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, which belongs to the field of atmospheric detection technology. The method includes: determining a radar differential attenuation rate according to X / Ka dual-band radar data within the detection range; correcting the radar differential attenuation rate using a predetermined X / Ka dual-band radar differential attenuation rate deviation, and inputting it into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model. The present invention uses an X / Ka dual-band radar to observe the same precipitation target within the detection range, collects more abundant radar data, and determines the radar differential attenuation rate and constructs a precipitation inversion model corresponding to the detection range according to radar data of two bands, the X-band and the Ka-band, to avoid the influence of factors such as noise in the radar data, and finally inputs the radar differential attenuation rate into the precipitation inversion model to obtain the estimated precipitation intensity, which greatly improves the accuracy of precipitation intensity estimation.

Description

Technical Field

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

[0002] Precipitation intensity estimation is one of the main contents of meteorological research and is widely used in weather forecasting and disaster warning, water resources management, agricultural production, urban planning and construction, and other fields.

[0003] At present, the estimation method of precipitation intensity is mainly based on single-band radar (such as Doppler weather radar and dual-polarization weather radar). The echo power data measured by the single-band radar is used in advance to calculate the radar reflectivity factor, differential reflectivity, differential propagation phase shift rate, attenuation rate and other polarization parameters, and a precipitation inversion model is established based on the polarization parameters. Finally, the single-band radar data is input into the precipitation inversion model to estimate the precipitation intensity.

[0004] However, the use of single-band radar data to establish a precipitation inversion model is affected by factors such as raindrop spectrum changes, radar noise and attenuation, resulting in unstable inversion relationships and inaccurate inversion results, resulting in low accuracy in precipitation intensity estimation. Summary of the invention

[0005] The present invention provides a precipitation intensity estimation method, device, electronic equipment and storage medium, which are used to solve the defect of low precipitation intensity estimation accuracy in the prior art.

[0006] In a first aspect, the present invention provides a precipitation intensity estimation method, comprising:

[0007] Determine the radar differential attenuation rate based on the X / Ka dual-band radar data within the detection range;

[0008] After the radar differential attenuation rate is corrected by using a predetermined differential attenuation rate deviation, the differential attenuation rate is input into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model.

[0009] According to a precipitation intensity estimation method provided by the present invention, the X / Ka dual-band radar data includes a radar near-end Ka-band reflectivity factor, a radar near-end X-band reflectivity factor, a radar far-end Ka-band reflectivity factor, and a radar far-end X-band reflectivity factor;

[0010] Determining the radar differential attenuation rate according to the X / Ka dual-band radar data within the detection range includes:

[0011] Determining a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determining a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor;

[0012] The radar differential attenuation rate is determined according to the first difference and the second difference.

[0013] According to a precipitation intensity estimation method provided by the present invention, the calculation formula for determining the radar differential attenuation rate according to the first difference and the second difference is:

[0014]

[0015] Wherein, k(X,Ka) is the radar differential attenuation rate, r1 is the radar near-end distance, r2 is the radar far-end distance, and Z m (X,r1) The radar near-end X-band reflectivity factor, Z m (X, r2) The radar far-end X-band reflectivity factor, Z m (Ka,r1) is the radar near-end Ka-band reflectivity factor, Z m (Ka, r2) is the radar far-end Ka-band reflectivity factor.

[0016] According to a precipitation intensity estimation method provided by the present invention, the differential attenuation rate deviation is obtained by pre-processing the raindrop spectrum data samples and X / Ka dual-band radar data samples in the detection range within any sampling period, specifically including:

[0017] Determine the number of raindrops per unit volume and per unit diameter of the detection range within any sampling period according to the raindrop spectrum data samples, so as to construct a T-matrix scattering model related to the detection range;

[0018] Inputting X-band radar parameters and Ka-band radar parameters into the T-matrix scattering model respectively to obtain X-band radar attenuation rate and Ka-band radar attenuation rate;

[0019] Calculating the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as a true value of the differential attenuation rate;

[0020] Determining a differential attenuation rate sample value according to the X / Ka dual-band radar data sample;

[0021] The differential attenuation rate deviation is determined according to the difference between the differential attenuation rate true value and the differential attenuation rate sample value.

[0022] According to a precipitation intensity estimation method provided by the present invention, the calculation formula of the T matrix scattering model is:

[0023]

[0024] Among them, A H is the radar attenuation rate, λ is the radar wavelength, Im is the integrated imaginary part, D is the raindrop diameter, N(D) is the number of raindrops per unit volume and per unit diameter, f H (D) is the horizontal forward scattering amplitude matrix.

[0025] According to a precipitation intensity estimation method provided by the present invention, the precipitation inversion model is constructed by fitting the historical X / Ka dual-band radar data and historical precipitation intensity data collected within the detection range;

[0026] The historical X / Ka dual-band radar data includes X / Ka dual-band radar data collected in multiple time windows;

[0027] The historical precipitation intensity data includes the precipitation intensity within each of the time windows.

[0028] According to a precipitation intensity estimation method provided by the present invention, the expression of the precipitation inversion model is:

[0029] R=2.836k(X,Ka) 1.113 ;

[0030] Wherein, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate.

[0031] In a second aspect, the present invention further provides a precipitation intensity estimation device, comprising:

[0032] The differential attenuation rate calculation unit is used to determine the radar differential attenuation rate based on the X / Ka dual-band radar data within the detection range;

[0033] The precipitation intensity estimation unit is used to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and then input the correction into a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.

