Precipitation intensity estimation method, device, electronic device and storage medium

Abstract

The present invention provides a precipitation intensity estimation method, device, electronic device, and storage medium, belonging to the technical field of precipitation intensity estimation. The method comprises: determining a set of precipitation intensity data based on target raindrop spectrum data; determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data; constructing a precipitation estimation formula to be determined; fitting the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined using the least squares method, updating the parameters of the precipitation estimation formula to be determined, and obtaining the precipitation estimation formula; and inputting the current dual-polarization radar data into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula. The precipitation intensity estimation method provided by the present invention solves the technical problem of low precision in quantitative precipitation estimation in related art precipitation intensity estimation methods.

Description

Technical Field

[0001] The present invention relates to the technical field of precipitation intensity estimation, and in particular to a precipitation intensity estimation method, device, electronic device and storage medium. Background Art

[0002] Precipitation is one of the meteorological factors that has a significant impact on human production and life. In recent years, extreme precipitation events have increased significantly, and abnormal precipitation has caused frequent meteorological disasters such as floods and mudslides. Currently, the most direct and commonly used method to obtain precipitation information is rain gauge observation. Although single-point accuracy is high, due to the limitations of sampling points, it is not enough to accurately grasp the fine spatial distribution and intensity changes of precipitation.

[0003] Effective precipitation information can be obtained by detecting physical quantities related to precipitation in the atmosphere (radar estimation and satellite observation inversion). Among them, radar-based high-temporal and spatial resolution quantitative precipitation estimation greatly compensates for the limitations of conventional rainfall estimation due to its advantages such as wide coverage and high temporal resolution. When faced with extreme weather conditions such as heavy rainfall, the precipitation intensity estimation methods in related technologies cannot accurately estimate precipitation, and the accuracy of quantitative precipitation estimation is low.

[0004] It can be seen that the precipitation intensity estimation method in the related art has a technical problem of low precision in quantitative precipitation estimation. Summary of the Invention

[0005] The present invention provides a precipitation intensity estimation method, device, electronic device and storage medium, which are used to solve the technical problem of low precision of quantitative precipitation estimation in precipitation intensity estimation methods in related technologies.

[0006] The present invention provides a precipitation intensity estimation method, comprising the following steps: determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during the rainfall period in the target area; constructing a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; fitting the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined using a least squares method, updating parameters of the precipitation estimation formula to be determined, and obtaining a precipitation estimation formula; and inputting current dual-polarization radar data into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0007] According to a precipitation intensity estimation method provided by the present invention, the step of constructing a precipitation estimation formula to be determined includes: constructing a first precipitation estimation formula to be determined: Wherein, Z is the radar reflectivity factor, R is the precipitation intensity, and a and b are the parameters of the first precipitation estimation formula to be determined; construct the second precipitation estimation formula to be determined: wherein KDP is the differential phase change rate, R is the precipitation intensity, and c and d are parameters of the second precipitation estimation formula to be determined; the step of fitting the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined using the least squares method, updating the parameters of the precipitation estimation formula to be determined, and obtaining the precipitation estimation formula comprises: fitting the set of precipitation intensity data and the set of radar reflectivity factor data based on the first precipitation estimation formula to be determined using the least squares method, Updating parameters of the first precipitation estimation formula to be determined to obtain a first precipitation estimation formula; fitting the set of precipitation intensity data and the set of differential phase change rate data based on the second precipitation estimation formula to be determined using a least squares method, updating parameters of the second precipitation estimation formula to be determined to obtain a second precipitation estimation formula; and combining the first precipitation estimation formula and the second precipitation estimation formula based on a threat score to obtain the precipitation estimation formula, wherein the threat score is used to evaluate the performance of the first precipitation estimation formula and the second precipitation estimation formula.

[0008] According to a precipitation intensity estimation method provided by the present invention, the first precipitation estimation formula and the second precipitation estimation formula are combined based on the threat score to obtain the precipitation estimation formula, including: determining a first threat score for the first precipitation estimation formula based on the set of precipitation intensity data and the set of radar reflectivity factor data; determining a second threat score for the second precipitation estimation formula based on the set of precipitation intensity data and the set of differential phase change rate data; determining a first weight and a second weight based on the ratio of the first threat score to the second threat score, wherein the ratio of the first threat score to the second threat score is positively correlated with the ratio of the first weight to the second weight, and the sum of the first weight and the second weight is 1; and multiplying the first precipitation estimation formula by the first weight, and adding the resultant result of multiplying the second precipitation estimation formula by the second weight, to obtain the precipitation estimation formula.

