A method and system for predicting short-term probability of precipitation based on oblique ellipsoid method

Abstract

The present invention belongs to the field of meteorological and energy cross-production, and specifically provides a method and system for predicting the short-term probability of precipitation based on the oblique ellipsoid method, wherein the method includes S1, obtaining the precipitation forecast of the target area of ​​the precipitation system; S2, determining the grid point position at the previous moment that is closest to the precipitation amount of the grid point position at the current moment, comparing the grid point position at the current moment and the grid point position at the previous moment and generating a moving vector; S3, constructing an oblique ellipsoid neighborhood in the X-Y-T space according to the moving vector and a preset time interval; S4, including all the grid points in the oblique ellipsoid neighborhood as set members, and using statistical methods to obtain a short-term precipitation probability prediction for the future period. The spatial range of the neighborhood is adjusted by the moving speed and direction of the precipitation system, forming an oblique ellipse shape as a new neighborhood range, and then adjusting the ellipsoid shape in the time-space coordinate system. The adjusted precipitation probability prediction is calculated using the grid point element values ​​within the new neighborhood range, with high accuracy and good TS score.

Description

Technical Field

[0001] The present invention relates to the field of meteorology and energy intersection technology, and more specifically, to a method and system for predicting the short-term probability of precipitation based on an oblique ellipsoid method. Background Art

[0002] The rapid development of atmospheric physics, meteorological numerical models, and computer technology has greatly improved the precision and accuracy of weather forecasts and has gradually led to the subdivision of meteorological numerical models. Forecasts can be categorized by duration: short-term forecasts, medium-term forecasts, and long-term forecasts; by spatial scope: global forecasts and regional forecasts; and by forecast method: deterministic forecasts and ensemble forecasts. Traditional deterministic forecasts only provide a definite value for a meteorological element at a specific location and time. Traditional ensemble forecasts, on the other hand, perturb the initial field or boundary conditions, drive and run a set of numerical models, and generate a collection of meteorological numerical forecasts. This collection of numerical forecasts is then statistically analyzed to generate the ensemble value, probability value, and dispersion of meteorological element forecasts. This provides a foundation for the interpretation and application of meteorological element forecasts and the production of rich, objective, and effective meteorological forecast products. For example, based on traditional ensemble forecast data, statistical methods can be applied to generate probabilistic forecast products for meteorological elements, inferring the likelihood of the occurrence and development of important weather events. Although traditional ensemble forecasting has rich forecast data, clear theories, mature technology, and clear physical processes, its forecasting technology is complex, consumes huge computing resources, has a large amount of input data, a large amount of output data, and poor forecast timeliness, making it difficult to achieve high-frequency rolling updates, lightweight applications, and local applications.

[0003] Given the aforementioned issues with traditional ensemble forecasting, the neighborhood method has been introduced to rapidly generate probabilistic forecast products for meteorological elements, particularly short-term nowcast probabilistic forecast products. This method considers the grid points surrounding a given grid point (in nearby space and forecast time) as relevant points. The meteorological element values ​​at these grid points form the meteorological element set at that grid point. Applying statistical methods to this set, probabilistic forecasts of meteorological elements can be obtained. Meteorological forecasts are subject to errors, manifesting as spatial offsets, temporal deviations, and differences in magnitude. Based on these errors, the neighborhood method incorporates meteorological element values ​​at nearby spatial and temporal points into the ensemble, which can improve forecast usability to a certain extent.

[0004] Conventional neighborhood methods typically select only uniform circles as their spatial range, which can be formed into an ellipsoid within the horizontal space-time coordinate system. This spatial range selection is simple and feasible for large-scale weather systems. However, for small- and medium-scale or local weather systems that frequently occur in summer, especially for spatially and temporally discontinuous quantities such as precipitation, this circular spatial range inevitably introduces a large number of useless grid points as ensemble members, resulting in large deviations in the predicted precipitation probability and limited application and indicative value. Summary of the Invention

[0005] The present invention aims to solve the technical problem in the prior art that the probability forecast products of meteorological elements generated by the conventional neighborhood method have large deviations.

