A weather radar networking precipitation estimation method and device and related equipment

Abstract

This application discloses a method for estimating precipitation in a weather radar network. The method includes: acquiring a first data quality index and a second data quality index, wherein the first data quality index indicates the reliability of observation data acquired by a first radar at different elevation angles, and the second data quality index indicates the reliability of observation data acquired by a second radar at different elevation angles; determining a first fusion weight corresponding to the first radar based on the first data quality index, and determining a second fusion weight corresponding to the second radar based on the second data quality index; fusing the first precipitation and the second precipitation based on the first and second fusion weights to obtain the final estimated precipitation for the target area, wherein the first precipitation is the precipitation estimated for the target area based on the observation data of the first radar, and the second precipitation is the precipitation estimated for the target area based on the observation data of the second radar. This method can improve the accuracy of precipitation estimation for the target area.

Description

Technical Field

[0001] This application relates to the field of meteorological measurement technology, and in particular to a method, device and related equipment for estimating precipitation using a weather radar network. Background Technology

[0002] When using a single radar to estimate precipitation, it is difficult to achieve high-precision and full-coverage precipitation estimation over a large area of ​​precipitation.

[0003] Currently, existing technologies utilize multiple radars to estimate precipitation over a large area. When the target area is covered by multiple radars, the precipitation estimation system performs an equal-weighted average of the precipitation estimates from multiple radars to obtain the final estimated precipitation for the target area.

[0004] However, this method has low accuracy in estimating precipitation in the target area. Summary of the Invention

[0005] This application provides a method for estimating precipitation using a weather radar network to improve the accuracy of precipitation estimation for target areas. Furthermore, this application also provides a corresponding precipitation estimation device, computer equipment, and computer-readable storage medium.

[0006] In a first aspect, this application provides a method for estimating precipitation using a weather radar network. The method includes: acquiring a first data quality index and a second data quality index, wherein the first data quality index indicates the reliability of observation data acquired by a first radar at different elevation angles; determining a first fusion weight corresponding to the first radar based on the first data quality index; determining a second fusion weight corresponding to the second radar based on the second data quality index; fusing a first precipitation value and a second precipitation value based on the first fusion weight and the second fusion weight to obtain a final estimated precipitation value for the target area, wherein the first precipitation value is the precipitation value for the target area estimated based on the observation data of the first radar, and the second precipitation value is the precipitation value for the target area estimated based on the observation data of the second radar.

[0007] In one possible implementation, the first data quality index includes a first sub-data quality index and a second sub-data quality index. The first sub-data quality index indicates the reliability of observation data acquired by the first radar at a first elevation angle, and the second sub-data quality index indicates the reliability of observation data acquired by the first radar at a second elevation angle. Acquiring the first data quality index includes: calculating the first sub-data quality index and the second sub-data quality index based on a layered obstruction rate, a layered beam height index, and a dynamic melting layer index, respectively. The layered obstruction rate characterizes the impact of radar beam obstruction on the reliability of the observation data; the layered beam height index characterizes the impact of radar beam height on the reliability of the observation data; and the dynamic melting layer index characterizes the impact of the melting layer on the reliability of the observation data. The first data quality index is then determined based on the first sub-data quality index and the second sub-data quality index.

[0008] In one possible implementation, determining the first fusion weight corresponding to the first radar based on the first data quality index includes: when both the first sub-data quality index and the second sub-data quality index can be mapped to the target area, determining the maximum value of the first sub-data quality index and the second sub-data quality index as the first fusion weight corresponding to the first radar.

[0009] In one possible implementation, the method further includes: calculating a first fusion attenuation rate and a second fusion attenuation rate, the fusion attenuation rate being used to characterize the overall attenuation degree of the electromagnetic waves emitted by the radar during propagation; and calculating the first precipitation and the second precipitation based on a precipitation calculation formula, the expression of which is... Where R is the precipitation and t is the preset duration. For fusion attenuation rate, The wavelength of the electromagnetic waves emitted by radar.

[0010] In one possible implementation, calculating the first fused attenuation rate includes: calculating a first attenuation rate and a second attenuation rate, wherein the first attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the first radar at a first elevation angle during propagation, and the second attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the first radar at a second elevation angle during propagation; and fusing the first attenuation rate and the second attenuation rate based on the first sub-data quality index and the second sub-data quality index to obtain the first fused attenuation rate.

[0011] In one possible implementation, calculating the first attenuation rate and the second attenuation rate includes: calculating a first slope and a second slope, wherein the first slope characterizes the rate of change of the first differential reflectivity with respect to the first horizontal reflectivity, and the second slope characterizes the rate of change of the second differential reflectivity with respect to the second horizontal reflectivity; determining the first attenuation rate based on the first slope and the first differential phase shift rate, and determining the second attenuation rate based on the second slope and the second differential phase shift rate; wherein the first differential reflectivity, the first horizontal reflectivity, and the first differential phase shift rate are observation data acquired by the first radar at a first elevation angle, and the second differential reflectivity, the second horizontal reflectivity, and the second differential phase shift rate are observation data acquired by the first radar at a second elevation angle.

[0012] In one possible implementation, calculating the first slope includes: obtaining a first horizontal attenuation rate, wherein the first horizontal attenuation rate is the observation data acquired by the first radar at a first elevation angle; dividing the first horizontal attenuation rate into multiple intervals according to a preset interval; determining the median of the first differential reflectivity corresponding to the multiple intervals, wherein one interval corresponds to one median of the first differential reflectivity; and performing linear fitting on the median of the first differential reflectivity corresponding to the multiple intervals to obtain the first slope.

