Method and system for evaluating and designing meteorological station network layout integrating multi-source observation requirements

Abstract

Embodiments of the present disclosure provide a weather station network layout evaluation and design method and system integrating multi-source observation requirements. The method is applied to the technical field of weather observation station network layout design, and includes five steps of requirement matching, efficiency evaluation, simulation optimization, layout decision and closed-loop feedback. In this way, the present application realizes efficient visual display by integrating multi-source observation requirements, constructing a comprehensive data management system and constructing multi-dimensional station network evaluation and design algorithms, and significantly improves the overall efficiency of weather station network data management and application.

Description

Technical Field

[0001] This disclosure relates to the field of meteorological observation network layout design technology, and in particular to a method and system for evaluating and designing meteorological station network layout that integrates multi-source observation needs. Background Technology

[0002] With the increasing severity of global climate change and the growing demand for meteorological information in economic and social development, the accurate acquisition and comprehensive analysis of meteorological information are crucial for scientific research, weather forecasting, and disaster prevention in today's digital age. To meet the increasing demand for meteorological data, promote the scientific and high-quality development of meteorological observation, and build a sophisticated meteorological observation system in my country, this is essential.

[0003] Focusing on pain points such as unbalanced layout of observation network, inefficient investment, insufficient coordination, and lack of evaluation, this project constructs five collaborative processes: demand matching, performance evaluation, simulation optimization, layout decision-making, and closed-loop feedback. It covers the entire lifecycle of services, from quantitative analysis of the current situation, precise demand matching, performance simulation and prediction to dynamic optimization of the plan. This not only supports the improvement of numerical forecast accuracy and the refinement of public services, but also helps to increase the efficiency of the energy industry and global observation collaboration, providing a solid data foundation and decision support for global meteorological disaster prevention and mitigation, climate action, and sustainable development. Summary of the Invention

[0004] This disclosure provides a method and system for evaluating and designing the layout of meteorological station networks that integrates multi-source observation needs. By constructing five collaborative processes—"demand matching, performance evaluation, simulation optimization, layout decision-making, and closed-loop feedback"—it covers the entire lifecycle service from quantitative analysis of the current situation, precise demand matching, performance simulation and prediction to dynamic optimization of the scheme. This solves the technical difficulties of multi-source assessment, achieves a scientific layout with "minimum input and maximum benefit," and provides a solid data foundation and decision support for meteorological disaster prevention and mitigation, climate action, and sustainable development.

[0005] According to a first aspect of this disclosure, a method for evaluating and designing the layout of a meteorological station network that integrates multi-source observation requirements is provided, comprising the following steps: Obtain diverse observation requirements data, and perform structured analysis on observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multidimensional requirement set. Based on the structured multidimensional demand set, the meteorological station network metadata, geographic information system topographic data, and historical observation assimilation products are called to calculate the spatial representativeness, temporal continuity, and element integrity of the station coverage, evaluate the station's observation capabilities and effects, and construct a multi-factor coupled observation capability evaluation matrix. Based on the observation capability assessment matrix, the effectiveness quantitative analysis of the candidate site deployment schemes is carried out, and the station network configuration scheme that meets the multidimensional requirements and has the best effectiveness is selected to generate a station network optimization candidate set. The candidate set for site network optimization is invoked, and the candidate schemes are ranked by multi-objective weighting based on regional business priority, construction cost constraints, and maintenance accessibility indicators. Highly adaptable and forward-looking site network layout strategies are identified, and the final recommended site network layout scheme is output.

[0006] Preferably, the structured multidimensional requirement set includes application domain labels, vertical hierarchy identifiers, element type codes, observation frequency requirements, and spatial resolution indicators; The observation capability assessment matrix includes spatial coverage, temporal continuity index, feature integrity score, terrain occlusion correction coefficient, FSO contribution weight, and EFSO sensitivity value. The candidate set for station network optimization includes candidate station coordinates, equipment configuration types, expected observation efficiency, and matching degree of social and natural factors. The recommended site network layout scheme includes site locations, equipment deployment list, implementation priority sequence, and dynamic update mechanism.

[0007] Preferably, the process of acquiring multi-dimensional observation requirement data, performing structured analysis on observation indicators divided by application field, vertical level, and element dimension, and extracting observation frequency, spatial resolution, element type, and vertical profile requirements to generate a structured multi-dimensional requirement set is as follows: Acquire the interface data of the "Tianyuan" system, the OSCAR requirement library XML document and the national and provincial planning texts. Convert unstructured or semi-structured requirements into a unified observation indicator system through semantic mapping rules. Extract the application scenarios, vertical hierarchies and meteorological elements corresponding to each requirement item to generate the original requirement item set. The original set of requirement items is invoked, and each item is labeled with a triplet according to the preset three-dimensional modeling framework to construct a multi-dimensional requirement cube. Consistency verification and fusion processing are performed on conflicting or redundant items to obtain a structured multi-dimensional requirement set. Based on the structured multidimensional requirement set, the update events of external requirement sources are monitored. When a new requirement version is detected, the incremental parsing process is triggered to inject the newly added or changed requirement items into the current requirement cube and mark their timeliness status.