[0006] The present invention provides a method for predicting the short-term probability of precipitation based on the oblique ellipsoid method, comprising the following steps:

[0007] S1, obtain the precipitation forecast of the target area of ​​the precipitation system;

[0008] S2, determining a grid point position at a previous moment that has the closest precipitation to the grid point position at the current moment, comparing the grid point position at the current moment with the grid point position at the previous moment, and generating a movement vector;

[0009] S3, constructing an oblique ellipsoid neighborhood in the XYT space according to the movement vector and a preset time interval;

[0010] S4, all grid points within the oblique ellipsoid neighborhood are included as ensemble members, and statistical methods are used to obtain the probability forecast of future short-term precipitation.

[0011] Preferably, the S1 specifically includes: obtaining precipitation forecast, 700hpa wind field, and 500hpa wind field from the meteorological center regional numerical forecast business system GRAPES-MESO.

[0012] Preferably, S2 specifically includes: obtaining precipitation in the target area at adjacent moments, defining a precipitation search area according to the grid point position at the current moment, and finding the grid point position with the largest precipitation correlation coefficient at the previous moment to obtain the grid point position at the previous moment.

[0013] Preferably, after S2 and before S3, the method further includes: in the three-dimensional space composed of the spatial axis X, the spatial axis Y, and the time axis T, taking the target grid point (x c ,y c ,t c ) as the center point and construct a regular ellipsoid neighborhood.

[0014] Preferably, all grid points within a certain radius around the target grid point on the XY plane are included as set members, and precipitation forecasts of the set members on the time axis T are obtained, thereby forming a regular ellipsoid neighborhood in the XYT space. The boundary of the regular ellipsoid neighborhood satisfies the following formula:

[0015]

[0016] Wherein, ΔX and ΔY are both preset radius ranges, and ΔT is a preset time interval.

[0017] Preferably, the S3 specifically includes:

[0018] The long axis direction of the oblique ellipsoid is set as the moving path direction of the precipitation system;

[0019] The length of the major axis ΔX is the speed of the precipitation system multiplied by the time interval;

[0020] The minor axis length ΔY is set as the major axis length multiplied by the sine of the angle between the moving direction and the X axis;

[0021] The time difference ΔT is the preset time interval;

[0022] Preferably, when the major axis length exceeds a preset threshold, the major axis length is set as the preset threshold; when the minor axis length is lower than the preset threshold, it is set as the preset threshold.

[0023] The present invention also provides a short-term precipitation probability prediction system based on the oblique ellipsoid method. The detection and extraction system is used to implement the short-term precipitation probability prediction method based on the oblique ellipsoid method, including:

[0024] A moving vector generation module is configured to obtain a precipitation forecast for a target area of ​​the precipitation system; determine a grid point position at a previous moment that is closest to the precipitation at the current moment, compare the current moment grid point position with the previous moment grid point position, and generate a moving vector;

[0025] An oblique ellipsoid neighborhood generation module, configured to construct an oblique ellipsoid neighborhood in an XYT space according to the movement vector and a preset time interval;

[0026] The precipitation probability prediction module is used to include all grid points within the oblique ellipsoid neighborhood as set members and use statistical methods to obtain the probability prediction of future short-term precipitation.

[0027] The present invention also provides an electronic device, comprising a memory and a processor, wherein the processor is configured to implement the steps of a method for predicting the short-term probability of precipitation based on an oblique ellipsoid method when executing a computer management program stored in the memory.

[0028] The present invention also provides a computer-readable storage medium on which a computer management program is stored. When the computer management program is executed by a processor, the steps of the method for predicting the short-term probability of precipitation based on the oblique ellipsoid method are implemented.

[0029] Beneficial effects: The present invention provides a method and system for predicting the short-term probability of precipitation based on the oblique ellipsoid method, wherein the method includes S1, obtaining the precipitation forecast for the target area of ​​the precipitation system; S2, determining the grid point position at the previous moment that is closest to the precipitation amount of the grid point position at the current moment, comparing the grid point position at the current moment and the grid point position at the previous moment and generating a moving vector; S3, constructing an oblique ellipsoid neighborhood in the XYT space according to the moving vector and a preset time interval; S4, including all the grid points in the oblique ellipsoid neighborhood as set members, and using statistical methods to obtain a prediction of the probability of short-term precipitation in the future. By adjusting the spatial range of the neighborhood according to the moving speed and direction of the precipitation system, an elliptical shape is formed as a new neighborhood range, and then the ellipsoid shape is adjusted in the time-space coordinate system. The adjusted precipitation probability prediction is calculated using the grid point element values ​​in the new neighborhood range. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flow chart of a method for predicting the short-term probability of precipitation based on the oblique ellipsoid method provided by the present invention;

