Extreme rainfall threshold analysis method based on meteorological rainfall station screening
By screening and inspecting meteorological rainfall sites, identifying effective sites and evaluating extreme precipitation thresholds, the problem of inconsistent data quality at meteorological sites is solved, and the accuracy of climate change analysis and the stability of extreme precipitation thresholds are improved.
Patent Information
- Application Number
- CN202510025379.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-08
- Publication Date
- 2025-04-25
AI Technical Summary
The data quality of existing meteorologically encrypted rainfall stations is uneven, resulting in inaccurate results of climate change analysis.
By filtering valid sites, deleting migrated sites, conducting missing measured values, range values, spatial consistency and availability checks, determining the third target site, and evaluating extreme precipitation thresholds using traditional sorting method, Z-exponent conversion method and square root transformation method.
Improve the accuracy of climate change analysis results, eliminate low-quality meteorological sites, and ensure the stability and representativeness of extreme precipitation thresholds.
Smart Images
Figure CN119882096A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of meteorological statistics technology, and in particular to an extreme precipitation threshold analysis method based on meteorological rainfall station screening. Background Art
[0002] Climate change can be analyzed based on the hourly precipitation data from the meteorological dense raingauges that have been established in some areas. However, the construction and relocation of these meteorological dense raingauges for various reasons and the uneven data quality lead to inaccurate results in climate change analysis.
[0003] Therefore, how to improve the accuracy of climate change analysis results is a technical problem that needs to be solved urgently. Summary of the invention
[0004] In view of the above problems, the present invention provides an extreme precipitation threshold analysis method based on meteorological rain gauge station screening, which overcomes the above problems or at least partially solves the above problems.
[0005] In a first aspect, the present invention provides an extreme precipitation threshold analysis method based on meteorological rainfall station screening, comprising: Obtain observation information for each meteorological station in the preset area; Determine a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time; Based on the valid site, delete the migration site to obtain a first target site; Based on the first target site, a second target site is obtained, and observation information of the second target site meets a preset condition; Based on the second target site, a third target site is obtained through missing value check, range value check, spatial consistency check and availability check; Based on the third target site, an extreme precipitation threshold of the preset area is determined.
[0006] Preferably, determining a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time, comprises: Based on the observation information of each meteorological station, determining whether the observation information is continuous; If yes, determine the effective observation duration based on the first difference between the start time of the observation information and the last record time; If not, extracting target observation information from the observation information, wherein the target observation information is continuous observation information with the longest continuous time; Determining effective observation time based on the target observation information; Based on the effective observation time, a valid site is determined, and the effective observation time of the valid site is greater than a preset time.
[0007] Preferably, based on the valid site, deleting the migration site to obtain the first target site includes: Based on the valid sites, determining whether the location information of each valid site has changed; If not, each valid site is determined as the first target site; If so, determining the position difference between the changed position information and the original position information; Determining whether the position difference is greater than a first preset value; If so, delete the valid site whose location information has changed, and obtain the first target site; If not, each valid site is determined as the first target site.
[0008] Preferably, based on the first target site, a second target site is obtained, and observation information of the second target site meets a preset condition, including: Based on the first target site, determining the number of available hourly precipitation data and the corresponding number of total precipitation data per year; Based on the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year, the completeness of the observation information of each first target station is determined according to the following formula: ; The completeness of the observation information of any first target site, is the year of the last record of observation information, Any year within the effective observation period; Determining whether the integrity is greater than a second preset value; If so, it is determined as the second target site.
[0009] Preferably, based on the second target site, obtaining the third target site through missing value check, range value check, spatial consistency check and availability check includes: Based on the second target site, obtaining real observation information of the second target site through missing value check and range value check; Based on the real observation information of the second target site, a percentile method and a spatial clustering method are used to perform a spatial consistency check to obtain spatial consistency observation information of the second target site; Based on the spatial consistency observation information of the second target site, an availability check is performed to obtain a third target site.