[0013] Secondly, this application provides a precipitation estimation device, the device comprising: an acquisition module, configured to acquire a first data quality index and a second data quality index, wherein the first data quality index is used to indicate the reliability of observation data acquired by a first radar at different elevation angles, and the second data quality index is used to indicate the reliability of observation data acquired by a second radar at different elevation angles; a determination module, configured to determine a first fusion weight corresponding to the first radar based on the first data quality index, and determine a second fusion weight corresponding to the second radar based on the second data quality index; and a fusion module, configured to fuse a first precipitation amount and a second precipitation amount based on the first fusion weight and the second fusion weight to obtain a final estimated precipitation amount for a target area, wherein the first precipitation amount is the precipitation amount for the target area estimated based on the observation data of the first radar, and the second precipitation amount is the precipitation amount for the target area estimated based on the observation data of the second radar.

[0014] In one possible implementation, the acquisition module is specifically used to calculate a first sub-data quality index and a second sub-data quality index based on the layered obstruction rate, the layered beam height index, and the dynamic melting layer index, respectively. The layered obstruction rate is used to characterize the degree of influence of radar beam obstruction on the reliability of observation data, the layered beam height index is used to characterize the degree of influence of radar beam height on the reliability of observation data, and the dynamic melting layer index is used to characterize the degree of influence of melting layer on the reliability of observation data; the first data quality index is determined based on the first sub-data quality index and the second sub-data quality index.

[0015] In one possible implementation, the determining module is specifically used to determine the maximum value of the first sub-data quality index and the second sub-data quality index as the first fusion weight corresponding to the first radar, when both the first sub-data quality index and the second sub-data quality index can be mapped to the target area.

[0016] In one possible implementation, the device further includes: a fusion attenuation rate calculation module, used to calculate a first fusion attenuation rate and a second fusion attenuation rate, the fusion attenuation rate being used to characterize the overall attenuation degree of the electromagnetic waves emitted by the radar during propagation; and to calculate the first precipitation and the second precipitation based on a precipitation calculation formula, the expression of which is: Where R is the precipitation and t is the preset duration. For fusion attenuation rate, The wavelength of the electromagnetic waves emitted by radar.

[0017] In one possible implementation, the fusion attenuation rate calculation module is specifically used to calculate a first attenuation rate and a second attenuation rate. The first attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a first elevation angle during propagation, and the second attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a second elevation angle during propagation. The first attenuation rate and the second attenuation rate are fused based on the first sub-data quality index and the second sub-data quality index to obtain the first fusion attenuation rate.

[0018] In one possible implementation, the fusion attenuation rate calculation module is specifically used to calculate a first slope and a second slope, wherein the first slope is used to characterize the rate of change of the first differential reflectivity with respect to the first horizontal reflectivity, and the second slope is used to characterize the rate of change of the second differential reflectivity with respect to the second horizontal reflectivity; the first attenuation rate is determined based on the first slope and the first differential phase shift rate, and the second attenuation rate is determined based on the second slope and the second differential phase shift rate; wherein the first differential reflectivity, the first horizontal reflectivity, and the first differential phase shift rate are observation data acquired by the first radar at a first elevation angle, and the second differential reflectivity, the second horizontal reflectivity, and the second differential phase shift rate are observation data acquired by the first radar at a second elevation angle.

[0019] In one possible implementation, the fusion attenuation rate calculation module is specifically used to obtain a first horizontal attenuation rate, wherein the first horizontal attenuation rate is the observation data obtained by the first radar at a first elevation angle; divide the first horizontal attenuation rate into multiple intervals according to a preset interval; determine the median of the first differential reflectivity corresponding to the multiple intervals, wherein one interval corresponds to one median of the first differential reflectivity; and perform linear fitting on the median of the first differential reflectivity corresponding to the multiple intervals to obtain a first slope.

[0020] Thirdly, this application provides a computer device including a processor and a memory, the memory being used to store instructions, and the processor executing the instructions stored in the memory to cause the computer device to perform any of the aforementioned weather radar network precipitation estimation methods.

[0021] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the weather radar network precipitation estimation method as described in any of the above applications.

[0022] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0023] As can be seen from the above technical solutions, this application has the following beneficial effects:

[0024] The weather radar network precipitation estimation system can acquire observation data from a first radar at different elevation angles and from a second radar at different elevation angles. Subsequently, the system can calculate a first data quality index characterizing the reliability of the observation data acquired by the first radar and a second data quality index characterizing the reliability of the observation data acquired by the second radar. Further, the system can determine a first fusion weight for the first radar based on the first data quality index and a second fusion weight for the second radar based on the second data quality index. Finally, the system can fuse the first and second precipitation amounts according to the first and second fusion weights. The first precipitation amount is the precipitation amount estimated for the target area based on the observation data from the first radar, and the second precipitation amount is the precipitation amount estimated for the target area based on the observation data from the second radar. In this way, the weather radar network precipitation estimation system determines the first fusion weight and the second fusion weight respectively through the data quality index, and merges the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight. Compared with the equal weighted average fusion of the first precipitation and the second precipitation, this improves the accuracy of the precipitation estimation of the target area. Attached Figure Description

[0025] Figure 1 This is a schematic diagram illustrating a scenario where multiple radars are used to estimate precipitation over a large area, as provided in an embodiment of this application.

[0026] Figure 2 This application provides a schematic diagram of the structure of a weather radar network precipitation estimation system.

