A method, device and storage medium for estimating rainfall based on microwave links

Abstract

The application discloses a microwave link-based rainfall estimation method, which comprises the following steps: obtaining attenuation data through a microwave link; processing the attenuation data by using a pre-processing method to obtain rain-induced attenuation; and inputting the rain-induced attenuation into a pre-established rainfall inversion model to obtain the rainfall intensity of an estimated place. The application discloses a localized rainfall physical inversion model suitable for a microwave link for the first time, solves the defect that the existing method cannot reflect the climate difference of a region, and improves the precision of the rainfall estimation result. The model proposed by the application belongs to a semi-empirical and semi-physical model, and the demand for local historical rainfall data is less compared with a machine learning model. The application perfectly solves the problem that the existing physical model is not suitable for a short link, and improves the applicability of the microwave link rain measurement technology.

Description

TECHNICAL FIELD

[0001] The application relates to a microwave link-based rainfall estimation method, device and storage medium, and belongs to the technical field of meteorological monitoring devices. BACKGROUND

[0002] Rainfall monitoring by using a microwave link is a new monitoring method in recent years. Currently, there are two main rainfall inversion methods at home and abroad. One is a power law formula given by the International Telecommunication Union, and the other is a model obtained by combining local data with machine learning.

[0003] From the formula given by the International Telecommunication Union, the value of the parameter is greatly related to the local climate, but there is no localized parameter at present, and application shows that the formula is completely unsuitable for short links; machine learning can provide a localized model, but it is not a physical model, and the quality requirement for historical data is very high. SUMMARY

[0004] The application aims to provide a microwave link-based rainfall estimation method, device and storage medium, which can establish a localized semi-empirical and semi-physical model according to local climate conditions and improve the rainfall estimation accuracy.

[0005] A microwave link-based rainfall estimation method, the method comprising:

[0006] obtaining attenuation data through a microwave link;

[0007] processing the attenuation data by using a preprocessing method to obtain rain-induced attenuation;

[0008] inputting the rain-induced attenuation into a pre-established rainfall inversion model to obtain the rainfall intensity of the estimated place.

[0009] Further, the preprocessing method comprises:

[0010] distinguishing dry and wet periods by using a sliding standard deviation method to determine the wet period and the dry period;

[0011] subtracting the basic attenuation, when a certain moment is determined as the dry period, the basic attenuation is the attenuation at the moment; when a certain moment is determined as the wet period, the basic attenuation is the average of the attenuations at five moments in the previous dry period, if the previous dry period is less than five moments, the average of all attenuation data is directly taken to determine the basic attenuation value, and the basic attenuation value is subtracted from the original attenuation;

[0012] correcting the influence of the wet antenna, setting the value of the wet antenna influence WA as a constant, and combining the dry and wet period discrimination and the basic attenuation subtraction to obtain the corrected attenuation as the rain-induced attenuation AR, R A = A - BL - W.

[0013] Further, the sliding standard deviation method comprises:

[0014] The standard deviation σ of the attenuation data A of a period of time T before the moment is calculated, and the standard deviation threshold σ0 of the dry-wet conversion is calculated according to the local meteorological data. When σ>σ0, it is determined that the moment is a wet period; otherwise, it is determined to be a dry period.

[0015] Further, the rainfall inversion model establishment method comprises:

[0016] The rainfall exceeding probability corresponding to the rain-induced attenuation is obtained through the rain-induced attenuation;

[0017] The rainfall rate cumulative distribution curve is constructed through the relationship between the rainfall intensity and the rainfall exceeding probability;

[0018] The rainfall inversion model is established by combining the corresponding relationship between the rainfall rate cumulative distribution curve and the rain-induced attenuation and the rainfall exceeding probability.

[0019] Further, the rainfall exceeding probability calculation method comprises:

[0020] The rainfall intensity R corresponding to the inverse pre-inversion rainfall exceeding probability of 0.01% is obtained 0.01

[0021] The equivalent path d of the link is calculated eff

[0022]

[0023] Wherein, d is the actual length of the link; α is the parameter of the ITU power law model corresponding to the link, which is obtained by table lookup; f is the frequency of the link;

[0024] The rain-induced attenuation A corresponding to R 0.01 is calculated 0.01

[0025] Wherein, k is the parameter of the ITU power law model corresponding to the link;

[0026] The exceeding probability p corresponding to the rain-induced attenuation at a moment is calculated through the inverse function of the following equation:

[0027]

[0028] Wherein,

[0029]

[0030] C2=0.855C0+0.546(1-C0)

[0031] C3=0.139C0+0.043(1-C0) ​​

[0032]

[0033] Further, the rainfall intensity R 0.01 The frequency is obtained by long-term measured data or the value given in ITU-R P.837.

[0034] Further, the rainfall rate cumulative distribution curve is obtained by consulting the inversion meteorological manual or obtained by frequency sorting of long-term rainfall observation data.