[0009] According to a precipitation intensity estimation method provided by the present invention, before determining a set of precipitation intensity data based on target raindrop spectrum data, the method further includes: obtaining initial raindrop spectrum data; eliminating data in the initial raindrop spectrum data whose precipitation intensity is less than a preset precipitation intensity threshold; and / or eliminating data in the initial raindrop spectrum data whose total particle count is less than a preset particle number threshold; and determining the initial raindrop spectrum data after data elimination as processed raindrop spectrum data.

[0010] According to a precipitation intensity estimation method provided by the present invention, before determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, the method further includes: acquiring initial dual-polarization radar data; filtering the initial dual-polarization radar data to obtain processed dual-polarization radar data; and removing misaligned data from the processed raindrop spectrum data and the processed dual-polarization radar data to obtain the target raindrop spectrum data and the target dual-polarization radar data, wherein the misaligned data is data that exists only in the processed raindrop spectrum data or the processed dual-polarization radar data at the same data collection time.

[0011] According to a precipitation intensity estimation method provided by the present invention, the dual-polarization radar system uses a hybrid scanning technology to collect the target dual-polarization radar data. With the dual-polarization radar system as the center, the radar elevation angle values used for collecting radar data in an area within a first distance from the dual-polarization radar system include 0.5°, 1.5°, and 2.4°; the radar elevation angle values used for collecting radar data in an area within a second distance from the dual-polarization radar system beyond the first distance include 0.5° and 1.5°; and the radar elevation angle value used for collecting radar data in an area beyond the second distance from the dual-polarization radar system includes 0.5°; wherein the first distance is less than the second distance.

[0012] The present invention also provides a precipitation intensity estimation device, comprising the following modules: a first determination module, configured to determine a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; a second determination module, configured to determine a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during the rainfall period in the target area; a construction module, configured to construct a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is configured to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and precipitation intensity; a fitting module, configured to fit the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined using a least squares method, update parameters of the precipitation estimation formula to be determined, and obtain a precipitation estimation formula; and an execution module, configured to input current dual-polarization radar data into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0013] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the above-described precipitation intensity estimation methods is implemented.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described precipitation intensity estimation methods.

[0015] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements any one of the above-mentioned precipitation intensity estimation methods.

[0016] The precipitation intensity estimation method, device, electronic device, and storage medium provided by the present invention determine a set of precipitation intensity data based on target raindrop spectrum data; determine a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data; construct a precipitation estimation formula to be determined; use the least squares method to fit the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined, and update the parameters of the precipitation estimation formula to be determined. The obtained precipitation estimation formula can accurately reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity. The current dual-polarization radar data is input into the precipitation estimation formula, and the obtained estimated precipitation intensity can accurately reflect the precipitation intensity of the current time period. This solves the technical problem of low precision of quantitative precipitation estimation in precipitation intensity estimation methods in related technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced one by one below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 It is a flow chart of the precipitation intensity estimation method provided by the present invention.

[0019] Figure 2 It is a structural schematic diagram of the precipitation intensity estimation device provided by the present invention.

[0020] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] It should be noted that, in the description of the present invention, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element specified by the phrase "comprises a..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element. Terms such as "upper" and "lower" indicate positions or relationships based on those shown in the accompanying drawings and are intended solely to facilitate description and simplify the present invention. They are not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed, or operate in a specific orientation, and are therefore not to be construed as limiting the present invention. Unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be broadly construed, for example, to mean a fixed connection, a removable connection, or an integral connection; a mechanical connection or an electrical connection; a direct connection or an indirect connection through an intermediary; or internal communication between two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0023] The terms "first," "second," and so forth, used herein are used to distinguish similar objects, not to describe a specific order or precedence. It should be understood that such terms are interchangeable where appropriate, allowing embodiments of the present invention to be implemented in an order other than that illustrated or described herein. Furthermore, the terms "first," "second," and so forth generally distinguish objects of a single type, and do not limit the number of objects. For example, the first object may be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the connected objects.

[0024] Precipitation is one of the meteorological factors that has a significant impact on human production and life. In recent years, extreme precipitation events have increased significantly, and abnormal precipitation has caused frequent meteorological disasters such as floods and mudslides. Currently, the most direct and commonly used method to obtain precipitation information is rain gauge observation. Although single-point accuracy is high, due to the limitations of sampling points, it is not enough to accurately grasp the fine spatial distribution and intensity changes of precipitation.

