Rainfall early warning and classification method based on cloud radar

Abstract

The embodiment of the invention provides a rainfall early warning and classification method based on cloud radar. Relates to the field of atmospheric detection and remote sensing. The method comprises the following steps: detecting profile data of the hydrogel in real time through a cloud radar, and carrying out data processing and quality control to obtain real-time base data; judging the echo grounding condition at the current moment according to the real-time base data; if echoes at the current moment are grounded, inputting characteristic parameters at the current moment into a rainfall classification model, and obtaining a rainfall type at the current moment; if the echoes at the current moment are not grounded and the hydrogel profile data show that single echoes appear, calculating the centroid heights of corresponding echo areas at different moments by using the base data within the time span of the single echoes; according to the centroid heights of the echo areas corresponding to different moments, a fitting function is used for fitting the change relation of the centroid heights along with the time lapse; and determining a time lapse amount corresponding to intersection of the centroid height and the ground according to the fitting function, and predicting rainfall time according to the time lapse amount. Therefore, rainfall is accurately classified, and rainfall time is accurately predicted.

Description

Technical Field

[0001] The present disclosure relates to the fields of atmospheric detection and atmospheric remote sensing, and in particular to a precipitation warning and classification method based on cloud radar. Background Art

[0002] Precipitation refers to a weather process in which water vapor in the air condenses into clouds, cloud droplets collide and grow under specific conditions, and finally fall to the ground in the form of rain, snow, hail or other hydrometeors. Precipitation is affected by multiple factors such as geographical environment, atmospheric circulation, and weather system. It is the most basic link in the water cycle process and the basic parameter in the water balance equation. The uneven spatial distribution of precipitation and the instability of temporal changes are the direct causes of floods, waterlogging, and droughts. Conventional equipment for monitoring precipitation mainly includes tipping bucket rain gauges, weighing precipitation sensors, raindrop spectrometers, etc. Among them, tipping bucket rain gauges and weighing precipitation sensors only output precipitation data when the precipitation is greater than their resolution. They cannot effectively monitor trace precipitation, resulting in deviations in precipitation time. Due to the small sampling area, the raindrop spectrometer is not ideal for identifying precipitation types such as trace precipitation and hail. In addition, accurately predicting precipitation time and determining precipitation periods have always been the focus and difficulty of meteorological observation and services.

[0003] Millimeter-wave cloud radars operating in the Ka (35GHz) band can penetrate thicker clouds and obtain minute-level hydrometeor profiles by utilizing the scattering characteristics of electromagnetic waves when in vertical overhead observation mode, thus achieving refined observation of the entire evolution process of water vapor forming clouds and causing rain. Before precipitation, the precipitation time can be predicted by fitting and extrapolating the evolution characteristics of the cloud system, and during the precipitation period, the fuzzy logic combination of the basic data characteristics can be used to classify the precipitation type. Summary of the invention

[0004] The present invention provides a precipitation warning and classification method, device, equipment and storage medium based on cloud radar.

[0005] According to a first aspect of the present disclosure, a precipitation warning and classification method based on cloud radar is provided. The method comprises:

[0006] Real-time detection of hydrometeor profile data by cloud radar, data processing and quality control of the real-time detected hydrometeor profile data, and acquisition of real-time basic data;

[0007] Determine the echo grounding state at the current moment according to the real-time base data;

[0008] If the echo is grounded at the current moment, the characteristic parameters at the current moment are input into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein the characteristic parameters include base data and ground air temperature;

[0009] If the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a single echo, the base data within the time span of the single echo is used to calculate the centroid height of the echo area corresponding to different moments within the time span of the single echo;

[0010] According to the centroid height of the echo area corresponding to different moments within the time span of the single echo, the relationship between the centroid height and the time elapse is fitted using a fitting function;

[0011] The time lapse corresponding to the intersection of the centroid height and the ground is determined according to the fitting function; the precipitation time is predicted according to the time lapse and a precipitation warning is issued.

[0012] In some implementations of the first aspect, the base data includes:

[0013] Reflectivity factor, radial velocity, velocity spectrum width.

