Method and device for revising atmospheric reanalysis data
By using the orthogonal regression principle to correct wind speed data in the atmospheric reanalysis system, the problem of inaccurate atmospheric reanalysis data is solved, the accuracy of the data is improved, and the selection of wind power stations is supported.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUANENG (ZHEJIANG) ENERGY DEV CO LTD
- Filing Date
- 2022-12-30
- Publication Date
- 2026-06-05
AI Technical Summary
Atmospheric reanalysis data is not accurate enough, affecting applications in fields such as weather, climate, oceanography, and hydrology.
An atmospheric reanalysis system is constructed based on the high-precision meteorological simulation software WRF. The reanalysis atmospheric wind speed data is corrected by the orthogonal regression principle. An initial correction function y=a0x+b0 is set, and a0 and b0 are iteratively adjusted until the orthogonal regression target value is minimized, so as to obtain the corrected target atmospheric wind speed data.
This improves the accuracy of atmospheric reanalysis data, which is beneficial for subsequent applications such as the selection of wind power stations.
Smart Images
Figure CN116227145B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of atmospheric reanalysis, and more particularly to a method and apparatus for correcting atmospheric reanalysis data. Background Technology
[0002] Atmospheric reanalysis is the reproduction of past atmospheric conditions using meteorological observation data, numerical models, and data assimilation techniques. It is widely used in fields such as weather, climate, oceanography, and hydrology. However, the data obtained from atmospheric reanalysis may not be accurate enough. To improve the accuracy of the data, correcting atmospheric reanalysis data is an urgent problem to be solved. Summary of the Invention
[0003] This application aims to at least partially address one of the technical problems in the related art.
[0004] Therefore, one objective of this application is to propose a method for correcting atmospheric reanalysis data. This method involves constructing an atmospheric reanalysis system based on the high-precision meteorological simulation software WRF, reanalyzing and calculating the assimilated historical observation data corresponding to the target area, and obtaining atmospheric wind speed data to be corrected within a first preset time period. It also involves acquiring actual wind speed data collected from multiple monitoring points within the target area within a second preset time period, and performing data quality control on the actual wind speed data. The first and second preset time periods overlap. Furthermore, it involves acquiring pre-selected atmospheric wind speed data to be corrected and pre-selected actual wind speed data within the overlapping time periods, setting the initial correction function as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected at a certain moment within the overlapping time period, and y represents the theoretical value of the wind speed at that moment. The corrected wind speed data is obtained, where a0 and b0 are set values. Each of the pre-selected atmospheric wind speed data to be corrected and reanalyzed is substituted into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data within the overlapping time period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data. a0 and b0 are iteratively adjusted, and the orthogonal regression target value is adjusted accordingly until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected and reanalyzed into the adjusted initial correction function. The currently adjusted initial correction function is then determined as the final correction function. All atmospheric wind speed data to be corrected and reanalyzed are corrected according to the correction function to obtain the corrected target atmospheric wind speed data.
[0005] The second objective of this application is to provide a correction device for atmospheric reanalysis data.
[0006] The third objective of this application is to propose an electronic device.
[0007] The fourth objective of this application is to provide a non-transitory computer-readable storage medium.
[0008] The fifth objective of this application is to provide a computer program product.
[0009] To achieve the above objectives, the first aspect of this application proposes a method for correcting atmospheric reanalysis data, comprising: constructing an atmospheric reanalysis system based on the high-precision meteorological simulation software WRF; reanalyzing and calculating the assimilated historical observation data corresponding to the target area to obtain atmospheric wind speed data to be corrected within a first preset time period; acquiring actual wind speed data collected by multiple monitoring points within the target area within a second preset time period, and performing data quality control on the actual wind speed data, wherein the first preset time period and the second preset time period overlap; acquiring pre-selected atmospheric wind speed data to be corrected and pre-selected actual wind speed data within the overlapping time period, and setting the initial correction function as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected at a certain moment within the overlapping time period, and y represents the theoretical value of the actual wind speed at that moment. The corrected wind speed data is used, with a0 and b0 as set values. Each of the pre-selected atmospheric wind speed data to be corrected and reanalyzed is substituted into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data within the overlapping time period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data. a0 and b0 are iteratively adjusted, and the orthogonal regression target value is adjusted accordingly until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected and reanalyzed into the adjusted initial correction function. The currently adjusted initial correction function is then determined as the final correction function. All the atmospheric wind speed data to be corrected and reanalyzed are corrected according to the correction function to obtain the corrected target atmospheric wind speed data.
