CN116609753A - Artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation - Google Patents
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The embodiment of the disclosure provides an artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation. The method is applied to the technical field of data processing, and comprises the steps of acquiring first radar echo data of a target area when precipitation is not affected artificially; transmitting the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data; calculating the extrapolated radar echo data to obtain extrapolated precipitation data; acquiring second radar echo data of the target area when precipitation is artificially affected; and obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data. In this way, an improved accuracy in evaluating the artificially affected precipitation effect can be achieved on the basis of extrapolation of the artificially affected precipitation effect.
Description
Technical Field
The disclosure relates to the technical field of data processing, in particular to an artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation.
Background
The artificial influence on precipitation is to supplement necessary conditions for precipitation, promote the rapid condensation or collision of cloud droplets and increase the cloud droplets into rain droplets, and drop to the ground. The artificial influence precipitation effect evaluation refers to scientific evaluation and analysis of the effect of artificial intervention precipitation process to know the influence and effect of the artificial intervention on precipitation, and can determine the feasibility of the technology, provide knowledge of the effectiveness, stability and reliability of the technology, and help a decision maker judge whether to adopt or continue to popularize the technology; the influence of different strategies on precipitation amount, space-time distribution and the like can be known, so that the precipitation regulation strategy is optimized, and the effect and efficiency are improved; the rainfall distribution can be regulated and controlled, the risk of natural disasters is reduced, the optimal intervention measures are determined, and early warning and natural disasters treatment are performed in advance; the precipitation amount can be increased or reduced, an optimal water resource management strategy is determined, and the efficiency and the sustainability of water resource utilization are improved; the understanding of the climate system can be increased, the deep study of climate change and influence thereof is facilitated, and scientific basis is provided for formulating policies and measures for coping with the climate change.
At present, a randomization experiment is generally used for evaluating the effect of precipitation caused by artificial influence, the weather is controlled by setting up a test group to implement artificial intervention, no intervention is performed by setting up a control group, and test data are collected and counted from the test group and the control group in a region or a time period where the test is randomly allocated to participate. The method cannot avoid the influence of extreme values, so that the accuracy of evaluating the precipitation effect of the artificial influence is low.
Therefore, there is a need for a radar echo short-term extrapolation-based method for evaluating precipitation effects with high evaluation accuracy.
Disclosure of Invention
The disclosure provides an artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation.
According to a first aspect of the present disclosure, a method for evaluating precipitation effects based on radar echo short-term extrapolation is provided. The method comprises the following steps:
acquiring first radar echo data of a target area when precipitation is not affected artificially;
transmitting the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data;
calculating the extrapolated radar echo data to obtain extrapolated precipitation data;
acquiring second radar echo data of the target area when precipitation is artificially affected;
and obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data.
Further, after the acquiring the first radar echo data of the target area, the method further includes:
and denoising the first radar echo data through a wavelet denoising method.
Further, the method further comprises:
drawing a two-dimensional precipitation map according to the extrapolated precipitation data;
according to the first radar echo data and the second radar echo data, calculating to obtain error variances of all grid points in the two-dimensional precipitation map;
and obtaining a statistically significant effect of the corresponding grid points according to the error variance.
Further, the obtaining a statistically significant effect of the corresponding lattice point according to the error variance includes:
and if the absolute value of the difference value between the second radar echo data and the extrapolated radar echo data is greater than twice the error variance, the statistical significance effect of the corresponding lattice point is 95% statistical significance.
Further, the method further comprises:
if the statistical significance effect of the grid points is 95% of statistical significance, marking the grid points;
according to each grid point with the mark, obtaining the effective range of artificially influencing precipitation;
and obtaining the influence area of artificially influencing precipitation according to the effective range.
Further, the method further comprises:
obtaining a time sequence of each grid point for manually influencing the precipitation effect;
and obtaining the effective duration of the artificially influenced precipitation according to the time length of the time sequence.
Further, the method further comprises:
the method is adjusted by means of iterative analysis.
According to a second aspect of the present disclosure, there is provided an artificial influence precipitation effect evaluation device based on radar echo short-term extrapolation. The device comprises:
the first acquisition module is used for acquiring first radar echo data of the target area when precipitation is not affected manually;
the generation module is used for sending the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data;
the first calculation module is used for calculating the extrapolated radar echo data to obtain extrapolated precipitation data;
the second acquisition module is used for acquiring second radar echo data of the target area when precipitation is affected manually;
and the second calculation module is used for obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data.