[0035] Further, the frequency sorting method comprises:

[0036] The measured data is arranged from large to small, the exceeding frequency of each item is calculated, and the exceeding frequency is plotted on the frequency grid paper, and the exceeding frequency calculation formula is:

[0037]

[0038] Wherein, m is the serial number of the item, and n is the total number.

[0039] A calculator device comprising a processor and a storage medium;

[0040] The storage medium is used to store instructions;

[0041] The processor is used to operate according to the instructions to execute the steps of the above method.

[0042] A computer readable storage medium, having stored thereon a computer program, which is executed by a processor to implement the steps of any of the above methods.

[0043] Compared with the prior art, the beneficial effects achieved by the present application are:

[0044] The method provides a semi-physical and semi-experience-based localized rainfall inversion method, compared with the traditional physical method, the method can consider the local climate difference, and provide more accurate estimation result;

[0045] Compared with the machine learning model, the present application overcomes the time scale difference between the microwave data and the rainfall station data, and greatly reduces the data quantity requirement;

[0046] The method effectively solves the problem that the current model is not applicable to short links, and provides a theoretical basis for future short links for monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The flowchart of the method of the present application. DETAILED DESCRIPTION

[0048] The technical means, creative features, purposes and effects of the present application are described below in conjunction with specific embodiments.

[0049] As shown in the formula (I), a microwave link-based rainfall estimation method is disclosed, which comprises the following steps: Figure 1

[0050] Step 1: using a preprocessing method to process the attenuation data obtained by the microwave link to obtain rain-induced attenuation;

[0051] Step 2: obtaining the rainfall exceedance probability corresponding to the rain-induced attenuation;

[0052] Step 3: constructing a rainfall rate cumulative distribution curve through the relationship between rainfall intensity and rainfall exceedance probability;

[0053] Step 4: combining the rainfall rate cumulative distribution curve with the corresponding relationship between rain-induced attenuation and rainfall exceedance probability to establish a rainfall inversion model;

[0054] Step 5: inputting the rain-induced attenuation into the rainfall inversion model to estimate the rainfall intensity of the ground.

[0055] The above method is suitable for a localized rainfall physical inversion model of a microwave link, solves the defect that the existing method cannot reflect the climate difference of the region, and improves the accuracy of the rainfall estimation result;

[0056] In step 1, the preprocessing method comprises:

[0057] Dry and wet period discrimination, using a sliding standard deviation method to determine the wet period and the dry period;

[0058] Basic attenuation deduction, when a certain moment is determined as a dry period, the basic attenuation is the attenuation at the moment; when a certain moment is determined as a wet period, the basic attenuation is the average of the attenuations at five moments in the previous dry period, if the previous dry period is less than five moments, the average of all attenuation data is directly taken to determine the basic attenuation value, and the basic attenuation value is deducted from the original attenuation;

[0059] Wet antenna influence correction, setting the wet antenna influence WA as a constant, and combining the dry and wet period discrimination and the basic attenuation deduction to obtain the corrected attenuation as the rain-induced attenuation AR, A R =A-BL-W.

[0060] In the embodiment, the sliding standard deviation method comprises:

[0061] obtaining the standard deviation σ of the attenuation data A before a certain time T, according to the local meteorological data, the standard deviation threshold σ0 of the dry and wet conversion is counted, when σ>σ0, it is determined that the moment is a wet period; otherwise, it is determined as a dry period;

[0062] ​In step 2, the rainfall exceedance probability calculation method comprises:

[0063] Obtain rainfall intensity R when the pre-play rainfall exceedance probability is 0.01% 0.01

[0064] Calculate the equivalent path d of the link eff

[0065]

[0066] Wherein, d is the actual length of the link; a is the parameter of the ITU power law model corresponding to the link, which is obtained by table lookup; f is the frequency of the link;

[0067] Calculate the rain attenuation A corresponding to R 0.01 0.01

[0068] Wherein, k is the parameter of the ITU power law model corresponding to the link;

[0069] Calculate the exceedance probability p corresponding to the rain attenuation at a certain time through the inverse function of the following equation:

[0070]

[0071] Wherein,

[0072]

[0073] C2=0.855C0+0.546(1-C0)

[0074] C3=0.139C0+0.043(1-C0)

[0075] The rainfall intensity R 0.01 Obtained by frequency statistics of long-term measured data or using the value given in ITU-R P.837 recommendation.

[0076] In step 3, the rainfall intensity calculation method comprises:

[0077] Obtained by solving the inverse function of the following equation:

[0078]

[0079] Wherein,

[0080]

[0081]

[0082] ​​​Wherein, u is the parameter of the formula, determined by local climate conditions, and can be obtained through long-term observation data; if there is no long-term observation data, u takes the reference value 0.025.