[0027] Figure 3 A flowchart illustrating a weather radar network precipitation estimation method provided in this application embodiment;

[0028] Figure 4 A schematic diagram illustrating the mapping relationship between a data quality index and a ground area is provided in an embodiment of this application.

[0029] Figure 5 A flowchart for estimating precipitation in a target area within a preset time period based on observation data from a single radar, provided as an embodiment of this application;

[0030] Figure 6 A flowchart illustrating another method for estimating precipitation using a weather radar network, provided in this application embodiment;

[0031] Figure 7 This is a schematic diagram of a weather radar network precipitation estimation device provided in an embodiment of this application. Detailed Implementation

[0032] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate; this is merely a method of distinction used in describing objects with the same attributes in the embodiments of this application.

[0033] To make the above-mentioned objectives, features and advantages of the embodiments of this application more apparent and understandable, the embodiments of this application will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] When using a single radar to estimate precipitation, it is difficult to achieve high-precision and full-coverage precipitation estimation over a large area of ​​precipitation.

[0035] Currently, existing technologies utilize multiple radars to estimate precipitation over large areas. For example... Figure 1 As shown, Figure 1 This diagram illustrates a scenario where multiple radars are used to estimate precipitation over a large area. The target area is simultaneously covered by the detection areas of both the first and second radars. The precipitation estimation system performs an equal-weighted average of the precipitation estimates from the first and second radars to obtain the final estimated precipitation for the target area.

[0036] However, this method has low accuracy in estimating precipitation in the target area.

[0037] Based on this, this application provides a weather radar network precipitation estimation method, which determines the radar fusion weights according to the data quality index that characterizes the reliability of the observation data acquired by the radar, so as to improve the accuracy of precipitation estimation for the target area.

[0038] To facilitate understanding of the methods provided in the embodiments of this application, the following is combined with... Figure 2 The structural example of the weather radar network precipitation estimation system 20 shown is illustrated below. (See also...) Figure 2 As shown, this figure is a schematic diagram of an exemplary weather radar network precipitation estimation system 20 provided in an embodiment of this application. Figure 2 As shown, the weather radar network precipitation estimation system 20 may include a data quality control module 210, a fusion weight determination module 220, a single radar precipitation estimation module 230, and a network precipitation fusion module 240.

[0039] In specific implementation, the data quality control module 210 can acquire observation data from the first radar at different elevation angles and observation data from the second radar at different elevation angles. Subsequently, the data quality control module 210 can calculate a first data quality index characterizing the reliability of the observation data acquired by the first radar and a second data quality index characterizing the reliability of the observation data acquired by the second radar. Further, the fusion weight determination module 220 can determine a first fusion weight corresponding to the first radar based on the first data quality index and a second fusion weight corresponding to the second radar based on the second data quality index. Furthermore, the single-radar precipitation estimation module 230 can estimate the first precipitation in the target area based on the observation data from the first radar and the second precipitation in the target area based on the observation data from the second radar. Finally, the network precipitation fusion module 240 can fuse the first and second precipitation amounts according to the first and second fusion weights. In this way, the weather radar network precipitation estimation system 20 determines the first fusion weight and the second fusion weight respectively through the data quality index, and merges the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight. Compared with the equal weighted average fusion of the first precipitation and the second precipitation, the accuracy of the precipitation estimation of the target area is improved.

[0040] Those skilled in the art will understand that Figure 2 The schematic diagram of the weather radar network precipitation estimation system 20 shown is merely one example of an embodiment that can be implemented in this application. The scope of application of this application's embodiments is not limited by any aspect of this system.

[0041] To facilitate understanding of the embodiments of this application, the following description, in conjunction with the accompanying drawings, illustrates a weather radar network precipitation estimation method provided by the embodiments of this application.

[0042] See Figure 3 , Figure 3 This application provides a flowchart illustrating a method for estimating precipitation using a weather radar network, which can be applied to... Figure 3 The weather radar network precipitation estimation system 20 shown can be applied to other applicable precipitation estimation systems. For ease of understanding, the following example uses an application... Figure 2 The weather radar network precipitation estimation system 20 shown is used as an example for illustration.

[0043] in, Figure 3 The precipitation estimation method for the weather radar network shown can specifically include the following steps.

[0044] S301: The weather radar network precipitation estimation system 20 acquires a first data quality index and a second data quality index. The first data quality index is used to indicate the reliability of the observation data acquired by the first radar at different elevation angles, and the second data quality index is used to indicate the reliability of the observation data acquired by the second radar at different elevation angles.

[0045] When estimating precipitation in a target area, multiple radars can be used for detection. Compared to using a single radar to detect meteorological data, this improves the accuracy of precipitation estimation for the target area. This application embodiment uses a weather radar network precipitation estimation system 20 that uses two radars to estimate precipitation in a target area, i.e., the target area is covered by the detection areas of two radars, to illustrate the weather radar network precipitation estimation method provided in this application embodiment. The two radars can be a first radar and a second radar. It is worth noting that the number of radars in this application embodiment can be three or more; the number of radars is not specifically limited.

[0046] In practice, the data quality control module 210 in the weather radar network precipitation estimation system 20 can acquire observation data obtained by the first radar and the second radar at different elevation angles.

[0047] Specifically, the first radar can emit electromagnetic waves at multiple different elevation angles. For example, the first radar can emit electromagnetic waves at a first elevation angle to obtain first observation data, and emit electromagnetic waves at a second elevation angle to obtain second observation data. The first elevation angle and the second elevation angle are different. It is worth noting that the first radar can emit electromagnetic waves at two or more elevation angles to obtain two or more sets of observation data. This application embodiment uses the example of the first radar emitting electromagnetic waves at two different elevation angles to illustrate the precipitation estimation method for a weather radar network provided in the application embodiment.

