CN112948649A - Automatic GRAPES (generalized Grace-oriented error condition) area forecast mode significance testing method and system - Google Patents
Automatic GRAPES (generalized Grace-oriented error condition) area forecast mode significance testing method and system Download PDFInfo
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Abstract
The invention belongs to the technical field of product quality evaluation, and discloses an automatic GRAPES (generalized GRAPES error condition) area forecast mode significance testing method and system, wherein a user sets a testing mode, a testing algorithm, confidence, start and stop dates of data, longitude and latitude of an area range and a scoring card display style parameter on a browser page; the back end determines a storage path of the data file in the server according to the inspection mode and the data start-stop date, and reads a corresponding forecast source file according to the obtained path; cutting data in the Grib file according to the latitude and longitude range; calculating statistic of the forecast value and the observed value; and calculating the significance test scoring level of the two modes under different statistics through a significance test algorithm. The invention adopts the B/S architecture, does not need to install a client, can scientifically and efficiently carry out the difference test of forecast product quality, and improves the customization degree of the test.
Description
Technical Field
The invention belongs to the technical field of product quality evaluation, and particularly relates to an automatic GRAPES (generalized Grace temporal evolution) area prediction mode significance testing method and system.
Background
At present, for the quality comparative evaluation of the forecast products of different models of the GRAPES, the inspection scores such as the average error, the root mean square error and the standard deviation of the forecast products under the same time period can be calculated for comparative analysis. By drawing the grading curves of different modes on the same chart and using lines with different colors for distinguishing, a weather analyst can visually see the difference between the modes and analyze and evaluate the drawn chart. At the present stage, some rough inspection and scoring card methods are provided, which are used for calculating significance inspection p values (in a t inspection mode) of two mode forecast products with different timeliness under each score, comparing the significance inspection p values with a set confidence coefficient to obtain a scoring grade, corresponding the scoring grade with color blocks with different colors, and drawing the obtained results on the same chart. But it is difficult to scientifically and quantitatively analyze how large and obvious the differences between the modes are, and to observe the differences of the product quality of each forecast mode under different scores simultaneously; in addition, because the area cutting range of the forecast product is mostly fixed by the existing forecast inspection system and related tools, the forecast mode participating in the inspection can only carry out the calculation of the score in a limited fixed area, so that the weather analyst engaged in the scientific research is difficult to flexibly carry out the significance inspection of the forecast product quality in different modes in the self-defined range.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) the prior art cannot scientifically and quantitatively analyze the difference of forecast product quality between different modes, and cannot simultaneously observe the difference under different scores.
(2) The forecasting mode participating in the inspection cannot be dynamically selected, and the procedure needs to be modified when the mode is changed, so that the method is complicated and time-consuming; the geographical clipping area participating in the inspection is limited and fixed, and the clipping area cannot be defined by users.
(3) The used detection algorithm is fixed, and other algorithms except the bilateral t-test cannot be used for scientifically and quantitatively analyzing the difference of the mode forecast product quality.
The difficulty in solving the above problems and defects is:
the method has higher requirements on program developers, and the developers are required to know the related knowledge of weather forecast inspection and understand the program design and can write codes for realization.
Test data are difficult to obtain. The data required by program development should be actual observed and forecasted meteorological data, which is stored in the China meteorological data center and can be obtained by cooperation or application with the China meteorological data center.
The significance of solving the problems and the defects is as follows:
the difference between different forecast modes and the significance of the difference can be scientifically and quantitatively analyzed;
the forecast data and the specified inspection algorithm of the specified area can be flexibly tailored, so that analysts can more flexibly perform forecast inspection analysis and scientific research.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an automatic GRAPES regional forecast mode significance testing method and system.
The invention is realized in such a way that an automatic GRAPES area forecast mode significance inspection method comprises the following steps:
step one, a user sets a detection mode, a detection algorithm, confidence, start and stop dates of data, longitude and latitude of a region range and a mark card display style parameter on a browser page;
step two, the front end packages the parameters and communicates with the rear end through a flash frame, the rear end determines a storage path of the data file in the server according to a detected mode and a data start-stop date, and reads a corresponding forecast source file, namely a Grib file, according to the obtained path;
thirdly, the background cuts data in the Grib file according to the longitude and latitude range parameters to obtain a grid point forecast value of the specified area, and the grid point forecast value is used as a final forecast value of the area after mean value processing;
reading the observation data of the region, and calculating the statistics of root mean square error RMSE, mean error BIAS and standard deviation SD of different timeliness, which are reported each time, of the forecast value and the observation value in a set date range;
step five, calculating p values of significance test statistics of each aging of the two modes under different statistics RMSE, BIAS and SD by using the set significance test algorithm and confidence coefficient;
step six, comparing the p value with the confidence coefficient set by the user to obtain a grading level, and drawing color blocks with different colors at the front end according to the grading level; and simultaneously drawing the scoring results of different statistics in each meteorological field on the same chart.
