Visual information-based weak weather forced heavy rainfall event objective identification method

Abstract

The invention discloses a visual information-based weak weather forced heavy rainfall event objective identification method, which comprises the following steps of: acquiring data and visual information thereof, analyzing and utilizing the information to eliminate influence areas of a strong weather system, such as tropical cyclones, vortexes, tangent lines and frontal surfaces; further determining areas and time which are forced to be influenced by weak weather in the research area, and identifying corresponding heavy rainfall events; according to the method, traditional forecasting experience is effectively continued, and meanwhile support is provided for mechanism research and forecasting technology development of forced heavy rainfall events in weak weather.

Description

Technical Field

[0001] The present invention relates to the field of meteorological technologies, and particularly relates to an objective recognition method for weak weather forced heavy precipitation events based on visualization information. Background Art

[0002] The research on precipitation is of great significance for disaster prevention and reduction. Usually, heavy precipitation events are often accompanied by obvious strong weather systems, such as frontal surfaces or vortices, etc. However, when the weather system is not significant, that is, under the condition of weak weather forcing, the occurrence of heavy precipitation is relatively rare. Therefore, heavy precipitation events under weak weather forcing are likely to be missed, affecting the timeliness of disaster prevention and reduction, and may lead to casualties and economic losses.

[0003] The recognition of weak weather forced heavy precipitation events usually depends on the subjective judgment of forecasters, which is based on the occurrence of heavy precipitation events in a certain area without being affected by obvious or strong weather systems (such as tropical cyclones, frontal surfaces, vortices, shear lines, etc.). However, subjective recognition is not only time-consuming and laborious, but also difficult to standardize. Therefore, there is an urgent need to develop an objective recognition method to solve this problem.

[0004] Existing objective recognition methods are disjointed from the subjective judgment basis of forecasters. These methods usually only focus on a single physical quantity, ignoring the weather system characteristics relied on by subjective recognition; in addition, existing methods regard heavy precipitation events as independent grid point events, and fail to reflect the spatio-temporal distribution characteristics of heavy precipitation events consistent with subjective cognition. Therefore, there is an urgent need for an objective recognition method for weak weather forced heavy precipitation events based on subjective experience basis to continue traditional forecasting experience and at the same time provide support for future mechanism research and forecasting technology development for a large number of weak weather forced heavy precipitation events. Summary of the Invention

[0005] Object of the Invention: The object of the present invention is to provide an objective recognition method for weak weather forced heavy precipitation events based on visualization information, to solve the problem that existing methods usually only focus on a single physical quantity and ignore the weather system characteristics relied on by subjective recognition, and to solve the problem that existing methods regard heavy precipitation events as independent grid point events and fail to reflect the spatio-temporal distribution characteristics of heavy precipitation events consistent with subjective cognition.

[0006] Technical Solution: An objective recognition method for weak weather forced heavy precipitation events based on visualization information according to the present invention includes the following steps: (1) Acquisition and parsing of data and its visualization information; (2) Determining the position of the center of the tropical cyclone by using tropical cyclone path data; (3) Determining the area affected by the synoptic scale frontal surface by using the visualization information of equivalent potential temperature gradient, relative vorticity and thermal front parameters; (4) Utilize the visualization information of relative vorticity and wind field, and apply density-based clustering method and skeleton extraction method to determine the influence area of synoptic-scale shear line; (5) Utilize the visualization information of geopotential height to determine the influence area of synoptic-scale vortex; (6) Take the union of the influence areas of various weather systems at the same moment, and then take the union of the influence areas of weather systems on the same day to determine the influence area of strong weather forcing on that day. Finally, take the complement of the influence area of strong weather forcing to determine the influence area of weak weather forcing on that day; (7) Determine heavy precipitation events according to hourly precipitation, precipitation station location and number. If heavy precipitation events occur only in the influence area of weak weather forcing, they are identified as weak weather forcing heavy precipitation events.

