Method and device for obtaining vertical fine structure of wind field based on wind profile radar data

Abstract

The application relates to a wind field vertical fine structure acquisition method and device based on wind profile radar data, the method comprising the following steps: interpolating sounding data to the collection height layer of the wind profile radar data to obtain sounding true values; selecting quality control parameters of the wind profile radar data, and generating a plurality of quality control parameter combinations based on the intervals of the quality control parameters; based on the wind profile radar data, using Kalman filtering to traverse each quality control parameter combination within a set time range to obtain quality controlled wind profile radar data; based on the quality controlled wind profile radar data and the sounding true values, acquiring an optimal quality control parameter combination in each quality control parameter combination, determining optimal quality control data in the wind profile radar data, and determining the wind field vertical fine structure; the method solves the problem that the wind field vertical fine structure cannot be accurately acquired due to inaccurate original data of the wind profile radar, and improves the data quality control quality of the wind profile radar and the accuracy of the wind field vertical fine structure.

Description

Technical Field

[0001] This application relates to the field of environmental data quality control technology, and in particular to a method and apparatus for acquiring the vertical fine structure of wind fields based on wind profiler radar data. Background Technology

[0002] With the development of radar technology, various types of radar are increasingly being used in atmospheric environment monitoring. Wind profiler radar is a new type of Doppler wind radar, primarily targeting clear-sky atmosphere. Compared to conventional atmospheric detection, wind profiler radar offers advantages such as continuous unattended operation, all-weather monitoring of atmospheric wind fields, continuous detection capability, high precision, and high operational reliability. However, in actual detection processes, wind profiler radar is often affected by various non-meteorological factors such as ground clutter, precipitation, and radio signals, thus impacting the accuracy of its observation data.

[0003] The vertical fine structure of wind fields plays an important role in the development and evolution of clouds and convection. Wind profiler radar observation data can be used to detect the vertical fine structure of wind fields. Therefore, inaccuracies in wind profiler radar observation data will affect the detection of the vertical fine structure of wind fields. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and apparatus for acquiring the vertical fine structure of a wind field based on wind profiler radar data, which can improve the quality of wind profiler radar data and thus obtain a more accurate vertical fine structure of the wind field, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for obtaining the vertical fine structure of a wind field based on wind profiler radar data, the method comprising:

[0006] The sounding data is interpolated onto the acquisition height layer of the wind profiler radar data to obtain the sounding true value, which is at the same acquisition height as the wind profiler radar data.

[0007] Select the quality control parameters of the wind profiler radar data, and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters;

[0008] Based on the wind profiler radar data, Kalman filtering is used to iterate through each combination of quality control parameters within a set time range to obtain the quality-controlled wind profiler radar data.

[0009] Based on the quality-controlled wind profiler radar data and the sounding true value, the best quality control parameter combination among the various quality control parameter combinations is determined, and the best quality control data among the wind profiler radar data is determined based on the best quality control parameter combination.

[0010] Based on the highest quality control data, the vertical fine structure of the wind field is determined.

[0011] In one embodiment, selecting quality control parameters for the wind profiler radar data and generating a combination of quality control parameters for the wind profiler radar data based on the range of the quality control parameters includes:

[0012] Based on historical data of the wind profiler radar data, quality control parameters of the wind profiler radar data are selected, and the range of the quality control parameters is determined. The quality control parameters include observation variance, initial estimation variance, and process noise.

[0013] Based on the first data interval of the observed variance, the second data interval of the initial estimated variance, and the third data interval of the process noise, a combination of quality control parameters for the wind profile radar data is generated.

[0014] In one embodiment, the step of using Kalman filtering to traverse each combination of quality control parameters within a set time range based on the wind profiler radar data to obtain the quality-controlled wind profiler radar data includes:

[0015] Based on the acquisition height layer of the wind profiler radar data, the wind profiler radar data within a set time range is sorted to obtain the sorted wind profiler radar data.

[0016] Using Kalman filtering, the sorted wind profiler radar data is iterated through each of the quality control parameter combinations to obtain the quality-controlled wind profiler radar data.

[0017] In one embodiment, the step of using Kalman filtering to iterate through each of the quality control parameter combinations to obtain the quality-controlled wind profiler radar data includes:

[0018] The sorted wind profiler radar data is iterated through each of the quality control parameter combinations using the following formula to obtain the quality-controlled wind profiler radar data:

[0019]

[0020] Among them, the To obtain the current time-series wind profile radar data after quality control using the Kalman filter performed on the combination of quality control parameters, the... To obtain the wind profile radar data after the previous time step of quality control using the Kalman filter performed with the aforementioned quality control parameter combination, the r n Let z be the observation variance of the wind profiler radar data. n The p is the wind profile radar data before the current quality control. n,n-1 The variance of the updated wind profile radar data after quality control in the previous time period is estimated.

