CN111611678B - Typhoon wind direction fuzzy solution optimization method and device, electronic equipment and storage medium - Google Patents
Typhoon wind direction fuzzy solution optimization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a typhoon wind direction fuzzy solution optimization method and device, electronic equipment and a storage medium, and belongs to the technical field of ocean microwave remote sensing. The method comprises the following steps: acquiring the typhoon center position and the maximum wind ring radius of typhoon; determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius; for each wind vector unit, determining a fuzzy solution with the wind direction being closest to the model wind direction in fuzzy solutions of the wind vector units as an initial field of the wind vector unit; and filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result. The method improves the effect of removing the fuzzy solution of the typhoon wind direction under the typhoon condition.
Description
Technical Field
The application relates to the technical field of ocean microwave remote sensing, in particular to a typhoon wind direction fuzzy solution optimization method and device, electronic equipment and a storage medium.
Background
The ocean surface wind field is a basic parameter which influences active factors of sea waves, ocean currents and water masses and ocean dynamics, and has important significance in monitoring the global ocean wind field, preventing and reducing disasters in coastal areas, guaranteeing the ocean environment and promoting ocean related scientific research. The satellite scatterometer has become the most important observation means of the global sea surface wind field due to the characteristics of all-time, all-weather, high space-time resolution, large coverage range and the like.
In the process of wind vector inversion, a plurality of wind vector solutions can satisfy the target function expression, wherein only one solution is a real solution, and the rest are called fuzzy solutions. Therefore, after the wind vector which enables the objective function to obtain the local maximum is obtained, multi-solution removal of the wind direction, namely fuzzy solution removal, is carried out, and then the real solution can be obtained.
The round number filtering algorithm of the fuzzy solution removing algorithm adopted in the scatterometer business processing algorithm is essentially a noise filtering algorithm, and when the algorithm is used for wind field fuzzy solution removing, the quality of a fuzzy solution initial field directly determines the effect of the fuzzy solution removing. If the round median filtering fuzzy solution removing algorithm achieves a good effect, the conditions that at least more than 50% of solutions in an initial field are correct solutions and flaky fuzzy can not occur need to be met. In general, the above conditions are satisfied, but under typhoon conditions, the wind direction changes violently and is generally influenced by rainfall and the like, and the initial field of the scatterometer fuzzy solution generally appears a flaky 180 ° fuzzy, and in this case, the round number filtering algorithm cannot achieve the effect of removing the fuzzy solution well, and in this case, the existing fuzzy solution removing algorithm needs to be improved.
Disclosure of Invention
In view of this, an object of the embodiments of the present application is to provide a method and an apparatus for optimizing a fuzzy solution of a typhoon direction, an electronic device, and a storage medium, so as to solve the problem in the prior art that a fuzzy solution removing effect is poor under a typhoon condition.
The embodiment of the application provides a typhoon wind direction fuzzy solution optimization method, which comprises the following steps: acquiring the typhoon center position and the maximum wind ring radius of typhoon; determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius; for each wind vector unit, determining a fuzzy solution with the wind direction being closest to the model wind direction in fuzzy solutions of the wind vector units as an initial field of the wind vector unit; and filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
In the implementation mode, the fuzzy solution of the wind vector unit is screened through the model wind direction of the typhoon model, so that the initial field is determined, and then the initial field is subjected to filtering optimization, so that the fuzzy solution removing efficiency in the satellite scatterometer wind field inversion process is improved, the sheet fuzzy can be removed, and the fuzzy solution removing effect is improved.
Optionally, the acquiring a typhoon center position and a maximum wind ring radius of the typhoon includes: determining a typhoon center position of the typhoon based on typhoon wind speed information in the fast scatterometer data of the typhoon and/or a backscattering coefficient in the fast scatterometer data; and taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon.
In the implementation mode, the typhoon center position and the maximum wind ring radius are determined through typhoon wind speed information and/or backscattering coefficients in typhoon fast scatterometer data, and accuracy and applicability of key data determination are improved.
Optionally, the determining, by using a typhoon model, a model wind direction of each wind vector unit based on the typhoon center position and the maximum wind ring radius includes: determining the typhoon wind vector of each wind vector unit by adopting gradient wind in a typhoon model based on the typhoon center position and the maximum wind ring radius; and superposing the typhoon moving speed on the typhoon vector of each wind vector unit to obtain the model wind direction of each wind vector unit.