[0025] Effective precipitation information can be obtained by detecting atmospheric physical quantities related to precipitation (radar estimation and satellite observation inversion). Radar-based high-temporal and spatial resolution quantitative precipitation estimation, with its wide coverage and high temporal resolution, significantly overcomes the limitations of conventional rainfall estimation. However, in extreme weather conditions such as heavy rainfall, precipitation intensity estimation methods used in related technologies cannot accurately estimate precipitation, resulting in low precision in quantitative precipitation estimation. This indicates that the precipitation intensity estimation methods used in related technologies suffer from the technical problem of low precision in quantitative precipitation estimation.

[0026] In order to at least solve some of the above problems, the following Figure 1-Figure 3 The present invention describes a precipitation intensity estimation method, device, electronic device, and storage medium.

[0027] The precipitation intensity estimation method provided in this embodiment can be applied to scenarios where precipitation intensity is estimated based on dual-polarization radar data.

[0028] The precipitation intensity estimation method in this embodiment can be executed by a server; the server obtains target raindrop spectrum data and target dual-polarization radar data, and after constructing a fitting precipitation estimation formula based on the acquired data, the server calculates and outputs the estimated precipitation intensity based on the current dual-polarization radar data.

[0029] Figure 1 It is a flow chart of the precipitation intensity estimation method provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps:

[0030] Step 101 : determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area.

[0031] In this embodiment, the target raindrop spectrum data is data collected by a laser raindrop spectrometer during the rainfall period in the target area; the laser raindrop spectrometer is used to measure the size and speed of precipitation particles (such as raindrops), and the target raindrop spectrum data may include data such as particle diameter, particle speed, particle mass, particle number, particle volume, and particle distribution of precipitation particles.

[0032] Determine a set of precipitation intensity data based on the target raindrop spectrum data; specifically, the precipitation intensity can be calculated according to the following formula: ; Where D represents the diameter of raindrops, N represents the number of raindrops with a diameter of D, V represents the velocity of raindrops, and I represents the precipitation intensity; after the precipitation intensity is calculated, the calculated precipitation intensity is converted into common units, such as millimeters per hour (mm / h).

[0033] Step 102 : determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by the dual-polarization radar system during a rainfall period in the target area.

[0034] Dual-polarization radar systems detect and classify precipitation particles in the atmosphere by sending and receiving horizontally and vertically polarized electromagnetic waves. This system provides richer information than traditional single-polarization radars and can more accurately estimate precipitation type, intensity, and distribution.

[0035] Target dual-polarization radar data is collected by the dual-polarization radar system during rainfall periods in the target area. This data includes radar reflectivity factor data and differential phase change rate data. The radar reflectivity factor is a measure of radar echo intensity, typically expressed as Z or dBZ (decibel reflectivity factor). It reflects the ability of precipitation particles to reflect radar waves and is an important parameter for estimating precipitation intensity and type. The differential phase change rate refers to the rate of change of the phase difference between the horizontally polarized wave and the vertically polarized wave with distance as the radar wave passes through the precipitation area. It is a parameter unique to dual-polarization radar and is the rate of change of the differential propagation phase with distance. It is typically expressed as KDP. KDP is related to the size of precipitation particles and can be used to estimate the average particle size.

[0036] It should be noted that the target raindrop spectrum data and target dual-polarization radar data are both collected during the same rainfall period in the target area.

[0037] Step 103: construct a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity.

[0038] A formula for estimating the precipitation to be determined is constructed, which links the radar reflectivity factor and the differential phase change rate to the precipitation intensity. The general form of the formula can be: I = f(Z, KDP); where I represents the precipitation intensity, f represents the functional relationship to be determined, Z represents the radar reflectivity factor, and KDP represents the differential phase change rate.

[0039] Step 104 : Fitting a set of precipitation intensity data, a set of radar reflectivity factor data, and a set of differential phase change rate data based on the precipitation estimation formula to be determined using the least squares method, updating the parameters of the precipitation estimation formula to be determined, and obtaining the precipitation estimation formula.

[0040] In this embodiment, the least squares method is used to perform parameter fitting on the precipitation estimation formula to be determined. This step requires a set of precipitation intensity data obtained in step 101, a set of radar reflectivity factor data obtained in step 102, and a set of differential phase change rate data as input. By minimizing the error between the observed value and the model prediction value, the parameters of the precipitation estimation formula to be determined are updated to obtain the final precipitation estimation formula.

[0041] Specifically, a least squares optimization algorithm (such as the gradient descent method, the Levenberg-Marquardt algorithm, etc.) is used to adjust the parameters of the precipitation estimation formula to be determined so as to minimize the error between the observed value and the model predicted value; in each iteration, the parameters of the precipitation estimation formula to be determined are updated according to the result of error minimization; a convergence criterion is set, for example, the error change is less than a preset threshold or the maximum number of iterations is reached; when the convergence criterion is met, the parameter update is stopped to obtain the final precipitation estimation formula.

