Precipitation extrapolation forecasting method and device, electronic equipment and storage medium

Abstract

The invention provides a rainfall extrapolation forecasting method and device, electronic equipment and a storage medium. The rainfall extrapolation forecasting method comprises the steps that a radar jigsaw data file of a target area is acquired; determining the combined reflectivity of the target area at the current time and the combined reflectivity of the target area at the historical time according to the three-dimensional echo data at the current time and the three-dimensional echo data at the historical time in the radar jigsaw data file; according to the combined reflectivity at the current time and the combined reflectivity at the historical time, identifying thunderstorm parameter information existing at the current time in the target area; according to the thunderstorm parameter information, determining a thunderstorm development trend of the target area; and carrying out precipitation extrapolation according to the thunderstorm development trend to obtain an extrapolated precipitation amount. Therefore, the rainfall extrapolation is performed by introducing the thunderstorm development trend, the rainfall extrapolation error caused by the unclear development and evolution process of the rainfall rain cluster can be improved, and the accuracy of the heavy rainfall extrapolation is improved.

Description

Technical Field

[0001] The present application relates to the field of meteorological technology, and in particular to a precipitation extrapolation forecasting method, device, electronic equipment and storage medium. Background Art

[0002] At present, the severe convective weather short-term nowcasting system SWAN (Severe Weather Automatic Nowcasting) has been applied to meteorological stations at all levels across the country, providing technical support for severe convective nowcasting and forecasting services. The precipitation nowcasting subsystem in the SWAN system was first developed from the nowcasting module in the SWIFT (Severe Weather Integrated Forecasting Tools) system.

[0003] However, how to describe the development and evolution process of precipitation rain clusters has always been one of the bottlenecks of radar extrapolation forecasting technology, which leads to large errors in the precipitation nowcasting subsystem in the current SWAN system and low accuracy of the heavy precipitation extrapolation products provided. Summary of the invention

[0004] In view of this, the purpose of the present application is to provide a precipitation extrapolation forecast method, device, electronic device and storage medium, which can improve the precipitation extrapolation error caused by unclear development and evolution process of precipitation rain groups by introducing thunderstorm development trend for precipitation extrapolation, and improve the accuracy of heavy precipitation extrapolation.

[0005] The present application provides a precipitation extrapolation forecasting method, the method comprising:

[0006] Get radar mosaic data file of target area;

[0007] Determine the combined reflectivity of the target area at the current time and the combined reflectivity of the target area at the historical time according to the three-dimensional echo data at the current time and the three-dimensional echo data at the historical time in the radar jigsaw data file;

[0008] Identify thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time;

[0009] Determining the thunderstorm development trend of the target area according to the thunderstorm parameter information;

[0010] The precipitation is extrapolated according to the thunderstorm development trend to obtain the extrapolated precipitation.

[0011] Further, identifying thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time includes:

[0012] According to the combined reflectivity at the current time and the combined reflectivity at the historical time, a storm identification algorithm is used to identify the thunderstorm existing at the current time in the target area;

[0013] The wind field inverted by the optical flow method is used to track the thunderstorms existing at the current time and the thunderstorms existing at the historical time to determine the thunderstorm parameter information existing at the current time; wherein the thunderstorm parameter information includes at least one of the following items: location, intensity, and the increase in intensity compared to the historical time.

[0014] Further, determining the thunderstorm development trend of the target area according to the thunderstorm parameter information includes:

[0015] Determine at least one thunderstorm evolution feature of each thunderstorm cell in the target area according to the thunderstorm parameter information; wherein the thunderstorm evolution feature includes: whether there is a strong gradient area in front of the thunderstorm, whether there is a new cell in front of the gradient, whether the echo area of ​​the first intensity range increases relative to the first n individual scans, and whether the echo area of ​​the second intensity range increases relative to the first n individual scans;

[0016] Determine the development state of the thunderstorm cell in the target area by weighting according to the at least one thunderstorm evolution feature and the weight of each thunderstorm evolution feature;

[0017] Determining the intensity change value of the thunderstorm cell according to the thunderstorm parameter information;

[0018] The development state, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the thunderstorm cell.