[0014] In some implementable manners of the first aspect, determining the echo grounding state at a current moment according to the real-time base data includes:

[0015] If the mean value of the reflectivity factor within the preset altitude range at the current moment is greater than or equal to 0dBZ, and the ratio of the number of echoes with radial velocity greater than the preset velocity threshold at the current moment to the total number of echoes within the preset altitude range at the current moment is greater than or equal to 70%, then the echo is grounded at the current moment; wherein, the echo grounding indicates precipitation has occurred.

[0016] In some implementations of the first aspect, the centroid heights of the echo regions corresponding to the time span of the single echo at different times are calculated using the basic data within the time span of the single echo by the following formula:

[0017]

[0018] Among them, γ m is the centroid height of the echo area corresponding to time m; wherein the echo area corresponding to time m is the area covered by the echo signal in the time period from time mn to time m; the echo area includes at least one monomer echo;

[0019] Z ij H is the reflectivity factor corresponding to the distance library with height j at time i; ij Z ij The height at which the echo is located; b represents the lower boundary of the echo area corresponding to time i; t represents the upper boundary of the echo area corresponding to time x.

[0020] In some implementations of the first aspect, the precipitation classification model is established by the following method:

[0021] The characteristic parameters at each moment of historical precipitation are used as training sample sets, and the precipitation types at each moment of historical precipitation are used as annotations;

[0022] The precipitation classification model is trained according to the training sample set and annotations; wherein,

[0023] The precipitation classification model adopts fuzzy logic algorithm.

[0024] In some implementations of the first aspect, the fuzzy logic algorithm is implemented by the following steps:

[0025] Establishing a membership function between each parameter in the characteristic parameters at each moment during the historical precipitation period and the precipitation type corresponding to each moment during the historical precipitation period, performing fuzzy processing on the parameters corresponding to each membership function, and obtaining a fuzzy set corresponding to each parameter in the characteristic parameters at each moment during the historical precipitation period;

[0026] Perform fuzzy reasoning on the fuzzy set according to preset fuzzy rules to obtain a fuzzy conclusion;

[0027] The fuzzy conclusion is defuzzified using a conversion function to obtain the precipitation type corresponding to each moment during the historical precipitation period.

[0028] In some implementations of the first aspect, the monomer echo time span is a time period from the appearance of the monomer echo to the end of the monomer echo or the grounding of the monomer echo.

[0029] In some possible implementations of the first aspect, predicting the precipitation time according to the time lapse amount and issuing a precipitation warning includes:

[0030] The sum of the time lapse corresponding to the intersection of the centroid height with the ground and the time when the single-echo appears is taken as the predicted precipitation time, and precipitation warning is issued based on the predicted precipitation time.

[0031] In some implementations of the first aspect, the data processing includes analog-to-digital conversion, pulse compression, coherent integration, fast Fourier transform, incoherent integration, and moment estimation;

[0032] The quality control includes the identification and elimination of pulse compression range sidelobe echoes and clear sky echoes, filtering and denoising, time and space continuity checking, and extreme value checking.

[0033] According to a second aspect of the present disclosure, a precipitation warning and classification device based on cloud radar is provided. The device comprises:

[0034] A base data acquisition module is used to detect hydrometeor profile data in real time through cloud radar, perform data processing and quality control on the real-time detected hydrometeor profile data, and obtain real-time base data;

[0035] An echo grounding determination module is used to determine the echo grounding state at the current moment according to the real-time base data;

[0036] A precipitation classification module is used to input the characteristic parameters at the current moment into a pre-established precipitation classification model to obtain the precipitation type at the current moment if the echo is grounded at the current moment; wherein the characteristic parameters include base data and ground air temperature;

[0037] The centroid height calculation module is used to calculate the centroid height of the echo area corresponding to different times within the time span of the monomer echo using the basic data within the time span of the monomer echo if the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a monomer echo;

[0038] The function fitting module is used to fit the relationship between the centroid height and the time lapse according to the centroid height of the echo area corresponding to different moments within the time span of the single echo using the fitting function;

[0039] The precipitation time prediction module is used to determine the time lapse corresponding to the intersection of the centroid height and the ground according to the fitting function; predict the precipitation time according to the time lapse and issue a precipitation warning.

