Raindrop spectrum and dual-polarization radar-based visibility quantitative estimation method
Through quantitative estimation of visibility based on raindrop spectra and dual polarization radar, the problem of difficulty in monitoring three-dimensional space visibility in the existing technology is solved, and accurate monitoring and inversion of visibility during precipitation is achieved, which improves the safety and efficiency of low-meteorological services.
Patent Information
- Application Number
- CN202510418251.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-03
- Publication Date
- 2025-06-13
AI Technical Summary
The prior art is difficult to effectively monitor and estimate the visibility of three-dimensional space during precipitation, especially in the field of low-meteorological services, where existing equipment is costly and it is difficult to achieve accurate visibility observations.
The quantitative estimation visibility method based on raindrop spectroscopy and dual polarization radar is adopted. By obtaining the data of laser raindrop spectrometer, dual polarization radar and ground visibility meter, data quality control and microphysical quantum parameter calculations are carried out, quantitative estimation visibility model is established, and real-time inversion of visibility is combined with dual polarization radar.
Complete monitoring of three-dimensional space visibility during precipitation is achieved, filling the gap in early warning monitoring and service of low-virtual weather, and improving the accuracy and applicability of low-meteorological services.
Smart Images
Figure CN120143128A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of atmospheric remote sensing monitoring and analysis, and particularly to a method for quantitatively estimating visibility based on a raindrop size distribution and a dual-polarization radar. Background Art
[0002] Visibility observation is crucial in fields such as meteorology and transportation. Currently, common visibility observation technologies mainly include transmissive and scattering visibility meters, lidar, digital imaging, digital video measurement, and manual visual observation, etc. The above-mentioned equipment technologies are mainly for ground visibility observation or the business investment cost is expensive. The low-altitude economy is booming vigorously. In this process, the importance of low-altitude meteorological services is becoming increasingly prominent; accurate meteorological data is the key to ensuring low-altitude flight safety, which can avoid risks and optimize routes for various low-altitude economic activities such as unmanned aerial vehicle logistics, low-altitude tourism, and airports, and become an important support for the steady progress of the low-altitude economy. A laser raindrop size spectrometer and a dual-polarization radar can measure precipitation such as falling rain, snow, and hail, and can calculate rain intensity, rainfall, etc. for various rainfall types, and are currently widely used in multiple fields such as meteorological monitoring and early warning and services, traffic meteorology, water conservancy and hydrology.
[0003] To achieve the estimation of three-dimensional space visibility during precipitation based on the instruments already in use for meteorological observation operations, a method for quantitatively estimating visibility based on a raindrop size distribution and a dual-polarization radar is proposed, which will have important application value in the field of low-altitude meteorological services. Summary of the Invention
[0004] The purpose of the present invention is to overcome the deficiencies of existing observation means and existing technologies, and based on existing observation instruments, establish a quantitative visibility estimation model to complete the visibility monitoring of three-dimensional space, and provide a method for quantitatively estimating visibility based on a raindrop size distribution and a dual-polarization radar to solve the problems raised in the above background art.
[0005] To achieve the above purpose, the present invention provides the following technical solution: A method for quantitatively estimating visibility based on a raindrop size distribution and a dual-polarization radar, specifically including the following steps: S1: Data preparation, that is, obtaining the data of a laser raindrop size spectrometer, a dual-polarization radar, and a ground visibility meter in the observation area, and performing quality control on the raindrop size distribution and dual-polarization radar data; S2: Using the quality-controlled data of the raindrop size spectrometer to calculate microphysical quantity parameters, and using the least squares method or machine learning method to calculate the fitting relationship between each parameter and visibility, and establishing a quantitative visibility estimation model; S3: The dual-polarization radar performs parameter networking based on vertical direction interpolation technology, and based on the fitting result, establishes a quantitative visibility estimation model of the dual-polarization radar to quantitatively estimate the visibility of each vertical layer in the target area.
[0006] Preferably, in S1, the data of the laser disdrometer, dual-polarization radar, and ground visibility meter in the observation area are decoded, and the data of the OTT Parsivel laser disdrometer are processed into a raindrop spectrum data format with 32 levels of particle size and 32 levels of particle velocity.