[0019] Further, determining at least one thunderstorm evolution characteristic of the target area according to the thunderstorm parameter information includes:

[0020] For each echo point in the thunderstorm whose intensity exceeds a first preset threshold, a search calculation of the echo gradient is performed in multiple directions with the echo point as the center, and a boundary range where the echo gradient exceeds a second preset threshold is identified; if the length of the boundary range exceeds the preset length threshold, it is determined that a strong gradient area exists in the thunderstorm;

[0021] According to the starting position and the ending position of the boundary range, it is determined whether the direction of the strong gradient zone of the thunderstorm is consistent with the moving direction of the thunderstorm; if consistent, it is determined that there is a strong gradient zone on the front side of the thunderstorm moving;

[0022] If the newly formed thunderstorm cell is located in the thunderstorm moving direction and the distance from the boundary range is less than the preset distance threshold, it is determined that there is a newly formed cell in front of the gradient;

[0023] Determine the echo area of ​​the first intensity range and the echo area of ​​the second intensity range at the current time, and compare them with the echo area of ​​the first intensity range at the historical time to determine whether the echo area of ​​the first intensity range has increased relative to the previous n individual scans and whether the echo area of ​​the second intensity range has increased relative to the previous n individual scans.

[0024] Further, determining the thunderstorm development trend of the target area according to the thunderstorm parameter information further includes:

[0025] Determining whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information;

[0026] If so, comprehensively analyzing the development status of each thunderstorm cell included in the linear convective thunderstorm to determine the development status of the linear convective thunderstorm;

[0027] The development state of the linear convective thunderstorm, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the linear convective thunderstorm.

[0028] Further, determining whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information includes:

[0029] For the centroid of each thunderstorm cell in the target area identified by the storm identification algorithm, clustering the thunderstorm cells according to the distance between the centroids;

[0030] For each type of thunderstorm cell obtained by clustering, the least square method is used for linear fitting to determine whether the thunderstorm cell constitutes a linear convective thunderstorm;

[0031] If so, it is determined that there is a linear convective thunderstorm in the target area.

[0032] Furthermore, precipitation is extrapolated according to the thunderstorm development trend to obtain extrapolated precipitation, including:

[0033] Spatially matching the wind field inverted by the optical flow method with the thunderstorm in the target area to determine the average moving vector of the thunderstorm;

[0034] According to the development trend of the thunderstorm cell and the development trend of the linear convective thunderstorm, the radar echo is extrapolated in combination with the average moving vector of the thunderstorm to obtain the extrapolated radar echo;

[0035] The extrapolated precipitation is obtained according to the extrapolated radar echo and the ZI relationship of the dynamic fitting, wherein Z represents the radar reflectivity factor and I represents the precipitation intensity.

[0036] The present application also provides a precipitation extrapolation forecasting device, the device comprising:

[0037] An acquisition module is used to acquire radar mosaic data files of the target area;

[0038] A first determination module is used to determine the combined reflectivity of the target area at the current time and the combined reflectivity of the historical time according to the three-dimensional echo data of the current time and the three-dimensional echo data of the historical time in the radar jigsaw data file;

[0039] An identification module, used for identifying thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time;

[0040] A second determination module is used to determine the thunderstorm development trend of the target area according to the thunderstorm parameter information;

[0041] The extrapolation module is used to perform precipitation extrapolation according to the thunderstorm development trend to obtain extrapolated precipitation.

[0042] An embodiment of the present application also provides an electronic device, including: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory through the bus, and when the machine-readable instructions are executed by the processor, the steps of the precipitation extrapolation forecasting method as described above are performed.

[0043] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the precipitation extrapolation forecasting method as described above are executed.

[0044] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A flow chart of a precipitation extrapolation forecasting method provided in an embodiment of the present application is shown;

[0047] Figure 2 A schematic diagram of the structure of a precipitation extrapolation forecasting device provided in an embodiment of the present application is shown;

[0048] Figure 3A schematic structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0049] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0050] According to research, the severe convective weather short-term forecasting system SWAN (Severe Weather Automatic Nowcasting) has been applied to meteorological stations at all levels across the country, providing technical support for severe convective nowcasting monitoring and forecasting services. The precipitation nowcasting subsystem in the SWAN system was first developed from the nowcasting module in the SWIFT (Severe Weather Integrated Forecasting Tools) system.

[0051] However, how to describe the development and evolution process of precipitation rain clusters has always been one of the bottlenecks of radar extrapolation forecasting technology. This leads to large errors in the precipitation nowcasting subsystem in the current SWAN system and low accuracy of the heavy precipitation extrapolation products provided, especially the 1-2h extrapolation results.

[0052] Based on this, an embodiment of the present application provides a precipitation extrapolation forecasting method to reduce precipitation extrapolation errors and improve the accuracy of heavy precipitation extrapolation.

[0053] See also Figure 1 , Figure 1 This is a flow chart of a precipitation extrapolation forecasting method provided in an embodiment of the present application. Figure 1 As shown in , the precipitation extrapolation method provided in the embodiment of the present application includes:

[0054] S101, obtaining a radar mosaic data file of a target area.