Further, in step four, the reading of the observation data of the area, and the calculation of statistics of root mean square error RMSE, mean error BIAS and standard deviation SD of different timeliness that are reported each time in a set date range from the forecast value and the observation value includes:
(1) analyzing the timeliness contained in the initial forecast file by the background, and obtaining forecast data of the timeliness;
(2) and comparing the observation data at the corresponding moment, and calculating the conventional statistics of root mean square error RMSE, average error BIAS and standard deviation SD.
Further, in step five, the calculating the value p of the significance test statistic of each aging of the two modes under different statistics RMSE, BIAS and SD by using the set significance test algorithm and the confidence includes:
(1) the user sets the date range of the data, two modes to be checked and a checking algorithm at the front end;
(2) the background calculates the statistics RMSE, BIAS and SD of each aging of the two modes in the range according to the setting,
(3) and (4) performing significance test on each statistic by using a specified test algorithm to obtain a result p value.
Further, in the sixth step, the user can specify a grading grade threshold, if the grading result is higher than the grading grade, the front end automatically uses a red frame to draw out the area, and the user is helped to analyze the timeliness and the level of the mode forecast quality difference; the user sets the drop-down box through the style of the front end, and switches the display setting from color block to shape, namely, different shapes are used for representing different grading levels.
Another object of the present invention is to provide an automatic GRAPES area forecast pattern saliency test system applying the automatic GRAPES area forecast pattern saliency test method, the automatic GRAPES area forecast pattern saliency test system comprising: the system comprises a user layer, an application layer, a functional layer, an odd layer and a support layer;
a user layer including a user interface;
the application layer is used for processing background data and comprises inspection mode setting, inspection algorithm setting, confidence coefficient setting, data date setting, area longitude and latitude setting and display style setting;
the functional layer is used for realizing data path extraction, grading grade division, Grib file analysis, geographic region cutting and significance test calculation;
the technical layer comprises a foreground technology, a background technology, data interaction and a database; the foreground technology comprises HTML, CSS, JavaScript and Vue frames, the background technology comprises Python3, NumPy, Pandas and SciPy, the data interaction utilizes a flash frame, and the database is MySQL;
and the support layer comprises hardware, a network and a communication protocol.
Further, the automatic GRAPES region forecasting mode significance inspection system adopts a B/S framework, a front-end page is constructed by using an Vue framework, a back-end page is compiled by using python codes, and a NumPy, Pandas and SciPy third-party library is used for data processing and inspection calculation; and performing data interaction of a foreground and a background by adopting a flash framework, and performing persistence processing on background data by using a MySQL database.
Another object of the present invention is to provide a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to execute the automatic GRAPES area forecast pattern significance checking method.
Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to execute the automatic aspes area prediction mode significance checking method.
Another object of the present invention is to provide an information data processing terminal for implementing the automatic GRAPES area prediction mode saliency verification method.
By combining all the technical schemes, the invention has the advantages and positive effects that: the automatic GRAPES regional forecast mode significance inspection method provided by the invention adopts a B/S framework, a front-end page is constructed by using an Vue framework, a back end is compiled by using python codes, and data processing, inspection and calculation are performed by using third-party libraries such as NumPy, Pandas, SciPy and the like; in addition, the invention adopts a flash frame to carry out data interaction between the foreground and the background, and uses a MySQL database to carry out persistence processing on background data, and all functions of the system can be applied directly through a browser without installing a client.
In the application of the invention in quality comparison inspection of GRAPES regional mode forecast products, aiming at the characteristics that the existing rough inspection method is inconvenient to scientifically and quantitatively analyze the difference of forecast product quality among different modes, replace forecast modes participating in inspection, difficultly carry out self-defined geographic region cutting on forecast data or difficultly utilize the existing program to replace an obvious inspection algorithm and the like, the invention designs an automatic GRPAES regional forecast mode obvious inspection method based on a B/S framework, a user can conveniently modify parameters such as inspection modes, data dates, inspection algorithms and the like through a front-end page, a system can analyze and process the parameters, automatically read a required source forecast data file from a server, cut out a required geographic region and analyze the forecast value of the region, compare the required geographic region with the actual observation value of the region, and calculate the root mean square error, And calculating the difference of the forecast product quality of the two forecast modes within a time range based on the statistics such as average error, standard deviation and the like by using a significance test algorithm specified by a user. The invention ensures that the difference test of the forecast product quality can be scientifically and efficiently carried out by the meteorological analyst only by simply interacting with the front end, thereby greatly improving the customization degree of the test and avoiding the trouble that the analyst needs to modify the bottom program every time the analyst needs to adjust the relevant parameters of the test.