In the implementation mode, the model wind direction of each wind vector unit is determined based on the gradient wind and the typhoon moving speed, so that the determination accuracy of the model wind direction under the typhoon condition is improved.
Optionally, before the determining the typhoon vector of each wind vector unit by using the gradient wind in the typhoon model based on the typhoon center position and the maximum wind ring radius, the method further includes: determining an internal flow angle factor based on a typhoon internal flow angle of the typhoon; and for each wind vector unit, taking the product of the gradient wind at each wind vector unit multiplied by the internal flow angle factor as the gradient wind for determining the typhoon wind vector of the wind vector unit.
In the implementation mode, the accuracy of the gradient wind data is further improved by referring to the internal flow angle factor in the gradient wind acquisition process.
Optionally, the determining, for each wind vector unit, a fuzzy solution of which the wind direction is closest to the model wind direction among fuzzy solutions of the wind vector units as an initial field of the wind vector unit includes: determining a preset number of undetermined fuzzy solutions from the fuzzy solutions of each wind vector unit according to the sequence from high to low; and for each wind vector unit, determining a to-be-determined fuzzy solution closest to the model wind direction of the wind vector unit in the to-be-determined fuzzy solutions of the wind vector units as an initial field of the wind vector unit.
In the implementation mode, the data product of the satellite microwave scatterometer generally provides a plurality of wind speed and wind direction fuzzy solutions for each wind vector unit, the solutions are arranged in the order of likelihood values from high to low, the undetermined fuzzy solutions are selected from high to low to ensure the accuracy of the selected fuzzy solutions, and meanwhile, the solution which is closest to the model wind direction of the wind vector unit in the undetermined fuzzy solutions is used as the initial field of the wind vector unit, so that the fuzzy solution removing accuracy of the wind vector unit is improved.
Optionally, for each wind vector unit, determining, among the solutions to be determined of the wind vector units, a solution to be determined that is closest to the model wind direction of the wind vector unit as an initial field of the wind vector unit includes: determining, for each wind vector unit, an absolute value of a difference between each undetermined fuzzy solution of the wind vector unit and a model wind direction of the wind vector unit; and aiming at each wind vector unit, taking the undetermined fuzzy solution with the minimum absolute value in each wind vector unit as the initial field closest to the wind vector unit.
In the implementation mode, the undetermined fuzzy solution closest to the model wind direction is determined by calculating the absolute value of the difference between the undetermined fuzzy solution and the model wind direction, and the accuracy and the calculation efficiency of the initial field are improved.
Optionally, the filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result includes: and performing circle median filtering on the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
In the implementation mode, after the initial field is determined to complete the optimization of the fuzzy solution, the number-of-circles filtering is performed, so that the fuzzy solution removing effect is improved, and the flaky fuzzy condition is avoided.
The embodiment of the application also provides a typhoon wind direction fuzzy solution optimizing apparatus, the apparatus includes: the acquisition module is used for acquiring the typhoon center position and the maximum wind ring radius of the typhoon; the model wind direction determining module is used for determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius; the initial field determining module is used for determining a fuzzy solution with the wind direction being closest to the model wind direction in the fuzzy solutions of the wind vector units as an initial field of each wind vector unit; and the filtering module is used for filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
In the implementation mode, the fuzzy solution of the wind vector unit is screened through the model wind direction of the typhoon model, so that the initial field is determined, and then the initial field is subjected to filtering optimization, so that the fuzzy solution removing efficiency in the satellite scatterometer wind field inversion process is improved, the sheet fuzzy can be removed, and the fuzzy solution removing effect is improved.
Optionally, the obtaining module is specifically configured to: determining a typhoon center position of the typhoon based on typhoon wind speed information in the fast scatterometer data of the typhoon and/or a backscattering coefficient in the fast scatterometer data; and taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon.
In the implementation mode, the typhoon center position and the maximum wind ring radius are determined through typhoon wind speed information and/or backscattering coefficients in typhoon fast scatterometer data, and accuracy and applicability of key data determination are improved.
Optionally, the model wind direction determining module is specifically configured to: determining the typhoon wind vector of each wind vector unit by adopting gradient wind in a typhoon model based on the typhoon center position and the maximum wind ring radius; and superposing the typhoon moving speed on the typhoon vector of each wind vector unit to obtain the model wind direction of each wind vector unit.
In the implementation mode, the model wind direction of each wind vector unit is determined based on the gradient wind and the typhoon moving speed, so that the determination accuracy of the model wind direction under the typhoon condition is improved.