[0010] Preferably, based on the real observation information of the second target site, a percentile method and a spatial clustering method are used to perform a spatial consistency check to obtain the spatial consistency observation information of the second target site, including: Based on the real observation information of the second target site, determining whether any site in the second target site has an adjacent site; If yes, the precipitation data of the current hour and the previous three hours of the adjacent station are combined into a precipitation data sequence of the adjacent station; Based on the precipitation data sequence of the adjacent station, the quality control coefficient is calculated: ; in, is the precipitation data of the current hour at any of the second target sites, , and They are respectively 25%, 50% and 75% of the precipitation data in the precipitation data series of the neighboring stations of any of the above-mentioned stations; Determine whether the quality control coefficient satisfies the following formula to obtain a determination result: ; in, is the absolute value function; Based on the judgment result, the optimal neighborhood radius and the minimum sample number combination are adopted, and the analysis is performed based on the DBSCAN algorithm to obtain the spatial consistency observation information of the second target site.
[0011] Preferably, performing an availability check based on the spatial consistency observation information of the second target site to obtain the third target site includes: Based on the spatial consistency observation information of the second target site, the number of available hourly precipitation data and the corresponding number of total precipitation data per year are determined, and the availability check is performed according to the following formula: ; availability of observation information for the second target site; Determining whether the availability is greater than or equal to a third preset value; If so, it is determined to be the third target site.
[0012] Preferably, based on the third target site, determining the extreme precipitation threshold of the preset area includes: Based on the third target site, the extreme precipitation threshold of each third target site determined by the following method is evaluated by discreteness to obtain an evaluation result: Traditional sorting method, Z index transformation method and square root transformation method; Based on the evaluation results, determining a target approach; Based on the target method, an extreme precipitation threshold for a preset area is determined.
[0013] Preferably, before evaluating the extreme precipitation threshold of each third target site determined by the following method based on the third target site by discreteness and obtaining the evaluation result, the method further includes: The traditional sorting method, Z index conversion method and square root transformation method were used to determine the extreme precipitation threshold of each third target station.
[0014] Preferably, based on the evaluation results, determining a target method comprises: When the evaluation result shows that the discreteness corresponding to any one of the traditional sorting method, the Z index conversion method and the square root transformation method is stable, it is determined as the target method.
[0015] In a second aspect, the present invention further provides an extreme precipitation threshold analysis device based on meteorological rainfall station screening, comprising: An acquisition module is used to obtain observation information of each meteorological station in a preset area; A first determination module is used to determine a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time; A first obtaining module is used to delete the migration site based on the valid site to obtain a first target site; A second obtaining module is used to obtain a second target site based on the first target site, and the observation information of the second target site meets a preset condition; A third obtaining module, configured to obtain a third target site based on the second target site through missing value check, range value check, spatial consistency check and availability check; The second determination module is used to determine the extreme precipitation threshold of the preset area based on the third target site.
[0016] In a third aspect, the present invention further provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the first aspect when executing the program.
[0017] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0018] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: The present invention provides an extreme precipitation threshold analysis method for meteorological rain gauge station screening, comprising: obtaining observation information of each meteorological station in a preset area; determining valid stations based on the observation information of each meteorological station, wherein the effective observation time of the valid stations is greater than the preset time; deleting migration stations based on the valid stations to obtain a first target station; obtaining a second target station based on the first target station, wherein the observation information of the second target station meets a preset condition; obtaining a third target station based on the second target station through missing value check, range value check, spatial consistency check and availability check; determining the extreme precipitation threshold of the preset area based on the third target station, and then eliminating meteorological stations with lower quality through layer-by-layer screening to improve the accuracy of climate change analysis results. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Also, throughout the accompanying drawings, the same reference figures are used to represent the same components. In the drawings: Figure 1 A schematic diagram of the steps of the extreme precipitation threshold analysis method based on meteorological rainfall station screening in an embodiment of the present invention is shown; Figure 2 A schematic diagram of the structure of an extreme precipitation threshold analysis device based on meteorological rainfall station screening in an embodiment of the present invention is shown; Figure 3 A schematic diagram of a computer device for implementing an extreme precipitation threshold analysis method based on meteorological rainfall station screening in an embodiment of the present invention is shown. DETAILED DESCRIPTION
[0020] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0021] Embodiment 1 The embodiment of the present invention provides an extreme precipitation threshold analysis method based on meteorological rainfall station screening, such as Figure 1 As shown, including: S101, obtaining observation information of each meteorological station in a preset area; S102, determining a valid station based on the observation information of each meteorological station, wherein the effective observation time of the valid station is greater than a preset time; S103, based on the valid sites, delete the migration site to obtain the first target site; S104, based on the first target site, obtaining a second target site, where observation information of the second target site meets a preset condition; S105, based on the second target site, obtain a third target site through missing value check, range value check, spatial consistency check and availability check; S106: Determine an extreme precipitation threshold of a preset area based on the third target site.