[0048] Similarly, the second radar can transmit electromagnetic waves at multiple different elevation angles. For example, the second radar can transmit electromagnetic waves at a third elevation angle to obtain third observation data, and transmit electromagnetic waves at a fourth elevation angle to obtain fourth observation data. The third and fourth elevation angles are different; the third elevation angle can be the same as or different from the first or second elevation angle. Likewise, the fourth elevation angle can be the same as or different from the first or second elevation angle.

[0049] The first observation data may include a first horizontal reflectance, a first vertical reflectance, a first differential reflectance, a first specific differential phase shift rate, and a first correlation coefficient. The second observation data may include a second horizontal reflectance, a second vertical reflectance, a second differential reflectance, a second specific differential phase shift rate, and a second correlation coefficient. The third observation data may include a third horizontal reflectance, a third vertical reflectance, a third differential reflectance, a third specific differential phase shift rate, and a third correlation coefficient. The fourth observation data may include a fourth horizontal reflectance, a fourth vertical reflectance, a fourth differential reflectance, a fourth specific differential phase shift rate, and a fourth correlation coefficient.

[0050] Horizontal reflectivity is the echo reflection factor obtained from the radar transmitting and receiving horizontally polarized electromagnetic waves. Horizontal reflectivity can be used to indicate the concentration of particles or the size of particles, which can be raindrops, snowflakes, or dry ice crystals.

[0051] Vertical reflectivity is the reflection factor of the echo obtained from radar transmitting and receiving vertically polarized electromagnetic waves. Vertical reflectivity can also be used to indicate the concentration of particle numbers or the size of particle diameter.

[0052] The relationship between differential reflectance and horizontal and vertical reflectance is: differential reflectance = 10lg(horizontal reflectance / vertical reflectance). Differential reflectance can be used to indicate the geometry of a particle, such as whether the particle is spherical, oblate, or other irregularly shaped.

[0053] Specific differential phase shift rate is the rate of change of the cumulative differential phase shift of a horizontally or vertically polarized wave along its propagation path with distance. Specific differential phase shift rate can be used to distinguish between liquid and solid particles.

[0054] The correlation coefficient can be used to distinguish between single-phase precipitation and mixed-phase precipitation. It can also be used to distinguish between regular and irregular shaped particles.

[0055] The data quality control module 210 can identify and filter out ground clutter and anomalous echoes based on the observation data. Ground clutter can be, for example, waves reflected from buildings or mountains, while anomalous echoes can be, for example, waves reflected from flying animals.

[0056] Furthermore, the data quality control module 210 can use a linear programming method to correct the differential phase to ensure the non-negativity of the differential phase shift rate in the liquid precipitation region, providing high-quality observation data for subsequent precipitation estimation by the weather radar network precipitation estimation system 20. Here, differential phase refers to the cumulative phase difference generated after the signal corresponding to the horizontally or vertically polarized electromagnetic wave emitted by the radar propagates in precipitation particles.

[0057] S302: Weather radar network precipitation estimation system 20 determines the first fusion weight corresponding to the first radar based on the first data quality index, and determines the second fusion weight corresponding to the second radar based on the second data quality index.

[0058] The first data quality index may include a first sub-data quality index, a second sub-data quality index, a third sub-data quality index, and a fourth sub-data quality index. The first sub-data quality index can be used to indicate the reliability of observation data acquired by the first radar at a first elevation angle. The second sub-data quality index can be used to indicate the reliability of observation data acquired by the first radar at a second elevation angle. The third sub-data quality index can be used to indicate the reliability of observation data acquired by the second radar at a third elevation angle. The fourth sub-quality index can be used to indicate the reliability of observation data acquired by the second radar at a fourth elevation angle.

[0059] The fusion weight determination module 220 in the weather radar network precipitation estimation system 20 can first calculate the first sub-data quality index, the second sub-data quality index, the third sub-data quality index and the fourth sub-data quality index through the data quality calculation formula.

[0060] The specific expression for the data quality calculation formula is as follows:

[0061] ;

[0062] ;

[0063] ;

[0064] ;

[0065] in, For layered occlusion rates; Beam blocking rate; For layered beam height index; This is the current elevation beam center height; The height attenuation factor is an empirical reference height that can be used to control the rate at which beam height affects data quality by setting different values. For dynamic melting layer index; The altitude at which the real-time temperature is zero degrees Celsius; The thickness of the melt layer; The standard deviation of the zero-degree layer, This is a data quality index, used to characterize the reliability of observed data.

[0066] It should be noted that the layered obstruction rate can be used to characterize the impact of beam obstruction of radar-transmitted electromagnetic waves on the reliability of observation data. The layered beam height index can be used to characterize the impact of the beam height of radar-transmitted electromagnetic waves on the reliability of observation data. The dynamic melting layer index can be used to characterize the impact of the melting layer on the reliability of observation data.

[0067] After the fusion weight determination module 220 completes the calculation of the data quality index for all observation data acquired by all radars at all elevation angles, it can map the first sub-data quality index and the second sub-data quality index to multiple regions on the ground, respectively. These multiple regions are multiple regions in polar coordinates.

[0068] like Figure 4 As shown, Figure 4 A schematic diagram illustrating the mapping relationship between a data quality index and a ground area is shown. For a first radar, both the first and second sub-data quality indices can be mapped to the same ground area. When the first elevation angle is less than the second elevation angle, some ground areas may only be mapped by the first sub-data quality index.