[0031] Figure 2 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention;

[0032] Figure 3 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention;

[0033] Figure 4 The projection diagram of the oblique ellipsoid neighborhood on the XY plane of the short-term probability prediction method of precipitation based on the oblique ellipsoid method provided by the present invention;

[0034] Figure 5 The present invention provides a TS score map for predicting different levels of precipitation in a certain area using the oblique ellipsoid neighborhood method and the conventional ellipsoid neighborhood method. DETAILED DESCRIPTION

[0035] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0036] Figure 1The present invention provides a method for predicting the short-term probability of precipitation based on the oblique ellipsoid method, wherein the method includes S1, obtaining a precipitation forecast for a target area of ​​a precipitation system; S2, determining the grid point position at the previous moment that is closest to the precipitation amount at the current moment grid point position, comparing the current moment grid point position and the previous moment grid point position and generating a movement vector; S3, constructing an oblique ellipsoid neighborhood in XYT space according to the movement vector and a preset time interval; S4, including all grid points in the oblique ellipsoid neighborhood as set members, and using statistical methods to obtain a prediction of the probability of future short-term precipitation.

[0037] By adjusting the spatial extent of the neighborhood based on the speed and direction of the precipitation system, an elliptical shape is formed as the new neighborhood, which is then adjusted within the time-space coordinate system. The adjusted precipitation probability forecast is calculated using the grid feature values ​​within the new neighborhood, the oblique ellipse. This method is effective in predicting local, short-term, and impending precipitation probabilities and has broad applications in fields requiring precipitation probability forecasts, such as meteorology and power grids. It lies at the intersection of meteorology and energy.

[0038] The specific principles are as follows:

[0039] (1) Calculate the moving vector of the precipitation system. First, extract the precipitation forecast, 700 hPa wind field, and 500 hPa wind field from the numerical forecast. The comprehensive precipitation information of the precipitation system can be calculated, including the precipitation moving direction, precipitation moving speed, and precipitation amount, that is, the moving vector of the precipitation system at adjacent moments.

[0040] (2) Then, in the three-dimensional space composed of the spatial axis X, the spatial axis Y, and the time axis T, with the target grid point (xc, yc, tc) as the center point, a regular ellipsoid neighborhood is constructed: all grid points within a certain radius range (ΔX) around the target grid point on the XY plane are included as set members, and the forecast of the approaching time (ΔT) is considered on the time axis T, thereby forming an ellipsoid in the XYT space, and the ellipsoid boundary satisfies the following formula.

[0041]

[0042] (3) Considering that precipitation is a spatially discontinuous quantity, the grid points around the main movement path of the precipitation system, especially the small and medium-scale precipitation system, have a greater impact on the precipitation forecast of the target grid point, while the grid points in other areas have a smaller impact. Therefore, the shape of the neighborhood ellipsoid can be changed (for example, the oblique ellipsoid method attempts to rotate the major axis angle and change the ellipsoid orientation) to absorb more effective set members. The oblique ellipsoid of this scheme is obtained by changing the shape of the neighborhood ellipsoid.

[0043] Specifically, the major and minor axes of the oblique ellipsoid on the XY plane are constructed. ① The major axis direction of the oblique ellipsoid is set as the main moving path direction of the precipitation system. ② The major axis length ΔX is the moving speed of the precipitation system multiplied by the time interval (that is, the length of the moving vector between the current grid position and the previous grid position in the precipitation system at adjacent moments). When the major axis length exceeds the preset threshold, the major axis length is set to the preset threshold. ③ The minor axis length ΔY is defined as the major axis length multiplied by the sine of the angle between the moving direction and the X-axis (the acute angle formed by the moving direction and the X-axis). When the minor axis length is lower than the preset threshold, it is set to the preset threshold. ④ The minor axis of the ellipsoid on the T axis is still set to ΔT (for example, considering the forecast two hours before and after the current forecast time, ΔT = 2). ⑤ After completing the setting of the oblique ellipsoid neighborhood, all grid points within the oblique ellipsoid neighborhood are included as set members, and the precipitation probability forecast is obtained using statistical methods. The oblique ellipsoid neighborhood method preliminarily considers the spatial distribution characteristics of atmospheric physical processes and meteorological element fields, focuses on the high-impact grids around the target grid, eliminates low-impact grids as much as possible, and improves the effectiveness of the set members.