[0055] Here, the target area refers to the area where precipitation extrapolation forecast is required, such as an area within a certain latitude and longitude range, or an area within a certain administrative division. In this step, the radar mosaic data file of the target area can be downloaded from the relevant public website. The radar mosaic data file includes both the current time data and the historical time data, such as the data of the previous m times (m is a positive integer).

[0056] S102. Determine the combined reflectivity of the target area at the current time and the combined reflectivity of the target area at the historical time according to the three-dimensional echo data at the current time and the three-dimensional echo data at the historical time in the radar jigsaw data file.

[0057] Here, the radar puzzle is a binary data file, which needs to be parsed to read the three-dimensional echo data of the current time and the three-dimensional echo data of the historical time; then, based on the three-dimensional echo data of the current time and the three-dimensional echo data of the historical time, the combined reflectivity of the target area at the current time and the combined reflectivity of the historical time are determined by numerical calculation. Combined reflectivity (CR): It is the product of projecting the maximum reflectivity factor onto the Cartesian grid in the radar volume scan, which represents the maximum value of the projection of echoes at different heights on the grid. The specific method for determining the combined reflectivity can be implemented with reference to the relevant technical implementation of the prior art, and this application does not impose any restrictions here.

[0058] It should be noted that research has found that heavy precipitation during the flood season is usually caused by convective weather, and the development and evolution of precipitation rain groups in precipitation extrapolation can be understood as the development and evolution of thunderstorms. Thunderstorms are local convective weather accompanied by lightning and lightning. The thunderstorms of severe convective weather systems vary greatly in different regions and seasons, which further increases the difficulty of extrapolating thunderstorms. Therefore, the key point to improve the accuracy of precipitation extrapolation is how to determine the future development state and intensity of thunderstorms. Whether the development of thunderstorms that have occurred is enhanced or weakened can be reflected in the comparison of radar intensity echoes, but how to determine the evolution of future thunderstorms based on the echoes at the current moment is the key point and difficulty of precipitation extrapolation. How to determine the evolution of future thunderstorms, i.e., the development trend of thunderstorms, in the embodiments of the present application will be specifically introduced below.

[0059] S103: Identify thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time.

[0060] In specific implementation, step S103 may include:

[0061] According to the combined reflectivity of the current time and the combined reflectivity of the historical time, a storm identification algorithm is used to identify the thunderstorm existing in the target area at the current time; the wind field inverted by the optical flow method is used to track the thunderstorm existing at the current time and the thunderstorm existing at the historical time to determine the thunderstorm parameter information existing at the current time.

[0062] Here, first, the SCIT storm identification algorithm can be used to identify thunderstorms based on the combined reflectivity of the current time and the combined reflectivity of the historical time. The SCIT algorithm can identify the center of a strong storm and separate storm cells from a storm cluster.

[0063] Afterwards, the wind field inverted based on the optical flow method at the current time is obtained, and the thunderstorms identified at the current time and the thunderstorms identified at the historical time are tracked to determine the thunderstorm parameter information existing at the current time. The optical flow method is a technology used in the field of computer vision and image processing to detect and analyze the motion of objects in the field of vision. It finds the corresponding relationship between the previous frame and the current frame by studying the changes in pixels in the time domain in the image sequence and the correlation between adjacent frames, thereby calculating the motion information of objects between adjacent frames. In an embodiment of the present application, the optical flow method can be used to track the corresponding relationship between the thunderstorms existing at the current time and the thunderstorms existing at the previous time. Wherein, the thunderstorm parameter information includes at least one of the following items: position, intensity, and the increase in intensity compared to the historical time (such as the adjacent previous and next time).

[0064] S104: Determine the thunderstorm development trend of the target area according to the thunderstorm parameter information.

[0065] In specific implementation, for a common thunderstorm cell, step S104 may include:

[0066] S1041. Determine at least one thunderstorm evolution feature of each thunderstorm cell in the target area according to the thunderstorm parameter information; wherein the thunderstorm evolution feature includes: whether there is a strong gradient area in front of the thunderstorm movement, whether there is a new cell in front of the gradient, whether the echo area of ​​the first intensity range increases relative to the previous n individual scans, and whether the echo area of ​​the second intensity range increases relative to the previous n individual scans; n is a positive integer, such as 2; the first intensity range and the second intensity range can be set as needed, such as the first intensity range is above 45dBz, and the second intensity range is 35-45dBz.

