CN113988466A - Correction method, device and equipment for forecast rainfall data - Google Patents
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Abstract
The invention relates to a correction method, a device and equipment for forecasting rainfall data, wherein the method comprises the following steps: acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area; acquiring actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data; and (4) correcting and analyzing the predicted precipitation through the difference precipitation data to obtain the corrected predicted precipitation. According to the correction method of the forecast rainfall data, error rainfall data are obtained by obtaining and comparing the original forecast rainfall with the actual rainfall, the forecast rainfall is corrected according to the error rainfall data, so that the error between the corrected forecast rainfall and the actual rainfall can be ignored, the accuracy of the forecast rainfall is improved, and the corrected forecast rainfall provides a guide direction for accurate weather forecast.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a correction method, a correction device and correction equipment for forecasting rainfall data.
Background
The rainfall is important disaster data, and is also an important basis for calculating the water resource amount of a region, so that it is necessary to find an accurate and simple method for predicting the rainfall, and the measurement and prediction accuracy of the rainfall are important. However, in the conventional rainfall measurement, data may vary depending on weather factors, and if the obtained data is not corrected, the stored historical data is erroneous data, and the predicted rainfall is also erroneous.
Disclosure of Invention
The embodiment of the invention provides a method, a device and equipment for correcting forecast rainfall data, which are used for solving the technical problem that the acquired rainfall data has errors because the existing measured rainfall data is not corrected.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
a correction method for forecast rainfall data comprises the following steps:
acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area;
acquiring actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data;
correcting and analyzing the predicted precipitation according to the error precipitation data to obtain a corrected predicted precipitation;
wherein, the water content calculation formula is as follows:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is the height difference between the top and bottom cloud layers, wxThe predicted precipitation for region x.
Preferably, the correcting and analyzing the predicted precipitation amount through the error precipitation data, and obtaining the corrected predicted precipitation amount includes: analyzing the error precipitation data and the cloud cluster data by adopting Fourier transform to obtain corrected height difference; and obtaining the corrected predicted precipitation through the corrected height difference and the water content calculation formula.
Preferably, the correcting and analyzing the predicted precipitation amount through the error precipitation data, and obtaining the corrected predicted precipitation amount includes: and analyzing the cloud cluster data by adopting a multi-scale optical flow technology based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, taking a predicted precipitation corresponding to the corrected error precipitation data as the corrected predicted precipitation.
Preferably, the correcting and analyzing the predicted precipitation amount through the error precipitation data, and obtaining the corrected predicted precipitation amount includes: and analyzing the cloud cluster data by adopting an accumulative distribution function based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, taking the predicted precipitation corresponding to the corrected error precipitation data as the corrected predicted precipitation.
Preferably, the method for correcting the forecast rainfall data comprises: and acquiring each cloud cluster data of a certain area by adopting radar scanning, wherein each cloud cluster data comprises water content, measurement area, movement probability, movement distance, top layer height and bottom layer height in each unit thickness area.
Preferably, the method for correcting the forecast rainfall data comprises: the actual precipitation of the area is obtained from the rain water measuring system.
The invention also provides a correcting device for forecasting rainfall data, which comprises a forecast data acquisition module, an actual data acquisition module and a correcting module;
the prediction data acquisition module is used for acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area;
the actual data acquisition module is used for acquiring the actual precipitation of the area and obtaining error precipitation data by performing difference processing on the predicted precipitation and the actual precipitation;
the correction module is used for correcting and analyzing the predicted precipitation through the error precipitation data to obtain the corrected predicted precipitation;
wherein, the water content calculation formula is as follows:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is the height difference between the top and bottom cloud layers, wxThe predicted precipitation for region x.
Preferably, the correction module is configured to analyze the error precipitation data and the cloud data by using fourier transform to obtain a corrected height difference; and obtaining the corrected predicted precipitation through the corrected height difference and the water content calculation formula.
Preferably, the correction module is configured to analyze the cloud cluster data by using a multi-scale optical flow technique or an accumulative distribution function based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, a predicted precipitation amount corresponding to the corrected error precipitation data is used as the corrected predicted precipitation amount.
The invention also provides a correcting device for forecasting rainfall data, which comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is used for executing the method for correcting the forecast rainfall data according to the instructions in the program codes.