[0021] The updated estimated variance is the updated value of the initial estimated variance of the wind profiler radar data during the Kalman filtering process, and the formula for the updated estimated variance is:

[0022]

[0023] q represents the process noise of the wind profiler radar data.

[0024] In one embodiment, determining the optimal combination of quality control parameters among the various combinations of quality control parameters based on the quality-controlled wind profiler radar data and the sounding true value includes:

[0025] Based on the true sounding value, calculate the root mean square error between the quality-controlled wind profile radar data and the true sounding value;

[0026] Based on the minimum value of the root mean square error, the optimal quality control parameter combination is determined among the various combinations of quality control parameters.

[0027] In one embodiment, interpolating the radiosonde data to the acquisition altitude of the wind profiler radar data to obtain the true radiosonde value includes:

[0028] Based on the acquisition height layer of the wind profiler radar data, the zonal wind component and radial wind component of the sounding data are interpolated onto the acquisition height layer to obtain the true sounding value.

[0029] Secondly, this application also provides a device for acquiring the vertical fine structure of a wind field based on wind profiler radar data, the device comprising:

[0030] The radiosonde data interpolation module is used to interpolate radiosonde data onto the acquisition height layer of the wind profiler radar data to obtain the radiosonde true value, which is at the same acquisition height as the wind profiler radar data.

[0031] The quality control combination generation module is used to select the quality control parameters of the wind profiler radar data and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters.

[0032] The quality control combination traversal module is used to traverse each combination of quality control parameters using Kalman filtering within a set time range based on the wind profiler radar data, so as to obtain the quality-controlled wind profiler radar data.

[0033] The optimal data determination module is used to determine the best quality control parameter combination among the various quality control parameter combinations based on the quality-controlled wind profiler radar data and the sounding true value, and to determine the best quality control data in the wind profiler radar data based on the best quality control parameter combination.

[0034] The wind field structure determination module is used to determine the fine vertical structure of the wind field based on the highest quality control data.

[0035] Thirdly, this application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the contents of the first aspect described above.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the contents of the first aspect described above.

[0037] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the contents of the first aspect described above.

[0038] The aforementioned method and apparatus for acquiring the vertical fine structure of wind fields based on wind profiler radar data obtains the radiosonde ground truth by interpolating radiosonde data to the acquisition height layer of the wind profiler radar data. The radiosonde ground truth is at the same acquisition height as the wind profiler radar data. Quality control parameters for the wind profiler radar data are selected, and a combination of quality control parameters is generated based on the intervals of these parameters. Based on the wind profiler radar data, Kalman filtering is used to traverse each combination of quality control parameters within a set time range to obtain quality-controlled wind profiler radar data. Based on the quality-controlled wind profiler radar data and the radiosonde ground truth, the optimal quality control parameter combination among the various combinations is determined, and the optimal quality control data in the wind profiler radar data is determined based on this optimal control parameter combination. Based on the optimal quality control data, the vertical fine structure of the wind field is determined. This method solves the problem of inaccurate acquisition of the vertical fine structure of the wind field due to inaccurate raw data from the wind profiler radar, improving the data quality control quality of the wind profiler radar and the accuracy of the vertical fine structure of the wind field. Attached Figure Description

[0039] Figure 1 This is an application environment diagram of a wind field vertical fine structure acquisition method based on wind profiler radar data in one embodiment.

[0040] Figure 2 This is a flowchart illustrating a method for acquiring the vertical fine structure of a wind field based on wind profiler radar data in one embodiment.

[0041] Figure 3 This is a schematic diagram of the specific process of step S202 in one embodiment;

[0042] Figure 4 This is a schematic diagram of the specific process of step S203 in one embodiment;

[0043] Figure 5 This is a schematic diagram of the specific process of step S204 in one embodiment;

[0044] Figure 6 This is a schematic diagram of the interpolated sounding data in step S201 of one embodiment;

[0045] Figure 7 A graph of the original wind profile radar data in an example embodiment;

[0046] Figure 8 This is a distribution chart of the root mean square error improvement value in an example embodiment;

[0047] Figure 9 A graph of wind profile radar data after quality control in an example embodiment;

[0048] Figure 10 This is a structural block diagram of a wind field vertical fine structure acquisition device based on wind profiler radar data in one embodiment.