[0007] Further, step (1) is specifically as follows: Obtain precipitation, tropical cyclone track, the magnitude of the gradient of equivalent potential temperature at 850 hPa , thermal front parameter , wind (u, v), relative vorticity , and geopotential height data at 500 hPa, 850 hPa and 925 hPa. Visualize the geopotential height at 500 hPa, 850 hPa and 925 hPa and the TFP at 850 hPa respectively; where the geopotential height interval is 2 dagpm, and only TFP = 0 is plotted; Output the visualization information variable c, that is, the position information of points on each isoline. Judge whether the first and last grid points of the isoline are at the same position. If they are at the same position, it is a closed isoline. If they are not at the same position, it is an open isoline; Calculate the length of the isoline; Calculate the area of the closed isoline; Judge whether the grid points on a closed isoline are inside other closed isolines, and judge whether a closed isoline is inside another closed isoline.

[0008] Further, in step (2), the area within 1000 km of the tropical cyclone center is the tropical cyclone influence area.

[0009] Further, step (3) is specifically as follows: Remove and area, screen out non-closed isolines with a length exceeding 1000 km and TFP = 0, and determine the area within 200 km of the isoline as the influence area of synoptic-scale frontal surface.

[0010] Further, step (4) is specifically as follows: Calculate relative vorticity using the 850 hPa wind field, and at the grid points of , determine to meet one of the three situations according to the wind direction: a1: and ; a2: , and ; a3: and where the subscript is shifted 1 grid point eastward from i, and the subscript that is shifted 1 grid point northward from j is the cyclonic shear point; Then, according to the positions of the shear edge points, the density-based clustering method is used to merge the shear points into several groups, the groups with the distance between the two farthest grid points exceeding 500 km are screened out, the backbone line is determined using the skeleton extraction method, and finally the area within 200 km of the backbone line is determined as the area affected by the synoptic-scale shear line.

[0011] Furthermore, step (5) is specifically as follows: Using the geopotential height fields at 500 hPa, 850 hPa, and 925 hPa, the contour information of the geopotential height fields is visualized at an interval of 2 dagpm, and the nested closed contours are merged into the same set. Among the sets of closed contours with the geopotential height values decreasing centripetally, the area enclosed by the outermost closed contour exceeding 196000 km 2 is the area affected by the synoptic-scale vortex.

[0012] Furthermore, step (6) is specifically as follows: Take the union of the areas affected by various weather systems at the same moment, and then take the union of the areas affected by the weather systems on the same day, which is the area affected by the severe weather forcing on that day. If the area affected by the severe weather forcing is less than 50% of the area of the study region, then the study region on that day is identified as the area affected by weak weather forcing.

[0013] Furthermore, step (7) is specifically as follows: Using precipitation data, determine the set of grid points where the maximum hourly precipitation reaches 20 mm, the distance is less than 100 km, and the precipitation periods overlap. When the number of grid points in the set exceeds 3, it is identified as a spatiotemporally continuous heavy precipitation event. Determine the earliest precipitation occurrence time in the set of grid points as the start time of the event, and the latest precipitation end time as the end time of the event; if the entire heavy precipitation event occurs only on the day affected by weak weather forcing, then it is identified as a weak weather forcing heavy precipitation event.

[0014] An electronic device according to the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements an objective recognition method for weak weather forcing heavy precipitation events based on visualization information according to any one of the above.

[0015] A storage medium according to the present invention stores a computer program, and when the computer program is executed by a processor, it implements an objective recognition method for weak weather forced heavy precipitation events based on visualization information as described in any one of the above.