According to the technical scheme, the embodiment of the invention has the following advantages: the method, the device and the equipment for correcting the forecast rainfall data comprise the following steps: acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area; acquiring actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data; and (4) correcting and analyzing the predicted precipitation through the difference precipitation data to obtain the corrected predicted precipitation. According to the method for correcting the forecast rainfall data, the original forecast rainfall is obtained and compared with the actual rainfall, error rainfall data is obtained, the forecast rainfall is corrected according to the error rainfall data, so that the error between the corrected forecast rainfall and the actual rainfall can be ignored, the accuracy of the forecast rainfall is improved, the corrected forecast rainfall provides a guide direction for accurate weather prediction later, a mode for guiding weather data staff to measure and calculate subsequent weather data is provided, the weather data is analyzed and corrected, and the technical problem that the obtained rainfall data is wrong due to the fact that the existing measured rainfall data is not corrected is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart illustrating steps of a method for correcting forecast rainfall data according to an embodiment of the present invention;
fig. 2 is a block diagram of a correction device for forecasting rainfall data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the application provides a method, a device and equipment for correcting forecast rainfall data, which are used for solving the technical problem that the acquired rainfall data has errors because the existing measured rainfall data is not corrected.
The first embodiment is as follows:
fig. 1 is a flowchart illustrating steps of a method for correcting forecast rainfall data according to an embodiment of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for correcting forecast rainfall data, including the following steps:
s1, acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area.
It should be noted that the water content calculation formula is:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is the height difference between the top and bottom cloud layers, wxThe predicted precipitation for region x. In the embodiment of the present invention, in step S1, each cloud data of a certain area is obtained mainly by using radar scanning, and each cloud data includes a moisture content per unit thickness area, a measurement area, a movement probability, a movement distance, a top layer height, a bottom layer height, and the like.
And S2, acquiring the actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data.
It should be noted that, in step S2, the actual precipitation amount of the area is mainly obtained from the existing rainwater measurement system. In this embodiment, the contents of the rainwater measurement system measuring the actual precipitation of the area are: after water reaches certain volume in the funnel, can lose the equilibrium under the effect of gravity and turn over, continue the splendid attire after empting the adjustment funnel, the rainfall of this part is noted once more to rainwater measurement system, so analogize in proper order, finally measure actual precipitation.
In the embodiment of the invention, the predicted precipitation is compared with the actual precipitation to obtain error precipitation data. The value of the error precipitation data may be positive, 0 or negative.
And S3, correcting and analyzing the predicted precipitation through the error precipitation data to obtain the corrected predicted precipitation.
It should be noted that, in step S3, the acquired cloud cluster data is mainly corrected according to the error precipitation data, so that the error between the corrected predicted precipitation and the actual precipitation does not exceed the error threshold, which indicates that the predicted precipitation is accurate. The error threshold may be defined according to its own needs, and is not limited herein.
The invention provides a correction method for forecasting rainfall data, which comprises the following steps: acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area; acquiring actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data; and (4) correcting and analyzing the predicted precipitation through the difference precipitation data to obtain the corrected predicted precipitation. According to the method for correcting the forecast rainfall data, the original forecast rainfall is obtained and compared with the actual rainfall, error rainfall data is obtained, the forecast rainfall is corrected according to the error rainfall data, so that the error between the corrected forecast rainfall and the actual rainfall can be ignored, the accuracy of the forecast rainfall is improved, the corrected forecast rainfall provides a guide direction for accurate weather prediction later, a mode for guiding weather data staff to measure and calculate subsequent weather data is provided, the weather data is analyzed and corrected, and the technical problem that the obtained rainfall data is wrong due to the fact that the existing measured rainfall data is not corrected is solved.
In the embodiment of the invention, the correction method of the forecast rainfall data can correct the weather forecast data by obtaining the error rainfall data, thereby providing guiding suggestions for the weather forecast in the future. Accurate prediction precipitation data are obtained through error precipitation data correction, prediction error precipitation data are adjusted and reduced, and small errors in future prediction are avoided.
In an embodiment of the present invention, in step S3, performing a calibration analysis on the predicted precipitation amount according to the error precipitation data, and obtaining a calibrated predicted precipitation amount includes:
s31, analyzing the error precipitation data and the cloud cluster data by adopting Fourier transform to obtain a corrected height difference; obtaining the corrected predicted precipitation through the corrected height difference and water content calculation formula; or
S32, analyzing the cloud cluster data by adopting a multi-scale optical flow technology based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold value, taking a predicted precipitation corresponding to the corrected error precipitation data as a corrected predicted precipitation; or
And S33, analyzing the cloud cluster data by adopting an accumulative distribution function based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold value, taking the predicted precipitation corresponding to the corrected error precipitation data as the corrected predicted precipitation.