[0049] Figure 11 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or apparatuses. The term “multiple” used in this application refers to two or more.

[0052] The method for obtaining the vertical fine structure of wind fields based on wind profiler radar data provided in this application embodiment can be applied to, for example... Figure 1In the application environment shown, the wind profiler radar 102 communicates with the server 104 via a network. The data storage system 106 can store the wind profiler radar data and / or sounding data that the server 104 needs to process. The data storage system 106 can be integrated on the server 104, or it can be placed in the cloud or on other network servers. On server 104, radiosonde data is interpolated to the acquisition height layer of wind profiler radar data acquired by wind profiler radar 102 to obtain radiosonde ground truth values, which are at the same acquisition height as the wind profiler radar data. Next, quality control parameters for the wind profiler radar data are selected, and based on the intervals of these parameters, a combination of quality control parameters for the wind profiler radar data is generated. Then, based on the wind profiler radar data, a Kalman filter is used to traverse each combination of quality control parameters within a set time range to obtain quality-controlled wind profiler radar data. Finally, based on the quality-controlled wind profiler radar data and the radiosonde ground truth values, the optimal quality control parameter combination among the various quality control parameter combinations is determined, and the optimal quality control data in the wind profiler radar data is determined based on this optimal control parameter combination, thereby determining the vertical fine structure of the wind field. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0053] In one embodiment, such as Figure 2 As shown, a method for obtaining the fine vertical structure of wind fields based on wind profiler radar data is provided, and this method is applied to... Figure 1 Taking the server side as an example, the explanation includes the following steps:

[0054] S201, interpolate the radiosonde data to the acquisition height layer of the wind profiler radar data to obtain the true radiosonde value.

[0055] The radiosonde true value is acquired at the same altitude as the wind profiler radar data.

[0056] Radiosonde data is stored in the server's data storage system 106. Based on detection experience, radiosonde data has a high accuracy rate. However, due to the sparse vertical resolution during detection, it is impossible to obtain the fine vertical structure of the wind field solely from the radiosonde data. Although the original wind profiler radar data has a large amount of data, its data quality is not high, and it is also impossible to obtain the fine structure of the wind field. Therefore, the radiosonde data is used as the true value to perform quality control on the wind profiler radar data. The quality-controlled wind profiler radar data is then used to obtain the fine vertical structure of the wind field.

[0057] Specifically, radiosonde data and wind profiler radar data differ in their vertical atmospheric data levels. Standard radiosonde data includes information on wind speed, direction, air pressure, temperature, and humidity at irregular altitudes, such as 3 meters, 121 meters, 610 meters, 791 meters, 1219 meters, 1511 meters, 2438 meters, and 3128 meters. Standard radiosonde data is collected at 8:00 AM and 8:00 PM local time. Due to local climate variations, the altitude of these irregular altitudes changes constantly. Wind profiler radar data, on the other hand, is collected at a fixed altitude, according to the regulations for the region. Data is collected at predetermined intervals, at fixed altitude changes, until the highest possible altitude is reached. For example, the wind profiler radar in Shantou provides vertical data every 6 minutes, starting at 100 meters above ground and providing data every 60 meters up to 5920 meters. Therefore, by interpolating the radiosonde data to the acquisition height layer of the wind profiler radar data, the true radiosonde value at the same acquisition height as the wind profiler radar data can be obtained.

[0058] S202, Select the quality control parameters of the wind profiler radar data, and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters.

[0059] Specifically, the raw wind profiler radar data is inaccurate, so it needs to be filtered to remove interference. Therefore, it is necessary to select quality control parameters for the wind profiler radar and generate as many combinations of these parameters as possible based on their ranges.

[0060] S203, Based on the wind profiler radar data, Kalman filtering is used to traverse each of the quality control parameter combinations within a set time range to obtain the quality-controlled wind profiler radar data.

[0061] Specifically, Kalman filtering is a filtering algorithm that estimates the desired signal from extracted signal-related observations. Applying Kalman filtering to wind profiler radar data for quality control can effectively remove some high-frequency interference and fill in missing data points.

[0062] S204, based on the quality-controlled wind profiler radar data and the sounding true value, determine the best quality control parameter combination among the various quality control parameter combinations, and determine the best quality control data among the wind profiler radar data based on the best quality control parameter combination.