[0007] Preferably, in S1, quality control is performed on the raindrop spectrum and dual-polarization radar data, the first two scale bins are removed, the data with a difference in particle falling velocity and the velocity-diameter relationship greater than 60% are removed, quality control processing such as noise correction and systematic deviation correction is performed on the CINRAD-SA dual-polarization radar data, and interpolation processing is performed on the missing data of the data.
[0008] Preferably, in S2, the quality-controlled data of the disdrometer are used to calculate each microphysical quantity and dual-polarization parameter differential reflectivity factor of the raindrop spectrum in combination with the T-matrix method , differential propagation phase shift rate , specifically including: calculating the raindrop number concentration per unit volume and per unit scale interval using the raindrop spectrum data: ;
[0009] In the formula represents the th scale interval and the th velocity interval of raindrops, (unit: ), (unit: ) are the sampling area and sampling time interval respectively, (unit: ) respectively represent the scale interval of the th scale interval, (unit: ) represents the terminal velocity of raindrop fall in the th velocity interval. Thus calculate the raindrop spectrum characteristic parameter number concentration ( ), rainfall intensity R (unit: ), rainwater content W (unit: ), reflectivity factor Z (unit: ), arithmetic mean diameter , mass mean diameter and standardized parameter (unit: ) are respectively: ; ; ; ; ; ; ;
[0010] Calculation of dual polarization parameter differential reflectivity factor using T-matrix method , differential propagation phase shift rate : ; ; ;
[0011] Where "h" and "v" represent the horizontal and vertical directions respectively. is the radar wavelength, is the dielectric constant, , are the forward and backward scattering coefficients respectively, and Re represents the real part of the complex number.
[0012] Preferably, the least squares multivariate fitting or machine learning method is used in S2, combining the calculated raindrop spectrum characteristic parameter number and the dual polarization parameter differential reflectivity factor , differential propagation phase shift rate , and the 1-minute average visibility of the visibility meter are analyzed to develop a quantitative estimation visibility model.
[0013] Preferably, in the S3, the CINRAD-SA dual polarization radar in the observation area is networked based on the vertical interpolation technology to realize the CAPPI of each parameter, and the quantitative estimation of visibility of each vertical layer in the target area is completed according to the quantitative estimation visibility model.
[0014] Preferably, in the process of establishing a quantitative estimation visibility model in S2, a dynamic weight optimization method of fusing multi-source data is adopted to dynamically adjust the weight of each data source in the model according to the correlation coefficient between the laser raindrop spectrometer, dual-polarization radar data and visibility under different weather conditions.
[0015] Preferably, in the process of quantitatively estimating visibility by the S3 dual-polarization radar, digital elevation model (DEM) data is combined to perform a three-dimensional visibility correction on the estimation result taking into account terrain factors.
[0016] Preferably, the data quality control in S1 also includes detecting and processing outliers on the ground visibility meter data, and eliminating data that obviously deviates from the normal range.
[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. The method for quantitatively estimating visibility based on raindrop size distribution and dual-polarization radar uses the precipitation microphysical parameters in a laser raindrop size spectrometer and the ground visibility observations of a visibility meter to statistically obtain the internal relationship between visibility and precipitation microphysical processes under precipitation weather conditions, obtain a quantitative visibility estimation model, and combine with a dual-polarization radar for real-time visibility inversion, so as to obtain a complete three-dimensional visibility structure within the radar observation area, filling the warning monitoring and service for low visibility weather in three-dimensional space, and having important application value in the field of low-altitude meteorological services.
[0018] 2. The method for quantitatively estimating visibility based on raindrop size distribution and dual-polarization radar uses dynamic weight optimization to improve the accuracy of the model under different weather conditions, and the three-dimensional visibility correction considering terrain factors makes the estimation results more conform to the actual geographical environment, further enhancing the applicability of the method in complex terrain areas. BRIEF DESCRIPTION OF THE DRAWINGS
[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.