[0067] In this step, for the strong gradient area, for each echo point in the thunderstorm whose intensity exceeds the first preset threshold, the echo gradient can be searched and calculated in multiple directions with the echo point as the center, and the boundary range where the echo gradient exceeds the second preset threshold is identified; if the length of the boundary range exceeds the preset length threshold, it is determined that there is a strong gradient area in the thunderstorm; and then, according to the starting position and the end position of the boundary range, it can be judged whether the direction of the strong gradient area of ​​the thunderstorm is consistent with the moving direction of the thunderstorm; if consistent, it is determined that there is a strong gradient area in front of the thunderstorm. Exemplarily, with the echo point with an intensity of more than 35dBz in the thunderstorm as the center, the echo gradient is searched and calculated in 8 directions, all echo points with an intensity of more than 35dBz are traversed, and the echo points with a gradient exceeding 20dBz are retained to obtain the boundary range. If the length of the boundary range exceeds 20% of the circumference of the thunderstorm, the enhanced feature is confirmed, and combined with the optical flow wind field, it can be determined that there is a strong gradient area in front of the thunderstorm.

[0068] As for whether there is a new cell ahead of the gradient, the direction of the strong gradient zone of the thunderstorm can also be determined according to the starting position and the ending position of the boundary range; if the new thunderstorm cell is located in the moving direction of the thunderstorm and the distance from the boundary range is less than the preset distance threshold, it is determined that there is a new cell ahead of the gradient. For example, if the new thunderstorm cell is located in the direction of the strong gradient zone and the distance does not exceed 50km, it is confirmed that there is a new cell ahead of the gradient.

[0069] As to whether the echo area increases, the echo area of ​​the first intensity range and the echo area of ​​the second intensity range of the current time can be determined, and compared with the echo area of ​​the first intensity range of the historical time, to determine whether the echo area of ​​the first intensity range has increased relative to the previous n individual scans and whether the echo area of ​​the second intensity range has increased relative to the previous n individual scans. For example, through thunderstorm identification and wind field information, two consecutive thunderstorms are matched, and the echo area above 35dBz and the echo area above 45dBz of the thunderstorm are calculated to determine whether the current echo area above 35dBz has increased compared with the previous time. If it has increased, it is confirmed that the 35dBz area has increased; and determine whether the current echo area above 45dBz has increased compared with the previous time. If it has increased, it is confirmed that the 45dBz area has increased.

[0070] Furthermore, thunderstorm evolution characteristics may also include: enhanced echo development height, maximum echo intensity, etc.

[0071] S1042. Determine the development state of the thunderstorm cell in the target area by weighted method according to the at least one thunderstorm evolution feature and the weight of each thunderstorm evolution feature; wherein the thunderstorm development trend includes strengthening, weakening and maintaining.

[0072] In this step, the weighted characteristic value can be determined according to the characteristic value of each thunderstorm evolution characteristic through weighted calculation with preset weights; the development status of the thunderstorm cell in the target area can be determined by comparing the weighted characteristic value with the threshold interval corresponding to the preset different thunderstorm development trends.

[0073] Exemplarily, if there is a strong gradient area in front of the thunderstorm, the eigenvalue is 1, otherwise it is 0; if there is a new monomer in front of the gradient, the eigenvalue is 1, otherwise it is 0; if the echo area of ​​the first intensity range increases relative to the previous n individual scans, the eigenvalue is 1, otherwise it is 0; if the echo area of ​​the second intensity range increases relative to the previous n individual scans, the eigenvalue is 1, otherwise it is 0. Whether there is a strong gradient area in front of the thunderstorm, whether there is a new monomer in front of the gradient, and whether the echo area above 35dBz increases, the corresponding weights are all 0.2, and whether the echo area above 45dBz increases is 0.4. Afterwards, the weighted eigenvalue can be obtained by multiplying the eigenvalue with the corresponding weight and then summing the products.

[0074] S1043. Determine an intensity change value of a thunderstorm cell according to the thunderstorm parameter information.

[0075] In this step, the thunderstorm parameter information includes the average echo intensity. The intensity change value of the thunderstorm cell can be determined by the average echo intensity of two consecutive thunderstorms. At the same time, the maximum growth intensity value can also be set for the echo.

[0076] S1044: Determine the development state, the duration corresponding to the development state, and the intensity change value as the development trend of the thunderstorm cell.

[0077] In this step, the strengthening, weakening and maintenance time of the thunderstorm can be set to different fixed times for processing according to historical experience data. The development trend of each thunderstorm cell includes the development state, duration and intensity change value of the thunderstorm cell.