[0063] Specifically, the quality-controlled wind profiler radar data at all collected altitudes are compared and calculated with the true sounding values ​​to determine the optimal quality-controlled wind profiler radar data. The quality control parameter combination corresponding to this data is the optimal quality control parameter combination for Kalman filtering.

[0064] S205, Based on the highest quality control data, determine the vertical fine structure of the wind field.

[0065] Specifically, the highest quality control data in the wind profile radar data after quality control is analyzed and processed to determine the fine vertical structure of the wind field.

[0066] In the above-described method for acquiring the vertical fine structure of wind fields based on wind profiler radar data, the radiosonde data is interpolated to the acquisition height layer of the wind profiler radar data to obtain the radiosonde ground truth, which is at the same acquisition height as the wind profiler radar data. Quality control parameters for the wind profiler radar data are selected, and a combination of quality control parameters is generated based on the intervals of these parameters. Based on the wind profiler radar data, a Kalman filter is used to traverse each combination of quality control parameters within a set time range to obtain quality-controlled wind profiler radar data. Based on the quality-controlled wind profiler radar data and the radiosonde ground truth, the optimal quality control parameter combination among the various combinations is determined, and the optimal quality control data in the wind profiler radar data is determined based on this optimal control parameter combination. Based on the optimal quality control data, the vertical fine structure of the wind field is determined. This method solves the problem of inaccurate acquisition of the vertical fine structure of the wind field due to inaccurate raw data from the wind profiler radar, thus improving the data quality control quality of the wind profiler radar.

[0067] In one embodiment, such as Figure 3 As shown, S202 selects the quality control parameters of the wind profiler radar data, and generates a combination of quality control parameters for the wind profiler radar data based on the range of the quality control parameters, specifically including the following steps:

[0068] S302, Based on the historical data of the wind profiler radar data, select the quality control parameters of the wind profiler radar data, and determine the range of the quality control parameters. The quality control parameters include observation variance, initial estimation variance, and process noise.

[0069] Specifically, quality control of filtering wind profiler radar data requires setting quality control parameters. The selection and value of these parameters directly affect the filtering effect. In this embodiment, in addition to using the initial estimation variance and observation variance as quality control parameters for filtering the original wind profiler radar data, the influence of process noise on the filtering process is also considered, thereby improving the accuracy of the filtering.

[0070] S304, Based on the first data interval of the observation variance, the second data interval of the initial estimated variance, and the third data interval of the process noise, generate a combination of quality control parameters for the wind profile radar data.

[0071] Specifically, the first data interval of the observed variance and the second data interval of the initial estimated variance are determined by the historical data of the wind profiler radar data, while the third data interval of the process noise is determined by the data type of the wind profiler radar data. The data types include wind speed, wind direction, air pressure, temperature, and humidity, and different data types set different process noise parameters.

[0072] In this embodiment, a large number of Kalman filter quality control parameter combinations within the required range are obtained through historical data of wind profiler radar data, which is beneficial for obtaining the optimal quality control parameter combination more accurately in the next step.

[0073] In one embodiment, such as Figure 4 As shown, S203, based on the wind profiler radar data, uses Kalman filtering to traverse each combination of quality control parameters within a set time range to obtain the quality-controlled wind profiler radar data. Specifically, this includes the following steps:

[0074] S402, based on the acquisition height layer of the wind profiler radar data, sort the wind profiler radar data within a set time range to obtain the sorted wind profiler radar data.

[0075] Specifically, based on the acquisition height layer and acquisition time of the wind profiler radar data within a set time range, the wind profiler radar data is sorted to obtain an array format of [time, height layer].

[0076] S404, using Kalman filtering, the sorted wind profiler radar data is traversed through each of the quality control parameter combinations to obtain the quality-controlled wind profiler radar data.

[0077] Kalman filtering can be divided into two parts: the time update equation and the estimated variance update equation. The basic approach of Kalman filtering is to estimate the wind profiler radar data for the current time period using the previously collected wind profiler radar data, and then correct the estimated value based on the collected wind profiler radar data for the current time period, repeating this process cyclically.

[0078] Specifically, due to the long acquisition time of wind profiler radar data, Kalman filtering is performed on the wind profiler radar data within a set time range, traversing different combinations of quality control parameters. During the filtering process, the initial estimated variance is continuously updated to perform quality control on the wind profiler radar data, effectively removing some high-frequency interference and completing missing values ​​in the data. Finally, the wind profiler radar data after quality control using different combinations of quality control parameters is obtained.