[0010] According to one embodiment of this application, the assimilated historical observation data corresponding to the target area is reanalyzed and calculated to obtain atmospheric wind speed data to be corrected within a first preset time period, including: reanalyzing and calculating the assimilated historical observation data corresponding to the target area to obtain reanalyzed atmospheric wind speed data within a first preset time period; identifying outliers in the reanalyzed atmospheric wind speed data and removing the outliers to obtain the reanalyzed atmospheric wind speed data to be corrected after removal.
[0011] According to one embodiment of this application, determining outliers in reanalysis atmospheric wind speed data includes: obtaining the difference between each reanalysis atmospheric wind speed data and adjacent reanalysis atmospheric wind speed data; determining whether each reanalysis atmospheric wind speed data is within a preset wind speed range; and determining the reanalysis atmospheric wind speed data as an outlier in response to the difference being greater than a preset threshold or in response to the reanalysis atmospheric wind speed data not being within the preset wind speed range.
[0012] According to one embodiment of this application, data quality control of actual wind speed data includes: performing boundary value, extreme value, and internal consistency checks on the actual wind speed data.
[0013] According to one embodiment of this application, after obtaining the corrected target atmospheric wind speed data, the method further includes: analyzing the target atmospheric wind speed data of the target area to determine whether the wind force in the target area meets the power generation requirements; and, in response to the wind force in the target area meeting the power generation requirements, designating the target area as a candidate area for a power plant.
[0014] To achieve the above objectives, a second aspect of this application provides an atmospheric reanalysis data correction device, comprising: a reanalysis module, used to construct an atmospheric reanalysis system based on high-precision meteorological simulation software WRF, and to perform reanalysis calculations on assimilated historical observation data corresponding to a target area to obtain atmospheric wind speed data to be corrected within a first preset time period; a first acquisition module, used to acquire actual wind speed data collected by multiple monitoring points within the target area within a second preset time period, and to perform data quality control on the actual wind speed data, wherein the first preset time period and the second preset time period overlap; and a second acquisition module, used to acquire pre-selected atmospheric wind speed data to be corrected and pre-selected actual wind speed data within the overlapping time period, setting the initial correction function as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected at a certain moment within the overlapping time period, and y represents the theoretical value of the actual wind speed data within the overlapping time period. The system consists of: a) the corrected wind speed data corresponding to each time point, where a0 and b0 are set values; b) the third acquisition module, which substitutes each of the pre-selected reanalysis atmospheric wind speed data to be corrected into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data within the overlapping time period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data; c) the iteration module, which continuously iterates and adjusts a0 and b0, and adjusts the orthogonal regression target value accordingly, until the orthogonal regression target value is minimized after substituting the pre-selected reanalysis atmospheric wind speed data to be corrected into the adjusted initial correction function, and determines the currently adjusted initial correction function as the final determined correction function; and d) the correction module, which corrects all the reanalysis atmospheric wind speed data to be corrected according to the correction function to obtain the corrected target atmospheric wind speed data.
[0015] According to one embodiment of this application, the reanalysis module is further configured to perform reanalysis calculations on the assimilated historical observation data corresponding to the target area to obtain reanalysis atmospheric wind speed data within a first preset time period; determine outliers in the reanalysis atmospheric wind speed data, remove outliers, and obtain the reanalysis atmospheric wind speed data to be corrected after removal.
[0016] According to one embodiment of this application, the reanalysis module is further configured to obtain the difference between each reanalysis atmospheric wind speed data and adjacent reanalysis atmospheric wind speed data; determine whether each reanalysis atmospheric wind speed data is within a preset wind speed range; and determine the reanalysis atmospheric wind speed data as an outlier in response to the difference being greater than a preset threshold or in response to the reanalysis atmospheric wind speed data not being within the preset wind speed range.
[0017] According to one embodiment of this application, the first acquisition module is further configured to: perform data quality control on the actual wind speed data, including: performing boundary value, extreme value, and internal consistency checks on the actual wind speed data.