[0040] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor; and a memory connected to the at least one processor in communication; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can perform the method described above.

[0041] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the method as described above.

[0042] In the present disclosure, the hydrometeor profile data is detected in real time by cloud radar, and the real-time detected hydrometeor profile data is processed and quality controlled to obtain real-time base data; the echo grounding state at the current moment is judged according to the real-time base data; if the echo is grounded at the current moment, the characteristic parameters at the current moment are input into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein, the characteristic parameters include base data and ground air temperature; if the echo is not grounded at the current moment and the hydrometeor profile data show the presence of a single echo, the centroid height of the echo area corresponding to different moments within the time span of the single echo is calculated using the base data within the time span of the single echo; according to the centroid height of the echo area corresponding to different moments within the time span of the single echo, the relationship between the centroid height and the time lapse is fitted using a fitting function; the time lapse corresponding to the intersection of the centroid height and the ground is determined according to the fitting function; the precipitation time is predicted according to the time lapse and a precipitation warning is issued. In this way, precipitation can be accurately classified, especially trace precipitation can be identified very accurately, which helps technicians to understand the precipitation type in the precipitation area in real time and accurately predict the precipitation time.

[0043] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0045] Figure 1 A flow chart of a precipitation warning and classification method based on cloud radar provided in an embodiment of the present disclosure is shown;

[0046] Figure 2 A schematic diagram showing the relationship between the centroid height of the echo region corresponding to different moments within the time span of a single echo provided by an embodiment of the present disclosure and the time lapse amount;

[0047] Figure 3 A schematic diagram of a classification result based on a fuzzy logic algorithm provided by an embodiment of the present disclosure is shown;

[0048] Figure 4 A structural diagram of a precipitation warning and classification device based on cloud radar provided in an embodiment of the present disclosure is shown;

[0049] Figure 5 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0051] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0052] In view of the problems in the background technology, the embodiment of the present disclosure provides a precipitation warning and classification method and device based on cloud radar. Specifically, the cloud radar detects hydrometeor profile data in real time, performs data processing and quality control on the real-time detected hydrometeor profile data, and obtains real-time base data; the echo grounding state at the current moment is judged according to the real-time base data; if the echo is grounded at the current moment, the characteristic parameters at the current moment are input into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein the characteristic parameters include base data and ground air temperature; if the echo is not grounded at the current moment and the hydrometeor profile data shows that a single echo appears, the base data within the single echo time span is used to calculate the centroid height of the echo area corresponding to different moments within the single echo time span; according to the centroid height of the echo area corresponding to different moments within the single echo time span, the fitting function is used to fit the relationship between the centroid height and the time lapse; according to the fitting function, the time lapse corresponding to the intersection of the centroid height and the ground is determined; according to the time lapse, the precipitation time is predicted and a precipitation warning is issued. In this way, precipitation can be accurately classified, especially trace precipitation can be identified very accurately, which helps technicians to understand the precipitation type in the precipitation area in real time and accurately predict the precipitation time.

[0053] The following is a detailed description of the cloud radar-based precipitation warning and classification method and device provided by the embodiments of the present disclosure through specific embodiments in conjunction with the accompanying drawings.

[0054] Figure 1 A flowchart of a precipitation warning and classification method based on cloud radar provided in an embodiment of the present disclosure is shown. The method 100 includes the following steps:

[0055] S110, detecting hydrometeor profile data in real time by using a cloud radar, performing data processing and quality control on the real-time detected hydrometeor profile data, and acquiring real-time basic data.

[0056] In some embodiments, the base data includes:

[0057] Reflectivity factor, radial velocity, velocity spectrum width.

[0058] In some embodiments, data processing includes analog-to-digital conversion, pulse compression, coherent integration, fast Fourier transform, incoherent integration, and moment estimation;

[0059] Quality control, including the identification and elimination of pulse compression range sidelobe echoes and clear sky echoes, filtering and denoising, time and space continuity checks, and extreme value checks.

[0060] In some embodiments, the hydrometeor profile data is detected in real time by a vertical headspace observation mode of a cloud radar;

[0061] Furthermore, the temporal resolution of the hydrometeor profile data detected by the cloud radar is less than or equal to 1 minute, and the spatial resolution is less than or equal to 30 meters.