According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: the system comprises a memory and a processor, wherein the memory is stored with a computer program, and the processor realizes an artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation when executing the program.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method of artificially influencing precipitation effect evaluation based on radar echo short-cut extrapolation.
The method comprises the steps of obtaining first radar echo data of a target area when precipitation is not affected artificially; transmitting the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data; calculating the extrapolated radar echo data to obtain extrapolated precipitation data; acquiring second radar echo data of the target area when precipitation is artificially affected; according to the difference value of the second radar echo data and the extrapolated radar echo data, the artificially influenced precipitation effect is obtained, and the accuracy of evaluating the artificially influenced precipitation effect is improved on the basis of extrapolation of the artificially influenced precipitation effect.
It should be understood that the description in this summary is not intended to limit key or critical features of the disclosed embodiments, nor is it intended to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 illustrates a flow chart of a method of artificially influencing precipitation effect assessment based on radar echo short-term extrapolation in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a block diagram of an artificially affected precipitation effect assessment device based on radar echo short-term extrapolation in accordance with an embodiment of the present disclosure;
fig. 3 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to be within the scope of this disclosure.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Fig. 1 shows a flowchart of a method 100 for evaluating an effect of precipitation based on radar echo short-term extrapolation, the method 100 comprising:
s101, acquiring first radar echo data of a target area when precipitation is not affected artificially.
In some embodiments, the target area is monitored in real time by a weather radar, and first radar echo data of the target area when precipitation is not affected artificially is obtained; the type of weather radar may be a reflectivity weather radar, a doppler weather radar or a phase contrast weather radar.
In some embodiments, the format of the first radar return data may be BVS format or CDF format.
In some embodiments, after the acquiring the first radar echo data of the target area, further comprising: and denoising the first radar echo data through a wavelet denoising method.
In other embodiments, after the acquiring the first radar echo data of the target area, further includes: and denoising the first radar echo data by a median filtering method or a mean filtering method.
According to the embodiment of the disclosure, the first radar echo data is screened by denoising the first radar echo data, so that the subsequent evaluation efficiency of artificially influencing the precipitation effect is improved.
S102, the first radar echo data are sent to a preset short-term extrapolation model, and extrapolated radar echo data are obtained.
In some embodiments, before said sending the first radar echo data to a preset short-term extrapolation model, comprising: and acquiring ground observation station data and environmental factors, and establishing a preset short-term extrapolation model according to the first radar echo data, the ground observation station data and the environmental factors.
In some embodiments, the type of the preset short-term extrapolation model may be a feedforward neural network model, a convolutional neural network model, a recurrent neural network model, a long-short term memory network model, or a generation countermeasure network.
According to the embodiment of the disclosure, the first radar echo data is processed through the preset short-term extrapolation model, so that the data processing flow is simplified, the data processing efficiency is improved, and the evaluation efficiency of the artificially influenced precipitation effect is further improved.
And S103, calculating the extrapolated radar echo data to obtain extrapolated precipitation data.
In some embodiments, the calculating the extrapolated radar return data to obtain extrapolated precipitation data includes: calculating the extrapolated radar echo data by adopting a Z-R relation to obtain extrapolated precipitation data; for example, the expression of the Z-R relationship may be
R＝a×Z b
Wherein R is precipitation rate, and the unit is millimeter/hour; z is the radar echo factor; a is a first coefficient; b is a second coefficient.
In some embodiments, after the extrapolated precipitation data is obtained, parameters of the preset short-run extrapolation model are adjusted according to actually observed precipitation data to optimize the parameters, so that accuracy of the preset short-run extrapolation model is improved; the parameters may be convolutional layer parameters, pooling layer parameters, full-connection layer parameters, activation function parameters, or batch normalization parameters.
According to the embodiment of the disclosure, the parameters of the preset short-term extrapolation model are adjusted to optimize the preset short-term extrapolation model, so that the accuracy of the output result of the preset short-term extrapolation model is improved, and the accuracy of the evaluation of the effect of precipitation affected by manpower is further improved.