[0022] First, S101, obtain observation information of each meteorological station in a preset area, where the preset area refers to an area where meteorological analysis is required, such as a province, a city, or a region, etc.
[0023] First, the observation information of each meteorological station i (i=1, 2, 3, ..., m, m is the total number of meteorological stations) in the preset area is counted, including: the start time is recorded as T si , the last record time is recorded as T ei , then the effective observation time is recorded as T i , and T i =Diff(T ei -T si ).
[0024] Next, select valid sites and data: S102, based on the observation information of each meteorological station, determining the effective station, the effective observation time of the effective station is greater than the preset time.
[0025] Specifically, based on the observation information of each meteorological station, it is determined whether it is continuous observation information; If so, the effective observation time is determined by the first difference between the start time of the observation information and the last record time; If not, extract the target observation information from the observation information, where the target observation information is the continuous observation information with the longest continuous time; Determine the effective observation time based on the target observation information; Based on the effective observation time, the effective site is determined, and the effective observation time of the effective site is greater than the preset time.
[0026] Through statistics, the effective observation time T of each meteorological station is obtained. fi , and the start time T of the effective observation duration fsi and the last record time T fei When the observation information of meteorological station i is continuous, that is, there is no interruption in the observation information, the corresponding effective observation time T fi=T i , T fsi =T si , T fei =T ei .
[0027] When the observation information of meteorological station i is discontinuous, T fi =max(T i ), T fsi = T si (max(T i )), T fei = T ei (max(T i )).
[0028] Finally, determine T fi Is it greater than 10 years? If so, it is determined as a valid site.
[0029] Next, S103 is executed to delete the migration site based on the valid site to obtain the first target site.
[0030] Specifically, based on the valid sites, determining whether the location information of each valid site has changed; If not, each valid site is determined as the first target site; If yes, determine the valid site corresponding to the changed location information and obtain the first target site; If not, each valid site is determined as the first target site.
[0031] First, the locations of valid sites are counted as distinct (lon, lat) to form a location information set W j (lon p ,lat p ), where p is the number of longitude and latitude information.
[0032] According to the location information of the valid sites, it is determined whether the location information of each valid site has changed, specifically, whether the valid site has been relocated, that is, the valid site was previously at one location, and has changed its location due to relocation.
[0033] If there is no site migration, the valid site is determined as the first target site.
[0034] If there is a station relocation, the position difference between the changed position information and the original position information is determined.
[0035] That is, the position difference distance (lon p ,lat p ), lon p+1 ,lat p+1 )).
[0036] When the location difference distance <= 1km, it is determined that the location information of the valid site has not changed. When the location difference distance > 1km, it is determined that the location of the valid site has changed, that is, there is a site migration. The valid site with changed location information is deleted to obtain the first target site.
[0037] Next, S104 is executed to obtain a second target site based on the first target site, and the observation information of the second target site meets a preset condition.
[0038] Specifically, based on the first target site, the number of available hourly precipitation data and the corresponding number of total precipitation data are determined each year; Based on the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year, the completeness of the observation information of each first target station is determined according to the following formula: ; The completeness of the observation information of any first target site, is the year of the last record of observation information, Any year within the effective observation period; Determining whether the integrity is greater than a second preset value; If so, it is determined as the second target site.
[0039] If not, delete the first target site.
[0040] Specifically, the second preset value is 80%. When the integrity of any first target site is greater than 80%, it is determined as the second target site.