[0062] S120, determining the echo grounding state at the current moment according to the real-time basic data.

[0063] In some embodiments, judging the echo grounding state at the current moment according to the real-time base data includes:

[0064] If the mean value of the reflectivity factor within the preset altitude range at the current moment is greater than or equal to 0dBZ, and the ratio of the number of echoes with radial velocity greater than the preset velocity threshold at the current moment to the total number of echoes within the preset altitude range at the current moment is greater than or equal to 70%, then the echo is grounded at the current moment; otherwise, the echo is not grounded at the current moment; wherein, the echo grounding indicates precipitation has occurred.

[0065] S130, if the echo is grounded at the current moment, input the characteristic parameters at the current moment into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein the characteristic parameters include base data and ground air temperature.

[0066] In some embodiments, the surface air temperature is obtained by an automatic weather station.

[0067] In some embodiments, the precipitation classification model is established by:

[0068] The characteristic parameters at each moment of historical precipitation are used as training sample sets, and the precipitation types at each moment of historical precipitation are used as annotations;

[0069] The precipitation classification model is trained according to the training sample set and annotations; wherein,

[0070] The precipitation classification model adopts fuzzy logic algorithm;

[0071] Further, the fuzzy logic algorithm is implemented by the following steps:

[0072] Establishing a membership function between each parameter in the characteristic parameters at each moment during the historical precipitation period and the precipitation type corresponding to each moment during the historical precipitation period, performing fuzzy processing on the parameters corresponding to each membership function, and obtaining a fuzzy set corresponding to each parameter in the characteristic parameters at each moment during the historical precipitation period;

[0073] Perform fuzzy reasoning on the fuzzy set according to preset fuzzy rules to obtain a fuzzy conclusion;

[0074] The fuzzy conclusion is defuzzified using a conversion function to obtain the precipitation type corresponding to each moment during the historical precipitation period.

[0075] In some embodiments, fuzzy logic functions are mainly used for fuzzy reasoning; wherein, the fuzzy logic functions include S-shaped functions, bell-shaped functions, triangle functions, and trapezoidal functions; this embodiment uses an asymmetric trapezoidal function as the fuzzy logic function.

[0076] In some embodiments, the characteristic parameters at different times are input into the precipitation classification model, and the classification results obtained are as follows: Figure 3 As shown, the weighted sum of the probability of snowfall caused by the reflectivity factor Z (in dBZ), radial velocity V (in m / s), velocity spectrum width W (in m / s), and ground air temperature T (in °C) at a certain moment meets the first probability threshold, indicating that snowfall occurs. For example, the probability of snowfall when Z is -5 dBZ is 1, the probability of snowfall when V is -1 m / s is 1, the probability of snowfall when W is 4 m / s is 1, and the probability of snowfall when T is -5 °C is 1. The value after the weighted sum of these four probabilities meets the first probability threshold, indicating that snowfall occurs when Z = -5 dBZ, V = -1 m / s, W = 4 m / s, and T = -5 °C;

[0077] The weighted sum of the probability of rainfall occurring due to the reflectivity factor Z, radial velocity V, velocity spectrum width W, and ground air temperature T at a certain moment satisfies the second probability threshold, indicating rainfall has occurred. For example, the probability of rainfall occurring when Z is 16 dBZ is 1, the probability of rainfall occurring when V is -3 m / s is 1, the probability of rainfall occurring when W is 1 m / s is 1, and the probability of rainfall occurring when T is 8°C is 1. The weighted sum of these four probabilities satisfies the second probability threshold, indicating that rainfall has occurred when Z = 16 dBZ, V = -3 m / s, W = 1 m / s, and T = 8°C.

[0078] The weighted sum of the probability of heavy rainfall caused by the reflectivity factor Z, radial velocity V, velocity spectrum width W, and ground air temperature T at a certain moment meets the third probability threshold, indicating that heavy rainfall has occurred. For example, the probability of heavy rainfall when Z is 45dBZ is 1, the probability of heavy rainfall when V is -10m / s is 1, the probability of heavy rainfall when W is 3m / s is 1, and the probability of heavy rainfall when T is 10℃ is 1. The value of the weighted sum of these four probabilities meets the third probability threshold, indicating that heavy rainfall occurs when Z = 45dBZ, V = -10m / s, W = 3m / s, and T = 10℃.