[0078] Furthermore, the embodiments of the present application have found through research that in addition to ordinary thunderstorm cells, there may also be linear convective thunderstorms in the target area; linear convective thunderstorms include multiple thunderstorm cells and are formed by a linear arrangement of thunderstorms, which often form a train effect and produce heavy precipitation. Therefore, considering linear convective thunderstorms is of great significance for precipitation extrapolation forecasting, which can further improve the accuracy of precipitation extrapolation forecasting. In specific implementation, step S104 may also include:

[0079] Step 1: Determine whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information.

[0080] In this step, for the centroid of each thunderstorm cell in the target area identified by the storm identification algorithm, the thunderstorm cells are clustered according to the distance between the centroids; for each type of thunderstorm cell obtained by clustering, a least squares method is used for linear fitting to determine whether the thunderstorm cell of this type constitutes a linear convective thunderstorm; if so, it is determined that a linear convective thunderstorm exists in the target area.

[0081] Here, the storm identification algorithm can determine the centroid of thunderstorm cells when identifying thunderstorm cells; for the thunderstorm centroid, all thunderstorm cells can be classified and divided by using the closest distance within 30km to associate thunderstorm cells, that is, clustering them into multiple types of thunderstorm cells. The least squares method is used to perform linear fitting on each type of thunderstorm group. If the correlation coefficient exceeds a certain value (such as 0.6) and the number of thunderstorm cells in this type exceeds a certain number (such as 10), it is determined that this type of thunderstorm cell constitutes a linear convective thunderstorm, and there is a linear convective thunderstorm in the target area.

[0082] Step 2: If so, comprehensively consider the development status of each thunderstorm cell included in the linear convective thunderstorm to determine the development status of the linear convective thunderstorm.

[0083] Here, linear convection refers to a convection system arranged in a linear shape. According to the thunderstorm development trend of each thunderstorm cell, the overall thunderstorm development state of the linear convection thunderstorm can be comprehensively determined to determine whether it is strengthening, weakening or maintaining.

[0084] Step 3: Determine the development trend of the linear convective thunderstorm based on the development state of the linear convective thunderstorm, the duration corresponding to the development state, and the intensity change value.

[0085] Here. Linear convective thunderstorms have a larger impact range and a longer duration than single thunderstorms, and the impact is greater. Therefore, the duration corresponding to the development state of linear convective thunderstorms can be set to different fixed times based on historical experience data for separate processing. The intensity change value can be judged by the growth of the area above 45dBz for two consecutive times.

[0086] Furthermore, linear convective thunderstorms can also be identified through image segmentation and other methods.

[0087] S105. Extrapolate precipitation according to the thunderstorm development trend to obtain extrapolated precipitation.

[0088] In specific implementation, step S105 may include:

[0089] The wind field inverted by the optical flow method is spatially matched with the thunderstorm in the target area to determine the average moving vector of the thunderstorm; according to the development trend of the thunderstorm cell and the development trend of the linear convective thunderstorm, the radar echo is extrapolated in combination with the average moving vector of the thunderstorm to obtain the extrapolated radar echo; according to the extrapolated radar echo and the ZI relationship of dynamic fitting, the extrapolated precipitation is obtained; wherein Z represents the radar reflectivity factor and I represents the precipitation intensity.

[0090] In this step, the wind field inverted by the optical flow method is spatially matched with the thunderstorm to calculate the average moving vector of the thunderstorm; considering that the acquired thunderstorm speed is unstable, especially when the storm moves slowly and the echo data quality is poor, the wind speed of the echo with unclear convective properties has a large error, so the grid wind field inverted by the optical flow method can be filtered first to remove the abnormal wind field, and then spatially matched with the thunderstorm to improve the calculation accuracy. Afterwards, according to the evolution trend (development status, duration and intensity change value) of the single thunderstorm and linear convective thunderstorm at the current time, combined with the wind field inverted by the optical flow method, the radar echo extrapolation calculation is performed; the extrapolated rainfall is calculated using the dynamically fitted ZI relationship and the extrapolated reflectivity data.

[0091] Here, the dynamically fitted ZI relationship can be obtained in the following way: obtaining real-time precipitation data every minute (for example, 6 minutes) observed by a ground meteorological observation station, and fitting the ZI relationship using the matched echo and real-time precipitation data.

[0092] Furthermore, step S105 may also include:

[0093] The comprehensive echo intensity of the low-level echo is determined according to the terrain data file of the target area; precipitation is extrapolated according to the comprehensive echo intensity and the thunderstorm development trend to obtain the extrapolated precipitation.

