CN117741665A - Precipitation intensity estimation method and device, electronic equipment and storage medium - Google Patents

Abstract

The invention provides a precipitation intensity estimation method, a device, electronic equipment and a storage medium, and belongs to the technical field of atmosphere detection, wherein the method comprises the following steps: determining a radar differential attenuation rate according to the X/Ka dual-band radar data in the detection range; and correcting the radar differential attenuation rate by utilizing the predetermined X/Ka dual-band radar differential attenuation rate deviation, and inputting the radar differential attenuation rate to a precipitation inversion model corresponding to the detection range to obtain the estimated precipitation intensity output by the precipitation inversion model. According to the method, the same precipitation target in the detection range is observed by using the X/Ka dual-band radar, richer radar data are collected, the radar differential attenuation rate is determined according to the radar data of the X band and the Ka band, a precipitation inversion model corresponding to the detection range is constructed, the influence of factors such as noise in the radar data is avoided, and finally the radar differential attenuation rate is input into the precipitation inversion model to obtain estimated precipitation intensity, so that the accuracy of precipitation intensity estimation is greatly improved.

Description

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

Technical field

The invention relates to the technical field of atmospheric detection, and in particular to a precipitation intensity estimation method, device, electronic equipment and storage medium.

Background technique

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

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

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. There are problems such as unstable inversion relationships and inaccurate inversion results, resulting in low accuracy in precipitation intensity estimation.

Contents of the invention

The present invention provides a precipitation intensity estimation method, device, electronic equipment and storage medium to solve the problem of low precipitation intensity estimation accuracy in the prior art.

In a first aspect, the present invention provides a precipitation intensity estimation method, including:

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 radar differential attenuation rate is input to a precipitation inversion model corresponding to the detection range, and the estimated precipitation intensity output by the precipitation inversion model is obtained.

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 Radar far-end X-band reflectivity factor;

Determining the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range includes:

Determine the first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determine the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor The second difference between the reflectivity factors;

The radar differential attenuation rate is determined based on the first difference and the second difference.

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

Among them, k (X, Ka) is the differential attenuation rate of the radar, r ₁ is the near-end distance of the radar, r ₂ is the far-end distance of the radar, Z _m (X, r ₁ ) the near-end X of the radar Band reflectivity factor, Z _m (X,r ₂ ). The far-end X-band reflectivity factor of the radar, Z _m (Ka,r ₁ ) is the Ka-band reflectivity factor of the near-end radar, Z _m (Ka,r ₂ ) is the Ka-band reflectivity factor at the far end of the radar.

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

According to the raindrop spectrum data sample, determine the number of raindrops within the unit volume unit diameter range of the detection range in any sampling period, for constructing a T matrix scattering model related to the detection range;

Input the X-band radar parameters and Ka-band radar parameters into the T matrix scattering model respectively to obtain the X-band radar attenuation rate and the Ka-band radar attenuation rate;

Calculate the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as the true value of the differential attenuation rate;

Determine the differential attenuation rate sample value according to the X/Ka dual-band radar data sample;

The differential attenuation rate deviation is determined based on the difference between the differential attenuation rate true value and the differential attenuation rate sample value.

According to a precipitation intensity estimation method provided by the present invention, 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 imaginary part after integration, D is the raindrop diameter, N(D) is the number of raindrops within the unit diameter range of the unit volume, f _H (D) is the horizontal front Scattering amplitude matrix.

According to a precipitation intensity estimation method provided by the present invention, the precipitation inversion model is constructed by fitting 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 precipitation intensity within each of the time windows.

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

R＝2.836k(X,Ka) ^1.113 ;

Where, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate.

In a second aspect, the present invention also provides a precipitation intensity estimation device, including:

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 measurement unit is used to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation, then input it into a precipitation inversion model corresponding to the detection range, and obtain the output of the precipitation inversion model. Estimated precipitation intensity.

In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, any one of the above is implemented. Steps of precipitation intensity estimation method.

In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the precipitation intensity estimation methods described above are implemented.