[0018] According to one embodiment of this application, the atmospheric reanalysis data correction device further includes: an analysis module for analyzing target atmospheric wind speed data of a target area to determine whether the wind force in the target area meets the power generation requirements; and a determination module for, in response to the wind force in the target area meeting the power generation requirements, designating the target area as a candidate area for a power plant.
[0019] To achieve the above objectives, a third aspect of this application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to implement the atmospheric reanalysis data correction method as described in the first aspect of this application.
[0020] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the atmospheric reanalysis data correction method as described in the first aspect of this application.
[0021] To achieve the above objectives, a fifth aspect of this application provides a computer program product, including a computer program that, when executed by a processor, implements the atmospheric reanalysis data correction method as described in the first aspect of this application.
[0022] This application achieves at least the following beneficial effects:
[0023] This application corrects the reanalysis atmospheric wind speed data based on the orthogonal regression principle, making the final corrected target atmospheric wind speed data more accurate, and also facilitating the selection of wind power stations based on the target atmospheric wind speed data. Attached Figure Description
[0024] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:
[0025] Figure 1 This is a schematic flowchart illustrating an exemplary implementation of an atmospheric reanalysis data correction method according to one embodiment of this application;
[0026] Figure 2 This is a schematic diagram of an atmospheric reanalysis data correction device according to one embodiment of this application;
[0027] Figure 3 This is a schematic diagram of an electronic device according to one embodiment of this application. Detailed Implementation
[0028] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.
[0029] Terminology Explanation:
[0030] Atmospheric reanalysis uses advanced, fixed data assimilation systems and numerical weather prediction models to fuse model forecasts with historical observation data, thereby obtaining long-term historical weather data that is rich in variables, has complete spatial coverage, and is temporally uniform and stable. Atmospheric reanalysis utilizes mature numerical weather prediction models and assimilation analysis techniques to reprocess and analyze historical meteorological observation data, reproducing past atmospheric conditions. It has significant application value in the fields of weather, climate, environment, oceanography, and hydrology.
[0031] Figure 1 This is a schematic flowchart illustrating an exemplary implementation of a method for correcting atmospheric reanalysis data as shown in this application, such as... Figure 1 As shown, the correction method for this atmospheric reanalysis data includes the following steps:
[0032] S101, based on the high-precision meteorological simulation software WRF, constructs an atmospheric reanalysis system to reanalyze and calculate the assimilated historical observation data corresponding to the target area, and obtains the atmospheric wind speed data to be corrected within the first preset time period.
[0033] An atmospheric reanalysis system is constructed based on the high-precision meteorological simulation software WRF (Weather Research Forecasting) to reanalyze and calculate the assimilated historical observation data corresponding to the target area, and obtain the atmospheric wind speed data to be corrected within the first preset time period.
[0034] The target area refers to the region where atmospheric reanalysis will be performed, such as a sea area, a city, or a mountain.
[0035] Historical observation data may include observational data such as temperature, wind speed, and relative humidity.
[0036] The first preset duration can generally be a relatively long duration; for example, the first preset duration can be selected from 1 year to 30 years. For example, if the first preset duration is 10 years, the period from 2011 to 2021 can be selected.
[0037] S102, acquire the actual wind speed data collected by multiple monitoring points in the target area within a second preset time period, and perform data quality control on the actual wind speed data, wherein the first preset time period and the second preset time period overlap.
[0038] The system acquires actual wind speed data collected from multiple monitoring points within the target area within a second preset time period, and performs data quality control on the actual wind speed data. The first preset time period and the second preset time period overlap.
[0039] Among them, multiple monitoring points refer to multiple monitoring points that monitor wind speed in the target area. For example, target area 1 can correspond to 3 monitoring points, namely monitoring point 1, monitoring point 2 and monitoring point 3.
[0040] For example, if the first preset duration is 2011 to 2021, then the second preset duration can be the entire year of 2021.
[0041] Among them, data quality control of actual wind speed data refers to checking the limit values, extreme values, and internal consistency of actual wind speed data.
[0042] S103. Obtain the pre-selected atmospheric wind speed data to be corrected and the pre-selected actual wind speed data within the overlapping time period. Set the initial correction function as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected and the actual wind speed data at that time under theoretical conditions, and a0 and b0 are set values.