Optionally, the model wind direction determination module is further configured to: determining an internal flow angle factor based on a typhoon internal flow angle of the typhoon; and for each wind vector unit, taking the product of the gradient wind at each wind vector unit multiplied by the internal flow angle factor as the gradient wind for determining the typhoon wind vector of the wind vector unit.
In the implementation mode, the accuracy of the gradient wind data is further improved by referring to the internal flow angle factor in the gradient wind acquisition process.
Optionally, the initial field determining module is specifically configured to: determining a preset number of undetermined fuzzy solutions from the fuzzy solutions of each wind vector unit according to the sequence from high to low; and for each wind vector unit, determining a to-be-determined fuzzy solution closest to the model wind direction of the wind vector unit in the to-be-determined fuzzy solutions of the wind vector units as an initial field of the wind vector unit.
In the implementation mode, the data product of the satellite microwave scatterometer generally provides a plurality of wind speed and wind direction fuzzy solutions for each wind vector unit, the solutions are arranged in the order of likelihood values from high to low, the undetermined fuzzy solutions are selected from high to low to ensure the accuracy of the selected fuzzy solutions, and meanwhile, the solution which is closest to the model wind direction of the wind vector unit in the undetermined fuzzy solutions is used as the initial field of the wind vector unit, so that the fuzzy solution removing accuracy of the wind vector unit is improved.
Optionally, the initial field determining module is specifically configured to: determining, for each wind vector unit, an absolute value of a difference between each undetermined fuzzy solution of the wind vector unit and a model wind direction of the wind vector unit; and aiming at each wind vector unit, taking the undetermined fuzzy solution with the minimum absolute value in each wind vector unit as the initial field closest to the wind vector unit.
In the implementation mode, the undetermined fuzzy solution closest to the model wind direction is determined by calculating the absolute value of the difference between the undetermined fuzzy solution and the model wind direction, and the accuracy and the calculation efficiency of the initial field are improved.
Optionally, the filtering module is specifically configured to: and performing circle median filtering on the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
In the implementation mode, after the initial field is determined to complete the optimization of the fuzzy solution, the number-of-circles filtering is performed, so that the fuzzy solution removing effect is improved, and the flaky fuzzy condition is avoided.
An embodiment of the present application further provides an electronic device, where the electronic device includes a memory and a processor, where the memory stores program instructions, and the processor executes steps in any one of the above implementation manners when reading and executing the program instructions.
An embodiment of the present application further provides a storage medium, where computer program instructions are stored in the storage medium, and when the computer program instructions are read and executed by a processor, the steps in any one of the above implementation manners are performed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for optimizing a typhoon wind direction fuzzy solution according to an embodiment of the present application;
FIG. 2 is a schematic diagram of backscattering coefficients of a satellite of the fast scatterometer observed in a typhoon IOKE;
FIG. 3 is a schematic flow chart of a step of determining a wind direction of a model according to an embodiment of the present disclosure;
fig. 4 is a schematic block diagram of a typhoon wind direction ambiguity solution optimizing apparatus according to an embodiment of the present application.
Icon: 20-a typhoon wind direction fuzzy solution optimizing device; 21-an acquisition module; 22-a model wind direction determination module; 23-an initial field determination module; 24-a filtering module.
Detailed Description
The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
A satellite scatterometer is a calibrated radar that actively transmits electromagnetic waves to the sea surface and receives echo signals modulated by the sea surface. The radar echo signal will be determined by the transmitted signal together with the sea surface characteristics. When the wave length of the sea wave and the wave length of the electromagnetic wave transmitted by the radar meet the Bragg scattering condition, the phase of the backward scattering electromagnetic wave generated by each wave surface is the same, so that resonance is generated, and the echo energy is mainly determined by the electromagnetic wave generating the resonance. At the operating frequency of the microwave scatterometer, the sea surface wave satisfying the bragg resonance condition is a sea surface capillary wave, and the spectral density of the sea surface capillary wave is directly related to the wind speed on the sea surface. Therefore, the echo signal measured by the radar can acquire the information of the sea surface wind field. By processing the radar echo signals, a normalized backscattering coefficient sigma can be derived which is only related to the sea surface situation0σ measured from a scatterometer0And (4) a sea surface wind field can be further extracted, and the information extraction process of the sea surface wind field is called wind vector inversion.
Inverting the sea surface wind vector from the sea surface backscattering coefficients measured by the scatterometer requires solving three problems: and establishing a geophysical model, a wind vector solving algorithm and a fuzzy solving and removing algorithm.