[0094] In the specific implementation, the digital elevation model data file of the target area can be obtained as the terrain data file, and the terrain data file and the three-dimensional echo data after quality control are used to calculate the comprehensive echo intensity of the low-layer echo (below 3 km), which gives the low-layer echo a greater proportion; then, the terrain is combined to calculate the comprehensive echo intensity of the middle and low-layer echoes, the development trend of thunderstorm cells and the development trend of linear convective thunderstorms, and the average moving vector of the thunderstorm is combined to carry out heavy precipitation extrapolation to improve the accuracy of heavy precipitation extrapolation.

[0095] A precipitation extrapolation forecasting method provided in an embodiment of the present application reduces the precipitation nowcasting error from the perspective of the development and evolution process of precipitation rain clusters. Compared with the extrapolation method of the SWAN system, the judgment of the development trend of thunderstorms is introduced into the extrapolation algorithm, which improves the heavy precipitation extrapolation error caused by the unclear development and evolution process of precipitation rain clusters to a certain extent.

[0096] Among them, the judgment of thunderstorm development trend includes two aspects: 1) Judgment of the development state of thunderstorm enhancement, weakening and maintenance: It includes 4 influencing factors, namely whether there is a strong gradient area in front of the thunderstorm, whether there is a new single cell in front of the gradient, whether the echo area above 45dBz is increased relative to the previous two individual scans, and whether the echo area above 35-45dBz is increased relative to the previous two individual scans. Among them, the strong gradient area and the new single cell can reflect whether the front area of ​​​​the convection movement is conducive to the development and enhancement of convection, and the change of the echo area reflects the development state of the thunderstorm itself. Based on the identification and quantitative calculation of the above four factors, the thunderstorm development state is comprehensively judged accurately through different weights; 2) Based on the storm identification algorithm, the thunderstorm single cell is identified, the thunderstorm intensity and location and other information are calculated, and the thunderstorm centroid is associated, and the linear convection is fitted by the least squares method.

[0097] Furthermore, the judgment result of the thunderstorm development trend is used to provide support for the development and evolution of thunderstorms. The integration of this part of information can improve the precipitation extrapolation deviation caused by the development and evolution of precipitation rain clusters to a certain extent. From the perspective of precipitation extrapolation test during the flood season, this method can reflect the precipitation enhancement caused by the enhancement of thunderstorms to a certain extent, and can make a judgment on the enhancement of future precipitation. Experiments show that for precipitation forecasts above 20 mm, the TS score result of the method provided in the embodiment of the present application is better than SWAN, and due to the introduction of the judgment of the thunderstorm development trend, the improvement ratio of 1-2h heavy precipitation extrapolation is more obvious.

[0098] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a precipitation extrapolation forecasting device provided in an embodiment of the present application. Figure 2 As shown in , the precipitation extrapolation forecasting device 200 comprises:

[0099] An acquisition module 210 is used to acquire a radar mosaic data file of a target area;

[0100] A first determination module 220 is used to determine the combined reflectivity of the target area at the current time and the combined reflectivity of the target area at the historical time according to the three-dimensional echo data of the current time and the three-dimensional echo data of the historical time in the radar mosaic data file;

[0101] An identification module 230 is used to identify thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time;

[0102] A second determination module 240 is used to determine the thunderstorm development trend of the target area according to the thunderstorm parameter information;

[0103] The extrapolation module 250 is used to perform precipitation extrapolation according to the thunderstorm development trend to obtain extrapolated precipitation.

[0104] Further, when the identification module 230 is used to identify the thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time, the identification module 230 is used to:

[0105] According to the combined reflectivity at the current time and the combined reflectivity at the historical time, a storm identification algorithm is used to identify the thunderstorm existing at the current time in the target area;

[0106] The wind field inverted by the optical flow method is used to track the thunderstorms existing at the current time and the thunderstorms existing at the historical time to determine the thunderstorm parameter information existing at the current time; wherein the thunderstorm parameter information includes at least one of the following items: location, intensity, and the increase in intensity compared to the historical time.

[0107] Furthermore, when the second determination module 240 is used to determine the thunderstorm development trend of the target area according to the thunderstorm parameter information, the second determination module 240 is used to:

[0108] Determine at least one thunderstorm evolution feature of each thunderstorm cell in the target area according to the thunderstorm parameter information; wherein the thunderstorm evolution feature includes: whether there is a strong gradient area in front of the thunderstorm, whether there is a new cell in front of the gradient, whether the echo area of ​​the first intensity range increases relative to the first n individual scans, and whether the echo area of ​​the second intensity range increases relative to the first n individual scans;

[0109] Determine the development state of the thunderstorm cell in the target area by weighting according to the at least one thunderstorm evolution feature and the weight of each thunderstorm evolution feature;

[0110] Determining the intensity change value of the thunderstorm cell according to the thunderstorm parameter information;

[0111] The development state, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the thunderstorm cell.