[0042] Step 105: Input the current dual-polarization radar data into a precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0043] In this embodiment, the current dual-polarization radar data refers to the data collected by the dual-polarization radar system during the current rainfall period in the target area. The current dual-polarization radar data is input into the precipitation estimation formula to obtain the estimated precipitation intensity output by the precipitation estimation formula, and then the rainfall conditions in the target area during the current rainfall period can be monitored and evaluated based on the estimated precipitation intensity.

[0044] Optionally, the current dual-polarization radar data may also be data collected by the dual-polarization radar system during the current rainfall period, and the current dual-polarization radar data may not be limited to being obtained in the target area.

[0045] According to the embodiments provided by the present application, a set of precipitation intensity data is determined based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; a set of radar reflectivity factor data and a set of differential phase change rate data are determined based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during a rainfall period in the target area; a precipitation estimation formula to be determined is constructed, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data are fitted based on the precipitation estimation formula to be determined using the least squares method, the parameters of the precipitation estimation formula to be determined are updated, and the precipitation estimation formula is obtained; the current dual-polarization radar data is input into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula; the technical problem of low precision of quantitative precipitation estimation in precipitation intensity estimation methods in related technologies is solved, and the precision of quantitative precipitation estimation is improved.

[0046] As an optional solution, a precipitation estimation formula to be determined is constructed, including:

[0047] S11, construct the first precipitation estimation formula to be determined: ; Wherein, Z is the radar reflectivity factor, R is the precipitation intensity, and a and b are the parameters of the first precipitation estimation formula to be determined;

[0048] S12, constructing a second precipitation estimation formula to be determined: ; Wherein, KDP is the differential phase change rate, R is the precipitation intensity, and c and d are the parameters of the second precipitation estimation formula to be determined;

[0049] The least squares method is used to fit a set of precipitation intensity data, a set of radar reflectivity factor data, and a set of differential phase change rate data based on the precipitation estimation formula to be determined, and the parameters of the precipitation estimation formula to be determined are updated to obtain the precipitation estimation formula, including:

[0050] S21, fitting a set of precipitation intensity data and a set of radar reflectivity factor data based on the first precipitation estimation formula to be determined using a least squares method, updating parameters of the first precipitation estimation formula to be determined, and obtaining a first precipitation estimation formula;

[0051] S22, fitting the set of precipitation intensity data and the set of differential phase change rate data based on the second precipitation estimation formula to be determined using the least squares method, updating the parameters of the second precipitation estimation formula to be determined, and obtaining a second precipitation estimation formula;

[0052] S23 , combining the first precipitation estimation formula and the second precipitation estimation formula based on the threat score to obtain a precipitation estimation formula, wherein the threat score is used to evaluate the performance of the first precipitation estimation formula and the second precipitation estimation formula.

[0053] In this embodiment, the precipitation estimation formula to be determined includes a first precipitation estimation formula to be determined and a second precipitation estimation formula to be determined; the first precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the precipitation intensity; the second precipitation estimation formula to be determined is used to reflect the relationship between the differential phase change rate and the precipitation intensity.

[0054] It should be noted that the parameters a and b of the first precipitation estimation formula to be determined use initial values when constructing it, and the least squares method is used to fit a set of precipitation intensity data and a set of radar reflectivity factor data based on the first precipitation estimation formula to be determined. After updating the parameters of the first precipitation estimation formula to be determined, the parameters of the updated first precipitation estimation formula to be determined constitute the final first precipitation estimation formula; similarly, the parameters c and d of the second precipitation estimation formula to be determined use initial values when constructing it, and the least squares method is used to fit a set of precipitation intensity data and a set of differential phase change rate data based on the second precipitation estimation formula to be determined. After updating the parameters of the second precipitation estimation formula to be determined, the parameters of the updated second precipitation estimation formula to be determined constitute the final second precipitation estimation formula.

[0055] Furthermore, the first precipitation estimation formula and the second precipitation estimation formula are combined based on the threat score (TS) to obtain a precipitation estimation formula. The threat score is used to evaluate the performance of the first precipitation estimation formula and the second precipitation estimation formula. The threat score is an indicator used in meteorology to evaluate the accuracy of precipitation forecasts or estimates. It is a statistic used to measure the degree of match between predicted or estimated precipitation events and actually observed precipitation events. The threat score can be used to compare different precipitation estimation formulas or models to determine which formula or model performs better in predicting precipitation.