[0034] In a third aspect, the present invention 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, the steps of any one of the above-mentioned precipitation intensity estimation methods are implemented.

[0035] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the precipitation intensity estimation methods described above.

[0036] The precipitation intensity estimation method, device, electronic device and storage medium provided by the present invention use an X / Ka dual-band radar to observe the same precipitation target within the detection range, collect more abundant radar data, and determine the radar differential attenuation rate and construct a precipitation inversion model corresponding to the detection range based on the radar data of the two bands of X band and Ka band, thereby avoiding the influence of factors such as noise in the radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, thereby greatly improving the accuracy of precipitation intensity estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 creative work.

[0038] Figure 1 It is one of the flow charts of the precipitation intensity estimation method provided by the present invention.

[0039] Figure 2 This is the second flow chart of the precipitation intensity estimation method provided by the present invention.

[0040] Figure 3 This is one of the schematic diagrams of the near-end reflectivity factor of the X / Ka dual-band radar provided by the present invention.

[0041] Figure 4 This is one of the schematic diagrams of the far-end reflectivity factor of the X / Ka dual-band radar provided by the present invention.

[0042] Figure 5 It is a schematic diagram of the radar differential attenuation rate provided by the present invention.

[0043] Figure 6 It is a flow chart of the method for determining the differential attenuation rate deviation provided by the present invention.

[0044] Figure 7 It is a schematic diagram of the corrected radar differential attenuation rate provided by the present invention and the true value of the differential attenuation rate obtained according to the measured raindrop spectrum.

[0045] Figure 8 It is a schematic diagram of the measured rainfall intensity provided by the present invention.

[0046] Fig. 9 It is a schematic diagram of the fitting precipitation inversion model provided by the present invention.

[0047] Fig.10 The precipitation intensity estimation result provided by the present invention is consistent with the actual precipitation intensity, R(AH ) method.

[0048] Fig.11 This is the second schematic diagram of the near-end reflectivity factor of the X / Ka dual-band radar provided by the present invention.

[0049] Fig.12 This is the second schematic diagram of the far-end reflectivity factor of the X / Ka dual-band radar provided by the present invention.

[0050] Fig.13 The precipitation intensity estimation result provided by the present invention is consistent with the actual precipitation intensity, R(A H )The second diagram comparing the estimation results of the method.

[0051] Fig.14 It is a structural schematic diagram of the precipitation intensity estimation device provided by the present invention.

[0052] Fig.15 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] It should be noted that in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0055] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" means at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0056] Precipitation intensity estimation is one of the main contents of meteorological research, and is currently mainly based on single-polarization weather radar and dual-polarization weather radar.

[0057] When estimating precipitation intensity based on single polarization weather radar (such as Doppler weather radar), the radar reflectivity factor Z (unit: mm) is generally calculated based on the measured echo power. 6 m -3 ), and then apply the radar meteorological equation according to the predetermined ZR relationship (Z = aR b ) is used to derive the precipitation intensity R (unit: mm / h). The typical relationship commonly used at present is Z = 200R 1.6 .

[0058] The value of radar reflectivity factor Z is directly related to the raindrop spectrum, while the rainfall rate R is related to the final falling velocity of raindrops in addition to the raindrop spectrum. Therefore, the same rainfall rate R may correspond to different raindrop spectra and different Z values.

[0059] Affected by the variability of the raindrop spectrum, the ZR relationship (Z = aR b ) is not stable, and its coefficients a and b vary with location, season, and precipitation type, and even within the same precipitation process. The instability of the ZR relationship greatly affects the accuracy of precipitation intensity estimation.

[0060] The precipitation intensity is estimated based on dual-polarization weather radar, which generally estimates the precipitation intensity by making different combinations of four polarization parameters.

[0061] The polarization parameters here include the radar reflectivity factor Z H (Unit: mm 6 m -3 ), differential reflectivity Z DR (Unit: dB), differential propagation phase shift rate K DP (Unit: ° / km) and attenuation rate A H (Unit: dB / km).

[0062] Here the different combinations can be roughly divided into five categories, namely R(ZH )、R(K DP )、R(Z H ,Z DR )、R(K DP ,Z DR ) and R(A H ), the specific relationship may include: R(Z H )=a1Z H b1 , R(K DP )=a2K DP b2 , R(Z H ,Z DR )=a3Z H b3 10 c3ZDR , R(K DP ,Z DR )=a4K DP b4 10 c4ZDR , R(A H )=a5A H b5 .

[0063] However, all five combination methods under dual-polarization weather radar have defects.

[0064] R(Z H ) method is the ZR relationship method under dual polarization weather radar, so R(Z H ) also faces the problem of inaccurate estimation of precipitation intensity due to the unstable ZR relationship.

[0065] Differential propagation phase shift rate K DP The differential propagation phase shift Φ DP Derivation yields, therefore, using K DP The method of measuring precipitation is not affected by absolute calibration errors and attenuation. However, if the rainfall rate R is not large, K DP Contains large noise information, resulting in K DP Inaccurate, thus affecting R(K DP ) and R(K DP ,Z DR ) is the accuracy of estimating precipitation in weak precipitation areas, so R(K DP ) and R(K DP ,Z DR ) is generally used for measurement in heavy precipitation areas. DP is Φ DP The derivative of the distance, but in actual calculation, the derivative can only be calculated over a finite distance, so based on K DP There is a trade-off between accuracy and range resolution in the estimation method of precipitation intensity.