[0041] Next, S105 is executed to obtain a third target site based on the second target site through missing value check, range value check, spatial consistency check and availability check.
[0042] Firstly, based on the second target site, the real observation information of the second target site is obtained through missing value check and range value check.
[0043] The missing value check specifically marks the missing value as 1, otherwise it is marked as 0. jt (t is time, and T fsj <=t<=T fej ) is zero, it is determined to be a missing value.
[0044] The range value check is to first convert the hourly precipitation data R jt Perform range value check, if 0≤R jt≤300mm, the hourly precipitation data R jt Data identification A jt is recorded as 0, otherwise it is recorded as 1.
[0045] Add the hourly precipitation data to the precipitation data marked as 0 in the previous 23 hours to get the 24-hour cumulative precipitation data R dt , if 0≤R dt If the precipitation data is less than or equal to 1000 mm, the 24-hour precipitation data identification remains unchanged; otherwise, the 24-hour precipitation data identification is all marked as 1.
[0046] Thus, the real observation information of the second target site is obtained.
[0047] Next, a spatial consistency check is performed, which includes percentile method and spatial clustering method.
[0048] Based on the real observation information of the second target site, the percentile method and spatial clustering method are used to perform spatial consistency check to obtain the spatial consistency observation information of the second target site.
[0049] Specifically, based on the real observation information of the second target site, determining whether any site in the second target site has a neighboring site; If yes, the precipitation data of the current hour and the previous 3 hours of the nearby station are combined into a precipitation data sequence of the nearby station; Based on the precipitation data series of nearby stations, the quality control coefficient is calculated: ; in, is the precipitation data of the current hour at any of the second target sites, , and They are respectively 25%, 50% and 75% of the precipitation data in the precipitation data series of the neighboring stations of any of the above-mentioned stations; Determine whether the quality control coefficient satisfies the following formula to obtain a determination result: ; in, is the absolute value function.
[0050] exist When Data Identifier It is recorded as 1, otherwise it is recorded as 0.
[0051] Next, spatial clustering: Based on the judgment results, the optimal neighborhood radius (ε) and the minimum number of samples (MinPts) are combined and analyzed based on the DBSCAN algorithm to determine the available observation information of the second target site; Based on the available observation information of the second target site, a third target site is obtained.
[0052] Among them, the DBSCAN algorithm (Density-Based Spatial Clustering of Applications with Noise) conducts a large number of tests based on existing error samples, selects the optimal neighborhood radius and the minimum number of samples, and then uses the DBSCAN algorithm to cluster each For analysis, if If the precipitation data is significantly different from the observations at surrounding stations, Data Identifier It is recorded as 1, otherwise it is recorded as 0.
[0053] Then Data Identifier If it is 0, it is determined as the precipitation data of station j at time t Available, that is, obtained at this time The spatial consistency observation information of the second target site is shown in Figure 2. Next, the availability is determined.
[0054] Specifically, based on the spatial consistency observation information of the second target site, an availability check is performed to obtain the third target site.
[0055] Among them, based on the spatial consistency observation information of the second target site, the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year are determined, and the availability check is performed according to the following formula:
[0056] is the availability of observation information of the second target station. If we take 24 hours a day and 1 precipitation data per hour as an example, the total number of precipitation data is 24, and the number of available hourly precipitation data is also 24.
[0057] Determine whether the availability is greater than or equal to a third preset value; if so, determine it as the third target site.
[0058] The third preset value of availability is 80%. When the availability is determined to be good, otherwise the availability is poor and it is discarded.
[0059] Thus, the third target site is obtained, wherein the availability data set of the third target site is D j (A data set whose data identifier is 0 during the effective observation time).
[0060] Next, S106 is executed to determine an extreme precipitation threshold of a preset area based on the third target site.
[0061] The determination of extreme precipitation thresholds is of great significance for the scientific assessment of extreme weather and climate events and the formulation of social defense standards for major meteorological disasters.
[0062] The extreme precipitation threshold refers to the process in statistics where a set of data is divided into 100 equal parts in order of size, and then a fixed percentile value of a long-term series is selected as the threshold. Values exceeding this threshold are considered extreme values, and the event is considered an extreme event.