[0044] Among them, the boundary of the oblique ellipsoid area satisfies the following equation:

[0045] Ax 2 +Bx 2 +Cx 2 +Dxy+Exy+Fyt+G=0

[0046] Among them, parameters A~G are oblique ellipsoid parameters, which can be obtained by rotating the coordinate system based on the three axis lengths of the regular ellipsoid. The oblique ellipsoid only considers the rotation of the XY coordinate system around the T axis, and its projection on the XY plane is shown as follows Figure 4 shown.

[0047] Next, we use this solution to conduct a prediction analysis on a specific area and compare it with the traditional method.

[0048] The original forecast data uses the deterministic precipitation forecast of the National Meteorological Center's regional numerical forecast system GRAPES-MESO.

[0049] Spatial resolution: 3 km. Coverage: 70°E–145°E, 15°N–65°N. Temporal resolution: 1 hour. Forecast validity: 36 hours into the future. Update frequency: Every 3 hours, for a total of 8 times per day. This solution was used to conduct experiments and validation of precipitation probability predictions using the oblique ellipsoid method for typical heavy rainfall events in the summer of 2019. The precipitation probability prediction products include the probability of precipitation of varying magnitudes (e.g., the probability of precipitation exceeding 10 mm, the probability of precipitation exceeding 50 mm, etc.) and ensemble percentiles of precipitation (e.g., the median and 75th percentile of the ensemble members). To facilitate comparison with deterministic precipitation forecast results, the 75th percentile of the ensemble members of the oblique ellipsoid method precipitation probability predictions was used in the evaluation and validation. Precipitation observation data required for the validation were obtained from 1-hour cumulative precipitation at meteorological observation stations. Validation metrics include the precipitation Threat Score (TS) and Mean Absolute Error (MAE). In order to obtain the precipitation forecast value at the station, the bilinear interpolation method is used to interpolate the grid forecast data to the location of the meteorological observation station.

[0050] like Figure 5 As shown in the figure, by selecting typical heavy rainfall cases from the summer of 2019, the precipitation probability prediction results based on numerical forecast precipitation data and the oblique ellipsoid neighborhood method were evaluated. The original precipitation forecast data used six batches of forecasts at 00:00, 03:00, 06:06:09:01, and 15:00 UTC on May 17, 2019. The main purpose was to test the forecast performance of each batch for different levels of precipitation during the evening and nighttime of that day. The scoring results shown below are the average of the six batches. One-hour precipitation levels are divided into: light rain (0.1mm-2.0mm), moderate rain (2.1mm-5.0mm), and heavy rain (5.1mm-10.0mm).

[0051] From the evening to the night of May 17, 2019 (5:00 PM to 11:00 PM Beijing time), eastern Beijing (Miyun, Pinggu, Shunyi, and Tongzhou) experienced a brief period of heavy rainfall, accompanied by localized severe convective weather such as thunderstorms, strong winds, and hail. Accumulated precipitation exceeded 150 mm in parts of Tongzhou. The movement trajectory and precipitation distribution indicate that the precipitation system moved perpendicular to its major axis.

[0052] Overall, the TS score for precipitation forecasts using the oblique ellipsoid neighborhood method is higher than that of both the original and conventional ellipsoid neighborhood methods. For light to moderate rain, the TS score for the oblique ellipsoid neighborhood method can reach as high as 0.18 in the first six hours, while the TS scores for the original and conventional ellipsoid neighborhood methods are only around 0.1 or well below. In particular, the TS score for precipitation forecasts using the conventional ellipsoid method is generally lower than that of the deterministic forecast. After six hours, the performance of the three forecast methods is comparable. For heavy rain, the oblique ellipsoid method successfully captures the precipitation distribution in the first two hours, with a TS score above 0.25, significantly higher than the other two methods.