[0066] Differential reflectivity Z DR It is a relative power measurement value, that is, the ratio of horizontal polarization power to vertical polarization power. DR Any method of precipitation measurement must be combined with Z H or K DP Use, resulting in R(Z H ,Z DR ) and R(K DP ,Z DR ) also faces R(Z H ) and R(K DP )The problem of inaccurate estimation of precipitation intensity.

[0067] R(A H The key to estimating precipitation intensity using the ) method is to invert and obtain accurate A H , currently generally through K DP Inversion (A H =a6K DP b6 ) or Z H Inversion (A H =a7Z H b7 ) is obtained. On the one hand, K DP Easily contaminated by noise, resulting in DP The inverted A H The data quality is poor. On the other hand, for H Inversion gets A H The inversion coefficient of the method is relatively unstable due to the different precipitation types, which leads to the inversion of A H There is also a large error. Therefore, due to the use of single-band radar to invert A H There are cases where the inversion relationship is unstable and the inversion results are inaccurate, even with Z H , Z DR and K DP Compared with the established relationship for estimating precipitation intensity, R(A H ) method has the advantage of being insensitive to changes in raindrop spectrum. H ) method still has the problem of inaccurate estimation of precipitation intensity.

[0068] Aiming at the problem that the current precipitation intensity estimation method has low accuracy, the present invention provides a precipitation intensity estimation method, device, electronic device and storage medium with high accuracy based on X / Ka dual-band radar.

[0069] It should be noted that the execution subject of the precipitation intensity estimation method provided in the embodiment of the present invention can be a server, a computer device, such as a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook or a personal digital assistant (PDA), etc., and can also be various weather forecast computers, etc.

[0070] Combine the following Figure 1-Figure 15 The present invention describes a precipitation intensity estimation method, device, electronic device and storage medium.

[0071] Figure 1 is one of the flow charts 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:

[0072] Step 101: Determine the radar differential attenuation rate according to the X / Ka dual-band radar data within the detection range.

[0073] Generally speaking, radar signals will attenuate during propagation. First, the propagation of radar signals in free space follows the law of free space propagation loss. The power of radar signals decreases as the propagation distance increases, that is, radar signal attenuation occurs. Secondly, in addition to attenuation as the propagation distance increases, radar signals will also attenuate during propagation due to factors such as atmospheric attenuation, radar antenna loss or material properties, terrain undulations, and obstacles.

[0074] When precipitation occurs, if radar is used to observe precipitation in a certain area, the radar signal will be attenuated. Water droplets will absorb radar waves, causing the amplitude of the radar signal to gradually decrease. Water droplets will also scatter radar waves, causing the radar signal to become scattered and blurred. Generally speaking, the stronger the precipitation intensity, the more significant the radar signal attenuation caused by precipitation.

[0075] When estimating precipitation intensity, the present invention first collects X / Ka dual-band radar data within the detection range. Specifically, the X / Ka dual-band radar transmits X-band and Ka-band radar beams to the same precipitation target within the detection range through an antenna system. The two-band radar beams interact with the precipitation target to generate echo signals, which are then collected and processed by the X / Ka dual-band radar to obtain X / Ka dual-band radar data within the detection range.

[0076] Among them, X / Ka dual-band radar data refers to radar data collected and processed by the radar system using both X-band and Ka-band frequency bands.

[0077] Then, based on the radar data of the X-band and Ka-band frequency bands, the current radar differential attenuation rate can be determined by calculation.

[0078] Among them, the X-band has a longer wavelength, which is suitable for long-distance detection and can provide better ground resolution and penetration. The Ka-band has a shorter wavelength, which is suitable for close-range target detection and can provide higher resolution and target recognition capabilities. Therefore, X / Ka dual-band radar data can provide richer information and higher accuracy.

[0079] Step 102: After correcting the radar differential attenuation rate using a predetermined differential attenuation rate deviation, the differential attenuation rate is input into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model.

[0080] First, in the process of precipitation intensity estimation, since the X / Ka dual-band radar data will be affected by factors such as noise or non-Rayleigh scattering, the differential attenuation rate derived from the X / Ka dual-band radar data will have abnormal values. Therefore, it is necessary to use a predetermined differential attenuation rate deviation to correct the radar differential attenuation rate.

[0081] For example, according to the electromagnetic wave attenuation theory that the higher the frequency, the faster the attenuation, the radar differential attenuation rate should be positive, but affected by noise or non-Rayleigh scattering, the radar differential attenuation rate derived from X / Ka dual-band radar data may be negative. Therefore, the radar differential attenuation rate needs to be corrected.

[0082] Then, the corrected radar differential attenuation rate is input into the precipitation inversion model corresponding to the detection range.

[0083] Among them, the precipitation inversion model is constructed based on the historical precipitation data of the same precipitation target within the same detection range, which reflects the relationship between the precipitation intensity of the same precipitation target within the detection range and the radar differential attenuation rate.

[0084] Generally speaking, when using X / Ka dual-band radar to observe the same precipitation meteorological target within the same detection range, the type and setting position of the X / Ka dual-band radar are fixed, and the terrain and obstacles within the detection range are fixed. Therefore, before and after precipitation, the attenuation of radar signals caused by factors such as propagation distance, radar antenna loss or material characteristics, terrain undulations and obstacle shielding is certain, and the radar differential attenuation rate obtained from X / Ka dual-band radar data varies only according to different precipitation conditions.

