[0016] Wherein, γ(h) is the spatial correlation coefficient, N(h) is the number of observation sites within the interval distance h of each observation site, and Z(X i ) is any observation site X i The observed value of Z(X i +h) is the observation site X i Observations within distance h.

[0017] Furthermore, in the above-mentioned meteorological station data quality inspection method based on spatial correlation, the distance between each other is continuously adjusted to obtain the minimum value of the spatial correlation coefficient as the minimum spatial correlation coefficient.

[0018] Furthermore, in the above-mentioned meteorological site data quality inspection method based on spatial correlation, the first preset value is 0.75.

[0019] The second aspect of the present invention also provides a meteorological station data quality control device, comprising the following modules:

[0020] A data acquisition module is used to acquire a first data set, wherein the first data set includes observation data and location information of each observation site, wherein the observation data is composed of observation data of each observation site;

[0021] A minimum spatial correlation calculation module: used for calculating the minimum spatial correlation of the minimum spatial correlation of the first data set according to the interval distance between the first data set and each of the observation sites, wherein the interval distance corresponding to the minimum spatial correlation is a critical interval distance. If the minimum spatial correlation is greater than a first preset value, the first data set does not have spatial correlation, and the data acquisition step is entered to reacquire data.

[0022] Data expansion module: used for obtaining a second data set by performing spatial interpolation calculation on the first data set using a Kriging algorithm according to the calculation result of the minimum spatial correlation and taking the critical interval distance as the interval distance of the observation sites;

[0023] A module for obtaining a proofreading data set is used to perform a circular spatial query based on the calculation result of the minimum spatial correlation, with the longitude and latitude of any meteorological observation station in the first data set as the center and the critical interval distance as the radius, and select the observation data in the query range in the second data set as the proofreading data set;

[0024] Data quality control module: used to set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

[0025] (III) Beneficial effects

[0026] By analyzing the observation data based on the principle of spatial correlation and analyzing the spatial distribution characteristics and variability of the observation data at the observation site and other spatially adjacent observation data, we can more accurately judge unreasonable observation data, and then conduct accuracy quality inspections on data with different quality control codes, further improving the accuracy of meteorological observation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a schematic diagram of a process according to an embodiment of the present invention;

[0028] Figure 2 is a structural block diagram according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. In addition, in the following description, the description of well-known structures and technologies is omitted to avoid unnecessary confusion of the concept of the present invention.

[0030] Combine the following Figure 1 and Figure 2 , the embodiments of the present invention are described.

[0031] refer to Figure 1 , the embodiment shown in the present invention includes the following steps:

[0032] Data acquisition step: acquiring a first data set, the first data set comprising: observation data and the location of each observation site, wherein the observation data consists of observation data of each observation site;

[0033] In practice, abnormal data is often obtained due to equipment failure and other reasons. Therefore, after the data acquisition step, the following steps may also be included:

[0034] Data checking steps: Obtain and compare the observation data of each observation site with the observation data of adjacent observation sites according to the time range and meteorological elements, and determine whether they meet the statistical characteristics. If not, the observation data of the current observation site is abnormal data and the abnormal data is eliminated.

[0035] Minimum spatial correlation calculation steps: The minimum spatial correlation of the first data set is calculated based on the interval distance between the first data set and each observation site, and the interval distance corresponding to the minimum spatial correlation is the critical interval distance.

[0036] The calculation formula of spatial correlation coefficient is:

[0037]

[0038] Among them, γ(h) is the spatial correlation coefficient, N(h) is the number of observation stations within the interval h of each observation station, and Z(X i ) is any observation site X i The observed value of Z(X i +h) is the observation site X i Observations within distance h.

[0039] By adjusting the interval distance h from each observation site, the spatial correlation coefficient γ(h) of the first data set at different interval distances h can be obtained. The larger the spatial correlation coefficient γ(h), the smaller the correlation. By continuously adjusting the interval distance h, the minimum value of the spatial correlation coefficient is obtained, that is, the minimum spatial correlation coefficient γ(h).min , at this time the distance h is the critical distance h l When the minimum spatial correlation coefficient γ(h) min If it is greater than the first preset value, it means that the first data set does not have spatial correlation, and the method is not applicable, and it is necessary to enter the data acquisition step to acquire data again. In this embodiment, the first preset value is 0.75.

[0040] Data expansion step: According to the calculation results of the minimum spatial correlation, the critical interval distance h l The Kriging algorithm is used to perform spatial interpolation on the first data set to obtain the second data set, which is the distance from the observation site. Spatial interpolation can be used to expand a large amount of unobserved data to improve the accuracy of subsequent data processing.

[0041] Steps for obtaining the calibration data set: Based on the calculation result of the minimum spatial correlation, take the longitude and latitude of any meteorological observation station in the first data set as the center and the critical interval distance h as the center. l A circular space query is performed with the radius as the radius, and the observation data Y in the query range in the second data set is selected as the calibration data set of the observation data of the meteorological observation station.