Technical effect or experimental effect of comparison.
In the invention, the user only needs to appoint the date, algorithm, mode and other inspection parameters at the front end, and the system can immediately respond to the operation of the user and inform the user whether the parameter modification or setting is successful or not; when a user clicks a 'start drawing' button, the system can read required data according to specified parameters, carry out calculation, and finally visually display the result on a page, wherein the response process does not exceed 5 seconds, and the current processing progress is displayed in a progress bar form, so that the user experience is enhanced, and the inspection efficiency is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for inspecting significance of an automatic GRAPES area forecast pattern according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a method for inspecting significance of an automatic GRAPES area forecast pattern according to an embodiment of the present invention.
Fig. 3 is a block diagram of a system architecture for automatic significance verification of the GRAPES area prediction mode according to an embodiment of the present invention.
Fig. 4 is a meaning diagram that a user can click a "score card description" button in a tab to view different color blocks.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides an automatic GRAPES regional forecast mode significance testing method and system, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the automatic significance testing method for the GRAPES area prediction mode provided by the embodiment of the present invention includes the following steps:
s101, a user sets a detection mode, a detection algorithm, confidence, start and stop dates of data, longitude and latitude of a region range and a scoring card display style parameter on a browser page;
s102, the front end packages the parameters and communicates with the rear end through a flash frame, the rear end determines a storage path of the data file in the server according to a detected mode and a data start-stop date, and reads a corresponding forecast source file, namely a Grib file, according to the obtained path;
s103, the background cuts data in the Grib file according to the longitude and latitude range parameters to obtain a grid point forecast value of the designated area, and the grid point forecast value is used as a final forecast value of the area after mean value processing;
s104, reading the observation data of the area, and calculating the statistics of root mean square error RMSE, mean error BIAS and standard deviation SD of different timeliness, which are reported each time, of the forecast value and the observation value in a set date range;
s105, calculating p values of significance test statistics of each aging of the two modes under different statistics RMSE, BIAS and SD by using the set significance test algorithm and confidence coefficient;
s106, comparing the p value with the confidence coefficient set by the user to obtain a grading level, and drawing color blocks with different colors at the front end according to the grading level; and simultaneously drawing the scoring results of different statistics in each meteorological field on the same chart.
A schematic diagram of an automatic GRAPES area prediction mode significance testing method provided by the embodiment of the present invention is shown in fig. 2.
As shown in fig. 3, an automatic GRAPES area forecast pattern saliency verification system provided by an embodiment of the present invention includes: user layer, application layer, functional layer, odd layer and support layer.
A user layer including a user interface;
the application layer is used for processing background data and comprises inspection mode setting, inspection algorithm setting, confidence coefficient setting, data date setting, area longitude and latitude setting and display style setting;
the functional layer is used for realizing data path extraction, grading grade division, Grib file analysis, geographic region cutting and significance test calculation;
the technical layer comprises a foreground technology, a background technology, data interaction and a database; the foreground technology comprises HTML, CSS, JavaScript and Vue frames, the background technology comprises Python3, NumPy, Pandas and SciPy, the data interaction utilizes a flash frame, and the database is MySQL;
and the support layer comprises hardware, a network and a communication protocol.
The present invention will be further described with reference to the following examples.
The invention adopts the B/S architecture, does not need to install a client and can apply all functions of the system directly through a browser.
Step 1: a user designates a forecasting mode to be checked at the front end, and a background fetches corresponding forecasting data from a server according to the designated mode; when the user needs to change the forecast mode of the inspection, the user only needs to change the selection through the pull-down box at the front end, and the program does not need to be changed any more.
Step 2: a user specifies the start-stop longitude and latitude of an area needing to be cut at the front end, the background cuts out a corresponding area from a grib file of an initial forecast product according to the longitude and latitude range, and the predicted values of all grid points in the area are subjected to mean processing to serve as the final predicted value of the area.
And step 3: and analyzing the aging contained in the initial forecast file by the background, obtaining forecast data of the aging, comparing the forecast data with observation data at corresponding moments, and calculating the conventional statistics Root Mean Square Error (RMSE), the average error (BIAS) and the Standard Deviation (SD).