[0085] Finally, by inputting the radar differential attenuation rate determined and corrected according to the current X / Ka dual-band radar data into the precipitation inversion model, the estimated precipitation intensity output by the precipitation inversion model can be obtained.

[0086] The precipitation intensity estimation method provided by the present invention uses an X / Ka dual-band radar to observe the same precipitation target within the detection range, collects more abundant radar data, and determines the radar differential attenuation rate and constructs a precipitation inversion model corresponding to the detection range based on the radar data of the two bands of X band and Ka band, thereby avoiding the influence of factors such as noise in the radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, thereby greatly improving the accuracy of precipitation intensity estimation.

[0087] Figure 2 FIG. 2 is a flow chart of the precipitation intensity estimation method provided by the present invention. Figure 2 As shown, as an optional embodiment, the X / Ka dual-band radar data includes a radar near-end Ka-band reflectivity factor, a radar near-end X-band reflectivity factor, a radar far-end Ka-band reflectivity factor and a radar far-end X-band reflectivity factor.

[0088] The radar reflectivity factor is a parameter that characterizes the echo intensity of a meteorological target. The size of the radar reflectivity factor depends on the raindrop spectrum, that is, it is related to the size, number and phase state of precipitation particles in a unit volume of the precipitation target, and can be used to indicate precipitation intensity.

[0089] The radar near-end refers to the area close to the radar transmitter and receiver. In the near-end area, the distance between the target and the radar system is relatively short, the propagation path of the radar wave is relatively simple, and the signal strength is high.

[0090] The far end of radar refers to the area far away from the radar transmitter and receiver. In the far end area, the distance between the target and the radar system is relatively long, the propagation path of the radar wave is relatively complex, and it may experience multiple absorption, reflection and scattering, and the signal strength is weak.

[0091] The X / Ka dual-band radar is used to receive the X / Ka dual-band radar echo signal within the detection range. According to the received echo signal strength and arrival time, combined with the characteristics and parameters of the radar system, the X / Ka dual-band radar data such as the radar near-end Ka-band reflectivity factor, the radar near-end X-band reflectivity factor, the radar far-end Ka-band reflectivity factor and the radar far-end X-band reflectivity factor are obtained.

[0092] Furthermore, according to the X / Ka dual-band radar data within the detection range, the radar differential attenuation rate is determined, specifically, a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor is calculated, and a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor is calculated. Finally, the radar differential attenuation rate is determined according to the first difference and the second difference.

[0093] Figure 3It is one of the schematic diagrams of the near-end reflectivity factor of the X / Ka dual-band radar provided by the present invention. Among them, the X-band curve is a schematic curve of the near-end reflectivity factor of the X-band radar, and the Ka-band curve is a schematic curve of the near-end reflectivity factor of the Ka-band radar. The observation time is from 00:53 am to 07:53 am on May 11, 2022.

[0094] Figure 4 It is one of the schematic diagrams of the far-end reflectivity factor of the X / Ka dual-band radar provided by the present invention. Among them, the X-band curve is a schematic curve of the far-end reflectivity factor of the X-band radar, and the Ka-band curve is a schematic curve of the far-end reflectivity factor of the Ka-band radar. The observation time is from 00:53 am to 07:53 am on May 11, 2022.

[0095] The precipitation intensity estimation method provided by the present invention determines the radar differential attenuation rate according to the difference between the X-band and Ka-band reflectivity factors, overcomes the defect that the relationship between the single-band radar reflectivity factor and the precipitation intensity is unstable, makes the radar differential attenuation rate more reliable, and thus improves the accuracy of precipitation intensity estimation.

[0096] Based on the content of the above embodiment, as an optional embodiment, the calculation formula for determining the radar differential attenuation rate according to the first difference and the second difference is as follows:

[0097]

[0098] Wherein, k(X,Ka) is the radar differential attenuation rate, r1 is the radar near-end distance, r2 is the radar far-end distance, and Z m (X,r1) The radar near-end X-band reflectivity factor, Z m (X, r2) The radar far-end X-band reflectivity factor, Z m (Ka,r1) is the radar near-end Ka-band reflectivity factor, Z m (Ka, r2) is the radar far-end Ka-band reflectivity factor.

[0099] Figure 5 : is a schematic diagram of the radar differential attenuation rate provided by the present invention. Among them, the X / Ka dual-band radar data for calculating the radar differential attenuation rate was collected from 00:53 am to 07:53 am on May 11, 2022.

[0100] Figure 6 is a flow chart of the method for determining the differential attenuation rate deviation provided by the present invention, such as Figure 6As shown, as an optional embodiment, the differential attenuation rate deviation is obtained by pre-processing the raindrop spectrum data samples and X / Ka dual-band radar data samples in the detection range within any sampling period, specifically including:

[0101] Determine the number of raindrops per unit volume and per unit diameter of the detection range within any sampling period according to the raindrop spectrum data samples, so as to construct a T-matrix scattering model related to the detection range;

[0102] Inputting X-band radar parameters and Ka-band radar parameters into the T-matrix scattering model respectively to obtain X-band radar attenuation rate and Ka-band radar attenuation rate;

[0103] Calculating the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as a true value of the differential attenuation rate;

[0104] Determining a differential attenuation rate sample value according to the X / Ka dual-band radar data sample;

[0105] The differential attenuation rate deviation is determined according to the difference between the differential attenuation rate true value and the differential attenuation rate sample value.