[0056] Specifically, the threat score calculation formula is as follows: ; Among them, Hits (H) refers to the number of events where precipitation is forecast and observed, Misses (M) refers to the number of events where precipitation is observed but not forecast, and False Alarms (F) refers to the number of events where precipitation is forecast but not observed; the threat score value ranges from 0 to 1, where 1 indicates a perfect forecast (i.e., all observed precipitation events are forecasted, with no misses or false alarms), and 0 indicates that the forecast is completely inaccurate.

[0057] Here, the performance of the first and second precipitation estimation formulas is evaluated based on the threat score, and the formulas can be merged based on their performance. The merged formula can use weighted averaging or other statistical techniques to ensure that the final precipitation estimation formula can combine the advantages of the two original formulas. For example, if the threat score of the first precipitation estimation formula is higher than that of the second precipitation estimation formula, the first precipitation estimation formula can be given a higher weight.

[0058] Through this embodiment, by separately constructing a first precipitation estimation formula to be determined and a second precipitation estimation formula to be determined, and then merging the two formulas based on the threat score to obtain a precipitation estimation formula, the performance of the precipitation estimation formula can be improved, thereby improving the accuracy and reliability of the precipitation intensity estimation method of this embodiment.

[0059] As an optional solution, the first precipitation estimation formula and the second precipitation estimation formula are combined based on the threat score to obtain a precipitation estimation formula, including:

[0060] S31, determining a first threat score of a first precipitation estimation formula based on a set of precipitation intensity data and a set of radar reflectivity factor data;

[0061] S32, determining a second threat score of a second precipitation estimation formula based on a set of precipitation intensity data and a set of differential phase change rate data;

[0062] S33, determining a first weight and a second weight based on a ratio of the first threat score to the second threat score, wherein the ratio of the first threat score to the second threat score is positively correlated with the ratio of the first weight to the second weight, and the sum of the first weight and the second weight is 1;

[0063] S34: multiply the first precipitation estimation formula by the first weight, and add the second precipitation estimation formula by the second weight to obtain a precipitation estimation formula.

[0064] In this embodiment, specific steps are described on how to combine the first precipitation estimation formula and the second precipitation estimation formula based on the threat score to obtain the precipitation estimation formula. In this embodiment, a weighted method is used to combine the first precipitation estimation formula and the second precipitation estimation formula.

[0065] A first threat score of a first precipitation estimation formula is determined based on a set of precipitation intensity data and a set of radar reflectivity factor data. Specifically, the set of radar reflectivity factor data can be input into the first precipitation estimation formula, and the output result of the first precipitation estimation formula is compared with the set of precipitation intensity data to determine the first threat score. Similarly, a second threat score of a second precipitation estimation formula is determined based on a set of precipitation intensity data and a set of differential phase change rate data. Specifically, the set of differential phase change rate data can be input into the second precipitation estimation formula, and the output result of the second precipitation estimation formula is compared with the set of precipitation intensity data to determine the second threat score.

[0066] The first threat score and the second threat score represent the performance of the formula. The first weight and the second weight are determined based on the ratio of the first threat score to the second threat score. The ratio of the first threat score to the second threat score is positively correlated with the ratio of the first weight to the second weight, and the sum of the first weight and the second weight is 1. Specifically, a higher ratio of the first threat score to the second threat score indicates that the performance of the first precipitation estimation formula is stronger than that of the second precipitation estimation formula, and therefore the first weight can be increased. Optionally, the ratio of the first threat score to the second threat score is the same as the ratio of the first weight to the second weight.

[0067] Through this embodiment, the first weight and the second weight can be accurately determined, and then the precipitation estimation formula can be accurately obtained, thereby improving the accuracy and reliability of the precipitation intensity estimation method of this embodiment.

[0068] As an optional solution, before determining a set of precipitation intensity data based on the target raindrop spectrum data, the method further includes:

[0069] S41, obtaining initial raindrop spectrum data;

[0070] S42, removing data from the initial raindrop spectrum data whose precipitation intensity is less than a preset precipitation intensity threshold; and / or removing data from the initial raindrop spectrum data whose total particle count is less than a preset particle count threshold;

[0071] S43, determining the initial raindrop spectrum data after data elimination as processed raindrop spectrum data.

[0072] In this embodiment, before determining a set of precipitation intensity data based on the target raindrop spectrum data, it is also necessary to obtain initial raindrop spectrum data data. Here, the initial raindrop spectrum data data refers to the initial data of all raindrop spectrum data collected by the laser raindrop spectrometer during the rainfall period in the target area.