The precipitation intensity estimation method, device, electronic equipment and storage medium provided by the present invention use X/Ka dual-band radar to observe the same precipitation target within the detection range, and collect richer radar data, and according to the X-band and Ka-band The radar data in the two bands determine the radar differential attenuation rate and build a precipitation inversion model corresponding to the detection range to avoid the influence of noise and other factors in the radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain the estimated precipitation intensity, which greatly Improved accuracy of precipitation intensity estimation.

Description of drawings

In order to explain the present invention or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are of the present invention. For some embodiments of the invention, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is one of the flow diagrams of the precipitation intensity estimation method provided by the present invention.

Figure 2 is the second schematic flow chart of the precipitation intensity estimation method provided by the present invention.

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

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

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

FIG. 6 is a schematic flowchart of the method for determining the differential attenuation rate deviation provided by the present invention.

Figure 7 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 based on the measured raindrop spectrum.

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

Figure 9 is a schematic diagram of the fitted precipitation inversion model provided by the present invention.

Figure 10 is one of the schematic diagrams comparing the precipitation intensity estimation results provided by the present invention with the actual measured precipitation intensity and R( _AH ) method estimation results.

Figure 11 is the second schematic diagram of the near-end reflectivity factor of the X/Ka dual-band radar provided by the present invention.

Figure 12 is the second schematic diagram of the far-end reflectivity factor of the X/Ka dual-band radar provided by the present invention.

Figure 13 is the second schematic diagram comparing the precipitation intensity estimation results provided by the present invention with the measured precipitation intensity and R( _AH ) method estimation results.

Figure 14 is a schematic structural diagram of the precipitation intensity estimation device provided by the present invention.

Figure 15 is a schematic structural diagram of the electronic device provided by the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present invention more clear, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention. , not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts fall within the scope of protection of the present invention.

It should be noted that in the description of the embodiments of the present invention, the terms "comprising", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or equipment including a series of elements It includes not only those elements but also other elements not expressly listed or inherent in the process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must be Has a specific orientation, is constructed and operates in a specific orientation and is therefore not to be construed as limiting the 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 specific circumstances.

The terms "first", "second", etc. in the present invention are used to distinguish similar objects, but are not used to describe a specific order or sequence. It is to be understood that the figures so used are interchangeable under appropriate circumstances so that embodiments of the invention can be practiced in sequences other than those illustrated or described herein, and that "first," "second," etc. are distinguished Objects are usually of one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

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.

To estimate precipitation intensity based on single polarization weather radar (such as Doppler weather radar), the radar reflectivity factor Z (unit: mm ⁶ m ^-3 ) is generally calculated based on the measured echo power, and then the radar meteorological equation is applied. The ZR relationship (Z=aR ^b ) deduces the precipitation intensity R (unit: mm/h). The typical relationship formula commonly used at present is Z=200R ^1.6 .

The size of the radar reflectivity factor Z value is directly related to the raindrop spectrum, while the rainfall rate R is not only related to the raindrop spectrum, but also related to the final falling speed of the raindrops. Therefore, the same rainfall rate R may correspond to different raindrop spectra and may also correspond to different Z values.

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

Precipitation intensity is estimated based on dual-polarization weather radar. Precipitation intensity is generally estimated through different combinations of four polarization parameters.

The polarization parameters here include 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).

The different combinations here can be roughly divided into five categories, namely R(Z _H ), R(K _DP ), R(Z _H ,Z _DR ), R(K _DP ,Z _DR ) and R(A _H ). The specific relationship The formula may include: R(Z _H )＝a ₁ Z _H ^b1 , R(K _DP )＝a ₂ K _DP ^b2 , R(Z _H ,Z _DR )＝a ₃ Z _H ^b3 10 ^c3ZDR , R(K _DP , Z _DR )=a ₄ K _DP ^b4 10 ^{c4 ZDR} , R(A _H )=a ₅ A _H ^b5 .

However, the five-category combination methods under dual-polarization weather radar all have shortcomings.

The R(Z _H ) method is a ZR relationship method under dual-polarization weather radar. Therefore, R(Z _H ) also faces the problem of inaccurate precipitation intensity estimation caused by the unstable ZR relationship.