[0042] Data quality control steps: set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

[0043] The present invention also provides a meteorological site data quality inspection device based on spatial correlation, comprising the following modules:

[0044] A data acquisition module is used to acquire a first data set, wherein the first data set includes observation data and location information of each observation site, wherein the observation data is composed of observation data of each observation site;

[0045] Minimum spatial correlation calculation module: used to calculate the minimum spatial correlation of the first data set according to the interval distance between the first data set and each observation site, the interval distance corresponding to the minimum spatial correlation is the critical interval distance, if the minimum spatial correlation is greater than the first preset value, the first data set does not have spatial correlation, enter the data acquisition step and re-acquire data;

[0046] Data expansion module: used to obtain the second data set by performing spatial interpolation calculation on the first data set using the Kriging algorithm based on the calculation result of the minimum spatial correlation and taking the critical interval distance as the interval distance of the observation sites;

[0047] Acquisition of proofreading data set module: used to perform circular spatial query based on the calculation result of minimum spatial correlation, taking the longitude and latitude of any meteorological observation station in the first data set as the center and the critical interval distance as the radius, and select the observation data in the query range of the second data set as the proofreading data set;

[0048] Data quality control module: used to set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

[0049] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A method for checking meteorological station data quality based on spatial correlation, characterized in that: The steps include: Data acquisition step: acquiring a first data set, the first data set comprising: observation data and location information of each observation site, wherein the observation data consists of observation data of each observation site; Minimum spatial correlation calculation step: calculating the minimum spatial correlation of the first data set according to the interval distance between the first data set and each of the observation sites, the interval distance corresponding to the minimum spatial correlation is a critical interval distance, if the minimum spatial correlation is greater than a first preset value, the first data set does not have spatial correlation, and the data acquisition step is entered to acquire data again; Data expansion step: according to the calculation result of the minimum spatial correlation, taking the critical interval distance as the interval distance of the observation site, using the Kriging algorithm to perform spatial interpolation calculation on the first data set to obtain the second data set; The step of obtaining a proofreading data set is as follows: based on the calculation result of the minimum spatial correlation, a circular spatial query is performed with the longitude and latitude of any of the meteorological observation stations in the first data set as the center and the critical interval distance as the radius, and the observation data in the query range of the second data set is selected as the proofreading data set of the observation data of the meteorological observation station; Data quality control steps: set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

2. The method for verifying meteorological station data quality based on spatial correlation according to claim 1, characterized in that: The data supplementation step further includes: Data checking step: obtain and compare the observation data of each observation site with the observation data of the adjacent observation site according to the time range and meteorological elements, and determine whether they meet the statistical characteristics. If not, the observation data of the current observation site is abnormal data and the abnormal data is eliminated.

3. The method for verifying meteorological station data quality based on spatial correlation according to claim 1, characterized in that: The calculation formula of the spatial correlation coefficient in the minimum spatial correlation calculation step is: Wherein, γ(h) is the spatial correlation coefficient, N(h) is the number of observation sites within the interval distance h of each observation site, and Z(X i ) is any observation site X i The observed value of Z(X i +h) is the observation site X i Observations within distance h.

4. The method for checking meteorological station data quality based on spatial correlation according to claim 3 is characterized in that: The interval distance is continuously adjusted to obtain the minimum value of the spatial correlation coefficient as the minimum spatial correlation coefficient.

5. The method for verifying meteorological station data quality based on spatial correlation according to claim 1, characterized in that: The first preset value is 0.

75.

6. A meteorological station data quality control device, characterized in that: Includes the following modules: A data acquisition module is used to acquire a first data set, wherein the first data set includes observation data and location information of each observation site, wherein the observation data is composed of observation data of each observation site; A minimum spatial correlation calculation module is used to calculate the minimum spatial correlation of the first data set according to the interval distance between the first data set and each of the observation sites, wherein the interval distance corresponding to the minimum spatial correlation is a critical interval distance. If the minimum spatial correlation is greater than a first preset value, the first data set does not have spatial correlation, and the data acquisition step is entered to reacquire data. Data expansion module: used for obtaining a second data set by performing spatial interpolation calculation on the first data set using a Kriging algorithm according to the calculation result of the minimum spatial correlation and taking the critical interval distance as the interval distance of the observation sites; A module for obtaining a proofreading data set is used to perform a circular spatial query based on the calculation result of the minimum spatial correlation, with the longitude and latitude of any meteorological observation station in the first data set as the center and the critical interval distance as the radius, and select the observation data in the query range in the second data set as the proofreading data set; Data quality control module: used to set the time range and meteorological elements, select any data in the first data set as the first observation data, then select any data in the proofreading data set as the second observation data, calculate the absolute value of the difference between the first observation data and the second observation data, if it is smaller than the second preset value, it meets the requirements, if it is larger than the second preset value, the first observation data is discarded.

Images