[0063] First, any of the following methods can be used to calculate the extreme precipitation threshold: the extreme precipitation threshold of each third target station can be determined by using the traditional sorting method, the Z index conversion method, and the square root transformation method.
[0064] The first one is the traditional sorting method, which sorts the hourly precipitation from small to large, and obtains , ,…… , percentile threshold :
[0065] in, is the sequence number of the hourly precipitation in ascending order, is the rounding function, is the corresponding percentile, is the weight coefficient, .
[0066] The second method is the Z index conversion method, which first converts the actual precipitation data into a standard normal distribution through the Z index. The conversion method is as follows:
[0067] in, , , is the actual hourly precipitation data, is the skewness coefficient, is the sample size, is the sample mean, is the sample standard deviation.
[0068] According to the Z index value of the standard normal distribution percentile, the corresponding precipitation data is converted according to the following formula:
[0069] Then, according to the obtained precipitation data, an appropriate percentile is selected as the extreme precipitation threshold, for example, 95%, 98% or 99%.
[0070] The third method is the square root transformation method. The actual hourly precipitation data is transformed into a general normal distribution through the square root, and then transformed into a standard normal distribution. According to the percentile value of the standard normal distribution, it is transformed into the value of the general normal distribution, and then the transformed value is squared to obtain the corresponding hourly precipitation data. The square root transformation is:
[0071] in, is the transformed hourly precipitation data, Actual hourly precipitation data.
[0072] Based on the transformed hourly precipitation data, the appropriate percentile is selected as the extreme precipitation threshold.
[0073] The above actual precipitation is obtained through the data of the third target station.
[0074] Based on the third target site, the extreme precipitation threshold of the preset area is determined, including: Based on the third target station, the extreme precipitation thresholds of each third target station determined by the following method are evaluated by discreteness to obtain the evaluation results: Traditional sorting method, Z index transformation method and square root transformation method; Based on the evaluation results, determine the target approach; Based on the target method, the extreme precipitation threshold of the preset area is determined.
[0075] When determining the threshold for short-term heavy precipitation, the stability of the threshold in different time periods must be ensured. According to the threshold determination requirements for extreme climate events, it is necessary to determine whether the average value of the threshold in a certain climate stage is representative and whether the average value of the threshold calculated year by year is stable.
[0076] Each method is measured by discreteness, the formula is as follows:
[0077] in, is the standard deviation of precipitation data, is the average value of precipitation data, is the discreteness, and Less than 1, The smaller the value, the smaller the variation of the thresholds calculated in different years, which means stability, while reproduction means that the thresholds are poorly representative and unstable.
[0078] Therefore, when the evaluation results show that the discreteness corresponding to any one of the traditional sorting method, the Z index conversion method, and the square root transformation method is stable, it is determined as the target method.
[0079] By using the following example, to evaluate.
[0080] Two cases, hourly precipitation > 0 mm and hourly precipitation > 1 mm, were selected to compare and analyze the above three methods for determining the extreme short-term heavy rainfall threshold.
[0081] As can be seen from Tables 1 and 2, regardless of the hourly precipitation > 0 mm (i.e., precipitation) or hourly precipitation > 1 mm, the multi-year threshold average is less than the threshold, and as the percentile increases, the dispersion tends to increase, that is, the threshold is less representative and unstable. At the same time, the threshold for hourly precipitation > 0 mm does not exceed 40 mm, while the percentile 99.9% threshold for hourly precipitation > 1 mm is close to 50 mm. From the analysis of historical data, the average maximum hourly precipitation exceeds 60 mm. From the extreme analysis, the threshold for hourly precipitation > 0 mm is quite different from the actual precipitation. From the three threshold determination methods at the same percentile, the threshold and average threshold of the traditional sorting method are the largest, followed by the square root transformation method, and finally the Z index transformation method, which also confirms the unevenness of extreme precipitation in each year and the abnormality of climate. From the perspective of dispersion, the difference is very small at low percentiles (i.e., < 99.8%), and the dispersion of the Z index transformation method is the smallest at higher percentiles (>= 99.8%).