[0069] When both the first sub-data quality index and the second sub-data quality index are mapped to the target area on the ground, the fusion weight determination module 220 can determine the maximum value of the first sub-data quality index and the second sub-data quality index as the first fusion weight corresponding to the first radar.

[0070] Similarly, the fusion weight determination module 220 can also map the third sub-data quality index and the fourth sub-data quality index to multiple areas on the ground. When both the third and fourth sub-data quality indices are mapped to target areas on the ground, the fusion weight determination module 220 can determine the maximum value of the third and fourth sub-data quality indices as the second fusion weight corresponding to the second radar.

[0071] S303: Weather radar network precipitation estimation system 20 fuses the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight to obtain the final estimated precipitation of the target area. The first precipitation is the precipitation of the target area estimated based on the observation data of the first radar, and the second precipitation is the precipitation of the target area estimated based on the observation data of the second radar.

[0072] First, the weather radar network precipitation estimation system 20 can estimate the precipitation in the target area within a preset time period based on the observation data of the first radar, that is, calculate the first precipitation. Furthermore, the weather radar network precipitation estimation system 20 can estimate the precipitation in the target area within a preset time period based on the observation data of the second radar, that is, calculate the second precipitation.

[0073] like Figure 5 As shown, Figure 5 This paper demonstrates a method for estimating precipitation in a target area over a preset time period based on observation data from a single radar.

[0074] S501: Filter the observation data of a single radar.

[0075] The single-radar precipitation estimation module 230 of the weather radar network precipitation estimation system 20 can first filter the observation data acquired by the first radar and the second radar at different elevation angles.

[0076] Specifically, the single-radar precipitation estimation module 230 can retain horizontal reflectivity greater than a first threshold and less than a second threshold, correlation coefficient greater than a third threshold, and differential reflectivity greater than a fourth threshold and less than a fifth threshold. The first threshold is less than the second threshold, and the fourth threshold is less than the fifth threshold.

[0077] S502: Calculate the slope corresponding to each elevation layer of a single radar.

[0078] The single-radar precipitation estimation module 230 can calculate a first slope, a second slope, a third slope, and a fourth slope respectively. The first slope characterizes the rate of change of the first differential reflectivity with respect to the first horizontal reflectivity; the second slope characterizes the rate of change of the second differential reflectivity with respect to the second horizontal reflectivity; the third slope characterizes the rate of change of the third differential reflectivity with respect to the third horizontal reflectivity; and the fourth slope characterizes the rate of change of the fourth differential reflectivity with respect to the fourth horizontal reflectivity.

[0079] Specifically, the single-radar precipitation estimation module 230 can divide the first horizontal attenuation rate into multiple intervals according to a preset interval. Then, the single-radar precipitation estimation module 230 can determine the median of the first differential reflectivity corresponding to each interval, with one interval corresponding to one median of the first differential reflectivity. Finally, the single-radar precipitation estimation module 230 can perform linear fitting on the medians of the first differential reflectivity corresponding to these multiple intervals to obtain the first slope.

[0080] Similarly, the specific calculation methods for the second, third, and fourth slopes can be found in the calculation method for the first slope, and the details will not be repeated here.

[0081] S503: Calculate the attenuation rate for each elevation level of a single radar. The attenuation rate is used to indicate the degree of attenuation of the electromagnetic waves emitted by the radar during propagation.

[0082] The single-radar precipitation estimation module 230 can calculate the first attenuation rate, the second attenuation rate, the third attenuation rate, and the fourth attenuation rate according to the attenuation rate formula. The first attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the first radar at a first elevation angle during propagation; the second attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the first radar at a second elevation angle during propagation; the third attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the second radar at a third elevation angle during propagation; and the fourth attenuation rate characterizes the degree of attenuation of the electromagnetic wave emitted by the second radar at a fourth elevation angle during propagation.

[0083] Specifically, the single radar precipitation estimation module 230 can determine a first attenuation based on a first slope and a first differential phase shift rate, a second attenuation rate based on a second slope and a second differential phase shift rate, a third attenuation rate based on a third slope and a third differential phase shift rate, and a fourth attenuation rate based on a fourth slope and a fourth differential phase shift rate.

[0084] The specific expression of the attenuation rate calculation formula is as follows:

[0085] ;

[0086] in, The attenuation rate, The slope For differential phase shift rate

[0087] S504: Based on the data quality index corresponding to each elevation angle layer, the attenuation rate corresponding to each elevation angle layer is fused to obtain the fused attenuation rate. The fused attenuation rate is used to characterize the overall attenuation degree of the electromagnetic waves emitted by the radar during the propagation process.

[0088] Furthermore, the single-radar precipitation estimation module 230 can fuse the first attenuation rate and the second attenuation rate according to the attenuation rate fusion formula and based on the first sub-data quality index and the second sub-data quality index to obtain the first fused attenuation rate of the first radar. Additionally, the single-radar precipitation estimation module 230 can fuse the third attenuation rate and the fourth attenuation rate according to the attenuation rate fusion formula and based on the third sub-data quality index and the fourth sub-data quality index to obtain the second fused attenuation rate of the second radar.

[0089] The specific expression of the attenuation rate fusion calculation formula is as follows:

[0090] ;

[0091] in, For fusion attenuation rate, Let K be the attenuation rate corresponding to the k-th elevation angle of the radar. is the data quality index of the observation data acquired by the radar at the k-th elevation angle.