[0073] Eliminate data in the initial raindrop spectrum data whose precipitation intensity is less than a preset precipitation intensity threshold, optionally, the preset precipitation intensity threshold is 0.5 mm / h; and / or eliminate data in the initial raindrop spectrum data whose total number of particles is less than a preset particle number threshold, optionally, the preset particle number threshold is 50.

[0074] Furthermore, the initial raindrop spectrum data after data elimination is determined as the processed raindrop spectrum data. It can be understood that the quality of the processed raindrop spectrum data can be improved through the quality control steps of this embodiment. Specifically, by eliminating data with precipitation intensity less than a preset precipitation intensity threshold, it is possible to exclude cases where precipitation is very slight, which may not have a significant impact on the estimation of the total precipitation amount, and can also reduce the impact of noise and error on the analysis results. By eliminating data with a total particle number less than a preset particle number threshold, it is possible to exclude cases where data collection is incomplete or the sample size is too small, because these data may not have statistical significance and cannot accurately reflect the precipitation characteristics.

[0075] Through this embodiment, the quality of raindrop spectrum data can be improved, the influence of noise and error on the analysis results can be reduced, and the accuracy and reliability of the precipitation intensity estimation method of this embodiment can be improved.

[0076] As an optional solution, before determining a set of radar reflectivity factor data and a set of differential phase change rate data based on the target dual-polarization radar data, the method further includes:

[0077] S51, acquiring initial dual-polarization radar data;

[0078] S52, performing filtering processing on the initial dual-polarization radar data to obtain processed dual-polarization radar data;

[0079] S53, removing misaligned data from the processed raindrop spectrum data and the processed dual-polarization radar data to obtain target raindrop spectrum data and target dual-polarization radar data, wherein the misaligned data is data that only exists in the processed raindrop spectrum data or the processed dual-polarization radar data at the same data collection time.

[0080] In this embodiment, before determining a set of radar reflectivity factor data and a set of differential phase change rate data based on the target dual-polarization radar data, it is also necessary to obtain initial dual-polarization radar data. Here, the initial dual-polarization radar data refers to the initial data collected by the dual-polarization radar system during the rainfall period in the target area.

[0081] The initial dual-polarization radar data is then filtered to produce processed dual-polarization radar data. This process aims to improve data quality and reduce the effects of noise and outliers. Specific operations include applying digital filters to remove random noise; detecting and removing outliers, such as non-precipitation echoes caused by radar sidelobe echoes and ground object echoes; smoothing the data to reduce data fluctuations caused by radar system or environmental factors; and correcting the data to eliminate systematic errors introduced by radar hardware or atmospheric propagation effects.

[0082] The time of the data in the initial raindrop spectrum data and the initial dual-polarization radar data corresponds, that is, both reflect the precipitation characteristics of the target area during the target period; however, the processed raindrop spectrum data and the processed dual-polarization radar data obtained through the above steps have eliminated some data compared to their respective initial data, so they may each contain some misaligned data. Here, misaligned data refers to data that only exists in the processed raindrop spectrum data or the processed dual-polarization radar data at the same data collection time. By eliminating the misaligned data in the processed raindrop spectrum data and the processed dual-polarization radar data, the acquisition times of the raindrop spectrum data and the dual-polarization radar data can be made to correspond one to one, and then can be used as fitting data for fitting the precipitation formula to be determined.

[0083] Through this embodiment, the accuracy and quality of the obtained target raindrop spectrum data and target dual-polarization radar data can be improved, thereby improving the accuracy and reliability of the precipitation intensity estimation method of this embodiment.

[0084] As an optional solution, the dual-polarization radar system uses a hybrid scanning technology to collect target dual-polarization radar data. With the dual-polarization radar system as the center, the radar elevation angle values used for collecting radar data in an area within a first distance from the dual-polarization radar system include 0.5°, 1.5°, and 2.4°; the radar elevation angle values used for collecting radar data in an area outside the first distance but within a second distance from the dual-polarization radar system include 0.5° and 1.5°; the radar elevation angle value used for collecting radar data in an area outside the second distance from the dual-polarization radar system includes 0.5°; wherein the first distance is less than the second distance.

[0085] In this embodiment, hybrid scanning technology is a radar data processing method used to solve the problem of terrain obstruction. In particular, in areas with complex terrain such as mountainous areas, the radar beam at low elevation angles may be blocked by the terrain, resulting in the loss of Z (radar reflectivity factor) and KDP (differential phase change rate) data; therefore, different elevation angles (such as 0.5°, 1.5° and 2.4°) can be used during data collection to scan using a dual-polarization radar system to obtain precipitation data within different distance ranges.

[0086] Optionally, the first distance is 50 km and the second distance is 115 km.