S104, acquiring second radar echo data of the target area when precipitation is artificially affected.
S105, obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data.
In some embodiments, a two-dimensional precipitation map is drawn from the extrapolated precipitation data; calculating to obtain error variances of all grid points in the two-dimensional precipitation map according to the extrapolated radar echo data and the second radar echo data; and obtaining a statistically significant effect of the corresponding grid points according to the error variance. For example, if the absolute value of the difference between the extrapolated radar echo data and the second radar echo data is greater than 2 times the error variance, then the effect of artificially affecting precipitation at the corresponding grid point is 95% statistically significant.
Specifically, the two-dimensional rainfall for forecasting can be used(time resolution is 6 minutes, +.>Representing a precipitation forecast result of (i, j) at a geographical location grid point at a time of k x 6 min) for characterizing a rainfall condition under an assumption that no artificial influence of the rainfall is present; precipitation data R from actual observations t+6min，(i，j) …R t+k*6min，(i,j) …R t+n*6min，(i，j) Comparison (time resolution 6min, R) t+k*6min，(i，j) Representing the actual measured precipitation result with grid points (i, j) at the geographic position at the moment of k x 6min, and representing the rainfall condition under the real artificial influence rainfall condition.
The difference between the two is a two-dimensional lattice point quantitative index (expressed by IF) which artificially influences the precipitation effect,represented at i.times.6The precipitation effect is artificially influenced by the grid point (i, j) at the geographical position at the moment of min.
Artificially influencing rainfall effect IF by comparing each two-dimensional lattice point k*6min，(i，j) And the error variance sigma of the forecast for each grid point (i，j) IF |IF k*6min，(i，j) |＞2*σ (i，j) The effect of artificially influencing rainfall is considered to be 95% statistically significant. Statistically significant ranges are indicated in the figures, representing the effective range of artificially affected rainfall. The area of influence can be calculated from the effective range.
The effective duration of one or more times of artificially affected rainfall operations can be obtained by analyzing the time series of the two-dimensional lattice point quantitative index IF artificially affecting the rainfall effect.
In some embodiments, if the statistically significant effect of the grid points is 95% statistically significant, the grid points are labeled; according to each grid point with the mark, obtaining the effective range of artificially influencing precipitation; and obtaining the influence area of artificially influencing precipitation according to the effective range. For example, the area of influence may be calculated from the sum of the areas of the marked grid points, or the total area of the marked grid points.
In some embodiments, the method further comprises: obtaining a time sequence of each grid point for manually influencing the precipitation effect; and obtaining the effective duration of the artificially influenced precipitation according to the time length of the time sequence. For example by obtaining a time series of artificially influenced precipitation effects; determining the time sequence; and obtaining the time length with the statistical significant effect larger than 0, and obtaining the effective duration of the corresponding grid point.
In some embodiments, the method further comprises: the method is adjusted by means of iterative analysis. The iterative analysis refers to a process of gradually improving and optimizing a system or algorithm through multiple iterations, and the accuracy of evaluating the precipitation effect of artificial influence is improved by continuously adjusting and improving to approach the optimal solution. For example, before and during each execution of the artificial influence precipitation operation, iterative analysis is performed according to the method to perform optimization adjustment on the evaluation of the artificial influence precipitation effect, so as to realize fine management of the artificial influence precipitation effect, and an auxiliary decision-making team adjusts strategies and schemes of the artificial influence precipitation.
According to the embodiment of the disclosure, first radar echo data of a target area when precipitation is not affected manually are acquired; transmitting the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data; calculating the extrapolated radar echo data to obtain extrapolated precipitation data; acquiring second radar echo data of the target area when precipitation is artificially affected; according to the difference value of the second radar echo data and the extrapolated radar echo data, the artificially influenced precipitation effect is obtained, and the accuracy of evaluating the artificially influenced precipitation effect is improved on the basis of extrapolation of the artificially influenced precipitation effect.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present disclosure is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present disclosure. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all alternative embodiments, and that the acts and modules referred to are not necessarily required by the present disclosure.
The foregoing is a description of embodiments of the method, and the following further describes embodiments of the present disclosure through examples of apparatus.