The differential propagation phase shift rate K _DP is derived from the differential propagation phase shift Φ _DP . Therefore, the method of measuring precipitation using K _DP is not affected by absolute calibration error and attenuation. However, when the rainfall rate R is not large, K _DP contains large noise information, causing K _DP to be inaccurate, which in turn affects the estimation of precipitation by R (K _DP ) and R (K _DP , Z _DR ) in weak precipitation areas. Therefore, R(K _DP ) and R(K _DP ,Z _DR ) are generally used for measurements in heavy precipitation areas. In addition, K _DP is the derivative of Φ _DP with respect to distance, but in actual calculations, the derivation can only be performed at a limited distance. Therefore, the method of estimating precipitation intensity based on K _DP has a trade-off problem between accuracy and distance resolution.

Differential reflectance Z _DR is a relative power measurement, that is, the ratio of horizontal polarization power to vertical polarization power. Any method of precipitation measurement using Z _DR must be used in conjunction with Z _H or K _DP , resulting in R (Z _H , Z _DR ) and R(K _DP ,Z _DR ) also face the problem of inaccurate estimation of precipitation intensity by R(Z _H ) and R(K _DP ).

The key to estimating precipitation intensity using the R(A _H ) method is to invert and obtain accurate A _H. Currently, K _DP inversion (A _H = a ₆ K _DP ^b6 ) or Z _H inversion (A _H = a ₇ Z _H ^b7 ) obtained. On the one hand, K _DP is easily contaminated by noise, resulting in poor quality of A _H data obtained through K _DP inversion. On the other hand, for the method of obtaining A _H through Z _H inversion, due to different precipitation types, the inversion coefficient has great instability, resulting in large errors in the inverted A _H. Therefore, due to the instability of the inversion relationship and inaccurate inversion results using single-band radar to invert A _H , even compared with the precipitation intensity estimation relationships established by Z _H , Z _DR and K _DP , R(A _H ) method has the advantage of being insensitive to changes in raindrop spectrum, but the R(A _H ) method still has the problem of inaccurate estimation of precipitation intensity.

In view of the problem of low accuracy in current precipitation intensity estimation methods, the present invention provides a precipitation intensity estimation method, device, electronic equipment and storage medium with high accuracy based on X/Ka dual-band radar.

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

The precipitation intensity estimation method, device, electronic equipment and storage medium provided by the present invention will be described below with reference to Figures 1-15.

Figure 1 is one of the flow diagrams of the precipitation intensity estimation method provided by the present invention. As shown in Figure 1, it includes but is not limited to the following steps:

Step 101: Determine the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range.

Generally speaking, radar signals will attenuate during propagation. First of all, the propagation of radar signals in free space follows the law of free space propagation loss. The power of radar signals weakens with the increase of propagation distance, that is, the attenuation of radar signals occurs. Secondly, in addition to weakening as the propagation distance increases, radar signals are also attenuated by factors such as atmospheric attenuation, radar antenna loss or material properties, terrain relief, and obstruction by obstacles.

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

When the present invention estimates precipitation intensity, it 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 the antenna system. The radar beams of the two bands interact with the precipitation target to generate echo signals. These echo signals It is then collected and processed by X/Ka dual-band radar to obtain X/Ka dual-band radar data within the detection range.

Among them, X/Ka dual-band radar data is the radar data collected and processed by the radar system using both X-band and Ka-band frequency bands.

Then based on the radar data in the X-band and Ka-band frequency bands, the current radar differential attenuation rate can be determined through calculation.

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

Step 102: After correcting the radar differential attenuation rate using the predetermined differential attenuation rate deviation, input it into the precipitation inversion model corresponding to the detection range, and obtain the estimated precipitation output by the precipitation inversion model. strength.

First, during the precipitation intensity estimation process, 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 outliers, so it is necessary to The radar differential attenuation rate is corrected using a predetermined differential attenuation rate deviation.

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. However, due to the influence of noise or non-Rayleigh scattering, the radar differential attenuation rate derived from X/Ka dual-band radar data may be negative. value. Therefore, the radar differential attenuation rate needs to be corrected.

The corrected radar differential attenuation rate is then input into the precipitation inversion model corresponding to the detection range.

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 and the radar differential attenuation rate of the same precipitation target within the detection range.

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. The terrain and obstacles within the detection range It is fixed. Therefore, in the before and after stages of precipitation, the attenuation of radar signals caused by factors such as propagation distance, radar antenna loss or material properties, terrain fluctuations, and obstacle obstruction is certain. The radar differential attenuation rate obtained based on X/Ka dual-band radar data is only Varies depending on precipitation conditions.