[0087] Specifically, with the dual-polarization radar system as the center, within a range of 0-50 km from the dual-polarization radar system, data can be collected at elevation angles of 0.5°, 1.5°, and 2.4°; the average value of the data collected at these three elevation angles is calculated as the radar data within this distance range.

[0088] Furthermore, when the distance to the dual-polarization radar system is within a range of 50-115 km, data can be collected at elevation angles of 0.5° and 1.5°; the average value of the data collected at these two elevation angles is calculated as the radar data within the distance range.

[0089] Furthermore, when the distance from the dual-polarization radar system is beyond 115 km, an elevation angle of 0.5° can be used for data collection. Because the radar beam at a high elevation angle may be affected by more severe signal attenuation and ground clutter at a longer distance, using only the reflectivity value at an elevation angle of 0.5° can improve the quality of the collected radar data.

[0090] Through this embodiment, the quality of the acquired radar data can be effectively improved, thereby improving the accuracy and reliability of the precipitation intensity estimation method of this embodiment.

[0091] Figure 2 Schematic diagram of the structure of the precipitation intensity estimation device provided by the present invention, such as Figure 2 As shown, including but not limited to the following modules:

[0092] A first determining module 201 is configured to determine a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area;

[0093] a second determining module 202 for determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by the dual-polarization radar system during a rainfall period in the target area;

[0094] A construction module 203 is used to construct a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity;

[0095] A fitting module 204 is configured to fit a set of precipitation intensity data, a set of radar reflectivity factor data, and a set of differential phase change rate data based on the precipitation estimation formula to be determined using a least squares method, update parameters of the precipitation estimation formula to be determined, and obtain the precipitation estimation formula;

[0096] The execution module 205 is configured to input the current dual-polarization radar data into a precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0097] According to an embodiment of the present application, a set of precipitation intensity data is determined based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; a set of radar reflectivity factor data and a set of differential phase change rate data are determined based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during a rainfall period in the target area; a precipitation estimation formula to be determined is constructed, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data are fitted based on the precipitation estimation formula to be determined using the least squares method, the parameters of the precipitation estimation formula to be determined are updated, and the precipitation estimation formula is obtained; the current dual-polarization radar data is input into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula; the technical problem of low precision of quantitative precipitation estimation in the precipitation intensity estimation method in the related art is solved, and the precision of quantitative precipitation estimation is improved.

[0098] It should be noted that the precipitation intensity estimation device provided by the present invention can execute the precipitation intensity estimation method of any of the above embodiments during specific operation, which will not be described in detail in this embodiment.

[0099] Figure 3 Schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 3As shown, the electronic device may include: a processor 310 , a communications interface 320 , a memory 330 and a communication bus 340 , wherein the processor 310 , the communications interface 320 and the memory 330 communicate with each other via the communication bus 340 . The processor 310 can call logic instructions in the memory 330 to execute a precipitation intensity estimation method, which includes: determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during a rainfall period in the target area; constructing a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; using the least squares method to fit the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined, updating the parameters of the precipitation estimation formula to be determined, and obtaining the precipitation estimation formula; and inputting the current dual-polarization radar data into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0100] Furthermore, the logic instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0101] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the precipitation intensity estimation method provided by the above embodiments, which includes: determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein The target dual-polarization radar data is data collected by the dual-polarization radar system during the rainfall period in the target area; a precipitation estimation formula to be determined is constructed, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; a set of precipitation intensity data, a set of radar reflectivity factor data, and a set of differential phase change rate data are fitted based on the precipitation estimation formula to be determined using the least squares method, and the parameters of the precipitation estimation formula to be determined are updated to obtain the precipitation estimation formula; the current dual-polarization radar data is input into the precipitation estimation formula to obtain the estimated precipitation intensity output by the precipitation estimation formula.

[0102] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program is implemented to perform the precipitation intensity estimation method provided in the above embodiments, the method comprising: determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during a rainfall period in the target area; constructing a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; using the least squares method to fit the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined, updating the parameters of the precipitation estimation formula to be determined, and obtaining the precipitation estimation formula; and inputting the current dual-polarization radar data into the precipitation estimation formula to obtain an estimated precipitation intensity output by the precipitation estimation formula.