[0043] The system acquires atmospheric wind speed data to be corrected and reanalyzed within overlapping time periods as pre-selected atmospheric wind speed data to be corrected and reanalyzed, and acquires actual wind speed data within overlapping time periods as pre-selected actual wind speed data. For example, if the first preset time period is 2011 to 2021 and the second preset time period is the entire year of 2021, then the atmospheric wind speed data to be corrected and reanalyzed for 2021 will be used as pre-selected atmospheric wind speed data to be corrected and reanalyzed, and the actual wind speed data for 2021 will be used as pre-selected actual wind speed data.
[0044] The initial correction function is set as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected and reanalyzed at a certain moment within the overlapping period, y represents the corrected wind speed data corresponding to that moment under theoretical conditions, and a0 and b0 are set values.
[0045] S104. Substitute each pre-selected atmospheric wind speed data to be corrected and reanalyzed into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data in the overlapping period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the straight line of the actual wind speed data curve corresponding to the pre-selected actual wind speed data.
[0046] Substitute each pre-selected atmospheric wind speed data to be corrected into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data in the overlapping period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the straight line of the actual wind speed data curve corresponding to the pre-selected actual wind speed data.
[0047] In this context, the horizontal axis corresponding to the coordinate points of the wind speed data can be the time axis, and the vertical axis can be the wind speed data.
[0048] S105, continuously iterate and adjust a0 and b0, and adjust the orthogonal regression target value accordingly, until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. Then, determine the current adjusted initial correction function as the final correction function.
[0049] The orthogonal regression target value is adjusted iteratively for a0 and b0 until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. The adjusted initial correction function is then determined as the final correction function, and the final correction function is denoted as y = ax + b.
[0050] In the process of iteratively adjusting a0 and b0, the maximum and minimum values corresponding to a0 and b0, as well as the minimum precision value for each change of a0 and b0, can be preset. Based on the minimum precision value for each change of a0 and b0, a0 and b0 are iteratively adjusted until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. The adjusted initial correction function is then determined as the final correction function.
[0051] S106, Correct all atmospheric wind speed data to be corrected and reanalyzed according to the correction function, and obtain the corrected target atmospheric wind speed data.
[0052] Based on the correction function y = ax + b obtained above, all atmospheric wind speed data to be corrected and reanalyzed are corrected, and the corrected atmospheric wind speed data to be corrected and reanalyzed are recorded as the target atmospheric wind speed data.
[0053] Since the correction function has been determined above, that is, a and b are constant values, we only need to substitute each atmospheric wind speed data to be corrected into the correction function as x, and the generated y is the target atmospheric wind speed data corresponding to the atmospheric wind speed data to be corrected.
[0054] The embodiments of this application correct the reanalysis atmospheric wind speed data based on the orthogonal regression principle, so that the final corrected target atmospheric wind speed data is more accurate, and it is also beneficial for subsequent selection of wind power stations based on the target atmospheric wind speed data.
[0055] Furthermore, when reanalyzing and calculating the assimilated historical observation data corresponding to the target area to obtain the reanalyzed atmospheric wind speed data to be corrected within the first preset time period, the assimilated historical observation data corresponding to the target area is first reanalyzed and calculated to obtain the reanalyzed atmospheric wind speed data within the first preset time period. It should be noted that the reanalyzed atmospheric wind speed data at this time may contain some abnormal data. In order to improve the accuracy of the reanalyzed atmospheric wind speed data, it is necessary to identify the outliers in the reanalyzed atmospheric wind speed data and remove the outliers to obtain the reanalyzed atmospheric wind speed data to be corrected after removal.
[0056] In identifying outliers in reanalyzed atmospheric wind speed data, the difference between each reanalyzed atmospheric wind speed data point and its adjacent counterparts can be obtained, or it can be determined whether each reanalyzed atmospheric wind speed data point falls within a preset wind speed range. If the difference exceeds a preset threshold, or if the reanalyzed atmospheric wind speed data point is not within a preset wind speed range, the reanalyzed atmospheric wind speed data point is identified as an outlier. For example, if a data point in the reanalyzed atmospheric wind speed data indicates an average wind speed of 20 meters per second for that hour, but a comparison reveals that the average wind speed in the target area for the previous and next hours of that hour is less than 3 meters per second, then the average wind speed of 20 meters per second for that hour is considered a sudden change in data, and this data point is deleted as an outlier. For example, if a data point in the reanalyzed atmospheric wind speed data indicates an average wind speed of 2000 meters per second for that hour, this data point clearly exceeds human physical cognition and is obviously unreasonable data, then this data point is deleted as an outlier.