The geophysical model function describes the relationship between the sea surface wind vector and the radar backscattering coefficient. The general form of the geophysical model function is:
σ0＝F(V,χ,...,f,p,θ)
wherein sigma0Representing the backscattering coefficient corresponding to the sea surface, wherein V is the wind speed, chi is the relative azimuth angle of the wind direction, f is the working frequency of the scatterometer, p is the polarization mode, and theta is the incident angle of the antenna.
The wind vector solving algorithm is mainly used for obtaining a wind vector solution of the sea surface through a geophysical model function and normalized backscattering coefficient observed values of different azimuth angles of a sea surface wind vector surface element. Due to the non-linear characteristic of the biccosine distribution characteristic of the geophysical model, the solution of the wind vector cannot be directly obtained by directly substituting the backscattering coefficient into the geophysical model. The general solving algorithm adopts a maximum likelihood solving algorithm, a wind vector which enables the objective function shown in the formula to obtain a local maximum value is searched to be used as a solution, and the solutions are sequenced according to the corresponding maximum likelihood values.
Wherein sigmaoiFor the normalized backscattering coefficient, σ, actually measured by a scatterometermAnd predicting a backscattering coefficient when the corresponding wind speed for the model is V and the relative wind direction is chi, wherein N is the total number of backscattering coefficient measurement results for wind vector inversion.
Due to the biccosine distribution characteristics of the geophysical model function itself and the effects of various measurement noises of the scatterometer, the solution algorithm will generally obtain multiple wind vector solutions (i.e., fuzzy solutions). Wind direction multi-solution elimination is to select the wind vector solution closest to the real wind vector from a series of multi-solution wind vectors.
The scatterometer radar is used for accurately measuring sea surface backscattering, and a wind direction inversion error caused by near symmetry of downwind measurement and upwind measurement in the measurement process is similar to the problem of noise elimination in image processing. In a uniform and smooth image, a randomly-appearing bright spot or dark spot is easy to automatically distinguish, for the noise of the pulse type, a median filtering method is one of effective methods for eliminating, and similarly, in a uniform wind field, if a certain wind vector is opposite to or has a larger difference with the surrounding wind vectors, the median filtering method is also an effective method. At present, the wind direction multi-solution removal of the business operation scatterometer adopts a circle number filtering algorithm.
In order to improve the above problem, an embodiment of the present application provides a typhoon wind direction fuzzy solution optimization method, please refer to fig. 1, and fig. 1 is a schematic flow diagram of the typhoon wind direction fuzzy solution optimization method provided by the embodiment of the present application. The method for optimizing the typhoon wind direction fuzzy solution comprises the following specific steps:
step S12: and acquiring the typhoon center position and the maximum wind ring radius of the typhoon.
Typhoons have a distinct structural feature with respect to the ordinary weather process: the structure of the typhoon wind field is of a cyclone vortex characteristic; the typhoon wind speed on the equidistant radius does not change greatly by taking the typhoon eye as the center, the wind direction basically makes quasi-uniform change along the tangential direction of the equidistant circular arc, and the typhoon wind direction has a certain internal rotation angle on the basis of the circular arc tangent line because of the influence of the earth rotation effect. According to the structural characteristics of typhoon, the position of the typhoon center can be obtained from the wind vector information obtained by inverting the typhoon observation result from the fast scatterometer data of the fast scatterometer satellite (Quick scatterometer, QUIKSCAT) or directly based on the backscattering coefficient information obtained by observing the typhoon by the fast scatterometer satellite.
Specifically, step S12 may include the following sub-steps:
step S121: the typhoon center position of the typhoon is determined based on the typhoon wind speed information in the fast scatterometer data of the typhoon and/or the backscattering coefficient in the fast scatterometer data.
Alternatively, the location of the typhoon center can be inferred from the distribution of wind speed: in the eye region, the wind is weak, dry and warm, and is less cloudy, and a ring-shaped maximum wind speed region is arranged around the eye region, and the average width is generally 8-50 km. The wind vector unit where the typhoon center position is located can be obtained by searching the local minimum value of the wind speed of the typhoon occurrence area, and the longitude and latitude information of the typhoon center position is determined through the longitude and latitude information attached to the wind vector unit.