[0092] S504: Calculate the precipitation in the target area estimated by a single radar within a preset time period based on the fusion attenuation rate.

[0093] The single-radar precipitation estimation module 230 can calculate the first precipitation by substituting the first fusion attenuation rate and the preset duration into the precipitation calculation formula, and calculate the second precipitation by substituting the second fusion attenuation rate and the preset duration into the precipitation calculation formula. The first precipitation is the precipitation in the target area estimated based on the observation data acquired by the first radar, and the second precipitation is the precipitation in the target area estimated based on the observation data acquired by the second radar.

[0094] The specific expression of the precipitation calculation formula is as follows:

[0095] ;

[0096] Where R is the precipitation and t is the preset duration. For fusion attenuation rate, The wavelength of the electromagnetic waves emitted by radar.

[0097] pass Figure 5 According to the provided embodiment, the weather radar network precipitation estimation system 20 can obtain the precipitation of the target area within a preset time period estimated based on the observation data of the first radar, namely the first precipitation, and the precipitation of the target area within a preset time period estimated based on the observation data of the second radar, namely the second precipitation.

[0098] Furthermore, when both the first fusion weight and the second fusion weight can be mapped to the target area, the network precipitation fusion module 240 of the weather radar network precipitation estimation system 20 can first interpolate the first fusion weight and the second fusion weight corresponding to the target area in polar coordinates to the target area in a unified plane rectangular coordinate system.

[0099] In one possible implementation, the network precipitation fusion module 240 can interpolate the first fusion weight and the second fusion weight corresponding to the target area in polar coordinates to the target area in a unified Cartesian coordinate system through bilinear interpolation or nearest neighbor interpolation, so as to ensure the consistency between the target area in the unified Cartesian coordinate system and the target area in polar coordinates.

[0100] Furthermore, the network precipitation fusion module 240 can substitute the first fusion weight, the second fusion weight, the first precipitation, and the second precipitation into the precipitation fusion formula to calculate the final estimated precipitation of the target area.

[0101] The specific expression of the precipitation fusion formula is as follows:

[0102] ;

[0103] in, M represents the number of radars used to estimate the final precipitation amount for the target area. The fusion weight is the weight corresponding to the j-th radar. This is the estimated precipitation amount for the target area based on observation data from the j-th radar.

[0104] Using the method provided in the above embodiments, the weather radar network precipitation estimation system 20 can acquire observation data acquired by a first radar at different elevation angles and observation data acquired by a second radar at different elevation angles. Subsequently, the weather radar network precipitation estimation system 20 can calculate a first data quality index characterizing the reliability of the observation data acquired by the first radar and a second data quality index characterizing the reliability of the observation data acquired by the second radar. Further, the weather radar network precipitation estimation system 20 can determine a first fusion weight corresponding to the first radar based on the first data quality index and a second fusion weight corresponding to the second radar based on the second data quality index. Finally, the weather radar network precipitation estimation system 20 can fuse the first precipitation amount estimated based on the observation data of the first radar and the second precipitation amount estimated based on the observation data of the second radar for the target area according to the first fusion weight and the second fusion weight. In this way, the weather radar network precipitation estimation system 20 determines the first fusion weight and the second fusion weight respectively through the data quality index, and merges the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight. Compared with the equal weighted average fusion of the first precipitation and the second precipitation, the accuracy of the precipitation estimation of the target area is improved.

[0105] To help those skilled in the art further understand the weather radar network precipitation estimation method provided in the embodiments of this application, Figure 6 A flowchart illustrating another method for estimating precipitation using a weather radar network is provided.

[0106] like Figure 6 As shown, another method for estimating precipitation using a weather radar network can include the following steps.

[0107] S601: Weather radar network precipitation estimation system 20 acquires observation data acquired by the first radar at different elevation angles and acquires observation data acquired by the second radar at different elevation angles.

[0108] S602: The weather radar network precipitation estimation system 20 can determine the sub-data quality index corresponding to each elevation angle layer of the first radar based on the observation data acquired by the first radar at different elevation angles and the terrain data around the first radar, and determine the sub-data quality index corresponding to each elevation angle layer of the second radar based on the observation data acquired by the second radar at different elevation angles and the terrain data around the second radar.

[0109] S603: The weather radar network precipitation estimation system 20 can determine a first fusion weight based on the sub-data quality index of each elevation layer of the first radar, and determine a second fusion weight based on the sub-data quality index of each elevation layer of the second radar.

[0110] Specifically, the weather radar network precipitation estimation system 20 can determine the largest sub-data quality index among all sub-data quality indices corresponding to all elevation angle layers of the first radar as the first fusion weight, and determine the largest sub-data quality index among all sub-data quality indices corresponding to all elevation angle layers of the second radar as the second fusion weight.

[0111] S604: The weather radar network precipitation estimation system 20 can control the quality of observation data acquired by the first radar and the second radar.

[0112] In one possible implementation, the weather radar network precipitation estimation system 20 can filter out observation data that do not contain meteorological echoes, or it can use a linear programming method to correct the phase difference.

[0113] S605: The weather radar network precipitation estimation system 20 can calculate the attenuation rate corresponding to each elevation angle layer of the first radar and the attenuation rate corresponding to each elevation angle layer of the second radar.

[0114] S606: The weather radar network precipitation estimation system 20 can obtain a first fused attenuation rate by fusing the attenuation rate corresponding to all elevation layers of the first radar based on the sub-data quality index corresponding to each elevation layer of the first radar. Furthermore, it can obtain a second fused attenuation rate by fusing the attenuation rate corresponding to all elevation layers of the second radar based on the sub-data quality index corresponding to each elevation layer of the second radar.