[0008] Preferably, the process of constructing a multi-factor coupled observation capability evaluation matrix by calling meteorological station network metadata, geographic information system topographic data, and historical observation assimilation products based on the structured multi-dimensional demand set, calculating the spatial representativeness, temporal continuity, and element completeness of station coverage, evaluating the station's observation capability and effect, and constructing the matrix is ​​as follows: By calling the meteorological station network element database and GIS terrain layer, the effective coverage area of ​​each station in the horizontal direction and the upper limit of detection capability in the vertical direction are calculated. Combined with DEM data, radar beam obstruction areas are identified, and spatial coverage masks are generated. The method of adjoint calculation is used to calculate the contribution of regional numerical accuracy of observation data at regular intervals, and outputs the quantitative evaluation results of the contribution rate of various observation numerical model forecasts within the specified area, and simultaneously outputs the FSO blacklist and whitelist. An interactive experimental method was used to quantitatively assess the quantitative contribution of designated existing stations to various weather processes; By integrating the spatial coverage mask, FSO blacklist / whitelist, and observation system test evaluation results, a weighted fusion algorithm is used to construct an observation capability evaluation matrix.

[0009] Preferably, the process of performing effectiveness quantitative analysis on candidate site deployment schemes based on the observation capability evaluation matrix, screening the station network configuration scheme that meets multi-dimensional requirements and has the best efficiency, and generating a candidate set for station network optimization is as follows: Based on the NatureRun high-resolution reanalysis field, a virtual observation environment containing atmospheric state, surface properties and socio-economic background is initialized, and the coordinates of candidate sites are mapped to three-dimensional grid points in the environment. The data acquisition process of various observation devices is simulated in the virtual observation environment to generate a simulated observation dataset, which is then input into the assimilation system for forecasting experiments. By comparing the forecast results after assimilation simulation observation with the NatureRun true values, the root mean square error, bias and skill score of key meteorological elements are calculated. Combined with the weighted values ​​of social and natural factors, the comprehensive effectiveness score of the candidate scheme is generated. Based on the comprehensive performance score, candidate solutions that are below the preset performance threshold are eliminated, and the configuration with the best performance and that meets the multi-dimensional requirements is retained to form a candidate set for station network optimization.

[0010] Preferably, the comprehensive performance score E is calculated as follows: in, This is the normalized root mean square error. Population weight Carbon sequestration weight, For aviation weighting.

[0011] Preferably, the process of calling the candidate set for site network optimization, performing multi-objective weighted ranking of candidate schemes based on regional business priority, construction cost constraints, and maintenance accessibility indicators, identifying highly adaptable and forward-looking site network layout strategies, and outputting the final recommended site network layout scheme is as follows: Based on the candidate set for station network optimization, the land use cost, power access difficulty and operation and maintenance accessibility scores of the candidate stations are obtained, and an economic feasibility assessment vector is constructed. In conjunction with regional business strategies, candidate solutions are assigned differentiated priority weights, and multi-attribute decision-making algorithms are used to comprehensively rank the solutions. Several top-ranked schemes are selected to generate a recommended site network layout scheme that includes site geographic coordinates, equipment type configuration, implementation phase division, and a rolling update mechanism for the next five years. The scheme is then displayed and interactively verified using a 3D visualization engine.

[0012] Preferably, the method further includes: Based on the actual operation observation data of the recommended station network layout scheme, the observation performance indicators and changes in user demand are collected and compared with the initial structured multidimensional demand set to form a dynamic iteration mechanism for the station network layout. The dynamic iteration mechanism includes four stages: demand deviation identification, capacity gap location, simulation re-optimization, and scheme fine-tuning.

[0013] Preferably, the process of collecting and observing performance indicators and changes in user demand based on the actual operation observation data of the recommended station network layout scheme, comparing the deviations with the initial structured multidimensional demand set, and forming a dynamic iterative mechanism for the station network layout is as follows: By calling actual operational observation data and user feedback logs, unmet observation requirements and substandard site records are extracted, and a requirement-capacity deviation report is generated. The deviation report is mapped to geospatial data, and combined with the existing station network topology, weak areas or emerging demand hotspots are identified to generate a set of optimization trigger areas. Using the set of optimized trigger regions as constraints, the simulation optimization process is partially restarted on the basis of the original station network to generate an incremental station network adjustment scheme. The incremental adjustment scheme is integrated with the original recommended scheme to generate an updated website layout recommendation scheme, and the version iteration log is recorded.