[0053] Table 1 Average absolute error of individual rainstorm cases in Beijing on May 17, 2019 (unit: mm)

[0054]

[0055]

[0056] Table 1 shows that the mean absolute error of the oblique ellipsoid neighborhood precipitation forecast is lower than that of the original and conventional ellipsoid neighborhood precipitation forecasts. For light rain, the mean absolute error of the oblique ellipsoid precipitation forecast is smaller than that of the original and conventional ellipsoid forecasts at every forecast moment. The mean absolute error decreases over time. Because light rain can roughly indicate the spatial extent of the precipitation system, this decrease in error over time indicates that summer convective precipitation systems develop rapidly, the precipitation range gradually decreases, and the precipitation system gradually dissipates. For moderate rain, the mean absolute error of the oblique ellipsoid precipitation forecast is smaller than that of the positive ellipsoid forecast at most moments, significantly outperforming the original forecast. For heavy rain, the oblique ellipsoid precipitation forecast has a clear advantage in the first two hours, after which the forecast performance of the three methods is essentially equivalent. This also means that through cyclic rolling updates, the oblique ellipsoid precipitation forecast has an advantage in short-term periods.

[0057] In summary, this scheme effectively establishes a precipitation probability forecast scheme based on numerical forecasts and the oblique ellipsoid method. Test results show that, after calculation using the oblique ellipsoid neighborhood method, all test indicators for the short-term probability forecast of precipitation outperform those of the original numerical forecast and the conventional ellipsoid method. In particular, the TS score for light to moderate rain can be improved from 0.1 to 0.18 in the first few hours, and the TS score for heavy rain can be improved from below 0.05 to 0.3. For light rain levels, the mean absolute error of the oblique ellipsoid precipitation forecast is significantly smaller than that of the original deterministic precipitation forecast and the conventional ellipsoid method. For moderate rain levels, the mean absolute error of the oblique ellipsoid precipitation forecast is smaller for most of the time. For heavy rain levels, the oblique ellipsoid precipitation forecast has a clear advantage in the first two hours, and the mean absolute errors of the three forecast methods are comparable for the subsequent forecast hours.

[0058] The embodiment of the present invention further provides a system for predicting the short-term probability of precipitation based on the oblique ellipsoid method. The detection and extraction system is used to implement the method for predicting the short-term probability of precipitation based on the oblique ellipsoid method, including:

[0059] A moving vector generation module is configured to obtain a precipitation forecast for a target area of ​​the precipitation system; determine a grid point position at a previous moment that is closest to the precipitation at the current moment, compare the current moment grid point position with the previous moment grid point position, and generate a moving vector;

[0060] An oblique ellipsoid neighborhood generation module, configured to construct an oblique ellipsoid neighborhood in an XYT space according to the movement vector and a preset time interval;

[0061] The precipitation probability prediction module is used to include all grid points within the oblique ellipsoid neighborhood as set members and use statistical methods to obtain the probability prediction of future short-term precipitation.

[0062] See also Figure 2 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 2 As shown, an embodiment of the present invention provides an electronic device, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1311, the following steps are implemented: S1, obtaining a precipitation forecast for a target area of ​​a precipitation system;

[0063] S2, determining a grid point position at a previous moment that is closest to the precipitation at the current grid point position, comparing the current grid point position with the grid point position at the previous moment and generating a movement vector;

[0064] S3, constructing an oblique ellipsoid neighborhood in the XYT space according to the movement vector and a preset time interval;

[0065] S4, all grid points within the oblique ellipsoid neighborhood are included as ensemble members, and statistical methods are used to obtain the probability forecast of future short-term precipitation.

[0066] See also Figure 3 Schematic diagram of an embodiment of a computer-readable storage medium provided by the present invention. Figure 3 As shown, this embodiment provides a computer-readable storage medium 1400 on which a computer program 1411 is stored. When the computer program 1411 is executed by a processor, the following steps are implemented: S1, obtaining a precipitation forecast for a target area of ​​a precipitation system;

[0067] S2, determining a grid point position at a previous moment that has the closest precipitation to the grid point position at the current moment, comparing the grid point position at the current moment with the grid point position at the previous moment, and generating a movement vector;

[0068] S3, constructing an oblique ellipsoid neighborhood in the XYT space according to the movement vector and a preset time interval;

[0069] S4, all grid points within the oblique ellipsoid neighborhood are included as ensemble members, and statistical methods are used to obtain the probability forecast of future short-term precipitation.