[0106] The raindrop spectrum data samples and X / Ka dual-band radar data samples are respectively obtained by measuring the raindrop spectrometer and the same precipitation target within the detection range of the X / Ka dual-band radar.

[0107] The sampling period can be determined comprehensively based on factors such as the performance parameters of the raindrop spectrometer, the performance parameters of the X / Ka dual-band radar, and specific scene measurement requirements, and the present invention does not impose any limitation on this.

[0108] It should be noted that the X / Ka dual-band radar data samples used to predetermine the differential attenuation rate deviation and the X / Ka dual-band radar data used to estimate the precipitation intensity are both obtained by measuring the same precipitation target within the same detection range by X / Ka dual-band radars of the same type and set position, and the sampling period of the former is earlier than the sampling period of the latter.

[0109] It should be noted that the raindrop spectrometer used for collecting raindrop spectrum data samples in the embodiment of the present invention includes but is not limited to a two-dimensional video raindrop spectrometer (2DVD), a laser raindrop spectrometer, a photoelectric raindrop spectrometer, an acoustic raindrop spectrometer, etc., and the present invention does not impose any limitation on this.

[0110] The T-matrix scattering model is a numerical method for calculating the scattered light of atmospheric particles or precipitation particles. It uses matrix operations to simulate the scattering characteristics of particles.

[0111] A T-matrix scattering model is constructed according to the raindrop spectrum collected by the raindrop spectrometer during the sampling period, and the X-band radar parameters and Ka-band radar parameters of the X / Ka dual-band radar are input into the T-matrix scattering model. The X-band radar attenuation rate and Ka-band radar attenuation rate of the raindrop spectrum can be simulated, and the difference between the two is taken as the true value of the differential attenuation rate.

[0112] In addition, for the X / Ka dual-band radar data samples collected by the X / Ka dual-band radar within the sampling period, the calculation formula of the radar differential attenuation rate is determined according to the aforementioned embodiment to calculate the differential attenuation rate sample value, and the difference between the differential attenuation rate true value and the differential attenuation rate sample value is obtained as the differential attenuation rate deviation.

[0113] The precipitation intensity estimation method provided by the present invention determines the differential attenuation rate deviation according to the T matrix scattering model constructed by the measured raindrop spectrum in the same sampling period and the corresponding measured X / Ka dual-band radar data, and uses the differential attenuation rate deviation to correct the radar differential attenuation rate measured and calculated in the precipitation estimation process, thereby eliminating the adverse effects of noise factors other than the raindrop spectrum on the precipitation intensity estimation result and improving the accuracy of precipitation intensity estimation.

[0114] Based on the content of the above embodiment, as an optional embodiment, the calculation formula of the T matrix scattering model is:

[0115]

[0116] Among them, A H is the radar attenuation rate, λ is the radar wavelength, Im is the integrated imaginary part, D is the raindrop diameter, N(D) is the number of raindrops per unit volume and per unit diameter, f H (D) is the horizontal forward scattering amplitude matrix.

[0117] According to the calculation formula of the aforementioned T-matrix scattering model, the X-band radar attenuation rate and Ka-band radar attenuation rate within the same detection range during the sampling period can be calculated respectively, and the difference between the two is the true value of the differential attenuation rate.

[0118] Figure 7 It is a schematic diagram of the corrected radar differential attenuation rate provided by the present invention and the true value of the differential attenuation rate obtained according to the measured raindrop spectrum. Among them, the k(X,Ka)-2DVD curve is the true value curve of the differential attenuation rate obtained according to the raindrop spectrum measured by the two-dimensional video raindrop spectrometer, and the k(X,Ka)-Radar curve is the corrected radar differential attenuation rate curve. The X / Ka dual-band radar data for calculating the radar differential attenuation rate was collected from 00:53 to 07:53 on May 11, 2022.

[0119] In one embodiment, the wavelength of the X-band corresponds to 3.2 cm, and the wavelength of the Ka-band corresponds to 8.6 mm.

[0120] In another embodiment, the sampling period is 1 minute, the sampling period of the raindrop spectrometer is 1 minute, and the sampling period of the X / Ka dual-band radar is 2 seconds. In any sampling period, the raindrop spectrometer collects 1 observation data, and the X / Ka dual-band radar can collect 30 observation data.

[0121] Therefore, when obtaining the true value of the differential attenuation rate, a T-matrix scattering model is constructed according to the collected raindrop spectrum data, and combined with radar parameters, the scattering is simulated according to the T-matrix scattering model to obtain the true value of the differential attenuation rate.

[0122] When obtaining the differential attenuation rate sample value, the aforementioned radar reflectivity factor is accumulated for the 30 radar observation data, and the radar differential attenuation rate is calculated using the accumulated radar reflectivity factor as the differential attenuation rate sample value.

[0123] Finally, the difference between the true value of the differential attenuation rate and the sample value of the differential attenuation rate is taken as the differential attenuation rate deviation.