[0014] According to a second aspect of this disclosure, a meteorological station network layout evaluation and design system integrating multi-source observation needs is provided, including: a demand matching module, an effectiveness evaluation module, a simulation optimization module, a layout decision module, and a closed-loop feedback module; The demand matching module is used to acquire multi-dimensional observation demand data, and perform structured analysis on observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multi-dimensional demand set. The performance evaluation module is used to evaluate the station's observation capabilities and effects based on the structured multidimensional demand set, by calling meteorological station network metadata, geographic information system topographic data and historical observation assimilation products, calculating the spatial representativeness, temporal continuity and element integrity of the station coverage, and constructing a multi-factor coupled observation capability evaluation matrix. The simulation optimization module is used to perform effectiveness quantitative analysis on candidate site deployment schemes based on the observation capability evaluation matrix, screen the site network configuration schemes that meet multidimensional requirements and have the best effectiveness, and generate a site network optimization candidate set. The layout decision module is used to call the station network optimization candidate set, and perform multi-objective weighted ranking of the candidate schemes based on regional business priority, construction cost constraints and maintenance accessibility indicators, identify highly adaptable and forward-looking station network layout strategies, and output the final station network layout recommendation scheme. The closed-loop feedback module is used to collect observation performance indicators and changes in user needs based on the actual operation observation data of the recommended station network layout scheme, compare the deviation with the initial structured multidimensional demand set, and form a dynamic iteration mechanism for the station network layout.

[0015] Compared with the prior art, the present invention has the following technical effects: This invention addresses the problems of static and unstructured demand expression in traditional methods by constructing a structured, multi-dimensional demand set, unifying heterogeneous observation demands scattered across the "Tianyuan" system, the OSCAR database, and national and provincial plans into a unified three-dimensional framework of application, verticality, and elements. It also overcomes the limitations of relying solely on single indicators such as spatial coverage by integrating terrain shading effects, FSO assimilation contribution rate, and EFSO sensitivity to construct a multi-factor coupled observation capability evaluation matrix. Furthermore, it drives OSSE simulations using NatureRun and couples surface features, population distribution, and other socio-natural factors for effectiveness quantification, shifting station network optimization from experience-driven to data- and model-driven. Finally, it introduces a closed-loop feedback mechanism to enable dynamic iteration of station network layout as demand evolves, ensuring the observation system continuously matches the differentiated, high-frequency, and multi-element observation demands of emerging fields such as carbon neutrality, smart cities, and aviation safety, significantly improving the scientific rigor, adaptability, and forward-looking nature of meteorological station network layout.

[0016] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0017] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A schematic flowchart of a meteorological station network layout assessment and design method for integrating multi-source observation requirements according to an embodiment of the present disclosure is shown. Figure 2 This illustration shows a requirement matching process of a meteorological station network layout assessment and design method integrating multi-source observation requirements according to an embodiment of the present disclosure; Figure 3 A schematic diagram of the effectiveness evaluation process of a meteorological station network layout assessment and design method that integrates multi-source observation requirements according to an embodiment of the present disclosure is shown. Figure 4 A schematic diagram of the simulation optimization process of a meteorological station network layout assessment and design method integrating multi-source observation requirements according to an embodiment of the present disclosure is shown. Figure 5 A schematic diagram of the layout decision-making process of a meteorological station network layout assessment and design method that integrates multi-source observation requirements according to an embodiment of the present disclosure is shown. Figure 6 This diagram illustrates a closed-loop feedback process for a meteorological station network layout assessment and design method that integrates multi-source observation requirements according to an embodiment of the present disclosure. Figure 7 A schematic diagram of a meteorological station network layout assessment and design system for integrating multi-source observation needs is shown according to an embodiment of the present disclosure. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 As shown, this embodiment provides a method for evaluating and designing the layout of a meteorological station network that integrates multi-source observation needs, including the following steps: S101. Obtain multi-dimensional observation requirement data, and perform structured analysis on the observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multi-dimensional requirement set. S102. Based on the structured multidimensional demand set, call the meteorological station network metadata, geographic information system topographic data and historical observation assimilation products to calculate the spatial representativeness, temporal continuity and element integrity of the station coverage, evaluate the station's observation capability and observation effect, and construct a multi-factor coupled observation capability evaluation matrix. S103. Based on the observation capability evaluation matrix, perform effectiveness quantitative analysis on the candidate site deployment schemes, screen out the station network configuration schemes that meet multidimensional requirements and have the best effectiveness, and generate a station network optimization candidate set. S104. Call the aforementioned candidate set for site network optimization, and perform multi-objective weighted sorting of the candidate schemes based on regional business priority, construction cost constraints, and maintenance accessibility indicators to identify highly adaptable and forward-looking site network layout strategies, and output the final recommended site network layout scheme.

[0021] Furthermore, the method in this embodiment also includes: S105. Based on the actual operation observation data of the station network layout recommendation scheme, collect the observation performance indicators and changes in user demand, compare the deviation with the initial structured multidimensional demand set, and form a dynamic iteration mechanism for the station network layout. The dynamic iteration mechanism includes four stages: demand deviation identification, capacity gap location, simulation re-optimization, and scheme fine-tuning.