It should be noted that the error threshold may be defined according to itself, and is not limited herein.
In step S31 of the embodiment of the present invention, cloud cluster data of radar scanning is obtained from error precipitation data, and Fast Fourier Transform (FFT) is used to perform spectrum analysis on cloud cluster data signals of radar scanning, so as to correct forecast precipitation data, thereby ensuring that displacement deviation between forecast precipitation and actual precipitation is corrected.
It should be noted that the fast fourier transform algorithm can effectively extract the characteristic quantities such as the frequency amplitude, the phase angle and the like of the periodic component signals contained in the cloud data signals scanned by the radar, and the fast fourier transform algorithm mainly decomposes the signals in the time domain into a form of superposing a plurality of sinusoidal signals with different amplitude phases, so as to realize the spectrum analysis of the cloud data signals scanned by the radar, and integrates the sinusoidal signals to obtain standard cloud layer data, so as to obtain the water accumulation in the cloud layer, namely, the prediction precipitation. In this embodiment, a cloud cluster data signal scanned by a radar is acquired, data components such as frequency amplitude and phase angle containing periodic components are acquired from the signal, a fourier transform algorithm is used to perform position conversion on the data components, that is, rotation is performed on a coordinate axis to correct the data components to a correct position, and then corresponding input data is acquired from the correct position to serve as corrected cloud cluster data.
In the embodiment of the invention, the Fourier transform is adopted to analyze error precipitation data and cloud cluster data, and the method mainly decomposes signals of precipitation data (cloud layer height, water quantity parameters contained in the cloud layer thickness predicted according to the precipitation quantity and water quantity density degree scanned by a radar) in a time domain of a region into a mode of superposing a plurality of sinusoidal signals with different amplitude phases, thereby realizing the spectrum analysis of cloud cluster data signals scanned by the radar, integrating the sinusoidal signals to obtain corrected cloud cluster data, and further obtaining the water accumulation quantity in the cloud layer, namely the corrected predicted precipitation quantity.
In step S32 of the embodiment of the present invention, cloud data scanned by a radar is obtained from error precipitation data, and the trend of the predicted precipitation and the small-range precipitation area are reasonably adjusted by using a multi-scale optical flow technique, and are gradually close to the actual precipitation, so that the predicted precipitation area is more consistent with the actual precipitation, and the purpose of correction is achieved.
It should be noted that, the motion vector of the cloud layer at each set resolution or scale can be obtained by the multi-scale optical flow technique, and the overall group velocity and the phase velocity of the individual echoes can also be reflected in the final analysis field; and then solving the error square minimum value of the translated forecast rainfall field and the actual rainfall field to obtain the optimal translation position of the forecast rainfall field, further obtaining the value of a phase correction vector, and finally obtaining the corrected forecast rainfall. Wherein, in the multi-scale optical flow technique, once the optimal translation is determined, the same mapping set parameters may be applied in the correction of the next temporal mode quantitative precipitation forecast field with the same initial field. In the embodiment of the invention, the adopted multi-scale optical rheological technology is that a cloud layer in a certain area is divided into a plurality of pieces, the cloud rainfall of each piece area is measured, the trend of the predicted rainfall and the small-range rainfall area are reasonably adjusted according to error rainfall data, so that the predicted rainfall data is gradually close to the actual rainfall, the predicted rainfall area is more consistent with the actual rainfall, and the correction purpose is achieved.
It should be noted that, the motion vector of the cloud layer at each set resolution or scale can be obtained by the multi-scale optical flow technique, and the overall group velocity and the phase velocity of the individual echoes can also be reflected in the final analysis field; the method mainly comprises the steps of obtaining the optimal translation position of the forecast rainfall field by solving the error square minimum of the forecast rainfall field and the actual rainfall field after translation, further obtaining the value of a phase correction vector, and then updating the rainfall forecast value. Once the optimal translation is determined, the same mapping set parameters may be applied in the correction of the next temporal mode quantitative precipitation forecast field with the same initial field.
In this embodiment, the precipitation amount of a certain area may also be analyzed by using a multi-scale optical flow technique, which mainly includes obtaining cloud cluster data and a corresponding picture by using the multi-scale optical flow technique, where the picture includes a central area and a peripheral area (each area corresponds to a position on a different road surface), analyzing the cloud cluster data and the picture, extracting upper data, measuring actual precipitation data, and performing a difference comparison between the actual precipitation data and a predicted precipitation amount to obtain error precipitation data.