[0124] Based on the content of the above embodiment, as an optional embodiment, the precipitation inversion model is constructed by fitting the historical X / Ka dual-band radar data and historical precipitation intensity data collected within the detection range;

[0125] The historical X / Ka dual-band radar data includes X / Ka dual-band radar data collected in multiple time windows;

[0126] The historical precipitation intensity data includes the precipitation intensity in each time window, which is obtained by measuring the same precipitation target within the detection range by a rain gauge.

[0127] The time window refers to the time range for collecting historical X / Ka dual-band radar data and historical precipitation intensity data before estimating precipitation intensity. The time window can be determined comprehensively based on factors such as the performance parameters of the rain gauge, the performance parameters of the X / Ka dual-band radar, and the measurement requirements of specific scenarios, and the present invention does not limit this.

[0128] It should be noted that the method of fitting scattered points into a curve in the present invention includes polynomial fitting, linear fitting, spline interpolation, least squares fitting, nonlinear fitting, Fourier series fitting and the like, and the present invention does not impose any limitation on this.

[0129] Figure 8 It is a schematic diagram of the measured rainfall intensity provided by the present invention. The observation time is from 00:53 am to 07:53 am on May 11, 2022.

[0130] Fig. 9 It is a schematic diagram of the fitted precipitation inversion model provided by the present invention, wherein the historical X / Ka dual-band radar data is the radar differential attenuation rate, and the curve is the fitted precipitation inversion model.

[0131] like Fig. 9 As shown in the figure, the historical precipitation intensity has a good correspondence with the historical X / Ka dual-band radar differential attenuation rate, and the above precipitation inversion model has good performance.

[0132] In one embodiment, the sampling period of the rain gauge is 1 minute, the sampling period of the X / Ka dual-band radar is 2 seconds, and the time window is 1 minute. In any time window, the rain gauge collects 1 historical precipitation intensity data, and the X / Ka dual-band radar collects 30 historical X / Ka dual-band radar data.

[0133] When obtaining the differential attenuation rate sample value, the radar reflectivity factors of 30 radar observation data are accumulated, and the radar differential attenuation rate is calculated using the accumulated radar reflectivity factors as the differential attenuation rate sample value.

[0134] The difference between the true value of the differential attenuation rate and the sample value of the differential attenuation rate is taken as the differential attenuation rate deviation.

[0135] The radar differential attenuation rate is corrected by the above differential attenuation rate deviation.

[0136] Finally, the precipitation inversion model is constructed by fitting the corrected historical radar differential attenuation rate in multiple time windows with the historical precipitation intensity data.

[0137] The precipitation intensity estimation method provided by the present invention uses X / Ka dual-band radar data of the same type and detection conditions in the process of precipitation intensity estimation and precipitation inversion model construction, and the X / Ka dual-band radar data is richer and more accurate, thereby constructing a more stable and reliable precipitation inversion model and improving the accuracy of the precipitation estimation method.

[0138] Based on the content of the above embodiment, as an optional embodiment, the expression of the precipitation inversion model is:

[0139] R=2.836k(X,Ka) 1.113 ;

[0140] Wherein, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate.

[0141] Based on the above embodiment, as an optional embodiment, after each interval (for example, 6 hours), the estimated difference between the estimated precipitation intensity output by the precipitation inversion model during the sampling period and the real-time precipitation intensity is compared with a preset threshold value, and when the estimated difference is greater than the preset threshold value, the differential attenuation rate deviation and the precipitation inversion model are re-determined. According to the above method for determining the differential attenuation rate deviation and the precipitation inversion model, the differential attenuation rate deviation and the precipitation inversion model are re-determined.

[0142] After redetermining the differential attenuation rate bias and reconstructing the precipitation inversion model, the radar differential attenuation rate is corrected using the redetermined differential attenuation rate bias and input into the reconstructed precipitation inversion model to finally obtain the estimated precipitation intensity that is appropriate to the current precipitation situation.

[0143] In another embodiment, if the estimated precipitation intensity output by the precipitation inversion model during the sampling period is greater than the real-time precipitation intensity, the differential attenuation rate deviation and the precipitation inversion model are re-determined when the absolute value of the estimated difference between the two is greater than a first preset threshold value. If the estimated precipitation intensity output by the precipitation inversion model during the sampling period is less than the real-time precipitation intensity, the differential attenuation rate deviation and the precipitation inversion model are re-determined when the absolute value of the estimated difference between the two is greater than a second preset threshold value.

[0144] The first preset score threshold is not equal to the second preset score threshold.

[0145] It should be noted that the preset threshold, the first preset sub-threshold, and the second preset sub-threshold can be determined based on factors such as historical precipitation intensity estimation results, performance and parameters of the measuring device, and the present invention does not impose any limitation on this.

[0146] By testing the estimated precipitation intensity output by the precipitation inversion model at regular intervals, when the difference between the estimated precipitation intensity and the measured precipitation intensity is too large, it indicates that the precipitation situation has changed. At this time, the differential attenuation rate deviation and the precipitation inversion model are re-determined. The present invention can adapt to application scenarios with complex precipitation conditions and large precipitation fluctuations.

[0147] Finally, the present invention also obtains the measured precipitation intensity by measuring the real-time precipitation intensity, and uses R(A H ) method to estimate the precipitation intensity and obtain R(A H ) method to estimate the results, and then the measured precipitation intensity, R(A H ) method is compared with the precipitation intensity estimation result of the present invention to evaluate the accuracy of the precipitation intensity estimation method provided by the present invention.