Finally, by inputting the radar differential attenuation rate determined and corrected based on 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.

The precipitation intensity estimation method provided by the present invention uses X/Ka dual-band radar to observe the same precipitation target within the detection range, collects richer radar data, and determines the radar based on the radar data of the X-band and Ka-band. Differential attenuation rate and build a precipitation inversion model corresponding to the detection range to avoid the influence of factors such as noise in radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain an estimated precipitation intensity, which greatly improves the accuracy of precipitation intensity estimation. Spend.

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

The radar reflectivity factor is a parameter that characterizes the echo intensity of meteorological targets. 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 per unit volume of the precipitation target, and can be used to express Precipitation intensity.

The near-end of the radar 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.

The far end of the 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, and the propagation path of the radar wave is relatively complex and may experience multiple absorption and reflections. and scattering, the signal strength is weak.

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 near-end Ka-band reflection of the radar is obtained. X/Ka dual-band radar data such as rate factor, radar near-end X-band reflectivity factor, radar far-end Ka-band reflectivity factor, and radar far-end X-band reflectivity factor.

Further, according to the X/Ka dual-band radar data within the detection range, the radar differential attenuation rate is determined, specifically by calculating the first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor. value, calculate the second difference between the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor, and finally determine the radar differential attenuation rate based on the first difference and the second difference.

Figure 3 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 to 07 a.m. on May 11, 2022. :53.

Figure 4 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 to 07 a.m. on May 11, 2022. :53.

The precipitation intensity estimation method provided by the present invention determines the radar differential attenuation rate based on the difference between the X-band and Ka-band reflectivity factors, overcomes the defect of an unstable relationship between the single-band radar reflectivity factor and precipitation intensity, and improves the radar differential attenuation rate. more reliable, thus improving the accuracy of precipitation intensity estimates.

Based on the contents of the above embodiments, as an optional embodiment, the calculation formula for determining the radar differential attenuation rate based on the first difference and the second difference is as follows:

Among them, k (X, Ka) is the differential attenuation rate of the radar, r ₁ is the near-end distance of the radar, r ₂ is the far-end distance of the radar, Z _m (X, r ₁ ) the near-end X of the radar Band reflectivity factor, Z _m (X,r ₂ ). The far-end X-band reflectivity factor of the radar, Z _m (Ka,r ₁ ) is the Ka-band reflectivity factor of the near-end radar, Z _m (Ka,r ₂ ) is the Ka-band reflectivity factor at the far end of the radar.

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 used to calculate the differential attenuation rate of the radar was collected from 00:53 in the morning to 07:53 in the morning on May 11, 2022.

Figure 6 is a schematic flowchart of the method for determining the differential attenuation rate deviation provided by the present invention. As shown in Figure 6, as an optional embodiment, the differential attenuation rate deviation is for the detection range in any sampling period. Raindrop spectrum data samples and X/Ka dual-band radar data samples are pre-processed, including:

According to the raindrop spectrum data sample, determine the number of raindrops within the unit volume unit diameter range of the detection range in any sampling period, for constructing a T matrix scattering model related to the detection range;

Input the X-band radar parameters and Ka-band radar parameters into the T matrix scattering model respectively to obtain the X-band radar attenuation rate and the Ka-band radar attenuation rate;

Calculate the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as the true value of the differential attenuation rate;

Determine the differential attenuation rate sample value according to the X/Ka dual-band radar data sample;

The differential attenuation rate deviation is determined based on the difference between the differential attenuation rate true value and the differential attenuation rate sample value.

Raindrop spectrum data samples and X/Ka dual-band radar data samples were measured by the raindrop spectrometer and the same precipitation target within the detection range of the X/Ka dual-band radar respectively.

The sampling period can be comprehensively determined based on the performance parameters of the raindrop spectrometer, the performance parameters of the X/Ka dual-band radar, specific scene measurement requirements and other factors, and the present invention does not limit this.

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 precipitation intensity are both X/Ka dual-band radar data of the same type and setting position. Ka dual-band radar is obtained by measuring the same precipitation target within the same detection range, and the sampling period of the former is earlier than the sampling period of the latter.