In step S33 of the embodiment of the present invention, cloud data scanned by a radar is obtained from error data, data related to the predicted precipitation amount and the actual precipitation amount is substituted into an accumulative distribution function, and each parameter factor causing the error data is adjusted so that the predicted precipitation amount is continuously close to the actual precipitation amount, thereby correcting the forecast precipitation field level intensity.
It should be noted that the precipitation intensity correction is adjusted by approximating the predicted precipitation amount to the actual precipitation amount. And (3) assuming that the forecast of the predicted precipitation and the actual precipitation meet the Weber distribution, and the cumulative distribution functions of the two fields of the predicted precipitation and the actual precipitation are the same. The cumulative distribution function is:
. Wherein x is a precipitation amount of more than 0, alpha, beta>0. Wherein beta is a shape parameter and alpha is a scale parameter. The shape parameters determine the basic shape of the precipitation distribution density curve; the scale parameters function to enlarge or reduce the curve, but do not affect the shape of the precipitation distribution. In each correction process, parameters alpha and beta of the Weber distribution are obtained by multi-sample operation solution of measured data. It is worth noting that in the process of correcting different precipitation cases, weber distribution parameters are different, and the intensity conditions of parameter factors of error precipitation data caused by each adjustment are different, so that the intensity adjustment of each case can be guaranteed to be effective.
In this embodiment, the rainfall in a certain area may also be analyzed through an accumulative distribution function, which is mainly to segment the cloud layer to obtain cloud cluster data, then find out a probability distribution function of the cloud cluster data according to a rule of the cloud cluster data (a distribution function obtained by measuring and calculating cloud cluster data values of each area for multiple times according to historical data), integrate the cloud cluster data, and obtain rainfall data of the whole cloud cluster to obtain actual data.
In the embodiment of the present invention, in steps S31 to S33, the data points where the forecast precipitation error occurs in the cloud map data are found based on the error precipitation data, and the found data points are adjusted to obtain the corrected forecast precipitation.
The method for obtaining the corrected predicted precipitation amount by performing the correction analysis of the predicted precipitation amount by using the error precipitation data may be any one of the steps S31, S32, and S33, or any two or more of them may be used in combination.
Example two:
fig. 2 is a block diagram of a correction apparatus for forecasting rainfall data according to an embodiment of the present invention.
As shown in fig. 2, an embodiment of the present invention further provides a correction device for forecasting rainfall data, which includes a predicted data obtaining module 101, an actual data obtaining module 102, and a correction module 103;
the forecast data acquisition module 101 is configured to acquire n cloud data of a certain area, and process the n cloud data by using a water content calculation formula to obtain a forecast precipitation of the area;
the actual data acquisition module 102 is configured to acquire an actual precipitation amount of the area, and perform difference processing on the predicted precipitation amount and the actual precipitation amount to obtain error precipitation data;
the correction module 103 is used for performing correction analysis on the predicted precipitation through the error precipitation data to obtain the corrected predicted precipitation;
wherein, the water content formula is:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is the height difference between the top and bottom cloud layers, wxThe predicted precipitation for region x.
In the embodiment of the invention, the correction module is used for analyzing the error precipitation data and the cloud cluster data by adopting Fourier transform to obtain the corrected height difference; and obtaining the corrected predicted precipitation through the corrected height difference and water content calculation formula.
In the embodiment of the invention, the correction module is used for analyzing the cloud cluster data by adopting a multi-scale optical flow technology or an accumulative distribution function based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, the predicted precipitation corresponding to the corrected error precipitation data is used as the corrected predicted precipitation.
It should be noted that the modules in the second embodiment correspond to the steps in the first embodiment, and the steps in the first embodiment have been described in detail in the first embodiment, and the contents of the modules in the second embodiment are not described in detail in this second embodiment.
Example three:
the embodiment of the invention provides a correction device for forecasting rainfall data, which comprises a processor and a memory, wherein the processor is used for processing rainfall data;
a memory for storing the program code and transmitting the program code to the processor;
and the processor is used for executing the correction method of the forecast rainfall data according to the instructions in the program codes.
It should be noted that the processor is configured to execute the steps in the embodiment of the correction method for forecasting rainfall data according to the instructions in the program code. Alternatively, the processor, when executing the computer program, implements the functions of each module/unit in each system/apparatus embodiment described above.