[0070] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0071] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for predicting the short-term probability of precipitation based on the oblique ellipsoid method, characterized in that: The following steps are involved: S1, obtain the precipitation forecast of the target area of ​​the precipitation system; S2, determining a grid point position at a previous moment that has the closest precipitation to the grid point position at the current moment, comparing the grid point position at the current moment with the grid point position at the previous moment, and generating a movement vector; S3, constructing an oblique ellipsoid neighborhood in the XYT space according to the movement vector and a preset time interval; S4, all grid points in the oblique ellipsoid neighborhood are included as ensemble members, and the probability of future short-term precipitation is predicted using statistical methods; The S3 specifically includes: The long axis direction of the oblique ellipsoid is set as the moving path direction of the precipitation system; The length of the major axis ΔX is the speed of the precipitation system multiplied by the time interval; The minor axis length ΔY is set as the major axis length multiplied by the sine of the angle between the moving direction and the X axis; The time difference ΔT is the preset time interval; When the major axis length exceeds the preset threshold, the major axis length is determined to be the preset threshold; when the minor axis length is lower than the preset threshold, it is determined to be the preset threshold.

2. The method for predicting short-term precipitation probability based on the oblique ellipsoid method according to claim 1, wherein: Said S1 specifically includes: obtaining precipitation forecast, 700hpa wind field, and 500hpa wind field from the meteorological center regional numerical forecast business system GRAPES-MESO.

3. The method for predicting short-term precipitation probability based on the oblique ellipsoid method according to claim 1, wherein: The S2 specifically includes: obtaining the precipitation of the target area at adjacent moments, demarcating a precipitation search area according to the grid point position at the current moment, and finding the grid point position with the largest precipitation correlation coefficient at the previous moment to obtain the grid point position at the previous moment.

4. The method for predicting short-term precipitation probability based on the oblique ellipsoid method according to claim 1, wherein: After S2 and before S3, the following further comprises: in the three-dimensional space composed of the spatial axis X, the spatial axis Y, and the time axis T, with the target grid point (x c ,y c ,t c ) as the center point and construct a regular ellipsoid neighborhood.

5. The method for predicting short-term precipitation probability based on the oblique ellipsoid method according to claim 4, characterized in that: On the XY plane, all grid points within a certain radius around the target grid point are included as ensemble members, and the precipitation forecast of the ensemble members on the time axis T is obtained, thereby forming a regular ellipsoid neighborhood in the XYT space. The boundary of the regular ellipsoid neighborhood satisfies the following formula: ， Wherein, ΔX and ΔY are both preset radius ranges, and ΔT is a preset time interval.

6. A short-term precipitation probability prediction system based on the oblique ellipsoid method, characterized in that: The short-term precipitation probability prediction system based on the oblique ellipsoid method is used to implement the short-term precipitation probability prediction method based on the oblique ellipsoid method according to any one of claims 1 to 5, comprising: A moving vector generation module is configured to obtain a precipitation forecast for a target area of ​​the precipitation system; determine a grid point position at a previous moment that is closest to the precipitation at the current moment, compare the current moment grid point position with the previous moment grid point position, and generate a moving vector; An oblique ellipsoid neighborhood generation module is configured to construct an oblique ellipsoid neighborhood in an XYT space based on the movement vector and the preset time interval; wherein the major axis direction of the oblique ellipsoid is set to be the movement path direction of the precipitation system; the major axis length ΔX is the movement speed of the precipitation system multiplied by the time interval; the minor axis length ΔY is set to be the major axis length multiplied by the sine value of the angle between the movement direction and the X-axis; the time difference ΔT is the preset time interval; when the major axis length exceeds a preset threshold, the major axis length is set to be the preset threshold; when the minor axis length is lower than the preset threshold, the minor axis length is set to be the preset threshold; The precipitation probability prediction module is used to include all grid points within the oblique ellipsoid neighborhood as set members and use statistical methods to obtain the probability prediction of future short-term precipitation.

7. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the processor is used to implement the steps of the method for predicting the short-term probability of precipitation based on the oblique ellipsoid method as described in any one of claims 1 to 5 when executing the computer management program stored in the memory.

8. A computer-readable storage medium, characterized in that A computer management program is stored thereon, and when the computer management program is executed by the processor, the steps of the method for predicting the short-term probability of precipitation based on the oblique ellipsoid method as described in any one of claims 1 to 5 are implemented.

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