It should be noted that the raindrop spectrometer used to collect 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, and an acoustic raindrop spectrometer. Etc., the present invention does not limit this.

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

Construct a T-matrix scattering model based on the raindrop spectrum collected by the raindrop spectrometer during the sampling period, and input the X-band radar parameters and Ka-band radar parameters of the X/Ka dual-band radar into the T-matrix scattering model to simulate the X-band of the raindrop spectrum. Radar attenuation rate and Ka-band radar attenuation rate, and the difference between the two is used as the true value of the differential attenuation rate.

In addition, for the X/Ka dual-band radar data samples collected by the X/Ka dual-band radar during the sampling period, the differential attenuation rate sample value is calculated according to the calculation formula for determining the radar differential attenuation rate in the aforementioned embodiment, and the true value of the differential attenuation rate is obtained The difference from the differential decay rate sample value is the differential decay rate deviation.

The precipitation intensity estimation method provided by the present invention determines the differential attenuation rate deviation based on the T matrix scattering model constructed from 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 predict precipitation. The radar differential attenuation rate measured and calculated during the estimation process is corrected, thereby eliminating the adverse effects of noise factors other than the raindrop spectrum on the precipitation intensity estimation results and improving the accuracy of the precipitation intensity estimation.

Based on the contents of the above embodiments, as an optional embodiment, 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 imaginary part after integration, D is the raindrop diameter, N(D) is the number of raindrops within the unit diameter range of the unit volume, f _H (D) is the horizontal front Scattering amplitude matrix.

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

Figure 7 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 based on the measured raindrop spectrum. Among them, the k(X,Ka)-2DVD curve is the true value curve of the differential attenuation rate obtained based on 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 used to calculate the differential attenuation rate of the radar was collected from 00:53 in the morning to 07:53 in the morning on May 11, 2022.

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

In another embodiment, assume that 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. During any sampling period, the raindrop spectrometer collects 1 piece of observation data, and the X/Ka dual-band radar can collect 30 pieces of observation data.

Therefore, when obtaining the true value of the differential attenuation rate, a T matrix scattering model is constructed based on the collected raindrop spectrum data, and combined with the radar parameters, the scattering is simulated according to the T matrix scattering model and the true value of the differential attenuation rate is obtained.

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

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.

Based on the contents of the above embodiments, 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. ;

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 time window, which is obtained by measuring the same precipitation target within the detection range with a rain gauge.

Among them, the time window refers to the time range set to collect historical X/Ka dual-band radar data and historical precipitation intensity data before estimating precipitation intensity. The time window can be comprehensively determined based on factors such as the performance parameters of the rain gauge, the performance parameters of the X/Ka dual-band radar, specific scene measurement requirements, etc., and the present invention does not limit this.

It should be noted that the method of fitting scattered points into curves in the present invention includes polynomial fitting, linear fitting, spline interpolation, least squares fitting, nonlinear fitting, Fourier series fitting and other methods. The present invention does not limit this.

Figure 8 is a schematic diagram of the measured rainfall intensity provided by the present invention. Among them, the observation time is from 00:53 in the morning to 07:53 in the morning on May 11, 2022.

Figure 9 is a schematic diagram of the fitted precipitation inversion model provided by the present invention. Among them, the historical X/Ka dual-band radar data is the radar differential attenuation rate, and the curve is the fitted precipitation inversion model.

As shown in Figure 9, there is a good correspondence between historical precipitation intensity and historical X/Ka dual-band radar differential attenuation rate, and the above precipitation inversion model has good performance.

In one embodiment, it is assumed that 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 collected 1 piece of historical precipitation intensity data, and the X/Ka dual-band radar collected 30 pieces of historical X/Ka dual-band radar data.

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

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.

The radar differential attenuation rate is corrected through the above differential attenuation rate deviation.

Finally, a precipitation inversion model is constructed based on fitting the corrected historical radar differential attenuation rate and historical precipitation intensity data within multiple time windows.

The precipitation intensity estimation method provided by the present invention uses X/Ka dual-band radar data with 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. Higher, thereby constructing a more stable and reliable precipitation inversion model and improving the accuracy of precipitation estimation methods.