The wind vector unit is extracted from satellite microwave scatterometer data, and the fast scatterometer data and the satellite microwave scatterometer data in the embodiment can be marine second satellite microwave scatterometer (HY2-SCAT) data. The HY2-SCAT is mainly used for observing global sea surface wind fields, the wind speed measuring range is 4-24 m/s, and the wind speed precision is 2m/s or 10%; the wind direction measuring range is 0 ~ 360, and the wind direction precision is 20. Currently available data products of the marine two-satellite scatterometer are classified into an L1B-grade product data product, an L2A-grade data product, an L2B-grade data product and an L3-grade data product. The L1B data are scatterometer observations stored in time order of telemetry frames. The L2A product file contains each radar backscatter coefficient measurement obtained by the satellite platform in one spatial orbit, and the backscatter coefficients in the L2A product are grouped in units of wind vectors, indexed by row-column number. The L2B product data files are organized in track units, i.e., the wind vector measurement data for each track constitutes one L2B file. The L3 data provides daily global sea surface wind field data in a grid of 0.25 ° x 0.25 ° size and separates the ascending and descending rails.
Optionally, the principle of directly obtaining the position of the typhoon center through the backscattering coefficient data is similar to the principle of deducing the position of the typhoon center through the distribution of the wind speed, because the stronger the wind speed is, the stronger the corresponding backscattering coefficient is under the condition that the observation azimuth angle is not changed much. The backscattering coefficient at the wind eye is much lower than that corresponding to a high wind zone around the wind eye. The wind vector unit where the typhoon center position is located can be obtained by searching the local minimum value of the backscattering coefficient, and the longitude and latitude information of the typhoon center position is determined through the longitude and latitude information attached to the wind vector unit.
It should be understood that the typhoon center position may also be determined according to the typhoon wind speed information and the backscattering coefficient, for example, two typhoon center positions are respectively obtained through the typhoon wind speed information and the backscattering coefficient, and then the center point of a straight line connecting the two typhoon center positions is taken as the finally determined typhoon center position.
In general, the typhoon center positions obtained by analyzing the wind speed or backscattering coefficient distribution in the fast scatterometer data are substantially close and relatively accurate. However, under some extreme conditions, due to influences of rainfall and other factors, the center positions of the typhoons determined by the wind direction and the wind speed may be misaligned, and the distribution of the wind speed may have a plurality of minimum values in the area where the wind speed is maximum. In this case, the position of the wind speed minimum point closest to the typhoon center determined by the wind direction may be selected as the typhoon center position in the range of the typhoon center determined by the wind direction and the radius of 4 wind vector bin lengths (i.e., 100 km).
Step S122: and taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon.
Referring to fig. 2, fig. 2 is a schematic diagram of backscattering coefficients observed by a fast scatterometer satellite for a typhoon IOKE (name of typhoon), wherein a, b, c, d are backscattering coefficients on a tangent line perpendicular to the satellite orbit direction passing through the typhoon center position, and e, f, g, h are backscattering coefficients on a tangent line parallel to the satellite orbit direction passing through the typhoon center position. The minimum value of the backscattering coefficient corresponding to the center position of the typhoon in the tangent line and the maximum value of the backscattering coefficient corresponding to the wall of the wind eye can be obviously distinguished from e and g, and the maximum wind circle radius of the typhoon can be estimated to be 69km according to the distance between the minimum value and the maximum value.
Step S14: and determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius.
Optionally, when one of the wind vector units is processed, the wind vector unit may be determined by the row number and the column number to extract related information of the wind vector unit, such as wind speed, observation longitude and latitude, and the like.
Referring to fig. 3, fig. 3 is a schematic flowchart of a step of determining a wind direction of a model according to an embodiment of the present application, where the step S14 may include the following sub-steps:
step S141: and determining the typhoon wind vector of each wind vector unit by adopting the gradient wind in the typhoon model based on the typhoon center position and the maximum wind ring radius.
It should be understood that the step of determining the gradient wind in the typhoon model may be as follows: determining an internal flow angle factor based on a typhoon internal flow angle of typhoon; and for each wind vector unit, taking the product of the gradient wind at each wind vector unit and the internal flow angle factor as the gradient wind of the typhoon vector of the determined wind vector unit.
Step S142: and superposing the typhoon moving speed on the typhoon vector of each wind vector unit to obtain the model wind direction of each wind vector unit.
The typhoon model in the embodiment can adopt a Holland typhoon model, the Holland typhoon model is used for describing a two-dimensional wind field structure for mature development of typhoon, and the described typhoon field has high precision and can meet the requirement of carrying out simulation analysis on the sea surface backscattering coefficient typical characteristics under the typhoon condition.