[0079] The weighted sum of the probabilities of hail occurring due to the reflectivity factor Z, radial velocity V, velocity spectrum width W, and ground air temperature T at a certain moment meets the fourth probability threshold, indicating that hail has occurred. For example, the probability of hail occurring is 1 when Z is 42dBZ, the probability of hail occurring is 1 when V is 13m / s, the probability of hail occurring is 1 when W is 4m / s, and the probability of hail occurring is 1 when T is 10℃. The weighted sum of these four probabilities meets the fourth probability threshold, indicating that hail occurs when Z=42dBZ, V=13m / s, W=4m / s, and T=10℃.

[0080] In some embodiments, the precipitation type includes rain, heavy rain, snow, and hail.

[0081] In some embodiments, the precipitation type at each moment during the historical precipitation period is obtained from a precipitation phenomenon meter and an automatic weather station's hourly precipitation; specifically, the precipitation is divided into rain, snow, and hail according to the precipitation phenomenon meter, and the precipitation is further divided into rain and heavy rain according to the automatic weather station's hourly precipitation.

[0082] In some embodiments, precipitation classification can be accurately and conveniently achieved using a precipitation classification model.

[0083] S140, if the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a single echo, the base data within the time span of the single echo is used to calculate the centroid heights of the echo regions corresponding to different moments within the time span of the single echo.

[0084] In some embodiments, the monomer echo time span is a time period from the appearance of the monomer echo to the end of the monomer echo or the monomer echo grounding.

[0085] In some embodiments, the centroid heights of the echo regions corresponding to different moments in the time span of the single echo are calculated using the basic data in the time span of the single echo by the following formula:

[0086]

[0087] Among them, γ mis the centroid height of the echo area corresponding to time m; wherein, the echo area corresponding to time m is the area covered by the echo signal in the time period from time mn to time m (n is an integer, in minutes); the echo area includes at least one monomer echo;

[0088] Z ij H is the reflectivity factor corresponding to the distance library with height j at time i; ij Z ij The height at which the echo is located; b represents the lower boundary of the echo area corresponding to time i; t represents the upper boundary of the echo area corresponding to time i;

[0089] Furthermore, the centroid of the echo region corresponding to time m is taken as the centroid of the echo at time m.

[0090] S150, fitting the relationship between the centroid height and the passage of time using a fitting function according to the centroid height of the echo area corresponding to different moments within the time span of the single echo.

[0091] In some embodiments, a linear or nonlinear fitting function may be used to fit the relationship between the change in the centroid height and the amount of time, such as Figure 2 As shown, the horizontal axis represents the time lapse N corresponding to time i i , the ordinate represents the centroid height γ of the echo area corresponding to time i i , the center of mass height corresponding to the center of mass of the echo at the first moment is γ1, and the time lapse is N1. The center of mass height corresponding to the center of mass of the echo at the second moment is γ2, and the time lapse is N2. The center of mass height corresponding to the center of mass of the echo at the nth moment is γ n , the time lapse is N n ; Figure 2 Among the three curves displayed, the top curve is the upper boundary of the echo area corresponding to time i, the bottom curve is the lower boundary of the echo area corresponding to time i, and the middle curve is the fitting function curve made using the fitting function; among them, the independent variable of the fitting function is the time lapse, and the dependent variable is the centroid height of the echo area corresponding to different times.

[0092] S160, determining the time lapse corresponding to the intersection of the centroid height and the ground according to the fitting function; predicting the precipitation time according to the time lapse and issuing a precipitation warning.

[0093] In some embodiments, the time lapse obtained when the dependent variable of the fitting function is 0 is the time lapse corresponding to the intersection of the centroid height with the ground.