[0079] In one embodiment, S404 uses Kalman filtering to iterate through each of the quality control parameter combinations of the sorted wind profiler radar data to obtain quality-controlled wind profiler radar data, specifically including the following:

[0080] The sorted wind profiler radar data is iterated through each of the quality control parameter combinations using the following formula to obtain the quality-controlled wind profiler radar data:

[0081]

[0082] Among them, the To obtain the current time-series wind profile radar data using Kalman filtering based on the aforementioned quality control parameter combination, the... To obtain the wind profile radar data after the previous time step of quality control using the Kalman filter performed with the aforementioned quality control parameter combination, the r n Let z be the observation variance of the wind profiler radar data. n The p is the wind profile radar data before the current quality control. n,n-1 The variance of the updated wind profile radar data after quality control in the previous time period is estimated.

[0083] The updated estimated variance is the updated value of the initial estimated variance of the wind profiler radar data during the Kalman filtering process, and the formula for the updated estimated variance is:

[0084]

[0085] q represents the process noise of the wind profiler radar data.

[0086] In one embodiment, such as Figure 5 As shown, S204 determines the optimal combination of quality control parameters among the various combinations of quality control parameters based on the quality-controlled wind profiler radar data and the sounding true values, specifically including the following steps:

[0087] S502, Based on the radiosonde true values, the wind profiler radar data after quality control is filtered.

[0088] S504, Calculate the root mean square error between the filtered wind profiler radar data and the true sounding value.

[0089] S506, Based on the minimum value of the root mean square error, determine the best quality control parameter combination among the various quality control parameter combinations.

[0090] Specifically, after Kalman filtering, based on the radiosonde true values, the quality-controlled wind profiler radar data within a set time range are filtered, and each data point is matched with the quality-controlled wind profiler radar data at the same altitude and time as the radiosonde true values. The root mean square error (RMSE) between the matched wind profiler radar data at all altitude levels and the corresponding radiosonde true values ​​is calculated. Based on the minimum RMSE, the optimal quality control parameter combination is determined among all quality control parameter combinations.

[0091] In one embodiment, after S504 calculates the root mean square error between the filtered wind profiler radar data and the true sounding value, the following steps are also included:

[0092] Based on the root mean square error of the wind profiler radar data and the true sounding value after quality control, the improvement value of the root mean square error of the wind profiler radar data and the true sounding value before quality control is calculated.

[0093] The improvement value reflects the degree of closeness between the quality-controlled wind profiler radar data and the true sounding value.

[0094] In this embodiment, by calculating the root mean square error (RMSE) between the wind profiler radar data and the true sounding value at each altitude level after quality control, and using the minimum value of the RMSE, the combination of quality control parameters is determined, which further improves the quality control quality of the wind profiler radar data. Furthermore, by using the RMSE to calculate the improvement value of the RMSE between the wind profiler radar data and the true sounding value before quality control, the quality control quality of the wind profiler radar data is more accurately reflected.

[0095] In one embodiment, S201 interpolates the radiosonde data to the acquisition altitude of the wind profiler radar data to obtain the true radiosonde value, specifically including the following:

[0096] Based on the acquisition height layer of the wind profiler radar data, the zonal wind component and radial wind component of the sounding data are interpolated onto the acquisition height layer to obtain the true sounding value.

[0097] Specifically, using one-dimensional cubic spline interpolation, the zonal and radial wind components of the sounding data are interpolated onto the wind profiler radar data. The cubic spline interpolation method allows the interpolated curve of discrete data points to be freely curved, conforming to the continuity of vertical atmospheric variations. For example... Figure 6As shown, the horizontal axis represents the radial and zonal wind speeds, and the vertical axis represents altitude. The large, dark dots A1-A5 in the graph represent the radial wind component of the sounding data. Smaller dots on the same continuous curve as the large, dark dots represent the radial wind component of the sounding data interpolated to the altitude layer of the wind profiler radar data. Similarly, the large, light-colored dots B1-B5 in the graph represent the zonal wind component of the sounding data. Smaller dots on the same continuous curve as the large, light-colored dots represent the zonal wind component of the sounding data interpolated to the altitude layer of the wind profiler radar data. The interpolated sounding data is more continuous in the vertical layer and can be used as true sounding data.

[0098] In one example embodiment, a method for obtaining the vertical fine structure of a large wind field based on wind profiler radar data is provided. Taking Shantou wind profiler radar data from April 2018 as an example, the optimal combination of Kalman filter quality control parameters is obtained using nearby Shantou radiosonde data. The method specifically includes the following steps:

[0099] S1, as Figure 7 As shown, raw wind profiler radar data for Shantou was acquired at an altitude of 100 meters from 00:00 on April 2, 2018 to 23:00 on April 30, 2018, totaling 696 hours (the raw wind profiler radar data to be quality controlled here is the hourly data of the wind profiler radar). The radiosonde data was interpolated to the acquisition altitude layer of the wind profiler radar data to obtain the true radiosonde values.