Based on the contents of the above embodiments, as an optional embodiment, the expression of the precipitation inversion model is:

R＝2.836k(X,Ka) ^1.113 ;

Where, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate.

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 calculated. The value is compared with the preset threshold. When the estimated difference is greater than the preset threshold, the differential attenuation rate deviation and precipitation inversion model are re-determined. According to the aforementioned method of determining the differential attenuation rate deviation and precipitation inversion model, the differential attenuation rate deviation and precipitation inversion model are re-determined.

After re-determining the differential attenuation rate deviation and re-constructing the precipitation inversion model, the re-determined differential attenuation rate deviation is used to correct the radar differential attenuation rate and input into the re-constructed precipitation inversion model to finally obtain an estimate suitable for the current precipitation situation. Measure precipitation intensity.

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, then when the absolute value of the estimated difference between the two is greater than the first preset threshold, re-determine Differential decay rate bias and precipitation inversion models. If the estimated precipitation intensity output by the precipitation inversion model during the sampling period is less than the real-time precipitation intensity, then when the absolute value of the estimated difference between the two is greater than the second preset threshold, the differential attenuation rate deviation and precipitation are re-determined. Inversion model.

Wherein, the first preset sub-threshold is not equal to the second preset sub-threshold.

It should be noted that the preset threshold, the first preset sub-threshold, and the second preset sub-threshold can be determined based on historical precipitation intensity estimation results, performance and parameters of the measurement device and other factors, and the present invention does not limit this.

By checking the estimated precipitation intensity output by the precipitation inversion model at intervals, when the difference between the estimated precipitation intensity and the measured precipitation intensity is too large, it means that the precipitation situation has changed. At this time, the differential attenuation rate deviation and Precipitation inversion model, this invention can adapt to application scenarios with complex precipitation conditions and large fluctuations in precipitation.

Finally, the present invention also obtains the measured precipitation intensity by measuring the real-time precipitation intensity, and uses the R( _AH ) method to estimate the precipitation intensity to obtain the R( _AH ) method estimation result, and then combines the measured precipitation intensity, R( _AH ) method The estimation results are compared with the precipitation intensity estimation results of the present invention, thereby evaluating the accuracy of the precipitation intensity estimation method provided by the present invention.

Since the attenuation of Ka-band electromagnetic waves in rain areas is extremely serious, and there is a meter scattering effect in Ka-band rain area observations, the R(A _H ) method based on the X-band attenuation rate is used to estimate the precipitation process.

The X-band attenuation rate A _H is calculated based on the commonly used inversion relationship. The calculation formula is as follows:

Among them, the unit of Z _H is mm ⁶ m ^-3 and the unit of A _H is dB/km.

The precipitation intensity R(A _H ) is calculated based on the currently commonly used inversion relationship. The calculation formula is as follows:

Figure 10 is one of the schematic diagrams comparing the precipitation intensity estimation results provided by the present invention with the actual measured precipitation intensity and R( _AH ) method estimation results. Among them, the R(k(X,Ka)) curve is the precipitation intensity estimation result curve of the present invention, the R _Measured curve is the measured precipitation intensity curve, and the R( _AH ) curve is the precipitation intensity estimated by the R( _AH ) method. The result curve, the observation time is from 00:53 in the morning to 07:53 in the morning on May 11, 2022.

As shown in Figure 10, the estimated precipitation intensity results obtained by the present invention have a small difference with the measured precipitation intensity, and the accuracy is much higher than the R(A _H ) method.

In addition, the accuracy of the present invention and the R( _AH ) method is also evaluated through the average deviation (AD) and correlation coefficient (CorrelationCoefficient, CC).

The average deviation AD and correlation coefficient CC are defined as follows:

Among them, Cov(R _Estimated -R _Measured ) is the covariance of the estimated precipitation intensity R _Estimated and the measured precipitation intensity R _Measured , Var[R _Estimated ] is the variance of R _Estimated , and Var[R _Measured ] is the variance of R _Measured .

Table 1 is one of the statistical tables of the results of precipitation estimation obtained by the present invention and the R( _AH ) method through the average deviation and correlation coefficient. As shown in Table 1, the R(k(X,Ka)) precipitation estimation method based on the dual-band differential attenuation rate of the present invention obtains an estimation result with smaller average deviation and higher correlation coefficient, which is significantly better than that based on the single-band attenuation. R(A _H ) method for estimating precipitation.