[0094] In some embodiments, predicting the precipitation time according to the time lapse amount and issuing a precipitation warning includes:

[0095] The sum of the time lapse corresponding to the intersection of the centroid height and the ground and the time when the single echo appears is taken as the predicted precipitation time, and precipitation warning is issued according to the predicted precipitation time; Figure 2 As shown, γ i The predicted precipitation time can be obtained by adding the time lapse corresponding to 0 to the time when the single echo appears;

[0096] Furthermore, the time lapse is the difference between the current time and the time when the single echo appears; the maximum value of the time lapse is the difference between the time corresponding to the intersection of the fitted centroid height with the ground and the time when the single echo appears.

[0097] According to an embodiment of the present disclosure, hydrometeor profile data is detected in real time by cloud radar, and data processing and quality control are performed on the real-time detected hydrometeor profile data to obtain real-time base data; the echo grounding state at the current moment is judged according to the real-time base data; if the echo is grounded at the current moment, the characteristic parameters at the current moment are input into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein the characteristic parameters include base data and ground air temperature; if the echo is not grounded at the current moment and the hydrometeor profile data show the presence of a single echo, the centroid height of the echo area corresponding to different moments within the time span of the single echo is calculated using the base data within the time span of the single echo; according to the centroid height of the echo area corresponding to different moments within the time span of the single echo, a fitting function is used to fit the relationship between the centroid height and the time lapse amount; according to the fitting function, the time lapse amount corresponding to the intersection of the centroid height and the ground is determined; according to the time lapse amount, the precipitation time is predicted and a precipitation warning is issued. In this way, precipitation can be accurately classified, especially trace precipitation can be identified very accurately, which helps technicians to understand the precipitation type in the precipitation area in real time and accurately predict the precipitation time.

[0098] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0099] The above is an introduction to the method embodiment. The following is a further explanation of the scheme disclosed in the present invention through an apparatus embodiment.

[0100] Figure 4 The structure diagram of a precipitation warning and classification device based on cloud radar provided by an embodiment of the present disclosure is shown. The device 400 includes:

[0101] The basic data acquisition module 410 is used to detect hydrometeor profile data in real time through cloud radar, perform data processing and quality control on the real-time detected hydrometeor profile data, and acquire real-time basic data.

[0102] In some embodiments, module 410 is specifically configured to:

[0103] Basic data, including:

[0104] Reflectivity factor, radial velocity, velocity spectrum width.

[0105] In some embodiments, module 410 is further configured to:

[0106] Data processing, including analog-to-digital conversion, pulse compression, coherent integration, fast Fourier transform, incoherent integration, and moment estimation;

[0107] Quality control, including the identification and elimination of pulse compression range sidelobe echoes and clear sky echoes, filtering and denoising, time and space continuity checks, and extreme value checks.

[0108] The echo grounding determination module 420 is used to determine the echo grounding state at the current moment according to the real-time basic data.

[0109] In some embodiments, module 420 is specifically configured to:

[0110] Determining the echo grounding state at the current moment according to the real-time base data includes:

[0111] If the mean value of the reflectivity factor within the preset altitude range at the current moment is greater than or equal to 0dBZ, and the ratio of the number of echoes with radial velocity greater than the preset velocity threshold at the current moment to the total number of echoes within the preset altitude range at the current moment is greater than or equal to 70%, then the echo is grounded at the current moment; wherein, the echo grounding indicates precipitation has occurred.

[0112] The precipitation classification module 430 is used to input the characteristic parameters of the current moment into a pre-established precipitation classification model to obtain the precipitation type of the current moment if the echo is grounded at the current moment; wherein the characteristic parameters include base data and ground air temperature.

[0113] In some embodiments, module 430 is specifically configured to:

[0114] The precipitation classification model is established by the following method:

[0115] The characteristic parameters at each moment of historical precipitation are used as training sample sets, and the precipitation types at each moment of historical precipitation are used as annotations;

[0116] The precipitation classification model is trained according to the training sample set and annotations; wherein,

[0117] The precipitation classification model adopts fuzzy logic algorithm.

[0118] In some embodiments, module 430 is further configured to:

[0119] The fuzzy logic algorithm is implemented by the following steps:

[0120] Establishing a membership function between each parameter in the characteristic parameters at each moment during the historical precipitation period and the precipitation type corresponding to each moment during the historical precipitation period, performing fuzzy processing on the parameters corresponding to each membership function, and obtaining a fuzzy set corresponding to each parameter in the characteristic parameters at each moment during the historical precipitation period;

[0121] Perform fuzzy reasoning on the fuzzy set according to preset fuzzy rules to obtain a fuzzy conclusion;

[0122] The fuzzy conclusion is defuzzified using a conversion function to obtain the precipitation type corresponding to each moment during the historical precipitation period.