And 4, step 4: the user sets a date range of data, two modes to be tested and a testing algorithm at the front end, the background calculates statistics (RMSE, BIAS and SD) of each aging of the two modes in the range according to the setting, and the statistics are respectively subjected to significance testing by using a specified testing algorithm to obtain a result p value.
And 5: comparing the p value with the confidence coefficient set by the user to obtain a grade, and drawing color blocks with different colors (or switching to different shapes) at the front end according to the grade; and on the same chart, the scoring results of different statistics in each meteorological field are drawn simultaneously, so that comparison and analysis are facilitated.
Step 6: the user can specify a grading grade threshold, if the grading result is higher than the grading grade, the front end automatically uses a red frame to draw out the area, and the user is further helped to analyze the timeliness and the level of the mode forecast quality difference.
The invention adopts a B/S framework, a front-end page is constructed by using an Vue framework, a back-end is compiled by adopting python codes, and a third-party library such as NumPy, Pandas, SciPy and the like is used for data processing and checking calculation; in addition, the invention adopts a flash framework to carry out data interaction of the foreground and the background, and uses a MySQL database to carry out persistence processing on background data. The system architecture of the present invention is shown in fig. 3.
The user can set parameters such as a detection mode, a detection algorithm, confidence coefficient, start and stop dates of data, longitude and latitude of a region range, a score card display style and the like on a browser page, and the parameters are packaged by the front end and are communicated with the rear end through a flash frame; the back end determines the storage path of the data file in the server according to the detected mode and the data start-stop date, and then reads a corresponding forecast source file (Grib file) according to the obtained path; then, the background cuts data in the Grib file according to the longitude and latitude range parameters to obtain a grid point forecast value of the designated area, and the grid point forecast value is used as a final forecast value of the area after mean value processing; reading the observation data of the area in the same way, thereby calculating statistic values of RMSE, BIAS, SD and the like of different timeliness, which are reported each time, of the forecast value and the observation value in a set date range; then, calculating the value (p value) of significance test statistic of each aging of the two modes under different statistics (RMSE, BIAS and SD) by using the set significance test algorithm and confidence coefficient; comparing the p-value with the set 3 confidences (including their negative values), the p-value can be mapped to 7 scoring levels, each represented by a solid square of a different color; finally, all the color block results are drawn on a chart, so that the analysis of a user is facilitated; the user can also set a drop-down box through the style of the front end, and the display setting is switched from the color block to the shape, namely different shapes are used for representing different grading levels.
In the application of the invention in quality comparison inspection of GRAPES regional mode forecast products, aiming at the characteristics that the existing rough inspection method is inconvenient to scientifically and quantitatively analyze the difference of forecast product quality among different modes, replace forecast modes participating in inspection, difficultly carry out self-defined geographic region cutting on forecast data or difficultly utilize the existing program to replace an obvious inspection algorithm and the like, the invention designs an automatic GRPAES regional forecast mode obvious inspection method based on a B/S framework, a user can conveniently modify parameters such as inspection modes, data dates, inspection algorithms and the like through a front-end page, a system can analyze and process the parameters, automatically read a required source forecast data file from a server, cut out a required geographic region and analyze the forecast value of the region, compare the required geographic region with the actual observation value of the region, and calculate the root mean square error, And calculating the difference of the forecast product quality of the two forecast modes within a time range based on the statistics such as average error, standard deviation and the like by using a significance test algorithm specified by a user. The invention ensures that the difference test of the forecast product quality can be scientifically and efficiently carried out by the meteorological analyst only by simply interacting with the front end, thereby greatly improving the customization degree of the test and avoiding the trouble that the analyst needs to modify the bottom program every time the analyst needs to adjust the relevant parameters of the test.
In the tab of the inspection system, the user can set relevant parameters of the inspection, including forecast modes participating in the inspection, start and stop dates of data, geographical region range needing to be inspected, inspection result visualization style and the like; in addition, a preset geographical range is provided, so that a user can directly select the geographical range without manual output; and also provides visual result export of PNG and SVG formats for users to select according to their own requirements.
The effects of the present invention will be further described below with reference to application examples.
Application example
In the drawn test result example, the height temperature field and the wind field of different layers are drawn on the same chart, so that the user can conveniently perform test analysis on the specified two modes.