In the Holland typhoon model, the gradient wind can be expressed as:
wherein, UgIs the gradient wind size at r from the center of the typhoon, p is the air density, p0Is the central air pressure of typhoon, pnIs the air pressure far away from the center of the typhoon, f is the Coriolis force,
B＝1.5+(980-p0)/120，Rmaxin units of km, p0The unit is mb.
Taking the typhoon internal flow angle of 25 degrees as an example, to obtain the average wind vector of 1 minute with the sea surface 10 meters high, a factor of 0.8 needs to be multiplied on the basis of gradient wind. Due to the movement of the typhoon center, the typhoon wind field is not a symmetrical structure, and in order to account for the influence of the typhoon moving speed, the typhoon moving speed needs to be superposed on a wind vector obtained through model calculation. The invention adopts a relatively simple processing method, namely, the typhoon moving speed is directly and linearly superposed on the model wind vector of the typhoon model.
The input parameters of the Holland typhoon model comprise the typhoon center position, the center air pressure, the maximum wind ring radius and the moving speed of the typhoon. Wherein, the typhoon central air pressure and typhoon moving speed can be inquired through a weather bureau website. When the typhoon center air pressure and the typhoon moving speed cannot be obtained, the default value ρ is 1.15 × 10-2, pn＝1000mb，p0920 mb; the typhoon moving speed is determined by the current typhoon historical moving speed; and if the historical movement speed of the typhoon cannot be acquired, setting the movement speed of the typhoon to be 0.
Step S16: and for each wind vector unit, determining a fuzzy solution with the wind direction closest to the model wind direction in the fuzzy solutions of the wind vector units as an initial field of the wind vector unit.
Alternatively, step S16 may include the following sub-steps:
step S161: and determining a preset number of the undetermined fuzzy solutions from the fuzzy solutions of each wind vector unit in the order from high to low.
Because the fuzzy solutions corresponding to the wind vector units in the L2B data product are arranged from high to low according to the maximum likelihood, a preset number of undetermined fuzzy solutions are determined from the fuzzy solutions according to the sequence from high to low, and the selection precision of the undetermined fuzzy solutions can be improved. Alternatively, the predetermined number is generally 2 to meet the accuracy requirement.
Step S162: and for each wind vector unit, determining the undetermined fuzzy solution closest to the model wind direction of the wind vector unit in the undetermined fuzzy solutions of the wind vector units as the initial field of the wind vector unit.
Specifically, step S162 may include: determining an absolute value of a difference between each undetermined fuzzy solution of the wind vector unit and the model wind direction of the wind vector unit aiming at each wind vector unit; and aiming at each wind vector unit, taking the undetermined fuzzy solution with the minimum absolute value in each wind vector unit as the initial field closest to the wind vector unit.
Step S18: and filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
The filtering in this embodiment may be circular median filtering, after circular median filtering based on the prior art, due to the influence of factors such as rainfall, the typhoon wind field at a part of positions may not exhibit the characteristic of the supposed vortex-like distribution, after circular median filtering based on the initial field, the wind direction of the flaky fuzzy area may be effectively corrected, and the whole typhoon wind direction exhibits obvious vortex-like distribution and is consistent with the structure of the mature typhoon wind field.
In order to cooperate with the above-mentioned typhoon wind direction fuzzy solution optimization method provided in the embodiment of the present application, this embodiment further provides a typhoon wind direction fuzzy solution optimization device 20.
Referring to fig. 4, fig. 4 is a schematic block diagram of a typhoon wind direction ambiguity solution optimizing apparatus according to an embodiment of the present application.
Typhoon wind direction fuzzy solution optimizing apparatus 20:
the acquiring module 21 is used for acquiring the typhoon center position and the maximum wind ring radius of the typhoon;
the model wind direction determining module 22 is configured to determine a model wind direction of each wind vector unit by using a typhoon model based on the typhoon center position and the maximum wind ring radius;
the initial field determining module 23 is configured to determine, for each wind vector unit, a fuzzy solution, of the wind vector units, in which a wind direction is closest to a model wind direction, as an initial field of the wind vector unit;
and the filtering module 24 is configured to filter the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
Optionally, the obtaining module 21 is specifically configured to: determining the typhoon center position of the typhoon based on typhoon wind speed information in the typhoon fast scatterometer data and/or backscattering coefficients in the fast scatterometer data; and taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon.