[0057] Furthermore, after obtaining the corrected target atmospheric wind speed data, the process also includes: analyzing the target atmospheric wind speed data of the target area to determine whether the wind force in the target area meets the power generation requirements; and, in response to the wind force in the target area meeting the power generation requirements, designating the target area as a candidate area for a power plant.
[0058] Figure 2 This is a schematic diagram of an atmospheric reanalysis data correction device shown in this application, such as... Figure 2 As shown, the atmospheric reanalysis data correction device 200 includes:
[0059] The reanalysis module 201 is used to construct an atmospheric reanalysis system based on the high-precision meteorological simulation software WRF, and to reanalyze and calculate the assimilated historical observation data corresponding to the target area to obtain the atmospheric wind speed data to be corrected within the first preset time period.
[0060] The first acquisition module 202 is used to acquire actual wind speed data collected by multiple monitoring points in the target area within a second preset time period, and to perform data quality control on the actual wind speed data, wherein the first preset time period and the second preset time period overlap.
[0061] The second acquisition module 203 is used to acquire the pre-selected atmospheric wind speed data to be corrected and reanalyzed within the overlapping time period and the pre-selected actual wind speed data within the overlapping time period. The initial correction function is set as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected and reanalyzed at a certain moment within the overlapping time period, y represents the corrected wind speed data corresponding to that moment under theoretical conditions, and a0 and b0 are set values.
[0062] The third acquisition module 204 is used to substitute each of the pre-selected atmospheric wind speed data to be corrected into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data in the overlapping period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data.
[0063] The iteration module 205 is used to continuously iterate and adjust a0 and b0, and the orthogonal regression target value is adjusted accordingly until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. The adjusted initial correction function is then determined as the final correction function.
[0064] The correction module 206 is used to correct all atmospheric wind speed data to be corrected and reanalyzed according to the correction function, and obtain the corrected target atmospheric wind speed data.
[0065] This device corrects the reanalysis atmospheric wind speed data based on the orthogonal regression principle, making the final corrected target atmospheric wind speed data more accurate. It is also beneficial for subsequent selection of wind power stations based on the target atmospheric wind speed data.
[0066] According to one embodiment of this application, the reanalysis module 201 is further configured to perform reanalysis calculation on the assimilated historical observation data corresponding to the target area to obtain reanalysis atmospheric wind speed data within a first preset time period; determine outliers in the reanalysis atmospheric wind speed data, remove outliers, and obtain the reanalysis atmospheric wind speed data to be corrected after removal.
[0067] According to one embodiment of this application, the reanalysis module 201 is further configured to obtain the difference between each reanalysis atmospheric wind speed data and adjacent reanalysis atmospheric wind speed data; determine whether each reanalysis atmospheric wind speed data is within a preset wind speed range; and determine the reanalysis atmospheric wind speed data as an outlier in response to the difference being greater than a preset threshold or in response to the reanalysis atmospheric wind speed data not being within the preset wind speed range.
[0068] According to one embodiment of this application, the first acquisition module 202 is further configured to: perform data quality control on the actual wind speed data, including: performing boundary value, extreme value, and internal consistency checks on the actual wind speed data.
[0069] According to one embodiment of this application, the atmospheric reanalysis data correction device 200 further includes: an analysis module 207, used to analyze the target atmospheric wind speed data of the target area and determine whether the wind force of the target area meets the power generation requirements; and a determination module 208, used to select the target area as a candidate area for a power plant in response to the wind force of the target area meeting the power generation requirements.
[0070] To implement the above embodiments, this application also proposes an electronic device 300, such as... Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 communicatively connected to the processor. The memory 302 stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor 301 to implement the atmospheric reanalysis data correction method as shown in the above embodiment.
[0071] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to implement the atmospheric reanalysis data correction method as shown in the above embodiments.
[0072] To implement the above embodiments, this application also proposes a computer program product, including a computer program that, when executed by a processor, implements the atmospheric reanalysis data correction method as shown in the above embodiments.
[0073] In the description of this application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.
[0074] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.