[0020] Figure 1 is the overall flowchart of the present invention; Figure 2 is a schematic diagram of a quantitative visibility estimation model using raindrop size distribution parameters in an embodiment of the present invention; Figure 3 is a schematic diagram of a quantitative visibility estimation model using dual-polarization radar parameters in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.
[0022] Please refer to Figures 1-3 , the present invention provides a technical solution: A method for quantitatively estimating visibility based on raindrop size distribution and dual-polarization radar: Step 1: Data preparation, obtain the data of a laser raindrop size spectrometer, a dual-polarization radar, and a ground visibility meter in the observation area, and perform quality control preprocessing on the data.
[0023] Step 2: Calculate the microphysical quantity parameters using the quality-controlled data of the disdrometer.
[0024] Step 3: Calculate the fitting relationship between each parameter and visibility, and establish a quantitative estimation visibility model.
[0025] Step 4: The dual-polarization radar networks each parameter based on the vertical direction interpolation technology.
[0026] Step 5: Based on the fitting result, establish a quantitative estimation visibility model for the dual-polarization radar, and conduct quantitative estimation of visibility for each vertical layer in the target area.
[0027] In this embodiment, it mainly includes the following processes: data preparation, decoding the data of the laser disdrometer, dual-polarization radar, and ground visibility meter within the observation area, and processing the OTT Parsivel laser disdrometer data into a raindrop spectrum data format with 32 levels of particle size and 32 levels of particle velocity.
[0028] Perform quality control on the raindrop spectrum and dual-polarization radar data. Eliminate the first two scale bins, and eliminate the data where the difference between the particle fall velocity and the velocity-diameter relationship ( , (unit ) respectively represent the raindrop particle diameter, (unit: ) represents the terminal velocity of raindrop fall) is greater than 60%. Conduct quality control processing on the noise correction and system deviation correction of the CINRAD-SA dual-polarization radar data. And perform interpolation processing on the missing data of the data.
[0029] Calculate the microphysical quantity parameters. Use the quality-controlled data of the disdrometer and combine the T-matrix method to calculate each microphysical quantity of the raindrop spectrum and the differential reflectivity factor of the dual-polarization parameter, of the differential propagation phase shift rate, ;
[0030] In the formula represents the th scale interval and the th velocity interval of raindrop number, (unit: ), (unit: ) are the sampling area and sampling time interval respectively, (unit: ) respectively represent the scale interval of the th scale interval, (unit: ) represents the Terminal velocities of raindrops in a velocity range. Thus Calculate the number concentration of characteristic parameters of the raindrop size distribution ( ), rainfall intensity R (unit: ), water content W (unit: ), reflectivity factor Z (unit: ), arithmetic mean diameter , mass mean diameter and normalized parameter (unit: ) are respectively: ; ; ; ; ; ; ;
[0031] Use the T-matrix method to calculate the dual-polarization parameter differential reflectivity factor , differential propagation phase shift rate : ; ; ;
[0032] In the formula, "h" and "v" represent the horizontal and vertical directions respectively, is the radar wavelength, is the dielectric constant, , are the forward and backward scattering coefficients respectively, and Re represents the real part of a complex number.
[0033] Use the least squares multiple fitting or random forest machine learning method, combined with the calculated number of characteristic parameters of the raindrop size distribution and the dual-polarization parameter differential reflectivity factor , differential propagation phase shift rate , and the 1-minute average visibility of the visibility meter, to analyze a quantitative visibility estimation model. Use the random forest machine learning method to calculate , R, W, , , , Z, , and the quantitative visibility estimation model of the minute visibility. The model results are as Figure 2, the predicted value and the actual value have a very high correlation, and the correlation coefficient reaches 0.982, indicating that there is a high correlation between visibility and raindrop size distribution under precipitation weather conditions. Since the polarization parameters observed by the dual-polarization radar also have the ability to invert the raindrop size distribution, only the dual-polarization parameters Z, , and the minute visibility are used to calculate a quantitative estimation visibility model, and the model results are as shown in Figure 3 .
[0034] For the CINRAD-SA dual-polarization radar in the observation area, each parameter is networked based on the vertical direction interpolation technology to realize the CAPPI of each parameter. According to the quantitative estimation visibility model of the dual-polarization parameters in the observation area, the quantitative estimation of visibility for each vertical layer of the target area is completed.