[0016] Beneficial effects: Compared with the prior art, the present invention has the following remarkable advantages: using an objective method to recognize weak weather forced heavy precipitation events based on subjective recognition experience, which not only continues the idea of manual recognition but also avoids the problems of energy consumption and difficulty in standardization brought by subjective recognition. The aim is to efficiently identify weak weather forced heavy precipitation events and provide strong support for future statistical research, mechanism analysis, and forecast technology development for a large number of weak weather forced heavy precipitation events. Description of the Drawings

[0017] Figure 1 is a flowchart of the present invention; Figure 2 is the daily variation of the total number of weak weather forced heavy precipitation events occurring during the period 2000 - 2022 in the present invention. Detailed Embodiments

[0018] The technical solution of the present invention will be further described below with reference to the drawings.

[0019] An embodiment of the present invention provides an objective recognition method for weak weather forced heavy precipitation events based on visualization information, as Figure 1 shown, including the following steps: The research area of the embodiment of the present invention is the Southeast China (SEC), and the research period is from June to August in the years 2000 - 2022.

[0020] Step 1: Data acquisition and acquisition and analysis of its visualization information: Acquire precipitation, tropical cyclone paths, the magnitude of the gradient of the equivalent potential temperature at 850 hPa the magnitude of , the thermal frontal parameter (Thermal Frontal Parameter, ), wind (u, v), relative vorticity , and data such as geopotential heights at 500 hPa, 850 hPa, and 925 hPa. Using Matlab, the geopotential heights at 500 hPa, 850 hPa, and 925 hPa (with an interval of 2 dagpm) and the TFP at 850 hPa (only plotting TFP = 0) are visualized respectively, and the TFP at 850 hPa (only plotting TFP = 0) is output. The visualized information variable c contains information such as the variable values represented by each contour line and the positions of the points on each contour line. Based on this, (a) judge whether the first and last grid points of the contour line are at the same position. If they are at the same position, it is a closed contour line; if not, it is an open contour line. (b) Calculate the length of the contour line. (c) Calculate the area of the closed contour line. (d) Judge whether the grid points on a closed contour line are inside other closed contour lines to determine whether one closed contour line is inside another closed contour line (nested relationship).

[0021] In this example, the precipitation data is CMORPH satellite precipitation estimation data, and the tropical cyclone track data comes from the Best Track Dataset BST of the Tropical Cyclone Data Center; the temperature field, specific humidity field, wind field, and geopotential height field data at each height level come from ERA5 reanalysis data.

[0022] The time resolution of the BST dataset is sometimes every 6 hours and sometimes every 3 hours, while the ERA5 and CMORPH datasets are hourly. For convenience of processing, assume that the movement of the tropical cyclone center within 6 hours and 3 hours is a uniform linear motion, and interpolate the BST into hourly data so that the time resolutions of each dataset are consistent. Since ERA5 does not It can be calculated using the air temperature T (unit: K) and specific humidity q (unit: kg / kg) at P = 850 hPa. First, calculate the vapor pressure using q , and based on e, the dew point temperature can be calculated ; then calculate the latent heat ratio of condensation using T ; based on q, e, , T, and L, the equivalent potential temperature can be calculated . Thus, TFP can be calculated using calculation. The wind field data can be used for diagnosis.

[0023] The visualization process of this example is implemented using Matlab. Taking the visualization of the geopotential height at 850 hPa as an example, its Matlab code: [c,~] = contour(longitude,latitude,geopotential_850hPa,'LevelList',0:2:1200); Among them, c is the output visualization information variable, longitude and latitude are the longitude and latitude respectively, geopotential_850hPa is the geopotential height at 850 hPa (unit: dagpm), and the parameter LevelList is set to 0:2:1200, indicating that an isoline is drawn every 2 dagpm, and the isoline information is included in c.

[0024] Step 2: Use the tropical cyclone track data to determine the position of the center of the tropical cyclone, and then determine the area within 1000 km from the center of the tropical cyclone as the tropical cyclone influence area.

[0025] Step 3: Remove and the area of, filter out the isolines that are more than 1000 km long, non-closed, and TFP = 0, and determine the area within 200 km from the isoline as the synoptic-scale frontal influence area.