[0022] Furthermore, in this embodiment, the structured multidimensional requirement set includes application domain labels, vertical hierarchy identifiers, feature type codes, observation frequency requirements, and spatial resolution indicators; the observation capability assessment matrix includes spatial coverage, temporal continuity index, feature integrity score, terrain occlusion correction coefficient, FSO contribution weight, and EFSO sensitivity value; the station network optimization candidate set includes candidate station coordinates, equipment configuration type, expected observation efficiency, and social and natural factor matching degree; and the recommended station network layout scheme includes station location, equipment deployment list, implementation priority sequence, and dynamic update mechanism.

[0023] like Figure 2 As shown, further, step S101 specifically includes: S1011. Obtain the interface data of the "Tianyuan" system, the OSCAR requirement library XML document and the national and provincial planning text. Convert unstructured or semi-structured requirements into a unified observation indicator system through semantic mapping rules. Extract the application scenarios, vertical layers (such as the ground layer, boundary layer, and troposphere) and meteorological elements (such as temperature, humidity, wind, and greenhouse gases) corresponding to each requirement item to generate the original requirement item set. The system retrieves a JSON-formatted data stream returned by the "Tianyuan" system interface. This stream contains observation requirements for the temperature and humidity profile of the "boundary layer" in the context of "smart city" applications. The element type is "temperature," the observation frequency is "10 minutes / time," and the spatial resolution is "1 kilometer." Simultaneously, it parses an XML document in the OSCAR requirements database related to "aviation safety" to extract the observation requirements for the "lower troposphere" wind field. The element type is "3D wind speed," the observation frequency is "5 minutes / time," and the spatial resolution is "3 kilometers." Furthermore, it identifies the monitoring requirements for greenhouse gases (CO2, CH4) in the "near-surface layer" of the "carbon neutrality pilot zone" from the provincial meteorological bureau's planning text. The observation frequency is "1 hour / time," and the spatial resolution is "5 kilometers." The above heterogeneous data is then converted into a unified observation indicator system through a predefined semantic mapping table (as shown in Table 1 below), generating the original requirement item set. S1012. Call the original set of requirements items, and according to the preset three-dimensional modeling framework (application-vertical-feature), label each item with a triplet, construct a multi-dimensional requirement cube, and perform consistency verification and fusion processing on conflicting or redundant items to obtain a structured multi-dimensional requirement set. Specifically, by calling the original set of requirement items, each item is labeled with a triplet. For example, "Smart City - Boundary Layer - Temperature" is labeled as (A1, V2, E1), "Aerospace Safety - Lower Troposphere - 3D Wind Speed" is labeled as (A2, V3, E3), and "Carbon Neutrality Pilot Zone - Near-Surface Layer - CO2" is labeled as (A3, V1, E4), where A... i V represents the application area. j E represents vertical hierarchy. k Represent the feature type; load all triples into the data structure of the multidimensional demand cube, which has three orthogonal dimensions: application, vertical, and feature; when multiple observation frequency requirements are detected for the same triple, for example, (A1, V2, E1) has two entries, "10 minutes / time" and "15 minutes / time", then perform a consistency check, retain the stricter "10 minutes / time" requirement, and mark the fusion log to finally obtain the structured multidimensional demand set.

[0024] S1013. Based on the structured multidimensional requirement set, monitor the update events of external requirement sources. When a new requirement version is detected, trigger the incremental parsing process, inject the newly added or changed requirement items into the current requirement cube, and mark the timeliness status to maintain the dynamic evolution capability of the requirement set. The system continuously monitors the API version number of the "Tianyuan" system, the update timestamp of the OSCAR library's FTP directory, and the revision date of provincial planning documents via a message queue. When it is detected that the OSCAR library released version v2.1 on April 1, 2025, which added the observation requirement for the "low-altitude airspace" turbulence intensity in the "drone logistics" application scenario, the incremental parsing process is triggered. Only the newly added XML node is parsed, its triple (A4, V2, E5) and the corresponding observation indicators are extracted, and they are injected into the corresponding position of the current multidimensional demand cube. At the same time, the timeliness status of the entry is marked as "effective", thereby maintaining the dynamic evolution capability of the demand set.

[0025] like Figure 3 As shown, further, step S102 specifically includes: S1021. Call the meteorological station network element database and GIS terrain layer to calculate the effective coverage area of ​​each station in the horizontal direction and the upper limit of the detection capability in the vertical direction. Combine the DEM data to identify the radar beam obstruction area and generate a spatial coverage mask. This embodiment extracts the latitude and longitude coordinates, equipment type, and antenna height of automatic weather stations, radiosonde stations, and wind profiler radars from the meteorological station network element database, and obtains 30-meter resolution digital elevation model (DEM) data from the GIS platform. For a C-band weather radar located at an altitude of 500 meters and an antenna height of 30 meters, the ray tracing algorithm is used in conjunction with the DEM data to calculate its beam path at an elevation angle of 0.5°, identifying the beam obstruction area caused by the mountain in front (altitude 800 meters). This area forms a fan-shaped blind zone at a distance of 60 kilometers from the radar, with an area of ​​approximately 120 square kilometers. The horizontal coverage disks of all stations are Boolean superimposed with the vertical detection cones, and the radar obstruction area is subtracted to generate a complete spatial coverage mask.