[0075] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0076] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A method for correcting atmospheric reanalysis data, characterized in that, include: An atmospheric reanalysis system is constructed based on the high-precision meteorological simulation software WRF to reanalyze and calculate the assimilated historical observation data corresponding to the target area, and obtain the atmospheric wind speed data to be corrected within the first preset time period. The actual wind speed data collected by multiple monitoring points within the target area within a second preset time period is obtained, and the actual wind speed data is subjected to data quality control, wherein the first preset time period and the second preset time period overlap. Acquire the pre-selected atmospheric wind speed data to be corrected and the pre-selected actual wind speed data within the overlapping time period. Set the initial correction function as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected and the actual wind speed data at a certain moment within the overlapping time period, y represents the corrected wind speed data at that moment under theoretical conditions, and a0 and b0 are set values. Substitute each of the pre-selected atmospheric wind speed data to be corrected into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data in the overlapping period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data. The orthogonal regression target value is adjusted accordingly by iteratively adjusting a0 and b0 until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. The adjusted initial correction function is then determined as the final correction function. The correction function is used to correct all the atmospheric wind speed data to be corrected and reanalyzed, and the corrected target atmospheric wind speed data is obtained.
2. The method according to claim 1, characterized in that, The process of reanalyzing and calculating the assimilated historical observation data corresponding to the target area to obtain the atmospheric wind speed data to be corrected within a first preset time period includes: The assimilated historical observation data corresponding to the target area are reanalyzed and calculated to obtain the reanalyzed atmospheric wind speed data within the first preset time period; Identify outliers in the reanalysis atmospheric wind speed data, remove the outliers, and obtain the reanalysis atmospheric wind speed data to be corrected after removal.
3. The method according to claim 1, characterized in that, The process of identifying outliers in the reanalysis atmospheric wind speed data includes: Obtain the difference between each reanalysis atmospheric wind speed data and the adjacent reanalysis atmospheric wind speed data; Determine whether each of the reanalyzed atmospheric wind speed data is within a preset wind speed range; If the difference is greater than a preset threshold or if the reanalysis atmospheric wind speed data is not within a preset wind speed range, the reanalysis atmospheric wind speed data is determined to be an outlier.
4. The method according to claim 1, characterized in that, The data quality control of the actual wind speed data includes: The actual wind speed data is checked for limit values, extreme values, and internal consistency.
5. The method according to claim 1, characterized in that, After obtaining the corrected target atmospheric wind speed data, the process also includes: Analyze the target atmospheric wind speed data of the target area to determine whether the wind force in the target area meets the power generation requirements; In response to the fact that the wind power in the target area meets the power generation requirements, the target area is selected as a candidate area for a power plant.
6. A correction device for atmospheric reanalysis data, characterized in that, include: The reanalysis module is used to build an atmospheric reanalysis system based on the high-precision meteorological simulation software WRF, and to reanalyze and calculate the assimilated historical observation data corresponding to the target area to obtain the atmospheric wind speed data to be corrected within the first preset time period. The first acquisition module is used to acquire actual wind speed data collected by multiple monitoring points in the target area within a second preset time period, and to perform data quality control on the actual wind speed data, wherein the first preset time period and the second preset time period overlap. The second acquisition module is used to acquire the pre-selected atmospheric wind speed data to be corrected and reanalyzed within the overlapping time period and the pre-selected actual wind speed data within the overlapping time period. The initial correction function is set as y = a0x + b0, where x represents the pre-selected atmospheric wind speed data to be corrected and reanalyzed at a certain moment within the overlapping time period, y represents the corrected wind speed data corresponding to that moment under theoretical conditions, and a0 and b0 are set values. The third acquisition module is used to substitute each of the pre-selected atmospheric wind speed data to be corrected and reanalyzed into the initial correction function to obtain the orthogonal regression target value corresponding to the corrected wind speed data in the overlapping period under theoretical conditions. The orthogonal regression target value refers to the sum of the distances from the coordinate points of the corrected wind speed data corresponding to each theoretical condition to the actual wind speed data curve corresponding to the pre-selected actual wind speed data. An iterative module is used to continuously adjust a0 and b0, and the orthogonal regression target value is adjusted accordingly until the orthogonal regression target value is minimized after substituting the pre-selected atmospheric wind speed data to be corrected into the adjusted initial correction function. The adjusted initial correction function is then determined as the final correction function. The correction module is used to correct all the atmospheric wind speed data to be corrected and reanalyzed according to the correction function, and obtain the corrected target atmospheric wind speed data.
7. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
8. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.
9. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.