Fig. 2 shows a block diagram of an artificial influence precipitation effect evaluation device 200 based on radar echo short-term extrapolation, the device 200 comprising:
a first acquisition module 201, configured to acquire first radar echo data of a target area when precipitation is not affected manually;
the generating module 202 is configured to send the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data;
the first calculation module 203 is configured to calculate the extrapolated radar echo data to obtain extrapolated precipitation data;
a second obtaining module 204, configured to obtain second radar echo data of the target area when precipitation is affected manually;
and the second calculation module 205 is configured to obtain an artificially affected precipitation effect according to the difference between the second radar echo data and the extrapolated radar echo data.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.
Fig. 3 shows a schematic block diagram of an electronic device 300 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in a ROM302 or a computer program loaded from a storage unit 308 into a RAM 303. In the RAM303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM302, and the RAM303 are connected to each other by a bus 304. I/O interface 305 is also connected to bus 304.
Various components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The calculation unit 301 performs the various methods and processes described above, such as the artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation. For example, in some embodiments, the method of artificially influencing the precipitation effect evaluation based on radar echo short-term extrapolation may be implemented as a computer software program, tangibly embodied on a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 300 via the ROM302 and/or the communication unit 309. When the computer program is loaded into RAM303 and executed by the computing unit 301, one or more steps of the above-described method of evaluating the effect of precipitation based on artificial influence of radar echo short-term extrapolation may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems-on-chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a readable storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The readable storage medium may be a machine-readable signal medium or a machine-readable storage medium. The readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: display means for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that the various forms of flow described above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. An artificial influence precipitation effect evaluation method based on radar echo short-term extrapolation is characterized by comprising the following steps:
acquiring first radar echo data of a target area when precipitation is not affected artificially;
transmitting the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data;
calculating the extrapolated radar echo data to obtain extrapolated precipitation data;
acquiring second radar echo data of the target area when precipitation is artificially affected;
and obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data.
2. The method for evaluating precipitation effects based on radar echo shorthand extrapolation according to claim 1, further comprising, after the acquiring the first radar echo data of the target area:
and denoising the first radar echo data through a wavelet denoising method.
3. The method for evaluating precipitation effects based on radar echo short-cut extrapolation according to claim 1, further comprising:
drawing a two-dimensional precipitation map according to the extrapolated precipitation data;
according to the first radar echo data and the second radar echo data, calculating to obtain error variances of all grid points in the two-dimensional precipitation map;
and obtaining a statistically significant effect of the corresponding grid points according to the error variance.
4. The method for evaluating the effect of precipitation based on the artificial influence of the radar echo short-cut extrapolation according to claim 3, wherein the obtaining the statistically significant effect of the corresponding lattice point according to the error variance comprises:
and if the absolute value of the difference value between the second radar echo data and the extrapolated radar echo data is greater than twice the error variance, the statistical significance effect of the corresponding lattice point is 95% statistical significance.
5. The method for evaluating precipitation effects based on radar echo short-cut extrapolation of claim 4, further comprising:
if the statistical significance effect of the grid points is 95% of statistical significance, marking the grid points;
according to each grid point with the mark, obtaining the effective range of artificially influencing precipitation;
and obtaining the influence area of artificially influencing precipitation according to the effective range.
6. The radar echo short-term extrapolation-based artificial influence precipitation effect assessment method according to claim 5, further comprising:
obtaining a time sequence of each grid point for manually influencing the precipitation effect;
and obtaining the effective duration of the artificially influenced precipitation according to the time length of the time sequence.
7. The method for evaluating precipitation effects based on radar echo short-cut extrapolation of claim 6, further comprising:
the method is adjusted by means of iterative analysis.
8. An artificial influence precipitation effect evaluation device based on radar echo short-term extrapolation is characterized by comprising:
the first acquisition module is used for acquiring first radar echo data of the target area when precipitation is not affected manually;
the generation module is used for sending the first radar echo data to a preset short-term extrapolation model to obtain extrapolated radar echo data;
the first calculation module is used for calculating the extrapolated radar echo data to obtain extrapolated precipitation data;
the second acquisition module is used for acquiring second radar echo data of the target area when precipitation is affected manually;
and the second calculation module is used for obtaining the artificially influenced precipitation effect according to the difference value of the second radar echo data and the extrapolated radar echo data.
9. An electronic device, comprising:
at least one processor;
a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
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