[0115] S607: The weather radar network precipitation estimation system 20 can calculate the first precipitation in the target area estimated by the first radar within a preset time period, and calculate the second precipitation in the target area estimated by the second radar within a preset time period.

[0116] S608: The weather radar network precipitation estimation system 20 can fuse the first precipitation and the second precipitation based on the first fusion weight corresponding to the first radar and the second fusion weight corresponding to the second radar to obtain the final estimated precipitation of the target area by the weather radar network precipitation estimation system 20.

[0117] It is worth noting that other reasonable combinations of steps that can be conceived by those skilled in the art based on the above description also fall within the scope of protection of this application. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to this application.

[0118] Based on the weather radar network precipitation estimation method provided in the above-described embodiments, this application also provides a weather radar network precipitation estimation device. The weather radar network precipitation estimation device will be described below with reference to the accompanying drawings. Since the principle of the device in this embodiment is similar to the weather radar network precipitation estimation method described above, the implementation of the device can refer to the implementation of the method; repeated details will not be repeated.

[0119] See Figure 7 As shown in the figure, this is a schematic diagram of the structure of a weather radar network precipitation estimation device provided in an embodiment of this application. Figure 7 As shown, the weather radar network precipitation estimation device 700 includes:

[0120] The acquisition module 701 is used to acquire a first data quality index and a second data quality index. The first data quality index is used to indicate the reliability of the observation data acquired by the first radar at different elevation angles, and the second data quality index is used to indicate the reliability of the observation data acquired by the second radar at different elevation angles.

[0121] The determination module 702 is used to determine the first fusion weight corresponding to the first radar based on the first data quality index, and to determine the second fusion weight corresponding to the second radar based on the second data quality index.

[0122] The fusion module 703 is used to fuse the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight to obtain the final estimated precipitation of the target area. The first precipitation is the precipitation of the target area estimated based on the observation data of the first radar, and the second precipitation is the precipitation of the target area estimated based on the observation data of the second radar.

[0123] In one possible implementation, the acquisition module 701 is specifically used to calculate a first sub-data quality index and a second sub-data quality index based on the layered occlusion rate, the layered beam height index, and the dynamic melting layer index, respectively. The layered occlusion rate is used to characterize the degree of influence of radar beam occlusion on the reliability of the observation data, the layered beam height index is used to characterize the degree of influence of radar beam height on the reliability of the observation data, and the dynamic melting layer index is used to characterize the degree of influence of the melting layer on the reliability of the observation data.

[0124] The first data quality index is determined based on the first sub-data quality index and the second sub-data quality index.

[0125] In one possible implementation, the determining module 702 is specifically used to determine the maximum value of the first sub-data quality index and the second sub-data quality index as the first fusion weight corresponding to the first radar, when both the first sub-data quality index and the second sub-data quality index can be mapped to the target area.

[0126] In one possible implementation, the weather radar network precipitation estimation device 700 further includes:

[0127] The fusion attenuation rate calculation module is used to calculate the first fusion attenuation rate and the second fusion attenuation rate. The fusion attenuation rate is used to characterize the overall attenuation of the electromagnetic waves emitted by the radar during the propagation process.

[0128] The first and second precipitation amounts are calculated based on the precipitation calculation formula, which is expressed as follows: ;

[0129] Where R is the precipitation and t is the preset duration. For fusion attenuation rate, The wavelength of the electromagnetic waves emitted by radar.

[0130] In one possible implementation, an attenuation rate calculation module is integrated, specifically used to calculate a first attenuation rate and a second attenuation rate. The first attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a first elevation angle during propagation, and the second attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a second elevation angle during propagation.

[0131] The first attenuation rate and the second attenuation rate are fused based on the first sub-data quality index and the second sub-data quality index to obtain the first fused attenuation rate.

[0132] In one possible implementation, the fusion attenuation rate calculation module is specifically used to calculate a first slope and a second slope, wherein the first slope is used to characterize the rate of change of the first differential reflectivity with respect to the first horizontal reflectivity, and the second slope is used to characterize the rate of change of the second differential reflectivity with respect to the second horizontal reflectivity.

[0133] The first attenuation rate is determined based on the first slope and the first ratio differential phase shift rate, and the second attenuation rate is determined based on the second slope and the second ratio differential phase shift rate.

[0134] The first differential reflectivity, the first horizontal reflectivity, and the first differential phase shift rate are the observation data acquired by the first radar at the first elevation angle, and the second differential reflectivity, the second horizontal reflectivity, and the second differential phase shift rate are the observation data acquired by the first radar at the second elevation angle.

[0135] In one possible implementation, the fusion attenuation rate calculation module is specifically used to obtain a first horizontal attenuation rate, which is the observation data obtained by the first radar at a first elevation angle.

[0136] The first level attenuation rate is divided into multiple intervals according to a preset interval;

[0137] Determine the median of the first differential reflectance corresponding to multiple intervals, with each interval corresponding to a median of the first differential reflectance;

[0138] The first slope is obtained by linearly fitting the median of the first differential reflectance corresponding to multiple intervals.

[0139] It should be noted that the specific implementation of each module in this embodiment can be found in the relevant descriptions in the above method embodiments. The module division in this application embodiment is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. In this application embodiment, each functional module can be concentrated in one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. For example, in the above embodiment, the first acquisition module and the holding module can be the same module or different modules.