[0026] S1022. The accompanying method is used to calculate the contribution of regional numerical accuracy of observation data at regular intervals, and output the quantitative evaluation results of the contribution rate of various observation numerical model forecasts within the specified area, and simultaneously output the FSO blacklist and whitelist. The Forecast Sensitive to Observations (FSO) module aims to use an adjoint method to periodically calculate the contribution of observational data to the accuracy of regional numerical weather predictions. It outputs a quantitative assessment of the contribution rate of various observations to numerical model forecasts within a specified area, and provides timely statistical analysis and visualization. This section also outputs the assimilation rate and assimilation blacklist / whitelist for FSO.

[0027] The assimilation rate of FSO is mainly analyzed in the form of charts to show its spatiotemporal distribution, including different time points, observation equipment, altitude layers and meteorological elements. It is calculated by the ratio of the number of times the station data is called to the number of times the station data is collected.

[0028] The FSO assimilation blacklist and whitelist are used to assess the assimilation status of various observation stations over a period of time (such as weekly, ten-day, monthly, quarterly, or yearly).

[0029] Simultaneously, based on actual needs, blacklists and whitelists for FSO assimilation are created, including sites with the lowest assimilation rates (blacklist) and sites with the highest assimilation rates (whitelist). The Observational Contribution to Forecast (EFSO) function primarily enables real-time operation and analysis of the impact assessment of observations on forecasts based on GRAPES ensemble forecasts.

[0030] S1023. Use interactive experimental methods to quantitatively evaluate the quantitative contribution of designated stations to various weather processes; This embodiment examines the actual impact of observational data in a real-world operational assimilation forecasting system, investigating the true contribution of the evaluated data to assimilation observation forecasting. The system designs and encapsulates the OSE experiment process, sets appropriate assimilation parameters, generates initial model values, designs the experiment's data interface and configuration mode, and develops experiment-driven tools, enabling users to conveniently conduct OSE experiments through a front-end interactive interface.

[0031] The OSE process is based on the WRF model and the WRFDA assimilation system. The OSE experimental workflow is designed and encapsulated, mainly comprising two parts: numerical modeling and task management. First, in the numerical modeling part, it is necessary to debug the parameter configurations of one or more WRF model dynamic frameworks and physical processes to ensure good simulation performance in the region of interest. Simultaneously, quality control of the observation data is performed, and the observation data is assimilated using WRFDA. Appropriate assimilation parameters are set, initial model values ​​are generated, the experimental data interface and configuration mode are designed, and experimental driving tools are developed to facilitate OSE experiments. Second, the deployed high-performance computing environment is managed. The interaction process between the observation system and OSE is containerized and encapsulated, and a task scheduling system is established to fully utilize high-performance computing resources.

[0032] S1024. By fusing the spatial coverage mask, assimilation efficiency weight vector, and forecast sensitivity score, an observation capability evaluation matrix is ​​constructed using a weighted fusion algorithm, wherein the weights of each item are dynamically adjusted according to the current business priorities. In this embodiment, the spatial coverage mask (binary matrix), the quantitative evaluation result of assimilation rate (one-dimensional vector), and the quantitative contribution (one-dimensional vector) are spatially aligned to generate three indicators for each grid point. During the flood season, the operational focus is on severe weather forecasting, and the assimilation weight α=0.5, the assimilation rate weight β=0.4, and the quantitative contribution weight γ=0.1 are set. The observation capability assessment matrix M is calculated using the following formula: (1) in, , , These represent the assimilation weight, assimilation rate, and quantitative contribution value of grid point (i,j), respectively.

[0033] like Figure 4 As shown, further, step S103 specifically includes; S1031. Based on the NatureRun high-resolution reanalysis field, initialize a virtual observation environment that includes atmospheric state, surface properties and socio-economic background, and map the candidate site coordinates to the three-dimensional grid points in the environment. In this embodiment, a NatureRun reanalysis field with a resolution of 3 kilometers and a time step of 1 hour is loaded. This field includes three-dimensional atmospheric variables such as temperature, humidity, wind, and air pressure, as well as two-dimensional surface attributes such as land use, vegetation cover, and population density. The latitude and longitude coordinates of a set of candidate stations (such as 100 proposed automatic stations) are mapped to the nearest three-dimensional grid point of NatureRun through bilinear interpolation to complete the initialization of the virtual observation environment.

[0034] S1032. Simulate the data acquisition process of various observation devices (automatic weather station, radiosonde, wind profiler radar, greenhouse gas monitor) in the virtual observation environment, generate a simulated observation dataset, and input it into the assimilation system for forecasting experiments; This embodiment uses the OSSE method to simulate observations in a virtual environment based on the equipment configuration type of the candidate stations: for automatic stations, variables such as 2-meter air temperature and 10-meter wind speed are extracted and Gaussian white noise that matches the instrument's accuracy is added; for radiosonde stations, temperature, pressure, humidity, and wind data are extracted along the vertical profile and the time delay of the ascent process is simulated; after generating a complete simulated observation dataset, it is input into the WRFDA assimilation system to drive the WRF model to conduct a 72-hour forecast experiment.