[0026] Step 4: Calculate the relative vorticity using the 850hPa wind field, and determine the grid points that meet one of the three cases according to the wind direction at the grid points: a1: and ; a2: , and ; a3: and , where the subscript moves 1 grid point eastward compared to i, and moves 1 grid point northward compared to j as the cyclonic shear point; Then, according to the positions of the shear points, use the density-based clustering method to merge the shear points into several groups, then screen out the groups where the distance between the two farthest grid points exceeds 500 km, then use the skeleton extraction method to determine the main lines of these groups, and finally determine the area within 200 km from the main lines as the synoptic-scale shear line influence area.

[0027] Step 5: Use the geopotential height fields at 500hPa, 850hPa, and 925hPa to visualize the isoline information of the geopotential height field at intervals of 2 dagpm, merge the nested closed isolines into the same set, and in the set of closed isolines where the isoline values decrease centripetally, determine the area enclosed by the outermost closed isoline whose enclosed area exceeds 196000 km 2 (with a diameter of about 500 km) as the synoptic-scale vortex influence area.

[0028] Step 6: Take the union of the influence areas of various weather systems at the same moment above, and then take the union of the influence areas of the weather systems on the same day, which is the influence area of strong weather forcing on that day. If the influence area of strong weather forcing is less than 50% of the SEC area, then identify the study area on that day as being affected by weak weather forcing.

[0029] Step 7: Using precipitation data, determine the set of grid points where the maximum hourly precipitation reaches 20 mm, the distance is less than 100 km, and the precipitation periods overlap. When the number of grid points in the set exceeds 3, it is identified as a spatiotemporally continuous heavy precipitation event. Furthermore, determine the earliest precipitation occurrence time in the set of grid points as the start time of the event, and the latest precipitation end time as the end time of the event. If the entire heavy precipitation event occurs only on a day affected by weak weather forcing, it is identified as a weak weather forcing heavy precipitation event.

[0030] As Figure 2 shown, 1550 weak weather forcing heavy precipitation events were identified from 2000 to 2022. Their start times are mainly concentrated from 14:00 to 18:00 in the afternoon, with more than 150 events in each period. Among them, the peak of 212 events is reached from 16:00 to 17:00. However, there are fewer weak weather forcing heavy precipitation events occurring at night and in the early morning, from 22:00 to 10:00 the next day, all less than 20 events, and the lowest value of 2 events is reached at 06:00.

[0031] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is loaded into the processor, it implements an objective recognition method for weak weather forcing heavy precipitation events based on visualization information according to any one of the above.

[0032] An embodiment of the present invention also provides a storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements an objective recognition method for weak weather forcing heavy precipitation events based on visualization information according to any one of the above.

Claims

1. An objective identification method for weak weather forced heavy precipitation events based on visual information, characterized in that: The following steps are involved: (1) Acquisition and analysis of data and its visualization information; (2) Determine the location of the center of the affected tropical cyclone using tropical cyclone track data; (3) Determine the weather-scale frontal impact area using visualization information of equivalent potential temperature gradient, relative vorticity, and heat front parameters; (4) Using the visualization information of relative vorticity and wind field, and applying density-based clustering and skeleton extraction methods, we can determine the impact area of ​​the weather-scale shear line; (5) Determine the impact area of ​​the weather-scale vortex using the visualization information of geopotential height; (6) Take the union of the areas affected by various weather systems at the same time, and then take the union of the areas affected by the weather systems on the same day to determine the area affected by strong weather forcing on that day. Finally, take the complement of the areas affected by strong weather forcing to determine the area affected by weak weather forcing on that day. (7) Heavy precipitation events are determined based on hourly precipitation, location and number of precipitation stations. If a heavy precipitation event only occurs in an area affected by weak weather forcing, it is identified as a heavy precipitation event with weak weather forcing.