[0082] In general, due to the large number of hourly precipitation samples, the data are basically close to the normal distribution, so the impact of whether to perform normal distribution conversion on the results is not obvious. For a certain basin, from the perspective of representativeness and stability, it is better to choose the Z index conversion method with hourly precipitation>1mm to determine the extreme short-term heavy precipitation threshold. From the perspective of computational efficiency, when there are more than 10 years of hourly data, it is also possible to use the traditional sorting method to determine the extreme short-term heavy precipitation threshold. The present invention adopts the Z index conversion method with hourly precipitation>1mm to determine the extreme short-term heavy precipitation threshold.
[0083] Table 1 Comparison of three threshold determination methods (hourly precipitation > 0 mm)
[0084] Table 2 Comparison of three threshold determination methods (hourly precipitation > 1 mm)
[0085] One or more technical solutions in the embodiments of the present invention have at least the following technical effects or advantages: The present invention provides an extreme precipitation threshold analysis method for meteorological rain gauge station screening, comprising: obtaining observation information of each meteorological station in a preset area; determining valid stations based on the observation information of each meteorological station, wherein the effective observation time of the valid stations is greater than the preset time; deleting migration stations based on the valid stations to obtain a first target station; obtaining a second target station based on the first target station, wherein the observation information of the second target station meets a preset condition; obtaining a third target station based on the second target station through missing value check, range value check, spatial consistency check and availability check; determining the extreme precipitation threshold of the preset area based on the third target station, and then eliminating meteorological stations with lower quality through layer-by-layer screening to improve the accuracy of climate change analysis results.
[0086] Embodiment 2 Based on the same inventive concept, the embodiment of the present invention also provides an extreme precipitation threshold analysis device based on meteorological rainfall station screening, such as Figure 2 As shown, including: The acquisition module 201 is used to acquire the observation information of each meteorological station in a preset area; A first determination module 202 is used to determine a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time; A first obtaining module 203 is used to delete the migration site based on the valid site to obtain a first target site; A second obtaining module 204 is used to obtain a second target site based on the first target site, and the observation information of the second target site meets a preset condition; A third obtaining module 205 is used to obtain a third target site based on the second target site through missing value check, range value check, spatial consistency check and availability check; The second determination module 206 is configured to determine an extreme precipitation threshold of the preset area based on the third target site.
[0087] In an optional implementation, the first determining module 202 is configured to: Based on the observation information of each meteorological station, determining whether the observation information is continuous; If yes, determine the effective observation duration based on the first difference between the start time of the observation information and the last record time; If not, extracting target observation information from the observation information, wherein the target observation information is continuous observation information with the longest continuous time; Determining effective observation time based on the target observation information; Based on the effective observation time, a valid site is determined, and the effective observation time of the valid site is greater than a preset time.
[0088] In an optional implementation, the first obtaining module 203 is used to: Based on the valid sites, determining whether the location information of each valid site has changed; If not, each valid site is determined as the first target site; If so, determining the position difference between the changed position information and the original position information; Determining whether the position difference is greater than a first preset value; If so, delete the valid site whose location information has changed, and obtain the first target site; If not, each valid site is determined as the first target site.
[0089] In an optional implementation, the second obtaining module 204 is used to: Based on the first target site, determining the number of available hourly precipitation data and the corresponding number of total precipitation data per year; Based on the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year, the completeness of the observation information of each first target station is determined according to the following formula: ; The completeness of the observation information of any first target site, is the year of the last record of observation information, Any year within the effective observation period; Determining whether the integrity is greater than a second preset value; If so, it is determined as the second target site.
[0090] In an optional implementation, the third obtaining module 205 is used to: Based on the second target site, obtaining the real observation information of the second target site through missing value check and range value check; Based on the real observation information of the second target site, a percentile method and a spatial clustering method are used to perform a spatial consistency check to obtain spatial consistency observation information of the second target site; Based on the spatial consistency observation information of the second target site, an availability check is performed to obtain a third target site.