[0100] S2, based on historical data from the original wind profiler radar data, obtain the observation variance r within the aforementioned time period. n The range is 0.2:0.2:2 (10 numbers in total), the process noise q is 0.01:0.01:0.5 (50 numbers in total), and the initial estimated variance p 0,0 The possible values ​​are: 0.2:0.2:2 (10 numbers in total), resulting in a total of 10×10×50=5000 combinations of quality control parameters.

[0101] Based on the original wind profiler radar data, Kalman filtering is performed using the following formula, and all combinations of quality control parameters are traversed to obtain the quality-controlled wind profiler radar data.

[0102]

[0103] in, To obtain the current time-series wind profile radar data after quality control using Kalman filtering with one of the quality control parameter combinations, To obtain the wind profile radar data after the previous time step using Kalman filtering with one of the quality control parameter combinations, the r n Let z be the observation variance of the wind profiler radar data. nFor the wind profile radar data prior to the current quality control, p n,n-1 To estimate the variance of the updated wind profile radar data after quality control from the previous time step to the current time step, the following formula is used to estimate the variance p. 0,0 Perform an update to obtain the updated estimated variance:

[0104]

[0105] Where q represents the process noise of the wind profiler radar data.

[0106] S3. Using the radiosonde true value, the quality-controlled wind profiler radar data is filtered to obtain quality-controlled wind profiler radar data at each acquisition altitude level for 58 time points. The root mean square error (RMSE) of the quality-controlled wind profiler radar data at all acquisition altitude levels for the 58 time points is calculated between the quality-controlled wind profiler radar data and the radiosonde true value. Based on the obtained RMSE, the RMSE of the uncontrolled wind profiler radar data at all acquisition altitude levels for the 58 time points is calculated between the quality-controlled wind profiler radar data and the radiosonde true value. The RMSEs before and after the two quality control steps are calculated together to obtain the error improvement value:

[0107]

[0108] Where "improvement" is the root mean square error improvement, representing the quality improvement of the data after Kalman filtering quality control compared to the data before quality control. before The root mean square error (RMSE) of the wind profiler radar data before quality control relative to the true sounding value. after It is the root mean square error of the wind profile radar data after quality control relative to the true sounding value.

[0109] like Figure 8 As shown, this is a distribution chart of the root mean square error improvement values ​​obtained by traversing 58 different time points corresponding to different combinations of quality control parameters. The color depth points represent the percentage improvement, and all improvement values ​​are positive, indicating that the wind profile radar data after Kalman filtering quality control is closer to the true sounding value.

[0110] S4. Determine the minimum root mean square error of the wind profiler radar data relative to the true sounding value after quality control. The combination of quality control parameters corresponding to this minimum value is the optimal combination of quality control parameters. The observation variance r in the combination is... n Initial estimated variance p 0,0 The process noise q is the Kalman filter quality control parameter that best improves the effect.

[0111] like Figure 9 The figure shows the curve of the highest quality control data obtained after Kalman filtering of the highest quality control parameter combination. Clearly, compared to the original wind profiler radar data, the quality-controlled wind profiler radar data is more continuous and smoother, with fewer outliers.

[0112] S5 calculates the fine vertical structure of the wind field based on the highest quality control data obtained.

[0113] It should be noted that the methods described above, which obtain the best quality control data of wind profiler radar data by acquiring the optimal combination of Kalman filter quality control parameters, can also be applied to evaluate the model's ability to predict wind fields and assess the atmospheric boundary layer height.

[0114] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0115] Based on the same inventive concept, this application also provides a wind field vertical fine structure acquisition device based on wind profiler radar data for implementing the above-mentioned method for acquiring the vertical fine structure of wind fields based on wind profiler radar data. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the wind field vertical fine structure acquisition device based on wind profiler radar data provided below can be found in the limitations of the wind field vertical fine structure acquisition method based on wind profiler radar data described above, and will not be repeated here.

[0116] In one embodiment, such as Figure 10 As shown, a device for acquiring the vertical fine structure of a wind field based on wind profiler radar data is provided, including: a sounding data interpolation module 100, a quality control combination generation module 200, a quality control combination traversal module 300, an optimal data determination module 400, and a wind field structure determination module 500, wherein:

[0117] The radiosonde data interpolation module 100 is used to interpolate radiosonde data onto the acquisition height layer of the wind profiler radar data to obtain the radiosonde true value, which is at the same acquisition height as the wind profiler radar data.