In addition, the user can click the "scorecard description" button in the tab to see the meaning represented by the different color blocks, as shown in fig. 4.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When used in whole or in part, can be implemented in a computer program product that includes one or more computer instructions. When loaded or executed on a computer, cause the flow or functions according to embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL), or wireless (e.g., infrared, wireless, microwave, etc.)). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that includes one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The above description is only for the purpose of illustrating the present invention and the appended claims are not to be construed as limiting the scope of the invention, which is intended to cover all modifications, equivalents and improvements that are within the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. An automatic GRAPES area prediction mode significance testing method is characterized by comprising the following steps:
setting a checking mode, a checking algorithm, a confidence coefficient, a start-stop date of data, longitude and latitude of a region range and a scoring card display style parameter on a browser page by a user;
the front end packages the parameters and communicates with the rear end through a flash frame, the rear end determines a storage path of the data file in the server according to a detected mode and a data start-stop date, and reads a corresponding forecast source file, namely a Grib file, according to the obtained path;
the background cuts data in the Grib file according to the longitude and latitude range parameters to obtain a grid point forecast value of the specified area, and the grid point forecast value is used as a final forecast value of the area after mean value processing;
reading the observation data of the region, and calculating the statistics of root mean square error RMSE, mean error BIAS and standard deviation SD of different timeliness, which are reported each time, of the forecast value and the observation value in a set date range;
calculating the p value of the significance test statistic of each time effect of the two modes under different statistics RMSE, BIAS and SD by using the set significance test algorithm and the confidence coefficient;
comparing the p value with the confidence coefficient set by the user to obtain a grading grade, and drawing color blocks with different colors at the front end according to the grading grade; and simultaneously drawing the scoring results of different statistics in each meteorological field on the same chart.
2. The automated GRAPES region prediction mode significance testing method of claim 1, wherein said reading observation data of said region, calculating the statistics of root mean square error RMSE, mean error BIAS and standard deviation SD of different time periods each time the predicted value and the observed value are in a set date range, comprises:
(1) analyzing the timeliness contained in the initial forecast file by the background, and obtaining forecast data of the timeliness;
(2) and comparing the observation data at the corresponding moment, and calculating the conventional statistics of root mean square error RMSE, average error BIAS and standard deviation SD.
3. The automated GRAPES region prediction mode significance testing method of claim 1, wherein said calculating the value p of significance test statistic for each aging of two modes under different statistics RMSE, BIAS and SD using set significance test algorithm and confidence comprises:
(1) the user sets the date range of the data, two modes to be checked and a checking algorithm at the front end;
(2) the background calculates the statistics RMSE, BIAS and SD of each aging of the two modes in the range according to the setting,
(3) and (4) performing significance test on each statistic by using a specified test algorithm to obtain a result p value.
4. The method as claimed in claim 1, wherein the user can specify a rating threshold, and if there is a rating result higher than the rating threshold, the front end will automatically outline the area with a red box to help the user analyze the time and level of the pattern prediction with significant quality difference; the user sets the drop-down box through the style of the front end, and switches the display setting from color block to shape, namely, different shapes are used for representing different grading levels.
5. An automated GRAPES area prediction mode saliency inspection system, characterized in that said automated GRAPES area prediction mode saliency inspection system comprises: a user layer, an application layer, a functional layer, a technical layer and a support layer;
a user layer including a user interface;
the application layer is used for processing background data and comprises inspection mode setting, inspection algorithm setting, confidence coefficient setting, data date setting, area longitude and latitude setting and display style setting;
the functional layer is used for realizing data path extraction, grading grade division, Grib file analysis, geographic region cutting and significance test calculation;
the technical layer comprises a foreground technology, a background technology, data interaction and a database; the foreground technology comprises HTML, CSS, JavaScript and Vue frames, the background technology comprises Python3, NumPy, Pandas and SciPy, the data interaction utilizes a flash frame, and the database is MySQL;
and the support layer comprises hardware, a network and a communication protocol.
6. The automated GRAPES area prediction mode saliency verification system of claim 5, characterized in that it employs B/S architecture, front end pages are built using Vue framework, back end is written in python code and data processing and verification calculations are performed using NumPy, Pandas and SciPy third party libraries.
7. The automated GRAPES area forecast mode saliency test system of claim 5, characterized in that said automated GRAPES area forecast mode saliency test system employs a flash framework for foreground and background data interaction and uses a MySQL database for persistence of background data.
8. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the automated GRAPES area prediction mode significance verification method of any one of claims 1-4.
9. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the automated GRAPES area prediction mode significance checking method of any one of claims 1 to 4.
10. An information data processing terminal, wherein the information data processing terminal is used for implementing the automatic GRAPES regional forecast mode significance testing method as claimed in any one of claims 1-4.
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