[0140] Thirdly, this application provides a computer device including a processor and a memory. The memory is used to store instructions, and the processor executes the instructions stored in the memory to cause the computer device to perform the weather radar network precipitation estimation method provided in any of the above embodiments.

[0141] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the weather radar network precipitation estimation method provided in any of the above embodiments.

[0142] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

[0143] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0144] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0145] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0146] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for estimating precipitation using a weather radar network, characterized in that, The method includes: A first data quality index and a second data quality index are obtained. The first data quality index is used to indicate the reliability of the observation data acquired by the first radar at different elevation angles, and the second data quality index is used to indicate the reliability of the observation data acquired by the second radar at different elevation angles. The first fusion weight corresponding to the first radar is determined based on the first data quality index, and the second fusion weight corresponding to the second radar is determined based on the second data quality index. Based on the first fusion weight and the second fusion weight, the first precipitation and the second precipitation are fused to obtain the final estimated precipitation of the target area. The first precipitation is the precipitation of the target area estimated based on the observation data of the first radar, and the second precipitation is the precipitation of the target area estimated based on the observation data of the second radar.

2. The method according to claim 1, characterized in that, The first data quality index includes a first sub-data quality index and a second sub-data quality index. The first sub-data quality index is used to indicate the reliability of the observation data acquired by the first radar at a first elevation angle, and the second sub-data quality index is used to indicate the reliability of the observation data acquired by the first radar at a second elevation angle. The process of obtaining the first data quality index includes: The first sub-data quality index and the second sub-data quality index are calculated based on the layered obstruction rate, the layered beam height index, and the dynamic melting layer index, respectively. The layered obstruction rate is used to characterize the degree of influence of radar beam obstruction on the reliability of observation data. The layered beam height index is used to characterize the degree of influence of radar beam height on the reliability of observation data. The dynamic melting layer index is used to characterize the degree of influence of melting layer on the reliability of observation data. The first data quality index is determined based on the first sub-data quality index and the second sub-data quality index.

3. The method according to claim 2, characterized in that, The step of determining the first fusion weight corresponding to the first radar based on the first data quality index includes: When both the first sub-data quality index and the second sub-data quality index can be mapped to the target area, the maximum value of the first sub-data quality index and the second sub-data quality index is determined as the first fusion weight corresponding to the first radar.

4. The method according to claim 2, characterized in that, The method further includes: Calculate the first fusion attenuation rate and the second fusion attenuation rate. The fusion attenuation rate is used to characterize the overall attenuation of the electromagnetic waves emitted by the radar during the propagation process. The first precipitation and the second precipitation are calculated based on the precipitation calculation formula, the expression of which is as follows: ; Where R is the precipitation and t is the preset duration. For fusion attenuation rate, The wavelength of the electromagnetic waves emitted by radar.

5. The method according to claim 4, characterized in that, The calculation of the first fusion attenuation rate includes: Calculate a first attenuation rate and a second attenuation rate. The first attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a first elevation angle during propagation. The second attenuation rate is used to characterize the degree of attenuation of the electromagnetic wave emitted by the first radar at a second elevation angle during propagation. The first attenuation rate and the second attenuation rate are fused based on the first sub-data quality index and the second sub-data quality index to obtain the first fused attenuation rate.

6. The method according to claim 5, characterized in that, The calculation of the first attenuation rate and the second attenuation rate includes: Calculate a first slope and a second slope, wherein the first slope is used to characterize the rate of change of the first differential reflectance with respect to the first horizontal reflectance, and the second slope is used to characterize the rate of change of the second differential reflectance with respect to the second horizontal reflectance. The first attenuation rate is determined based on the first slope and the first differential phase shift rate, and the second attenuation rate is determined based on the second slope and the second differential phase shift rate. Wherein, the first differential reflectivity, the first horizontal reflectivity, and the first differential phase shift rate are the observation data acquired by the first radar at a first elevation angle, and the second differential reflectivity, the second horizontal reflectivity, and the second differential phase shift rate are the observation data acquired by the first radar at a second elevation angle.

7. The method according to claim 6, characterized in that, The calculation of the first slope includes: Obtain a first horizontal attenuation rate, wherein the first horizontal attenuation rate is the observation data acquired by the first radar at a first elevation angle; The first horizontal attenuation rate is divided into multiple intervals according to a preset interval; Determine the median of the first differential reflectance corresponding to the plurality of intervals, with each interval corresponding to a median of the first differential reflectance; The first slope is obtained by linearly fitting the median of the first differential reflectance corresponding to the multiple intervals.

8. A precipitation estimation device, characterized in that, The device includes: The acquisition module is used to acquire a first data quality index and a second data quality index. The first data quality index is used to indicate the reliability of the observation data acquired by the first radar at different elevation angles, and the second data quality index is used to indicate the reliability of the observation data acquired by the second radar at different elevation angles. The determination module is used to determine the first fusion weight corresponding to the first radar based on the first data quality index, and to determine the second fusion weight corresponding to the second radar based on the second data quality index. The fusion module is used to fuse the first precipitation and the second precipitation based on the first fusion weight and the second fusion weight to obtain the final estimated precipitation of the target area. The first precipitation is the precipitation of the target area estimated based on the observation data of the first radar, and the second precipitation is the precipitation of the target area estimated based on the observation data of the second radar.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory being used to store instructions, and the processor executing the instructions stored in the memory to cause the computer device to perform the weather radar network precipitation estimation method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program, wherein the computer program, when executed by a processor, implements the weather radar network precipitation estimation method as described in any one of claims 1 to 7.

Images