[0118] The quality control combination generation module 200 is used to select the quality control parameters of the wind profiler radar data and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters.

[0119] The quality control combination traversal module 300 is used to traverse each combination of quality control parameters within a set time range based on the wind profiler radar data to obtain the quality-controlled wind profiler radar data.

[0120] The optimal data determination module 400 is used to determine the best quality control parameter combination among the various quality control parameter combinations based on the quality-controlled wind profiler radar data and the sounding true value, and to determine the best quality control data in the wind profiler radar data based on the best quality control parameter combination.

[0121] The wind field structure determination module 500 is used to determine the fine vertical structure of the wind field based on the highest quality control data.

[0122] In one embodiment, the data interpolation module 100 is further configured to perform the following steps:

[0123] Based on the acquisition height layer of the wind profiler radar data, the zonal wind component and radial wind component of the sounding data are interpolated onto the acquisition height layer to obtain the true sounding value.

[0124] In one embodiment, the observation variance is obtained based on historical data from the wind profiler radar, the process noise is determined based on the type of wind profiler radar data, and the quality control update module 200 is further configured to perform the following steps:

[0125] Based on historical data of the wind profiler radar data, quality control parameters of the wind profiler radar data are selected, and the range of the quality control parameters is determined. The quality control parameters include observation variance, initial estimation variance, and process noise.

[0126] Based on the first data interval of the observed variance, the second data interval of the initial estimated variance, and the third data interval of the process noise, a combination of quality control parameters for the wind profile radar data is generated.

[0127] In one embodiment, the data quality control module 300 is further configured to perform the following steps:

[0128] Based on the acquisition height layer of the wind profiler radar data, the wind profiler radar data within a set time range is sorted to obtain sorted wind profiler radar data.

[0129] Using Kalman filtering, the sorted wind profiler radar data is iterated through each of the quality control parameter combinations to obtain the quality-controlled wind profiler radar data.

[0130] In one embodiment, the data quality control module 300 is further configured to perform the following steps:

[0131] The sorted wind profiler radar data is iterated through each of the quality control parameter combinations using the following formula to obtain the quality-controlled wind profiler radar data:

[0132]

[0133] The To obtain the current time-series wind profile radar data after quality control using the Kalman filter performed on the combination of quality control parameters, the... To obtain the wind profile radar data after the previous time step of quality control using the Kalman filter performed with the aforementioned quality control parameter combination, the r n Let z be the observation variance of the wind profiler radar data. n The p is the wind profile radar data before the current quality control. n,n-1 The variance of the updated wind profile radar data after quality control in the previous time period is estimated.

[0134] The updated estimated variance is the updated value of the initial estimated variance of the wind profiler radar data during the Kalman filtering process, and the formula for the updated estimated variance is:

[0135]

[0136] q represents the process noise of the wind profiler radar data.

[0137] In one embodiment, the array selection module 400 is further configured to perform the following steps:

[0138] Based on the true sounding values, the quality-controlled wind profiler radar data is filtered; the root mean square error between the filtered wind profiler radar data and the true sounding values ​​is calculated; based on the minimum value of the root mean square error, the optimal quality control parameter combination among the various quality control parameter combinations is determined.

[0139] Each module in the aforementioned wind field vertical fine structure acquisition device based on wind profiler radar data can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, so that the processor can call and execute the corresponding operations of each module.

[0140] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 11As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores radiosonde data, wind profiler radar data, and all process data mentioned in the above embodiments. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for acquiring the vertical fine structure of wind fields based on wind profiler radar data.

[0141] Those skilled in the art will understand that Figure 11 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the methods described in the above embodiments.

[0143] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the methods described in the above embodiments.

[0144] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods described in the above embodiments.

[0145] It should be noted that the data involved in this application (including but not limited to data used for analysis, data stored, data displayed, etc.) are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0146] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0147] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0148] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for acquiring the vertical fine structure of wind fields based on wind profiler radar data, characterized in that, The method includes: The sounding data is interpolated onto the acquisition height layer of the wind profiler radar data to obtain the sounding true value, which is at the same acquisition height as the wind profiler radar data. Select the quality control parameters of the wind profiler radar data, and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters; Based on the wind profiler radar data, Kalman filtering is used to traverse each of the quality control parameter combinations within a set time range to obtain the quality-controlled wind profiler radar data. Based on the quality-controlled wind profiler radar data and the sounding true value, the best quality control parameter combination among the various quality control parameter combinations is determined, and the best quality control data among the wind profiler radar data is determined based on the best quality control parameter combination. Based on the highest quality control data, the vertical fine structure of the wind field is determined.