[0035] S1033. Compare the forecast results after assimilation simulation observation with the NatureRun true values, calculate the root mean square error, bias and skill score of key meteorological elements, and combine the weighted influence of social and natural factors (such as population density, carbon sink intensity and air corridor) to generate the comprehensive effectiveness score of candidate solutions. In this embodiment, the root mean square error (RMSE) for 24-hour cumulative precipitation is calculated to be 12.5 mm, the deviation for 2-meter temperature is +0.8℃, and the Anomaly Correlation Coefficient (ACC) skill score for 850 hPa wind speed is 0.72. Simultaneously, based on the population density (e.g., 1000 people / km²) and carbon sequestration intensity (e.g., 2.5 tC ha) of the candidate site's location area... -1 y r-1Whether it is located within an aviation corridor and whether it is within such a corridor are assigned social weights of 0.3, 0.2, and 0.5 respectively; the overall effectiveness score E is calculated using a weighted formula: (2) S1034. Based on the comprehensive performance score, candidate solutions that are lower than the preset performance threshold are eliminated, and the configuration with the best performance and that meets the multi-dimensional requirement constraints is retained to form a candidate set for station network optimization. Specifically, in this embodiment, the efficiency threshold is set to 0.65, and candidate schemes with a comprehensive efficiency score lower than this value are eliminated. At the same time, it is checked whether the remaining schemes meet the hard constraints of the structured multidimensional demand set, such as the requirement that the "smart city" area must have at least one temperature and humidity profile observation every 10 minutes. Finally, 20 candidate schemes that meet all conditions are retained to form a candidate set for station network optimization.

[0036] like Figure 5 As shown, further, step S104 includes; S1041. Obtain the land use cost, power access difficulty and operation and maintenance accessibility scores of candidate sites, and construct an economic feasibility assessment vector; By connecting to the national land and space planning database, the land use nature (such as construction land and forest land) of candidate sites is obtained, and the land use cost is estimated; the distance to the nearest substation is assessed through the power grid GIS system to quantify the difficulty of power access; the driving time to the nearest maintenance base is calculated through road network analysis and converted into an operation and maintenance accessibility score (0-1); the three indicators are normalized and weighted averaged to construct an economic feasibility assessment vector.

[0037] S1042. In conjunction with regional business strategies (such as smart city core area, carbon neutrality pilot area, aviation safety critical area), assign differentiated priority weights to candidate solutions, and use multi-attribute decision algorithms (such as TOPSIS or AHP) to comprehensively rank the solutions. In this specific application, the candidate sites in the "Smart City Core Area" are assigned a business priority weight of 1.0, the "Carbon Neutral Pilot Area" is assigned a weight of 0.8, and the "Aviation Safety Critical Area" is assigned a weight of 0.9. The TOPSIS algorithm is used, with the comprehensive efficiency score as the positive ideal solution and the economic feasibility as the negative ideal solution, to calculate the relative closeness of each candidate scheme and complete the comprehensive ranking.

[0038] S1043. Select several top-ranked schemes to generate a recommended network layout scheme that includes site geographic coordinates, equipment type configuration, implementation phase division and a rolling update mechanism for the next five years, and perform spatial display and interactive verification through a 3D visualization engine. This embodiment selects the top 5 schemes from TOPSIS rankings to generate a detailed station network layout recommendation document, which includes the WGS84 coordinates of each station, a list of equipment (such as "six-element automatic station + visibility meter"), a three-year construction schedule, and a rolling optimization mechanism based on annual demand updates. The scheme is visualized on the web using the CesiumJS 3D engine, supporting interactive verification of coverage effects by users.

[0039] like Figure 6 As shown, further, step S105 includes; S1051. Call the actual operation observation data and user feedback logs, extract the unmet observation requirements and the records of substandard sites, and generate a requirement-capacity deviation report. S1052. Map the deviation report to geospatial space, combine it with the existing station network topology, identify areas with weak capabilities or emerging demand hotspots, and generate a set of optimization trigger areas. S1053. Using the set of optimized triggering regions as constraints, the simulation optimization process is partially restarted on the basis of the original station network to generate an incremental station network adjustment scheme. S1054. Integrate the incremental adjustment scheme with the original recommended scheme to generate an updated website layout recommended scheme, and record the version iteration log.