[0091] In an optional implementation, the third obtaining module 205 is used to: Based on the real observation information of the second target site, determining whether any site in the second target site has an adjacent site; If yes, the precipitation data of the current hour and the previous three hours of the adjacent station are combined into a precipitation data sequence of the adjacent station; Based on the precipitation data sequence of the adjacent station, the quality control coefficient is calculated: ; in, is the precipitation data of the current hour at any of the second target sites, , and They are respectively 25%, 50% and 75% of the precipitation data in the precipitation data series of the neighboring stations of any of the above-mentioned stations; Determine whether the quality control coefficient satisfies the following formula to obtain a determination result: ; in, is the absolute value function; Based on the judgment result, the optimal neighborhood radius and the minimum sample number combination are adopted, and the analysis is performed based on the DBSCAN algorithm to obtain the spatial consistency observation information of the second target site.
[0092] In an optional implementation, the third obtaining module 205 is used to: Based on the spatial consistency observation information of the second target site, the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year are determined, and the availability check is performed according to the following formula: ; availability of observation information for the second target site; Determining whether the availability is greater than or equal to a third preset value; If so, it is determined to be the third target site.
[0093] In an optional implementation, the second determining module 206 is configured to: Based on the third target site, the extreme precipitation threshold of each third target site determined by the following method is evaluated by discreteness to obtain an evaluation result: Traditional sorting method, Z index transformation method and square root transformation method; Based on the evaluation results, determining a target approach; Based on the target method, an extreme precipitation threshold for a preset area is determined.
[0094] In an optional implementation manner, the second determining module 206 is further configured to: The traditional sorting method, Z index conversion method and square root transformation method were used to determine the extreme precipitation threshold of each third target station.
[0095] In an optional implementation manner, the second determining module 206 is further configured to: When the evaluation result shows that the discreteness corresponding to any one of the traditional sorting method, the Z index conversion method and the square root transformation method is stable, it is determined as the target method.
[0096] Embodiment 3 Based on the same inventive concept, an embodiment of the present invention provides a computer device, such as Figure 3 As shown, it includes a memory 304, a processor 302, and a computer program stored in the memory 304 and executable on the processor 302. When the processor 302 executes the program, the steps of the extreme precipitation threshold analysis method based on meteorological rainfall station screening are implemented.
[0097] Among them, Figure 3 In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown, and bus 300 may include any number of interconnected buses and bridges, and bus 300 links various circuits including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 306 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same element, namely a transceiver, providing a unit for communicating with various other devices on a transmission medium. Processor 302 is responsible for managing bus 300 and general processing, while memory 304 may be used to store data used by processor 302 when performing operations.
[0098] Embodiment 4 Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned extreme precipitation threshold analysis method based on meteorological rainfall station screening.
[0099] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages can be utilized to realize the content of the present invention described herein, and the description of the above specific languages is for disclosing the best mode of the present invention.
[0100] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.
[0101] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recorded in each embodiment. More specifically, as reflected in each embodiment, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.
[0102] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and further may be divided into a plurality of submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device so disclosed may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature that provides the same, equivalent or similar purpose.
[0103] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in a specific embodiment, any one of the claimed embodiments may be used in any combination.
[0104] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components of an extreme precipitation threshold analysis device based on meteorological rain gauge station screening and a computer device according to an embodiment of the present invention. The present invention may also be implemented as a device or device program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program for implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.
[0105] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.
Claims
1. A method for analyzing extreme precipitation thresholds based on meteorological rainfall station screening, characterized in that: include: Obtain observation information for each meteorological station in the preset area; Determine a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time; Based on the valid site, delete the migration site to obtain a first target site; Based on the first target site, a second target site is obtained, and observation information of the second target site meets a preset condition; Based on the second target site, a third target site is obtained through missing value check, range value check, spatial consistency check and availability check; Based on the third target site, an extreme precipitation threshold of the preset area is determined.