Illustratively, a computer program may be partitioned into one or more modules/units, which are stored in a memory and executed by a processor to accomplish the present application. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of a computer program in a terminal device.
The terminal device may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor, a memory. Those skilled in the art will appreciate that the terminal device is not limited and may include more or fewer components than those shown, or some components may be combined, or different components, e.g., the terminal device may also include input output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage may be an internal storage unit of the terminal device, such as a hard disk or a memory of the terminal device. The memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the terminal device. Further, the memory may also include both an internal storage unit of the terminal device and an external storage device. The memory is used for storing computer programs and other programs and data required by the terminal device. The memory may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A correction method for forecast rainfall data is characterized by comprising the following steps:
acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area;
acquiring actual precipitation of the area, and performing difference processing on the predicted precipitation and the actual precipitation to obtain error precipitation data;
correcting and analyzing the predicted precipitation according to the error precipitation data to obtain a corrected predicted precipitation;
wherein, the water content calculation formula is as follows:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is a cloud clusterHeight difference between top and bottom layers, wxThe predicted precipitation for region x.
2. The method of correcting forecasted rainfall data of claim 1, wherein the performing a correction analysis on the predicted rainfall by the error rainfall data to obtain a corrected predicted rainfall comprises: analyzing the error precipitation data and the cloud cluster data by adopting Fourier transform to obtain corrected height difference; and obtaining the corrected predicted precipitation through the corrected height difference and the water content calculation formula.
3. The method of correcting forecasted rainfall data of claim 1, wherein the performing a correction analysis on the predicted rainfall by the error rainfall data to obtain a corrected predicted rainfall comprises: and analyzing the cloud cluster data by adopting a multi-scale optical flow technology based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, taking a predicted precipitation corresponding to the corrected error precipitation data as the corrected predicted precipitation.
4. The method of correcting forecasted rainfall data of claim 1, wherein the performing a correction analysis on the predicted rainfall by the error rainfall data to obtain a corrected predicted rainfall comprises: and analyzing the cloud cluster data by adopting an accumulative distribution function based on the error precipitation data to obtain corrected error precipitation data, and if the corrected error precipitation data is smaller than an error threshold, taking the predicted precipitation corresponding to the corrected error precipitation data as the corrected predicted precipitation.
5. A method of correcting forecasted rainfall data according to claim 1, comprising: and acquiring each cloud cluster data of a certain area by adopting radar scanning, wherein each cloud cluster data comprises water content, measurement area, movement probability, movement distance, top layer height and bottom layer height in each unit thickness area.
6. A method of correcting forecasted rainfall data according to claim 1, comprising: the actual precipitation of the area is obtained from the rain water measuring system.
7. A correction device for forecasting rainfall data, comprising: the device comprises a prediction data acquisition module, an actual data acquisition module and a correction module;
the prediction data acquisition module is used for acquiring n cloud cluster data of a certain area, and processing the n cloud cluster data by adopting a water content calculation formula to obtain the predicted precipitation of the area;
the actual data acquisition module is used for acquiring the actual precipitation of the area and obtaining error precipitation data by performing difference processing on the predicted precipitation and the actual precipitation;
the correction module is used for correcting and analyzing the predicted precipitation through the error precipitation data to obtain the corrected predicted precipitation;
wherein, the water content calculation formula is as follows:
in the formula, a is the water content in each unit thickness area in cloud cluster data, s is the measurement area of the region, n is the measurement quantity of the region, i belongs to n, p is the moving probability of the cloud cluster, L is the moving distance of the cloud cluster, and h3Is the height difference between the top and bottom cloud layers, wxThe predicted precipitation for region x.
8. The apparatus of claim 7, wherein the calibration module is configured to analyze the error precipitation data and the cloud data using a fourier transform to obtain a calibrated height difference; and obtaining the corrected predicted precipitation through the corrected height difference and the water content calculation formula.
9. The apparatus of claim 7, wherein the calibration module is configured to analyze the cloud data using a multi-scale optical flow technique or an accumulative distribution function based on the error precipitation data to obtain calibrated error precipitation data, and if the calibrated error precipitation data is smaller than an error threshold, a predicted precipitation corresponding to the calibrated error precipitation data is used as the calibrated predicted precipitation.
10. A correction device for forecasting rainfall data comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the method of correcting forecasted rainfall data according to any one of claims 1-6, according to instructions in the program code.
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