Optionally, the model wind direction determining module 22 is specifically configured to: determining the typhoon wind vector of each wind vector unit by adopting the gradient wind in the typhoon model based on the typhoon center position and the maximum wind ring radius; and superposing the typhoon moving speed on the typhoon vector of each wind vector unit to obtain the model wind direction of each wind vector unit.
Optionally, the model wind direction determination module 22 is further configured to: determining an internal flow angle factor based on a typhoon internal flow angle of typhoon; and for each wind vector unit, taking the product of the gradient wind at each wind vector unit and the internal flow angle factor as the gradient wind of the typhoon vector of the determined wind vector unit.
Optionally, the initial field determining module 23 is specifically configured to: determining a preset number of undetermined fuzzy solutions from the fuzzy solutions of each wind vector unit according to the sequence from high to low; and for each wind vector unit, determining the undetermined fuzzy solution closest to the model wind direction of the wind vector unit in the undetermined fuzzy solutions of the wind vector units as the initial field of the wind vector unit.
Optionally, the initial field determining module 23 is specifically configured to: determining an absolute value of a difference between each undetermined fuzzy solution of the wind vector unit and the model wind direction of the wind vector unit aiming at each wind vector unit; and aiming at each wind vector unit, taking the undetermined fuzzy solution with the minimum absolute value in each wind vector unit as the initial field closest to the wind vector unit.
Optionally, the filtering module 24 is specifically configured to: and performing circle median filtering on the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
The embodiment of the application further provides electronic equipment, which comprises a memory and a processor, wherein program instructions are stored in the memory, and when the processor reads and runs the program instructions, the steps in any one of the methods for optimizing the typhoon wind direction fuzzy solution provided by the embodiment are executed.
It should be understood that the electronic device may be a Personal Computer (PC), a tablet PC, a smart phone, a Personal Digital Assistant (PDA), or other electronic device with a logic computation function, and the corresponding dynamic reference station may be a GNSS device. It is also possible that the data center is within the same equipment system as the static reference station.
The embodiment of the application also provides a readable storage medium, wherein computer program instructions are stored in the readable storage medium, and the computer program instructions are read by a processor and executed to execute the steps in the typhoon wind direction fuzzy solution optimization method.
To sum up, the embodiment of the present application provides a method and an apparatus for optimizing a fuzzy solution of a typhoon wind direction, an electronic device, and a storage medium, where the method includes: acquiring the typhoon center position and the maximum wind ring radius of typhoon; determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius; for each wind vector unit, determining a fuzzy solution with the wind direction being closest to the model wind direction in fuzzy solutions of the wind vector units as an initial field of the wind vector unit; and filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
In the implementation mode, the fuzzy solution of the wind vector unit is screened through the model wind direction of the typhoon model, so that the initial field is determined, and then the initial field is subjected to filtering optimization, so that the fuzzy solution removing efficiency in the satellite scatterometer wind field inversion process is improved, the sheet fuzzy can be removed, and the fuzzy solution removing effect is improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. The apparatus embodiments described above are merely illustrative, and for example, the block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of devices according to various embodiments of the present application. In this regard, each block in the block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams, and combinations of blocks in the block diagrams, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Therefore, the present embodiment further provides a readable storage medium, in which computer program instructions are stored, and when the computer program instructions are read and executed by a processor, the computer program instructions perform the steps of any of the block data storage methods. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a RanDom Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (9)
1. A typhoon wind direction fuzzy solution optimization method is characterized by comprising the following steps:
determining the typhoon center position of the typhoon based on typhoon wind speed information in the typhoon fast scatterometer data and/or backscattering coefficients in the fast scatterometer data;
taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon;
determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius;
for each wind vector unit, determining a fuzzy solution with the wind direction being closest to the model wind direction in fuzzy solutions of the wind vector units as an initial field of the wind vector unit;
and filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
2. The method of claim 1, wherein determining the model wind direction of each wind vector unit using a typhoon model based on the typhoon center position and the maximum wind circle radius comprises:
determining the typhoon wind vector of each wind vector unit by adopting gradient wind in a typhoon model based on the typhoon center position and the maximum wind ring radius;
and superposing the typhoon moving speed on the typhoon vector of each wind vector unit to obtain the model wind direction of each wind vector unit.
3. The method of claim 2, wherein prior to the determining the typhoon wind vector of each wind vector unit using the gradient wind in the typhoon model based on the typhoon center position and the maximum wind circle radius, the method further comprises:
determining an internal flow angle factor based on a typhoon internal flow angle of the typhoon;
and for each wind vector unit, taking the product of the gradient wind at each wind vector unit multiplied by the internal flow angle factor as the gradient wind for determining the typhoon wind vector of the wind vector unit.