2. The method according to claim 1, characterized in that Determining a valid site based on the observation information of each meteorological site, wherein the effective observation time of the valid site is greater than a preset time, comprises: Based on the observation information of each meteorological station, determining whether the observation information is continuous; If yes, determine the effective observation duration based on the first difference between the start time of the observation information and the last record time; If not, extracting target observation information from the observation information, wherein the target observation information is continuous observation information with the longest continuous time; Determining effective observation time based on the target observation information; Based on the effective observation time, a valid site is determined, and the effective observation time of the valid site is greater than a preset time.
3. The method according to claim 1, characterized in that Based on the valid site, deleting the migration site to obtain the first target site includes: Based on the valid sites, determining whether the location information of each valid site has changed; If not, each valid site is determined as the first target site; If so, determining the position difference between the changed position information and the original position information; Determining whether the position difference is greater than a first preset value; If so, delete the valid site whose location information has changed, and obtain the first target site; If not, each valid site is determined as the first target site.
4. The method according to claim 1, characterized in that Based on the first target site, a second target site is obtained, and observation information of the second target site meets a preset condition, including: Based on the first target site, determining the number of available hourly precipitation data and the corresponding number of total precipitation data per year; Based on the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year, the completeness of the observation information of each first target station is determined according to the following formula: ； The completeness of the observation information of any first target site, is the year of the last record of observation information, Any year within the effective observation period; Determining whether the integrity is greater than a second preset value; If so, it is determined as the second target site.
5. The method according to claim 1, characterized in that Based on the second target site, a third target site is obtained through missing value check, range value check, spatial consistency check and availability check, including: Based on the second target site, obtaining the real observation information of the second target site through missing value check and range value check; Based on the real observation information of the second target site, a percentile method and a spatial clustering method are used to perform a spatial consistency check to obtain spatial consistency observation information of the second target site; Based on the spatial consistency observation information of the second target site, an availability check is performed to obtain a third target site.
6. The method according to claim 5, characterized in that Based on the real observation information of the second target site, the percentile method and the spatial clustering method are used to perform a spatial consistency check to obtain the spatial consistency observation information of the second target site, including: Based on the real observation information of the second target site, determining whether any site in the second target site has an adjacent site; If yes, the precipitation data of the current hour and the previous three hours of the adjacent station are combined into a precipitation data sequence of the adjacent station; Based on the precipitation data sequence of the adjacent station, the quality control coefficient is calculated: ； in, is the precipitation data of the current hour at any of the second target sites, , and They are respectively 25%, 50% and 75% of the precipitation data in the precipitation data series of the neighboring stations of any of the above-mentioned stations; Determine whether the quality control coefficient satisfies the following formula to obtain a determination result: ； in, is the absolute value function; Based on the judgment result, the optimal neighborhood radius and the minimum sample number combination are adopted, and the analysis is performed based on the DBSCAN algorithm to obtain the spatial consistency observation information of the second target site.
7. The method according to claim 5, characterized in that Based on the spatial consistency observation information of the second target site, an availability check is performed to obtain a third target site, including: Based on the spatial consistency observation information of the second target site, the number of available hourly precipitation data per year and the corresponding number of total precipitation data per year are determined, and the availability check is performed according to the following formula: ； availability of observation information for the second target site; Determining whether the availability is greater than or equal to a third preset value; If so, the third target site is obtained.
8. The method according to claim 1, characterized in that Determining an extreme precipitation threshold of the preset area based on the third target site includes: Based on the third target site, the extreme precipitation threshold of each third target site determined by the following method is evaluated by discreteness to obtain an evaluation result: Traditional sorting method, Z index transformation method and square root transformation method; Based on the evaluation results, determining a target approach; Based on the target method, an extreme precipitation threshold for a preset area is determined.
9. The method according to claim 8, characterized in that Before obtaining the evaluation result, based on the third target site, evaluating the extreme precipitation threshold of each third target site determined by the following method by discreteness, the method further includes: The traditional sorting method, Z index conversion method and square root transformation method were used to determine the extreme precipitation threshold of each third target station.
10. The method according to claim 8, characterized in that Based on the assessment results, determine the target approach, including: When the evaluation result shows that the discreteness corresponding to any one of the traditional sorting method, the Z index conversion method and the square root transformation method is stable, it is determined as the target method.