[0112] Further, when the second determination module 240 is used to determine at least one thunderstorm evolution feature of each thunderstorm cell in the target area according to the thunderstorm parameter information, the second determination module is used to:

[0113] For each echo point in the thunderstorm whose intensity exceeds a first preset threshold, a search calculation of the echo gradient is performed in multiple directions with the echo point as the center, and a boundary range where the echo gradient exceeds a second preset threshold is identified; if the length of the boundary range exceeds the preset length threshold, it is determined that a strong gradient area exists in the thunderstorm;

[0114] According to the starting position and the ending position of the boundary range, it is determined whether the direction of the strong gradient zone of the thunderstorm is consistent with the moving direction of the thunderstorm; if consistent, it is determined that there is a strong gradient zone on the front side of the thunderstorm moving;

[0115] If the newly formed thunderstorm cell is located in the thunderstorm moving direction and the distance from the boundary range is less than the preset distance threshold, it is determined that there is a newly formed cell in front of the gradient;

[0116] Determine the echo area of ​​the first intensity range and the echo area of ​​the second intensity range at the current time, and compare them with the echo area of ​​the first intensity range at the historical time to determine whether the echo area of ​​the first intensity range has increased relative to the previous n individual scans and whether the echo area of ​​the second intensity range has increased relative to the previous n individual scans.

[0117] Furthermore, when the second determination module 240 is used to determine the thunderstorm development trend of the target area according to the thunderstorm parameter information, the second determination module 240 is also used to:

[0118] Determining whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information;

[0119] If so, comprehensively analyzing the development status of each thunderstorm cell included in the linear convective thunderstorm to determine the development status of the linear convective thunderstorm;

[0120] The development state of the linear convective thunderstorm, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the linear convective thunderstorm.

[0121] Further, when the second determination module 240 is used to determine whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information, the second determination module 240 is used to:

[0122] For the centroid of each thunderstorm cell in the target area identified by the storm identification algorithm, clustering the thunderstorm cells according to the distance between the centroids;

[0123] For each type of thunderstorm cell obtained by clustering, the least square method is used for linear fitting to determine whether the thunderstorm cell constitutes a linear convective thunderstorm;

[0124] If so, it is determined that there is a linear convective thunderstorm in the target area.

[0125] Furthermore, when the extrapolation module 250 is used to perform precipitation extrapolation according to the thunderstorm development trend to obtain the extrapolated precipitation, the extrapolation module 250 is used to:

[0126] Spatially matching the wind field inverted by the optical flow method with the thunderstorm in the target area to determine the average moving vector of the thunderstorm;

[0127] According to the development trend of the thunderstorm cell and the development trend of the linear convective thunderstorm, the radar echo is extrapolated in combination with the average moving vector of the thunderstorm to obtain the extrapolated radar echo;

[0128] The extrapolated precipitation is obtained according to the extrapolated radar echo and the ZI relationship of the dynamic fitting, wherein Z represents the radar reflectivity factor and I represents the precipitation intensity.

[0129] See also Figure 3 , Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown in , the electronic device 300 includes a processor 310 , a memory 320 and a bus 330 .

[0130] The memory 320 stores machine-readable instructions executable by the processor 310. When the electronic device 300 is running, the processor 310 communicates with the memory 320 via the bus 330. When the machine-readable instructions are executed by the processor 310, the above-mentioned Figure 1 The specific implementation of the steps of the precipitation extrapolation forecasting method in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0131] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 The specific implementation of the steps of the precipitation extrapolation forecasting method in the method embodiment shown can be found in the method embodiment, and will not be repeated here.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0133] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0136] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0137] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A precipitation extrapolation forecasting method, characterized in that: The method comprises: Get radar mosaic data file of target area; Determine the combined reflectivity of the target area at the current time and the combined reflectivity of the target area at the historical time according to the three-dimensional echo data at the current time and the three-dimensional echo data at the historical time in the radar jigsaw data file; Identify thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time; Determining the thunderstorm development trend of the target area according to the thunderstorm parameter information; The precipitation is extrapolated according to the thunderstorm development trend to obtain the extrapolated precipitation.

2. The method according to claim 1, characterized in that Identifying thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time, including: According to the combined reflectivity at the current time and the combined reflectivity at the historical time, a storm identification algorithm is used to identify the thunderstorm existing at the current time in the target area; The wind field inverted by the optical flow method is used to track the thunderstorms existing at the current time and the thunderstorms existing at the historical time to determine the thunderstorm parameter information existing at the current time; wherein the thunderstorm parameter information includes at least one of the following items: location, intensity, and the increase in intensity compared to the historical time.