Table 1 One of the statistical tables of precipitation estimation results

In order to better evaluate the accuracy of the present invention in estimating precipitation, 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. Provided by the present invention The precipitation intensity estimation method and the R( _AH ) method estimate the precipitation intensity during this period, compare the obtained precipitation intensity estimation results, and again use the average deviation and correlation coefficient to evaluate the present invention and the R(AH) method The accuracy of this process is explained below with reference to Figures 11-13.

Figure 11 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 :19.

Figure 12 is the second schematic diagram of the far-end reflectivity factor of the X/Ka dual-band radar provided. 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 04:19 am to 14 pm on June 11, 2022. :19.

Figure 13 is the second schematic diagram comparing the precipitation intensity estimation results provided by the present invention with the measured precipitation intensity and R( _AH ) method estimation results. Among them, the R(k(X,Ka)) curve is the precipitation intensity estimation result curve of the present invention, the R _Measured curve is the measured precipitation intensity curve, and the R( _AH ) curve is the precipitation intensity estimated by the R( _AH ) method. Result curve.

As shown in Figure 13, the estimated precipitation intensity results obtained by the present invention have a small difference with the measured precipitation intensity, and the accuracy is much higher than the R(A _H ) method.

Table 2 is the second statistical result of precipitation estimation obtained by the present invention and the R(A _H ) method through the average deviation and correlation coefficient. As shown in Table 2, the R(k(X,Ka)) precipitation estimation method based on the dual-band differential attenuation rate of the present invention obtains an estimation result with smaller average deviation and higher correlation coefficient, which is significantly better than that based on the single-band attenuation. R(A _H ) method for estimating precipitation.

Table 2 Precipitation estimation results statistical table two

Figure 14 is a schematic structural diagram of a precipitation intensity estimating device provided by the present invention. As shown in Figure 14, the present invention also provides a precipitation intensity estimating device, which mainly includes:

The differential attenuation rate measurement unit 1401 is used to determine the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range;

The precipitation intensity measurement unit 1402 is configured to use a predetermined differential attenuation rate deviation to correct the radar differential attenuation rate, then input it into a precipitation inversion model corresponding to the detection range, and obtain the precipitation inversion model based on the predetermined differential attenuation rate deviation. Outputs the estimated precipitation intensity.

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

The precipitation intensity estimation device provided by the present invention uses X/Ka dual-band radar to observe the same precipitation target within the detection range, collects richer radar data, and determines the radar based on the radar data of the X-band and Ka-band. Differential attenuation rate and build a precipitation inversion model corresponding to the detection range to avoid the influence of factors such as noise in radar data. Finally, the radar differential attenuation rate is input into the precipitation inversion model to obtain an estimated precipitation intensity, which greatly improves the accuracy of precipitation intensity estimation. Spend.

Based on the above embodiments, as an optional embodiment, the X/Ka dual-band radar data is collected using an X/Ka-band dual-antenna radar, and the X/Ka-band dual-antenna radar adopts an all-solid-state FM continuous wave radar. The mechanism works, transmitting and receiving are separated, and the X/Ka band shares two transmitting and receiving antennas.

Figure 15 is a schematic structural diagram of an electronic device provided by the present invention. As shown in Figure 15, the electronic device may include: a processor 1510, a communications interface 1520, a memory 1530 and a communication bus 1540. Among them, the processor 1510, the communication interface 1520, and the memory 1530 complete communication with each other through the communication bus 1540. The processor 1510 can call the logic instructions in the memory 1530 to execute the precipitation intensity estimation method. The method includes: determining the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range; using the predetermined differential attenuation rate. After the deviation corrects the radar differential attenuation rate, it is input to the precipitation inversion model corresponding to the detection range, and the estimated precipitation intensity output by the precipitation inversion model is obtained.

In addition, the above-mentioned logical instructions in the 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 this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are read by a computer, When executed, the computer can execute the precipitation intensity estimation method provided by the above embodiments. The method includes: determining the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range; using the predetermined differential attenuation rate deviation. After correcting the radar differential attenuation rate, it is input to the precipitation inversion model corresponding to the detection range, and the estimated precipitation intensity output by the precipitation inversion model is obtained.