[0148] Since the attenuation of Ka-band electromagnetic waves in rainy areas is extremely serious and there is Mie scattering effect in Ka-band rainy area observations, the R(A) based on the X-band attenuation rate is used. H) method to estimate the precipitation process.

[0149] X-band attenuation rate A H It is calculated based on the currently commonly used inversion relationship. The calculation formula is as follows:

[0150]

[0151] Among them, Z H Unit: mm 6 m -3 , A H The unit is dB / km.

[0152] Precipitation intensity R(A H ) is calculated according to the currently commonly used inversion relationship, and the calculation formula is as follows:

[0153]

[0154] Fig.10 The precipitation intensity estimation result provided by the present invention is consistent with the actual precipitation intensity, R(A H ) method estimation results. Among them, the R(k(X,Ka)) curve is the precipitation intensity estimation result curve of the present invention, R Measured The curve is the measured precipitation intensity curve, R(A H ) curve is R(A H ) method to estimate precipitation intensity. The observation time is from 00:53 am to 07:53 am on May 11, 2022.

[0155] like Fig.10 As shown in the figure, the estimated precipitation intensity obtained by the present invention is slightly different from the measured precipitation intensity, and its accuracy is much higher than R(A H )method.

[0156] In addition, the average deviation (AD) and correlation coefficient (CC) were used to evaluate the performance of the present invention and R (A H ) method’s accuracy.

[0157] The definitions of the average deviation AD and the correlation coefficient CC are as follows:

[0158]

[0159]

[0160] Among them, Cov(R Estimated -R Measured ) is the estimated precipitation intensity R Estimated Compared with the measured precipitation intensity RMeasured The covariance of Var[R Estimated ] is R Estimated Variance, Var[R Measured ] is R Measured The variance of .

[0161] Table 1 is obtained by the average deviation and correlation coefficient of the present invention and R (A H As shown in Table 1, the estimated results of the R(k(X,Ka)) precipitation estimation method based on the dual-band differential attenuation rate of the present invention have a smaller average deviation and a higher correlation coefficient, which is significantly better than the R(A) based on the single-band attenuation rate. H )Methods for estimating precipitation.

[0162] Table 1 Precipitation estimation results statistical table 1

[0163]

[0164] In order to better evaluate the accuracy of the precipitation estimation of the present invention, in one embodiment, the observation period is from 04:19 am to 14:19 pm on June 11, 2022, and the observation time span is 10 hours. H ) method to estimate the precipitation intensity in this period, compare the obtained precipitation intensity estimation results, and use the average deviation and correlation coefficient to evaluate the accuracy of the present invention and the R(AH) method again. Figure 11-13 This process is described below.

[0165] Fig.11 This is the second schematic diagram of the near-end reflectivity factor of the X / Ka dual-band radar provided by the present invention. Among them, the X-band curve is a schematic curve of the near-end reflectivity factor of the X-band radar, and the Ka-band curve is a schematic curve of the near-end reflectivity factor of the Ka-band radar. The observation time is from 04:19 am to 14:19 pm on June 11, 2022.

[0166] Fig.12 This is the second diagram of the X / Ka dual-band radar far-end reflectivity factor. The X-band curve is the X-band radar far-end reflectivity factor diagram, and the Ka-band curve is the Ka-band radar far-end reflectivity factor diagram. The observation time is from 04:19 am to 14:19 pm on June 11, 2022.

[0167] Fig.13 The precipitation intensity estimation result provided by the present invention is consistent with the actual precipitation intensity, R(A H ) method estimation results. Among them, the R(k(X,Ka)) curve is the precipitation intensity estimation result curve of the present invention, RMeasured The curve is the measured precipitation intensity curve, R(A H ) curve is R(A H ) method to estimate precipitation intensity.

[0168] like Fig.13 As shown in the figure, the estimated precipitation intensity obtained by the present invention is slightly different from the measured precipitation intensity, and its accuracy is much higher than R(A H )method.

[0169] Table 2 is obtained by the average deviation and correlation coefficient of the present invention and R (A H As shown in Table 2, the estimated results of the R(k(X,Ka)) precipitation estimation method based on the dual-band differential attenuation rate of the present invention have a smaller average deviation and a higher correlation coefficient, which is significantly better than the R(A) based on the single-band attenuation rate. H )Methods for estimating precipitation.

[0170] Table 2 Precipitation estimation results statistical table 2

[0171]

[0172] Fig.14 is a schematic diagram of the structure of the precipitation intensity estimation device provided by the present invention, such as Fig.14 As shown, the present invention also provides a precipitation intensity estimation device, which mainly includes:

[0173] The differential attenuation rate calculation unit 1401 is used to determine the radar differential attenuation rate according to the X / Ka dual-band radar data within the detection range;

[0174] The precipitation intensity estimation unit 1402 is used to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and then input it into a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.

[0175] It should be noted that the precipitation intensity estimation device provided in the embodiment of the present invention can execute the precipitation intensity estimation method described in any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0176] The precipitation intensity estimation device provided by the present invention uses an X / Ka dual-band radar to observe the same precipitation target within the detection range, collects more abundant radar data, and determines the radar differential attenuation rate and constructs a precipitation inversion model corresponding to the detection range based on the radar data of the two bands of X band and Ka band, thereby avoiding the influence of factors such as noise in the radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, thereby greatly improving the accuracy of precipitation intensity estimation.