3. The method according to claim 2, characterized in that Determining the thunderstorm development trend of the target area according to the thunderstorm parameter information includes: Determine at least one thunderstorm evolution feature of each thunderstorm cell in the target area according to the thunderstorm parameter information; wherein the thunderstorm evolution feature includes: whether there is a strong gradient area in front of the thunderstorm, whether there is a new cell in front of the gradient, whether the echo area of ​​the first intensity range increases relative to the first n individual scans, and whether the echo area of ​​the second intensity range increases relative to the first n individual scans; Determine the development state of the thunderstorm cell in the target area by weighting according to the at least one thunderstorm evolution feature and the weight of each thunderstorm evolution feature; Determining the intensity change value of the thunderstorm cell according to the thunderstorm parameter information; The development state, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the thunderstorm cell.

4. The method according to claim 3, characterized in that Determining at least one thunderstorm evolution characteristic of the target area according to the thunderstorm parameter information includes: For each echo point in the thunderstorm whose intensity exceeds a first preset threshold, a search calculation of the echo gradient is performed in multiple directions with the echo point as the center, and a boundary range where the echo gradient exceeds a second preset threshold is identified; if the length of the boundary range exceeds the preset length threshold, it is determined that a strong gradient area exists in the thunderstorm; According to the starting position and the ending position of the boundary range, it is determined whether the direction of the strong gradient area of ​​the thunderstorm is consistent with the moving direction of the thunderstorm; if consistent, it is determined that there is a strong gradient area on the front side of the thunderstorm moving; If the newly formed thunderstorm cell is located in the moving direction of the thunderstorm and the distance from the boundary range is less than the preset distance threshold, it is determined that there is a newly formed cell in front of the gradient; Determine the echo area of ​​the first intensity range and the echo area of ​​the second intensity range at the current time, and compare them with the echo area of ​​the first intensity range at the historical time to determine whether the echo area of ​​the first intensity range has increased relative to the previous n individual scans and whether the echo area of ​​the second intensity range has increased relative to the previous n individual scans.

5. The method according to claim 3, characterized in that: Determining the thunderstorm development trend of the target area according to the thunderstorm parameter information further includes: Determining whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information; If so, comprehensively analyzing the development status of each thunderstorm cell included in the linear convective thunderstorm to determine the development status of the linear convective thunderstorm; The development state of the linear convective thunderstorm, the duration corresponding to the development state, and the intensity change value are determined as the development trend of the linear convective thunderstorm.

6. The method according to claim 5, characterized in that Determining whether there is a linear convective thunderstorm in the target area according to the thunderstorm parameter information includes: For the centroid of each thunderstorm cell in the target area identified by the storm identification algorithm, clustering the thunderstorm cells according to the distance between the centroids; For each type of thunderstorm cell obtained by clustering, the least square method is used for linear fitting to determine whether the thunderstorm cell constitutes a linear convective thunderstorm; If so, it is determined that there is a linear convective thunderstorm in the target area.

7. The method according to claim 5, characterized in that According to the thunderstorm development trend, precipitation is extrapolated to obtain extrapolated precipitation, including: Spatially matching the wind field inverted by the optical flow method with the thunderstorm in the target area to determine the average moving vector of the thunderstorm; According to the development trend of the thunderstorm cell and the development trend of the linear convective thunderstorm, the radar echo is extrapolated in combination with the average moving vector of the thunderstorm to obtain the extrapolated radar echo; The extrapolated precipitation is obtained according to the extrapolated radar echo and the ZI relationship of the dynamic fitting, wherein Z represents the radar reflectivity factor and I represents the precipitation intensity.

8. A precipitation extrapolation forecasting device, characterized in that: The device comprises: An acquisition module is used to acquire radar mosaic data files of the target area; A first determination module is used to determine the combined reflectivity of the target area at the current time and the combined reflectivity of the historical time according to the three-dimensional echo data of the current time and the three-dimensional echo data of the historical time in the radar jigsaw data file; An identification module, used for identifying thunderstorm parameter information existing at the current time in the target area according to the combined reflectivity at the current time and the combined reflectivity at the historical time; A second determination module is used to determine the thunderstorm development trend of the target area according to the thunderstorm parameter information; The extrapolation module is used to perform precipitation extrapolation according to the thunderstorm development trend to obtain extrapolated precipitation.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to execute the steps of the precipitation extrapolation forecasting method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the precipitation extrapolation forecasting method according to any one of claims 1 to 7 are executed.

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