[0035] When establishing the quantitative estimation visibility model, a dynamic weight allocation mechanism is introduced. According to the differences in the influence of the laser raindrop size spectrometer and dual-polarization radar data on visibility under different weather conditions, the weights of each data source in the model are adjusted in real time using an adaptive algorithm. For example, in heavy rain weather, the raindrop size data has a greater impact on visibility, and its weight is appropriately increased; in light rain or drizzle weather, the polarization parameters of the dual-polarization radar may be more indicative of visibility, and its weight is adjusted accordingly. Through this dynamic weight optimization, the relationship between each data and visibility under different weather conditions can be more accurately reflected, improving the adaptability and accuracy of the model. The specific implementation steps are as follows: First, set the initial weights corresponding to the laser raindrop size spectrometer and dual-polarization radar data respectively. Then, collect a large number of sample data under different weather types, and calculate the correlation coefficient between the data of each data source and visibility. Dynamically adjust the weights according to the correlation coefficient, and the adjustment formula is: .
[0036] where is the adjusted weight.
[0037] Three-dimensional visibility correction considering terrain factors: Combining digital elevation model (DEM) data, the three-dimensional visibility quantitatively estimated by the dual-polarization radar is corrected for terrain. Since terrain undulations will affect the distribution of precipitation and the propagation of radar waves, and thus affect the accurate estimation of visibility. By obtaining the DEM data of the observation area, calculate the bending degree and occlusion situation of the radar beam propagation path at different terrain heights. According to the influence law of terrain on precipitation and radar waves, establish a terrain correction model to correct the quantitative estimation visibility results based on the dual-polarization radar, so that the estimation results are more in line with the actual three-dimensional space visibility distribution. In the specific calculation process, according to the intersection point of the radar beam propagation path and the terrain, calculate the actual distance of the beam propagation and the ideal straight-line distance difference, using the formula: ; Combined with factors such as precipitation intensity and raindrop spectrum characteristics, a terrain correction factor F is established, and the corrected visibility is: , where is the estimated visibility without considering terrain factors.
[0038] Specific steps to establish the terrain correction factor F:
[0039] Step 1: Analyze the influence mechanism of terrain on precipitation and radar wave propagation. Terrain undulations will change the distribution pattern of precipitation. For example, precipitation may increase on the windward slope of a mountain and decrease on the leeward slope. At the same time, the terrain will cause the radar beam propagation path to bend and be blocked, affecting the radar's detection of precipitation and the estimation of visibility. Therefore, factors such as terrain height, precipitation intensity, and raindrop spectrum characteristics need to be comprehensively considered to establish the terrain correction factor.
[0040] Step 2: Collect relevant data: Digital Elevation Model (DEM) data: Obtain high-precision DEM data of the observation area to determine the terrain height and undulation.
[0041] Precipitation intensity data: Obtain precipitation intensity information from a laser raindrop spectrometer or dual-polarization radar data, usually expressed by rainfall intensity R (unit: mm / h).
[0042] Raindrop spectrum characteristic data: Include raindrop number concentration , arithmetic mean diameter , mass mean diameter and other parameters, which can be calculated from raindrop spectrometer data.
[0043] Step 3: Determine the terrain influence index: Radar beam propagation path difference: Calculate the difference between the actual distance of radar beam propagation and the ideal straight-line distance .
[0044] Terrain height factor: Define the terrain height factor , which can be calculated according to the deviation of the terrain height from the average height. For example: ; where is the terrain height at the current location, is the average terrain height of the observation area.
[0045] Step 4: Establish an empirical formula for the terrain correction factor F: Considering precipitation intensity, raindrop spectrum characteristics, and terrain influence index comprehensively, establish the following empirical formula for the terrain correction factor F: ; Among them α、β , γ , δ are empirical coefficients, which need to be calibrated and optimized through a large amount of actual observation data. These coefficients reflect the influence degree of each factor on the terrain correction factor.