In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, and the computer program is implemented when executed by the processor to execute the precipitation intensity estimation method provided in the above embodiments, the The method includes: determining the radar differential attenuation rate based on 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 then inputting it into the data corresponding to the detection range. A precipitation inversion model is used to obtain the estimated precipitation intensity output by the precipitation inversion model.

The device embodiments described above are only illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.

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

Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be used Modifications are made to the technical solutions described in the foregoing embodiments, or equivalent substitutions are made to some of the technical features; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A precipitation intensity estimation method, characterized by including: 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 radar differential attenuation rate is input to a precipitation inversion model corresponding to the detection range, and the estimated precipitation intensity output by the precipitation inversion model is obtained. 2. The precipitation intensity estimation method according to claim 1, characterized in that 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 radar far-end X-band reflectivity factor; Determining the radar differential attenuation rate based on the X/Ka dual-band radar data within the detection range includes: Determine the first difference between the radar far-end X-band reflectivity factor and the radar far-end Ka-band reflectivity factor, and determine the radar near-end X-band reflectivity factor and the radar near-end Ka-band reflectivity factor The second difference between the reflectivity factors; The radar differential attenuation rate is determined based on the first difference and the second difference. 3. The precipitation intensity estimation method according to claim 2, characterized in that the calculation formula for determining the radar differential attenuation rate based on the first difference and the second difference is: Among them, k (X, Ka) is the differential attenuation rate of the radar, r ₁ is the near-end distance of the radar, r ₂ is the far-end distance of the radar, Z _m (X, r ₁ ) the near-end X of the radar Band reflectivity factor, Z _m (X,r ₂ ). The far-end X-band reflectivity factor of the radar, Z _m (Ka,r ₁ ) is the Ka-band reflectivity factor of the near-end radar, Z _m (Ka,r ₂ ) is the Ka-band reflectivity factor at the far end of the radar. 4. The precipitation intensity estimation method according to claim 1, characterized in that the differential attenuation rate deviation is based on raindrop spectrum data samples and X/Ka dual-band radar data samples of the detection range in any sampling period. Obtained by pre-processing, specifically including: According to the raindrop spectrum data sample, determine the number of raindrops within the unit volume unit diameter range of the detection range in any sampling period, for constructing a T matrix scattering model related to the detection range; Input the X-band radar parameters and Ka-band radar parameters into the T matrix scattering model respectively to obtain the X-band radar attenuation rate and the Ka-band radar attenuation rate; Calculate the difference between the X-band radar attenuation rate and the Ka-band radar attenuation rate as the true value of the differential attenuation rate; Determine the differential attenuation rate sample value according to the X/Ka dual-band radar data sample; The differential attenuation rate deviation is determined based on the difference between the differential attenuation rate true value and the differential attenuation rate sample value. 5. The precipitation intensity estimation method according to claim 4, 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 imaginary part after integration, D is the raindrop diameter, N(D) is the number of raindrops within the unit diameter range of the unit volume, f _H (D) is the horizontal front Scattering amplitude matrix. 6. The precipitation intensity estimation method according to claim 1, characterized in that the precipitation inversion model is simulated using historical X/Ka dual-band radar data and historical precipitation intensity data collected within the detection range. Constructed after merger; 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 precipitation intensity within each of the time windows. 7. The precipitation intensity estimation method according to claim 6, characterized in that the expression of the precipitation inversion model is: R＝2.836k(X,Ka) ^1.113 ; Where, R is the estimated precipitation intensity, and k(X,Ka) is the corrected radar differential attenuation rate. 8. A precipitation intensity estimating device, characterized by comprising: The differential attenuation rate measurement 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 measurement unit is used to correct the radar differential attenuation rate using a predetermined differential attenuation rate deviation, then input it into a precipitation inversion model corresponding to the detection range, and obtain the output of the precipitation inversion model. Estimated precipitation intensity. 9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that when the processor executes the computer program, the processor implements the claims as claimed in Precipitation intensity estimation method according to any one of 1 to 7. 10. A non-transitory computer-readable storage medium with a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method for estimating precipitation intensity according to any one of claims 1 to 7 is implemented. .