[0123] The centroid height calculation module 440 is used to calculate the centroid height of the echo area corresponding to different times within the time span of the monomer echo using the basic data within the time span of the monomer echo if the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a monomer echo.

[0124] In some embodiments, module 440 is specifically configured to:

[0125] The centroid height of the echo area corresponding to different moments in the time span of the single echo is calculated using the basic data in the time span of the single echo through the following formula:

[0126]

[0127] Among them, γ m is the centroid height of the echo area corresponding to time m; wherein the echo area corresponding to time m is the area covered by the echo signal in the time period from time mn to time m; the echo area includes at least one monomer echo;

[0128] Z ij H is the reflectivity factor corresponding to the distance library with height j at time i; ij Z ij The height at which the echo is located; b represents the lower boundary of the echo area corresponding to time i; t represents the upper boundary of the echo area corresponding to time x.

[0129] In some embodiments, module 440 is further configured to:

[0130] The single echo time span is the time period from the appearance of the single echo to the end of the single echo or the grounding of the single echo.

[0131] The function fitting module 450 is used to fit the relationship between the change of the centroid height and the passage of time using a fitting function according to the centroid height of the echo area corresponding to different moments within the time span of the single echo.

[0132] The precipitation time prediction module 460 is used to determine the time lapse corresponding to the intersection of the centroid height and the ground according to the fitting function; predict the precipitation time according to the time lapse and issue a precipitation warning.

[0133] In some embodiments, module 460 is specifically configured to:

[0134] Predicting the precipitation time and issuing a precipitation warning based on the time lapse amount includes:

[0135] The sum of the time lapse corresponding to the intersection of the centroid height with the ground and the time when the single-echo appears is taken as the predicted precipitation time, and precipitation warning is issued based on the predicted precipitation time.

[0136] Understandably, Figure 4 Each module / unit in the detection device 400 shown has the function of implementing each step in the detection method 100 provided in the embodiment of the present disclosure and can achieve its corresponding technical effect. For the sake of brevity, it will not be repeated here.

[0137] Figure 5 A block diagram of an exemplary electronic device capable of implementing an embodiment of the present disclosure is shown. Electronic device 500 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 500 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0138] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 can also be stored. The computing unit 501, the ROM 502, and the RAM 503 are connected to each other via a bus 504. An I / O interface 505 is also connected to the bus 504.

[0139] Multiple components in the electronic device 500 are connected to the I / O interface 505, including: an input unit 506, such as a keyboard, a mouse, etc.; an output unit 507, such as various types of displays, speakers, etc.; a storage unit 508, such as a disk, an optical disk, etc.; and a communication unit 509, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 509 allows the electronic device 500 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0140] The computing unit 501 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 501 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0141] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0142] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0143] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] It should be noted that the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute method 100 and achieve the corresponding technical effect achieved by executing the method in the embodiment of the present disclosure. For the sake of concise description, they are not repeated here.

[0145] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0146] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0147] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0148] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0149] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A precipitation warning and classification method based on cloud radar, characterized in that: include: Real-time detection of hydrometeor profile data by cloud radar, data processing and quality control of the real-time detected hydrometeor profile data, and acquisition of real-time basic data; Determine the echo grounding state at the current moment according to the real-time base data; If the echo is grounded at the current moment, the characteristic parameters at the current moment are input into a pre-established precipitation classification model to obtain the precipitation type at the current moment; wherein the characteristic parameters include base data and ground air temperature; If the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a single echo, the base data within the time span of the single echo is used to calculate the centroid height of the echo area corresponding to different moments within the time span of the single echo; According to the centroid height of the echo area corresponding to different moments within the time span of the single echo, the relationship between the centroid height and the time elapse is fitted using a fitting function; The time lapse corresponding to the intersection of the centroid height and the ground is determined according to the fitting function; the precipitation time is predicted according to the time lapse and a precipitation warning is issued.