[0103] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. That is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0104] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods of each embodiment or certain portions of the embodiments.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A precipitation intensity estimation method, characterized in that: include: Determining a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; Determining a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by the dual-polarization radar system during the rainfall period in the target area; Construct the first precipitation estimation formula to be determined: ; Wherein, Z is the radar reflectivity factor, R is the precipitation intensity, and a and b are the parameters of the first precipitation estimation formula to be determined; Construct the second precipitation estimation formula to be determined: ; Wherein, KDP is the differential phase change rate, R is the precipitation intensity, and c and d are the parameters of the second precipitation estimation formula to be determined; fitting the set of precipitation intensity data and the set of radar reflectivity factor data based on the first precipitation estimation formula to be determined using a least squares method, updating parameters of the first precipitation estimation formula to be determined, and obtaining a first precipitation estimation formula; fitting the set of precipitation intensity data and the set of differential phase change rate data based on the second precipitation estimation formula to be determined using a least squares method, updating parameters of the second precipitation estimation formula to be determined, and obtaining a second precipitation estimation formula; determining a first threat score for the first precipitation estimation formula based on the set of precipitation intensity data and the set of radar reflectivity factor data; determining a second threat score of the second precipitation estimation formula based on the set of precipitation intensity data and the set of differential phase rate of change data; determining a first weight and a second weight according to a ratio of the first threat score to the second threat score, wherein the ratio of the first threat score to the second threat score is positively correlated with the ratio of the first weight to the second weight, and the sum of the first weight and the second weight is 1; multiplying the first precipitation estimation formula by the first weight, and adding the second precipitation estimation formula by the second weight to obtain the precipitation estimation formula; The current dual-polarization radar data is input into the precipitation estimation formula to obtain the estimated precipitation intensity output by the precipitation estimation formula.

2. The precipitation intensity estimation method according to claim 1, characterized in that: Before determining a set of precipitation intensity data based on the target raindrop spectrum data, the method further includes: Obtain initial raindrop spectrum data; Eliminating data from the initial raindrop spectrum data whose precipitation intensity is less than a preset precipitation intensity threshold; and / or eliminating data from the initial raindrop spectrum data whose total particle count is less than a preset particle count threshold; The initial raindrop spectrum data after data elimination is determined as the processed raindrop spectrum data data.

3. The precipitation intensity estimation method according to claim 2, characterized in that: Before determining a set of radar reflectivity factor data and a set of differential phase change rate data based on the target dual-polarization radar data, the method further includes: Acquire initial dual-polarization radar data; performing filtering processing on the initial dual-polarization radar data to obtain processed dual-polarization radar data; The misaligned data in the processed raindrop spectrum data and the processed dual-polarization radar data are eliminated to obtain the target raindrop spectrum data and the target dual-polarization radar data, wherein the misaligned data is data that only exists in the processed raindrop spectrum data or the processed dual-polarization radar data at the same data collection time.

4. The precipitation intensity estimation method according to any one of claims 1 to 3, characterized in that: The dual-polarization radar system uses a hybrid scanning technology to collect dual-polarization radar data of the target. With the dual-polarization radar system as the center, the radar elevation angle values used for collecting radar data in an area within a first distance from the dual-polarization radar system include 0.5°, 1.5°, and 2.4°; the radar elevation angle values used for collecting radar data in an area within a second distance outside the first distance from the dual-polarization radar system include 0.5° and 1.5°; the radar elevation angle value used for collecting radar data in an area outside the second distance from the dual-polarization radar system includes 0.5°; wherein the first distance is less than the second distance.

5. A precipitation intensity estimation device for executing the precipitation intensity estimation method according to any one of claims 1 to 4, characterized in that: include: A first determination module is configured to determine a set of precipitation intensity data based on target raindrop spectrum data, wherein the target raindrop spectrum data is data collected by a laser raindrop spectrometer during a rainfall period in a target area; a second determining module, configured to determine a set of radar reflectivity factor data and a set of differential phase change rate data based on target dual-polarization radar data, wherein the target dual-polarization radar data is data collected by a dual-polarization radar system during the rainfall period in the target area; A construction module is used to construct a precipitation estimation formula to be determined, wherein the precipitation estimation formula to be determined is used to reflect the relationship between the radar reflectivity factor and the differential phase change rate, and the precipitation intensity; a fitting module, configured to fit the set of precipitation intensity data, the set of radar reflectivity factor data, and the set of differential phase change rate data based on the precipitation estimation formula to be determined using a least squares method, update parameters of the precipitation estimation formula to be determined, and obtain a precipitation estimation formula; The execution module is used to input the current dual-polarization radar data into the precipitation estimation formula to obtain the estimated precipitation intensity output by the precipitation estimation formula.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the precipitation intensity estimation method according to any one of claims 1 to 4 is implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the precipitation intensity estimation method according to any one of claims 1 to 4 is implemented.

8. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the precipitation intensity estimation method according to any one of claims 1 to 4 is implemented.

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