4. The method of claim 1, wherein the determining, for each wind vector unit, a ambiguity solution in the wind vector unit whose wind direction is closest to the model wind direction as the initial field of the wind vector unit comprises:
determining a preset number of undetermined fuzzy solutions from the fuzzy solutions of each wind vector unit according to the sequence from high to low;
and for each wind vector unit, determining a to-be-determined fuzzy solution closest to the model wind direction of the wind vector unit in the to-be-determined fuzzy solutions of the wind vector units as an initial field of the wind vector unit.
5. The method according to claim 4, wherein the determining, for each wind vector unit, a pending ambiguity solution that is closest to the model wind direction of the wind vector unit among the pending ambiguity solutions of the wind vector unit as the initial field of the wind vector unit comprises:
determining, for each wind vector unit, an absolute value of a difference between each undetermined fuzzy solution of the wind vector unit and a model wind direction of the wind vector unit;
and aiming at each wind vector unit, taking the undetermined fuzzy solution with the minimum absolute value in each wind vector unit as the initial field closest to the wind vector unit.
6. The method of claim 1, wherein the filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result comprises:
and performing circle median filtering on the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
7. A typhoon wind direction fuzzy solution optimizing device is characterized in that the device comprises:
the acquisition module is used for determining the typhoon center position of the typhoon based on typhoon wind speed information in typhoon fast scatterometer data and/or backscattering coefficients in the typhoon fast scatterometer data; taking the maximum value of the backscattering coefficient and the first distance of the center position of the typhoon as the maximum wind circle radius of the typhoon;
the model wind direction determining module is used for determining the model wind direction of each wind vector unit by adopting a typhoon model based on the typhoon center position and the maximum wind ring radius;
the initial field determining module is used for determining a fuzzy solution with the wind direction being closest to the model wind direction in the fuzzy solutions of the wind vector units as an initial field of each wind vector unit;
and the filtering module is used for filtering the initial field of each wind vector unit to obtain a fuzzy solution optimization result.
8. An electronic device comprising a memory having stored therein program instructions and a processor that, when executed, performs the steps of the method of any of claims 1-6.
9. A storage medium having stored thereon computer program instructions for executing the steps of the method according to any one of claims 1 to 6 when executed by a processor.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1309823B1 (en) * | 2000-06-23 | 2004-11-10 | VKR Holding A/S | Method and system for controlling ventilation |
| CN101853335A (en) * | 2010-06-01 | 2010-10-06 | 国家卫星海洋应用中心 | Point inversion method for ocean surface wind field inversion |
| CN103698750A (en) * | 2014-01-07 | 2014-04-02 | 国家卫星海洋应用中心 | HY-2 satellite scatterometer sea surface wind field retrieval method and device |
| CN104700456A (en) * | 2015-03-18 | 2015-06-10 | 广州中海达卫星导航技术股份有限公司 | Method and system for pixel screening and filtering of SeaWinds scatterometer wind vector inversion |
| CN108074014A (en) * | 2017-12-13 | 2018-05-25 | 宁波市镇海区气象局 | A kind of method for the phase fitful wind that detects a typhoon |
| CN109506880A (en) * | 2018-11-14 | 2019-03-22 | 杭州师范大学 | A kind of typhoon high wind circle extracting method |
-
2020
- 2020-03-27 CN CN202010231877.7A patent/CN111611678B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1309823B1 (en) * | 2000-06-23 | 2004-11-10 | VKR Holding A/S | Method and system for controlling ventilation |
| CN101853335A (en) * | 2010-06-01 | 2010-10-06 | 国家卫星海洋应用中心 | Point inversion method for ocean surface wind field inversion |
| CN103698750A (en) * | 2014-01-07 | 2014-04-02 | 国家卫星海洋应用中心 | HY-2 satellite scatterometer sea surface wind field retrieval method and device |
| CN104700456A (en) * | 2015-03-18 | 2015-06-10 | 广州中海达卫星导航技术股份有限公司 | Method and system for pixel screening and filtering of SeaWinds scatterometer wind vector inversion |
| CN108074014A (en) * | 2017-12-13 | 2018-05-25 | 宁波市镇海区气象局 | A kind of method for the phase fitful wind that detects a typhoon |
| CN109506880A (en) * | 2018-11-14 | 2019-03-22 | 杭州师范大学 | A kind of typhoon high wind circle extracting method |
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