[0040] like Figure 7 As shown in the embodiment, a meteorological station network layout evaluation and design system integrating multi-source observation needs is also provided, including: demand matching module 1, performance evaluation module 2, simulation optimization module 3, layout decision module 4 and closed-loop feedback module 5. The demand matching module 1 is used to acquire multi-dimensional observation demand data and perform structured analysis on observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multi-dimensional demand set. The performance evaluation module 2 is used to evaluate the station's observation capabilities and effects based on the structured multidimensional demand set, by calling meteorological station network metadata, geographic information system topographic data and historical observation assimilation products, calculating the spatial representativeness, temporal continuity and element integrity of the station coverage, and constructing a multi-factor coupled observation capability evaluation matrix. The simulation optimization module 3 is used to perform effectiveness quantitative analysis on the candidate site deployment schemes based on the observation capability evaluation matrix, screen the station network configuration schemes that meet multi-dimensional requirements and have the best effectiveness, and generate a station network optimization candidate set. The layout decision module 4 is used to call the station network optimization candidate set, and perform multi-objective weighted sorting of the candidate schemes based on regional business priority, construction cost constraints and maintenance accessibility indicators, identify highly adaptable and forward-looking station network layout strategies, and output the final recommended station network layout scheme. The closed-loop feedback module 5 is used to collect observation performance indicators and changes in user needs based on the actual operation observation data of the recommended station network layout scheme, compare the deviation with the initial structured multidimensional demand set, and form a dynamic iteration mechanism for the station network layout.

[0041] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0042] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0043] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for evaluating and designing the layout of a meteorological station network that integrates multi-source observation needs, characterized in that, Includes the following steps: Obtain diverse observation requirements data, and perform structured analysis on observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multidimensional requirement set. Based on the structured multidimensional demand set, the meteorological station network metadata, geographic information system topographic data, and historical observation assimilation products are called to calculate the spatial representativeness, temporal continuity, and element integrity of the station coverage, evaluate the station's observation capabilities and effects, and construct a multi-factor coupled observation capability evaluation matrix. Based on the observation capability assessment matrix, the effectiveness quantitative analysis of the candidate site deployment schemes is carried out, and the station network configuration scheme that meets the multidimensional requirements and has the best effectiveness is selected to generate a station network optimization candidate set. The candidate set for site network optimization is invoked, and the candidate schemes are ranked by multi-objective weighting based on regional business priority, construction cost constraints, and maintenance accessibility indicators. Highly adaptable and forward-looking site network layout strategies are identified, and the final recommended site network layout scheme is output.

2. The method according to claim 1, characterized in that, The structured multidimensional requirement set includes application domain labels, vertical hierarchy identifiers, element type codes, observation frequency requirements, and spatial resolution indicators. The observation capability assessment matrix includes spatial coverage, temporal continuity index, feature integrity score, terrain occlusion correction coefficient, FSO contribution weight, and EFSO sensitivity value. The candidate set for station network optimization includes candidate station coordinates, equipment configuration types, expected observation efficiency, and matching degree of social and natural factors. The recommended site network layout scheme includes site locations, equipment deployment list, implementation priority sequence, and dynamic update mechanism.

3. The method according to claim 1, characterized in that, The process of acquiring multi-dimensional observation requirement data, performing structured analysis on observation indicators categorized by application domain, vertical level, and element dimension, and extracting observation frequency, spatial resolution, element type, and vertical profile requirements to generate a structured multi-dimensional requirement set is as follows: Obtain the interface data of the "Tianyuan" system, the OSCAR requirement library XML document and the national and provincial planning texts. Convert unstructured or semi-structured requirements into a unified observation indicator system through semantic mapping rules. Extract the application scenarios, vertical layers and meteorological elements corresponding to each requirement item to generate the original requirement item set. The original set of requirement items is invoked, and each item is labeled with a triplet according to the preset three-dimensional modeling framework to construct a multi-dimensional requirement cube. Consistency verification and fusion processing are performed on conflicting or redundant items to obtain a structured multi-dimensional requirement set. Based on the structured multidimensional requirement set, the update events of external requirement sources are monitored. When a new requirement version is detected, the incremental parsing process is triggered to inject the newly added or changed requirement items into the current requirement cube and mark their timeliness status.

4. The method according to claim 1, characterized in that, The process of constructing a multi-factor coupled observation capability evaluation matrix by calling meteorological station network metadata, geographic information system topographic data, and historical observation assimilation products based on the structured multi-dimensional demand set, calculating the spatial representativeness, temporal continuity, and element completeness of station coverage, evaluating the station's observation capabilities and effects, and constructing the matrix is ​​as follows: By calling the meteorological station network element database and GIS terrain layer, the effective coverage area of ​​each station in the horizontal direction and the upper limit of detection capability in the vertical direction are calculated. Combined with DEM data, radar beam obstruction areas are identified, and spatial coverage masks are generated. The method of adjoint calculation is used to calculate the contribution of regional numerical accuracy of observation data at regular intervals, and outputs the quantitative evaluation results of the contribution rate of various observation numerical model forecasts within the specified area, and simultaneously outputs the FSO blacklist and whitelist. An interactive experimental method was used to quantitatively assess the quantitative contribution of designated existing stations to various weather processes; By integrating the spatial coverage mask, FSO blacklist / whitelist, and observation system test evaluation results, a weighted fusion algorithm is used to construct an observation capability evaluation matrix.