2. According to the objective identification method of weak weather forced heavy precipitation events based on visual information of claim 1, it is characterized in that: Step (1) is as follows: obtain precipitation, tropical cyclone path, and 850hPa equivalent potential temperature The gradient size , heat front parameters , wind (u, v), relative vorticity , as well as the potential heights of 500hPa, 850hPa and 925hPa, the potential heights of 500hPa, 850hPa and 925hPa and the TFP of 850hPa are visualized respectively; among them, the potential height interval is 2dagpm, and only TFP=0 is drawn; the visualization information variable c, that is, the position information of the points on each contour line, is output to determine whether the first and last grid points of the contour line are in the same position, if they are in the same position, it is a closed contour line, if not, it is a non-closed contour line; calculate the length of the contour line; calculate the area of ​​the closed contour line; determine whether the grid points on a closed contour line are inside other closed contour lines, and determine whether a closed contour line is inside another closed contour line.

3. According to the objective identification method of weak weather forced heavy precipitation events based on visual information in claim 1, it is characterized in that: In step (2), the area within 1000 km of the tropical cyclone center is defined as the tropical cyclone affected area.

4. According to the objective identification method of weak weather forced heavy precipitation events based on visual information in claim 1, it is characterized in that: Step (3) is as follows: remove and In the area, the contour lines with a length of more than 1000 km, non-closed, and TFP=0 were screened out, and the area within 200 km from the contour lines was determined as the weather-scale frontal influence area.

5. The objective identification method of weak weather forced heavy precipitation events based on visual information according to claim 1 is characterized in that: Step (4) is as follows: Calculate the relative vorticity using the 850hPa wind field ,exist The grid points of are determined according to the wind direction to meet one of the three conditions: a1: and ; a2: , and ; a3: and , where the footer Move one grid point east of i, The point one grid point north of j is the cyclonic shear point; Then, according to the location of the shear points, the density-based clustering method is used to merge the shear points into several groups, and the groups with the farthest two grid points more than 500 km away are screened out. The skeleton extraction method is used to determine the main lines, and finally the area 200 km away from the main lines is determined as the weather-scale shear line impact area.

6. The objective identification method of weak weather forced heavy precipitation events based on visual information according to claim 1 is characterized in that: Step (5) is as follows: Using the 500 hPa, 850 hPa and 925 hPa geopotential height fields, the contour information of the geopotential height fields is visualized at intervals of 2 dagpm, and the closed contour lines with nested relationships are merged into the same set. In the closed contour line set with centripetal decreasing contour line values, the area enclosed by the outermost closed contour line is determined to exceed 196,000 km. 2 The area enclosed by the set of contour lines is the area affected by the weather-scale vortex.

7. The objective identification method of weak weather forced heavy precipitation events based on visual information according to claim 1 is characterized in that: Step (6) is as follows: take the union of the areas affected by various weather systems at the same time, and then take the union of the areas affected by the weather systems on the same day, that is, the area affected by strong weather forcing on that day. If the area affected by strong weather forcing is less than 50% of the area of ​​the study area, then the study area on that day is identified as an area affected by weak weather forcing.

8. The objective identification method of weak weather forced heavy precipitation events based on visual information according to claim 1 is characterized in that: Step (7) is as follows: using precipitation data, determine a set of grid points with a maximum hourly precipitation of 20 mm, a distance of less than 100 km, and overlapping precipitation periods; when the number of grid points in the set exceeds 3, identify it as a spatiotemporally continuous heavy precipitation event; determine the earliest precipitation occurrence time in the grid point set as the start time of the event, and the latest precipitation end time as the end time of the event; if the entire heavy precipitation event only occurs on a day affected by weak weather forcing, identify it as a weak weather forcing heavy precipitation event.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into the processor, the objective identification method of weak weather forced heavy precipitation events based on visual information according to any one of claims 1 to 8 is implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, an objective identification method for weak weather forced heavy precipitation events based on visual information according to any one of claims 1 to 8 is implemented.

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