[0046] Step 5: Calibrate and optimize the empirical coefficients: Select the observation data under different representative terrains and precipitation conditions as the calibration samples. Use regression analysis methods such as the least squares method to compare the actually observed visibility with the uncorrected estimated visibility, and adjust the empirical coefficients α , β , γ , δ so that the error between the corrected visibility and the actual observed value is minimized.
[0047] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.
[0048] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar, characterized in that: The specific steps include: S1: Data preparation, i.e. obtaining data from a laser raindrop spectrometer, a dual-polarization radar and a ground visibility meter in the observation area, and performing quality control on the raindrop spectrometer and dual-polarization radar data; S2: Use the quality-controlled data of the raindrop spectrometer to calculate the microphysical parameters, and use the least squares method or machine learning method to calculate the fitting relationship between each parameter and visibility, and establish a quantitative estimation model for visibility; S3: The dual-polarization radar networks various parameters based on the vertical interpolation technology, establishes a quantitative estimation visibility model of the dual-polarization radar based on the fitting results, and quantitatively estimates the visibility of each vertical layer in the target area.
2. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In the S1, the data of the laser raindrop spectrometer, dual polarization radar and ground visibility meter in the observation area are decoded, and the OTT Parsivel laser raindrop spectrometer data is processed into a raindrop spectrum data format with 32 levels of particle size and 32 levels of particle speed.
3. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In S1, the raindrop spectrum and dual-polarization radar data are quality controlled, the first two scale files are eliminated, the data with a particle falling velocity and a velocity-diameter relationship that differ by more than 60% are eliminated, the noise correction and system deviation correction of the CINRAD-SA dual-polarization radar data are quality controlled, and the missing data of the data are interpolated.
4. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In S2, the raindrop spectrometer quality control data is combined with the T matrix method to calculate the microphysical quantities of the raindrop spectrum and the dual polarization parameter differential reflectivity factor. , differential propagation phase shift rate , specifically including: using raindrop spectrum data to calculate the number of raindrops per unit volume and per unit scale interval: ； In the formula Indicates The scale interval, The number of raindrops in the speed interval, (unit: )、 (unit: ) are the sampling area and sampling time interval, (unit: ) represent the The scale interval of the scale interval, (unit: ) indicates the The final falling speed of raindrops in the speed range. Calculate the number concentration of characteristic parameters of raindrop spectrum ( )、Rain intensity R(Unit: )、Rainwater content W(Unit: )、Reflectivity factor Z(Unit: ), arithmetic mean diameter , mass average diameter and standardized parameters (unit: ) are: ； ； ； ； ； ； ； Calculation of dual polarization parameter differential reflectivity factor using T-matrix method , differential propagation phase shift rate : ； ； ； Where "h" and "v" represent the horizontal and vertical directions respectively. is the radar wavelength, is the dielectric constant, , are the forward and backward scattering coefficients respectively, and Re represents the real part of the complex number.
5. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In S2, the least squares multivariate fitting or machine learning method is used to combine the calculated raindrop spectrum characteristic parameter number and the dual polarization parameter differential reflectivity factor , differential propagation phase shift rate , and the 1-minute average visibility of the visibility meter are analyzed to develop a quantitative estimation visibility model.
6. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In the S3, the CINRAD-SA dual-polarization radar in the observation area is networked based on the vertical interpolation technology to realize the CAPPI of each parameter, and the visibility of each vertical layer in the target area is quantitatively estimated according to the quantitative estimation visibility model.
7. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In the process of establishing a quantitative visibility estimation model in S2, a dynamic weight optimization method that integrates multi-source data is adopted to dynamically adjust the weight of each data source in the model according to the correlation coefficient between laser raindrop spectrometer, dual-polarization radar data and visibility under different weather conditions.
8. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: In the process of quantitatively estimating visibility by S3 dual-polarization radar, the digital elevation model (DEM) data is combined to make a three-dimensional visibility correction to the estimation result taking into account terrain factors.
9. The method for quantitatively estimating visibility based on raindrop spectrum and dual-polarization radar according to claim 1, characterized in that: The data quality control in S1 also includes detecting and processing outliers on the ground visibility meter data, and eliminating data that obviously deviates from the normal range.