2. The method for obtaining the vertical fine structure of wind fields based on wind profiler radar data according to claim 1, characterized in that, The process of acquiring quality control parameters for the wind profiler radar data and generating a combination of quality control parameters for the wind profiler radar data based on the range of the quality control parameters includes: Based on historical data of the wind profiler radar data, quality control parameters of the wind profiler radar data are selected, and the data range of the quality control parameters is determined. The quality control parameters include observation variance, initial estimation variance, and process noise. Based on the first data interval of the observed variance, the second data interval of the initial estimated variance, and the third data interval of the process noise, a combination of quality control parameters for the wind profile radar data is generated.

3. The method for obtaining the vertical fine structure of wind fields based on wind profiler radar data according to claim 1, characterized in that, The process of obtaining quality-controlled wind profiler radar data by using Kalman filtering to iterate through each combination of quality control parameters within a set time range based on the wind profiler radar data includes: Based on the acquisition height layer of the wind profiler radar data, the wind profiler radar data within a set time range is sorted to obtain sorted wind profiler radar data. Using Kalman filtering, the sorted wind profiler radar data is iterated through each of the quality control parameter combinations to obtain the quality-controlled wind profiler radar data.

4. The method for obtaining the vertical fine structure of wind fields based on wind profiler radar data according to claim 3, characterized in that, The process of using Kalman filtering to iterate through each of the quality control parameter combinations to obtain quality-controlled wind profiler radar data includes: The sorted wind profiler radar data is iterated through each of the quality control parameter combinations using the following formula to obtain the quality-controlled wind profiler radar data: The To obtain the current time-series wind profile radar data after quality control using the Kalman filter performed on the combination of quality control parameters, the... To obtain the wind profile radar data after the previous time step of quality control using the Kalman filter performed with the aforementioned quality control parameter combination, the r n Let z be the observation variance of the wind profiler radar data. n The p is the wind profile radar data before the current quality control. n,n-1 The variance of the updated wind profile radar data after quality control in the previous time period is estimated. The updated estimated variance is the updated value of the initial estimated variance of the wind profiler radar data during the Kalman filtering process, and the formula for the updated estimated variance is: p n,n =(1-K n ) n,n-1 +, q represents the process noise of the wind profiler radar data.

5. The method for obtaining the vertical fine structure of wind fields based on wind profiler radar data according to claim 1, characterized in that, The process of determining the optimal quality control parameter combination among the various quality control parameter combinations based on the quality-controlled wind profiler radar data and the sounding true value includes: Based on the aforementioned sounding true values, the quality-controlled wind profiler radar data is filtered. Calculate the root mean square error between the filtered wind profiler radar data and the true sounding values; Based on the minimum value of the root mean square error, the optimal quality control parameter combination is determined among the various combinations of quality control parameters.

6. The method for obtaining the vertical fine structure of a wind field based on wind profiler radar data according to claim 1, characterized in that, The process of interpolating the radiosonde data to the acquisition height of the wind profiler radar data to obtain the true radiosonde value includes: Based on the acquisition height layer of the wind profiler radar data, the zonal wind component and radial wind component of the sounding data are interpolated onto the acquisition height layer to obtain the true sounding value.

7. A device for acquiring the vertical fine structure of a wind field based on wind profiler radar data, characterized in that, The device includes: The radiosonde data interpolation module is used to interpolate radiosonde data onto the acquisition height layer of the wind profiler radar data to obtain the radiosonde true value, which is at the same acquisition height as the wind profiler radar data. The quality control combination generation module is used to select the quality control parameters of the wind profiler radar data and generate a combination of quality control parameters of the wind profiler radar data based on the range of the quality control parameters. The quality control combination traversal module is used to traverse each combination of quality control parameters using Kalman filtering within a set time range based on the wind profiler radar data, so as to obtain the quality-controlled wind profiler radar data. The optimal data determination module is used to determine the best quality control parameter combination among the various quality control parameter combinations based on the quality-controlled wind profiler radar data and the sounding true value, and to determine the best quality control data in the wind profiler radar data based on the best quality control parameter combination. The wind field structure determination module is used to determine the fine vertical structure of the wind field based on the highest quality control data.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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