[0083] In the embodiment, the rainfall rate cumulative distribution curve is obtained by consulting the inversion meteorological manual, and if there is no corresponding formula, it can also be obtained according to the measured long-term rainfall observation data, and the specific method comprises the following steps: arranging the measured data from large to small, calculating the exceedance frequency of each item, and plotting on the frequency grid paper, and the exceedance frequency calculation formula is:

[0084]

[0085] Wherein, m is the serial number of the item, n is the total number, and a suitable distribution line type is selected, generally gamma distribution, the frequency curve parameter preliminary estimation value is estimated by using the moment method or other methods, and then the parameter size is adjusted through visual estimation to make the curve fit well with each point.

[0086] In the embodiment, the function formula of the rainfall inversion model is solved as follows:

[0087] Taking the following rainfall rate cumulative distribution curve as an example, the inverse function of the following equation is solved, and the inverse function of the exceedance probability p is combined to form the final rainfall inversion model function formula:

[0088]

[0089] Wherein,

[0090]

[0091]

[0092] Wherein, u is the parameter of the formula, determined by local climate conditions, and can be obtained through long-term observation data; if there is no long-term observation data, u takes the reference value 0.025.

[0093] The application also provides a calculator device, comprising a processor and a storage medium.

[0094] The storage medium is used for storing instructions.

[0095] The processor is used for operating according to the instructions to execute the steps of the above method.

[0096] The application also provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of any one of the above methods.

[0097] Those skilled in the art will appreciate that embodiments of the application can be devised for a method, a system, or a computer program product. Accordingly, the present application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.

[0098] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0099] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams.

[0101] The above description is only preferred embodiments of the application. It should be pointed out that, for those skilled in the art, some improvements and modifications can be made without departing from the technical principles of the application, and these improvements and modifications should also be considered as falling within the scope of the application.

Claims

1. A method of rainfall estimation based on microwave links, characterized in that, The method comprises: Obtaining attenuation data through a microwave link; Processing the attenuation data by a preprocessing method to obtain rain-induced attenuation; Inputting the rain-induced attenuation into a pre-established rainfall inversion model to obtain estimated rainfall intensity; The preprocessing method comprises: Dry-wet period discrimination, using a sliding standard deviation method to determine wet and dry periods; Subtracting the base attenuation, when a certain time is determined to be a dry period, the base attenuation is the attenuation at that time; when a certain time is determined to be a wet period, the base attenuation is the average of the attenuations at five times in the previous dry period, if the previous dry period is less than five times, then directly take the average of all attenuation data to determine the base attenuation value, after determining the base attenuation value, subtract it from the original attenuation; The wet antenna influence correction sets the value of the wet antenna influence WA as a constant, and the corrected attenuation obtained by combining the dry-wet period discrimination and the basic attenuation deduction is the rain-induced attenuation AR. ; The sliding standard deviation method comprises: find the standard deviation of the attenuation data A in a time T before this moment , according to the local weather data statistics out of the standard deviation threshold of the dry-wet conversion , when , determine this moment as the wet period; otherwise, it is determined as the dry period; The rainfall inversion model establishment method comprises: Obtaining the rain-induced attenuation corresponding to the attenuation; Constructing a rainfall rate cumulative distribution curve through the relationship between rainfall intensity and rainfall exceedance probability; Combining the rainfall rate cumulative distribution curve with the corresponding relationship between rain-induced attenuation and rainfall exceedance probability to establish a rainfall inversion model; The rainfall exceedance probability calculation method comprises: Obtaining rainfall intensity R for 0.01% exceedance probability of counter-anticipation rainfall 0.01 The link equivalent path d is calculated eff : ； wherein d is the actual length of the link; is the parameter of the ITU power law model corresponding to the link, obtained by table lookup; f is the frequency of the link; Computing R 0.01 Corresponding rain attenuation A 0.01 : where k is a parameter of the ITU power law model corresponding to the link. Calculating the exceedance probability p corresponding to the rain-induced attenuation at a certain time through the inverse function of the following equation: ， wherein , ， ， 。 2. The microwave link based rainfall estimation method of claim 1, wherein, said rainfall intensity R 0.01 Obtained by statistical analysis of long-term measurements or using the values given in ITU-R P.837 Recommendation.

3. The microwave link based rainfall estimation method of claim 1, wherein, The rainfall rate cumulative distribution curve is obtained by consulting the inversion meteorological manual or obtained by frequency sorting according to the measured long-term rainfall observation data.

4. The microwave link based rainfall estimation method of claim 3, wherein, The frequency sorting method comprises: Arranging the measured data from large to small, calculating the exceedance frequency of each item, and plotting it on the frequency grid paper, the exceedance frequency calculation formula is: ， Wherein, m is the serial number of the item, and n is the total number.

5. A computing device, comprising: Including a processor and a storage medium; The storage medium is used to store instructions; The processor is used to operate according to the instructions to perform the steps of the method of any one of claims 1-4.

6. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to realize the steps of the method of any one of claims 1-4.

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