5. The method according to claim 1, characterized in that, The process of performing effectiveness quantitative analysis on candidate site deployment schemes based on the observation capability evaluation matrix, selecting the station network configuration scheme that meets multidimensional requirements and has the best effectiveness, and generating a candidate set for station network optimization is as follows: Based on the NatureRun high-resolution reanalysis field, a virtual observation environment containing atmospheric state, surface properties and socio-economic background is initialized, and the coordinates of candidate sites are mapped to three-dimensional grid points in the environment. The data acquisition process of various observation devices is simulated in the virtual observation environment to generate a simulated observation dataset, which is then input into the assimilation system for forecasting experiments. By comparing the forecast results after assimilation simulation observation with the NatureRun true values, the root mean square error, bias and skill score of key meteorological elements are calculated. Combined with the weighted values ​​of social and natural factors, the comprehensive effectiveness score of the candidate scheme is generated. Based on the comprehensive performance score, candidate solutions that are below the preset performance threshold are eliminated, and the configuration with the best performance and that meets the multi-dimensional requirements is retained to form a candidate set for station network optimization.

6. The method according to claim 5, characterized in that, The specific calculation method for the comprehensive performance score E is as follows: in, This is the normalized root mean square error. Population weight Carbon sequestration weight, For aviation weighting.

7. The method according to claim 1, characterized in that, The process of calling the candidate set for site network optimization, performing multi-objective weighted ranking of candidate schemes based on regional business priority, construction cost constraints, and maintenance accessibility indicators, identifying highly adaptable and forward-looking site network layout strategies, and outputting the final recommended site network layout scheme is as follows: Based on the candidate set for station network optimization, the land use cost, power access difficulty and operation and maintenance accessibility scores of the candidate stations are obtained, and an economic feasibility assessment vector is constructed. In conjunction with regional business strategies, candidate solutions are assigned differentiated priority weights, and multi-attribute decision-making algorithms are used to comprehensively rank the solutions. Several top-ranked schemes are selected to generate a recommended site network layout scheme that includes site geographic coordinates, equipment type configuration, implementation phase division, and a rolling update mechanism for the next five years. The scheme is then displayed and interactively verified using a 3D visualization engine.

8. The method according to claim 1, characterized in that, The method further includes: Based on the actual operation observation data of the recommended station network layout scheme, the observation performance indicators and changes in user demand are collected and compared with the initial structured multidimensional demand set to form a dynamic iteration mechanism for the station network layout. The dynamic iteration mechanism includes four stages: demand deviation identification, capacity gap location, simulation re-optimization, and scheme fine-tuning.

9. The method according to claim 8, characterized in that, The process of collecting and observing performance indicators and changes in user needs based on the actual operation observation data of the recommended station network layout scheme, comparing the deviations with the initial structured multidimensional demand set, and forming a dynamic iterative mechanism for the station network layout is as follows: By calling actual operational observation data and user feedback logs, unmet observation requirements and substandard site records are extracted, and a requirement-capacity deviation report is generated. The deviation report is mapped to geospatial data, and combined with the existing station network topology, weak areas or emerging demand hotspots are identified to generate a set of optimization trigger areas. Using the set of optimized trigger regions as constraints, the simulation optimization process is partially restarted on the basis of the original station network to generate an incremental station network adjustment scheme. The incremental adjustment scheme is integrated with the original recommended scheme to generate an updated website layout recommendation scheme, and the version iteration log is recorded.

10. A meteorological station network layout evaluation and design system integrating multi-source observation needs, implemented using the meteorological station network layout evaluation and design method integrating multi-source observation needs as described in any one of claims 1-9, characterized in that, include: Demand matching module (1), performance evaluation module (2), simulation optimization module (3), layout decision module (4) and closed-loop feedback module (5); The demand matching module (1) is used to acquire multi-dimensional observation demand data, and perform structured analysis on the observation indicators divided by application field, vertical level and element dimension to extract observation frequency, spatial resolution, element type and vertical profile requirements, and generate a structured multi-dimensional demand set. The performance evaluation module (2) is used to call meteorological station network metadata, geographic information system terrain data and historical observation assimilation products based on the structured multidimensional demand set, calculate the spatial representativeness, temporal continuity and element integrity of the station coverage, evaluate the station's observation capabilities and observation effects, and construct a multi-factor coupled observation capability evaluation matrix. The simulation optimization module (3) is used to perform efficiency quantitative analysis on the candidate site deployment schemes based on the observation capability evaluation matrix, screen the station network configuration schemes that meet multidimensional requirements and have the best efficiency, and generate a station network optimization candidate set. The layout decision module (4) is used to call the station network optimization candidate set, and perform multi-objective weighted sorting of the candidate schemes based on regional business priority, construction cost constraints and maintenance accessibility indicators, identify highly adaptable and forward-looking station network layout strategies, and output the final station network layout recommendation scheme. The closed-loop feedback module (5) is used to collect observation performance indicators and user demand changes based on the actual operation observation data of the recommended station network layout scheme, compare the deviation with the initial structured multidimensional demand set, and form a dynamic iteration mechanism for the station network layout.

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