2. The method according to claim 1, characterized in that The basic data include: Reflectivity factor, radial velocity, velocity spectrum width.

3. The method according to claim 2, characterized in that The step of determining the echo grounding state at the current moment according to the real-time base data includes: If the mean value of the reflectivity factor within the preset altitude range at the current moment is greater than or equal to 0dBZ, and the ratio of the number of echoes with radial velocity greater than the preset velocity threshold at the current moment to the total number of echoes within the preset altitude range at the current moment is greater than or equal to 70%, then the echo is grounded at the current moment; wherein, the echo grounding indicates precipitation has occurred.

4. The method according to claim 2, characterized in that: The centroid height of the echo area corresponding to different moments in the time span of the single echo is calculated using the basic data in the time span of the single echo through the following formula: Among them, γ m is the centroid height of the echo area corresponding to time m; wherein the echo area corresponding to time m is the area covered by the echo signal in the time period from time mn to time m; the echo area includes at least one monomer echo; Z ij H is the reflectivity factor corresponding to the distance library with height j at time i; ij Z ij The height at which the echo is located; b represents the lower boundary of the echo area corresponding to time i; t represents the upper boundary of the echo area corresponding to time i.

5. The method according to claim 1, characterized in that The precipitation classification model is established by the following method: The characteristic parameters at each moment of historical precipitation are used as training sample sets, and the precipitation types at each moment of historical precipitation are used as annotations; The precipitation classification model is trained according to the training sample set and annotations; wherein, The precipitation classification model adopts fuzzy logic algorithm.

6. The method according to claim 5, characterized in that The fuzzy logic algorithm is implemented by the following steps: Establishing a membership function between each parameter in the characteristic parameters at each moment during the historical precipitation period and the precipitation type corresponding to each moment during the historical precipitation period, performing fuzzy processing on the parameters corresponding to each membership function, and obtaining a fuzzy set corresponding to each parameter in the characteristic parameters at each moment during the historical precipitation period; Perform fuzzy reasoning on the fuzzy set according to preset fuzzy rules to obtain a fuzzy conclusion; The fuzzy conclusion is defuzzified using a conversion function to obtain the precipitation type corresponding to each moment during the historical precipitation period.

7. The method according to claim 1, characterized in that The monomer echo time span is the time period from the appearance of the monomer echo to the end of the monomer echo or the grounding of the monomer echo.

8. The method according to claim 7, characterized in that The predicting of precipitation time according to the time lapse amount and issuing a precipitation warning comprises: The sum of the time lapse corresponding to the intersection of the centroid height with the ground and the time when the single-echo appears is taken as the predicted precipitation time, and precipitation warning is issued based on the predicted precipitation time.

9. The method according to claim 1, characterized in that: The data processing includes analog-to-digital conversion, pulse compression, coherent integration, fast Fourier transform, incoherent integration, and moment estimation; The quality control includes the identification and elimination of pulse compression range sidelobe echoes and clear sky echoes, filtering and denoising, time and space continuity checking, and extreme value checking.

10. A precipitation warning and classification device based on cloud radar, characterized in that: include: A base data acquisition module is used to detect hydrometeor profile data in real time through cloud radar, perform data processing and quality control on the real-time detected hydrometeor profile data, and obtain real-time base data; An echo grounding determination module is used to determine the echo grounding state at the current moment according to the real-time base data; A precipitation classification module is used to input the characteristic parameters at the current moment into a pre-established precipitation classification model to obtain the precipitation type at the current moment if the echo is grounded at the current moment; wherein the characteristic parameters include base data and ground air temperature; The centroid height calculation module is used to calculate the centroid height of the echo area corresponding to different times within the time span of the monomer echo using the basic data within the time span of the monomer echo if the echo is not grounded at the current moment and the hydrometeor profile data shows the presence of a monomer echo; The function fitting module is used to fit the relationship between the centroid height and the time lapse according to the centroid height of the echo area corresponding to different moments within the time span of the single echo using the fitting function; The precipitation time prediction module is used to determine the time lapse corresponding to the intersection of the centroid height and the ground according to the fitting function; predict the precipitation time according to the time lapse and issue a precipitation warning.

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