[0177] Based on the above embodiment, as an optional embodiment, the X / Ka dual-band radar data is collected using an X / Ka band dual-antenna radar. The X / Ka band dual-antenna radar adopts a fully solid-state frequency-modulated continuous wave radar mechanism, with separate transmission and reception, and the X / Ka band shares two transmitting and receiving antennas.

[0178] Fig.15 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Fig.15 As shown, the electronic device may include: a processor 1510, a communication interface 1520, a memory 1530 and a communication bus 1540, wherein the processor 1510, the communication interface 1520 and the memory 1530 communicate with each other through the communication bus 1540. The processor 1510 may call the logic instructions in the memory 1530 to execute the precipitation intensity estimation method, which includes: determining the radar differential attenuation rate according to the X / Ka dual-band radar data within the detection range; correcting the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and inputting the correction to the precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model.

[0179] In addition, the logic instructions in the above-mentioned memory 1530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0180] 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 in the above-mentioned embodiments, the method including: determining a radar differential attenuation rate based on X / Ka dual-band radar data within a detection range; correcting the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and inputting the corrected radar differential attenuation rate into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model.

[0181] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the precipitation intensity estimation method provided in the above-mentioned embodiments, the method comprising: determining a radar differential attenuation rate based on X / Ka dual-band radar data within a detection range; correcting the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and inputting the corrected radar differential attenuation rate into a precipitation inversion model corresponding to the detection range, to obtain an estimated precipitation intensity output by the precipitation inversion model.

[0182] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0183] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0184] 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 embodiments of the present invention.

Claims

1. A precipitation intensity estimation method, characterized in that: include: Determine the radar differential attenuation rate based on the X / Ka dual-band radar data within the detection range; After correcting the radar differential attenuation rate using a predetermined differential attenuation rate deviation, the differential attenuation rate is input into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model; The X / Ka dual-band radar data includes a radar near-end Ka-band reflectivity factor, a radar near-end X-band reflectivity factor, a radar far-end Ka-band reflectivity factor, and a radar far-end X-band reflectivity factor; Determining the radar differential attenuation rate according to the X / Ka dual-band radar data within the detection range includes: Determine a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determine a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor; and determine the radar differential attenuation rate according to the first difference and the second difference.

2. The precipitation intensity estimation method according to claim 1, characterized in that: The calculation formula for determining the radar differential attenuation rate according to the first difference and the second difference is: Wherein, k(X,Ka) is the radar differential attenuation rate, r1 is the radar near-end distance, r2 is the radar far-end distance, and Z m (X,r1) The radar near-end X-band reflectivity factor, Z m (X, r2) The radar far-end X-band reflectivity factor, Z m (Ka,r1) is the radar near-end Ka-band reflectivity factor, Z m (Ka, r2) is the radar far-end Ka-band reflectivity factor.

3. The precipitation intensity estimation method according to claim 1, characterized in that: The differential attenuation rate deviation is obtained by pre-processing the raindrop spectrum data samples and X / Ka dual-band radar data samples in the detection range within any sampling period, specifically including: Determine the number of raindrops per unit volume and per unit diameter of the detection range within any sampling period according to the raindrop spectrum data samples, so as to construct a T-matrix scattering model related to the detection range; Inputting X-band radar parameters and Ka-band radar parameters into the T-matrix scattering model respectively to obtain X-band radar attenuation rate and Ka-band radar attenuation rate; Calculating the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as a true value of the differential attenuation rate; Determining a differential attenuation rate sample value according to the X / Ka dual-band radar data sample; The differential attenuation rate deviation is determined according to the difference between the differential attenuation rate true value and the differential attenuation rate sample value.

4. The precipitation intensity estimation method according to claim 3, characterized in that: The calculation formula of the T matrix scattering model is: Among them, A H is the radar attenuation rate, λ is the radar wavelength, Im is the integrated imaginary part, D is the raindrop diameter, N(D) is the number of raindrops per unit volume and per unit diameter, f H (D) is the horizontal forward scattering amplitude matrix.

5. The precipitation intensity estimation method according to claim 1, characterized in that: The precipitation inversion model is constructed by fitting the historical X / Ka dual-band radar data and historical precipitation intensity data collected within the detection range; The historical X / Ka dual-band radar data includes X / Ka dual-band radar data collected in multiple time windows; The historical precipitation intensity data includes the precipitation intensity within each of the time windows.

6. The precipitation intensity estimation method according to claim 5, characterized in that: The expression of the precipitation inversion model is: R=2.836k(X,Ka) 1.113 ; Wherein, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate.

7. A precipitation intensity estimation device, characterized in that: include: The differential attenuation rate calculation unit is used to determine the radar differential attenuation rate based on the X / Ka dual-band radar data within the detection range; A precipitation intensity estimation unit, configured to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation, and then input the correction into a precipitation inversion model corresponding to the detection range to obtain an estimated precipitation intensity output by the precipitation inversion model; The X / Ka dual-band radar data includes a radar near-end Ka-band reflectivity factor, a radar near-end X-band reflectivity factor, a radar far-end Ka-band reflectivity factor, and a radar far-end X-band reflectivity factor; The differential attenuation rate calculation unit is further used to determine a first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determine a second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor; and determine the radar differential attenuation rate according to the first difference and the second difference.

8. 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 6 is implemented.

9. 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 6 is implemented.

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