Atmosphere junction stability correction method, system, equipment and medium

Abstract

The invention belongs to the technical field of marine weather forecast, and particularly relates to an atmosphere structure stability correction method, system and device and a medium, and the method comprises the steps: analyzing the incidence relation between the atmosphere structure stability and the wind wave growth rate, and determining a core evaluation parameter; collecting a plurality of marine meteorological observation data, and preprocessing the plurality of marine meteorological observation data to construct a standardized training data set; constructing a wind wave growth rate initial correction model based on physical constraints; integrating the initial correction model into a wind energy input source item of a sea wave numerical mode so as to correct the stability of the atmosphere structure in real time and obtain correction stability; verifying the correction effect through long-term numerical simulation, and performing model performance verification on the initial correction model by adopting multi-index statistical evaluation to obtain a model verification result; and performing parameter sensitivity analysis and optimization on the initial correction model based on a model verification result to obtain an optimized correction model. According to the invention, the accuracy of wave numerical forecasting is improved.

Description

Technical Field

[0001] This application belongs to the field of marine meteorological forecasting technology, and in particular relates to an atmospheric stratification stability correction method, system, equipment and medium. Background Technology

[0002] Numerical wave forecasting is an important component of marine meteorological forecasting, and its accuracy directly affects maritime navigation safety, marine engineering operations, and military activities. Currently, mainstream numerical wave models (such as WAVEWATCH III and SWAN) typically rely on the neutral atmospheric stratification assumption when calculating wind energy inputs, which assumes that the air-sea interface temperature is equal and ignores the influence of atmospheric temperature and humidity stratification variations on air-sea momentum exchange.

[0003] In traditional numerical wave models, wind energy input terms are typically described using empirical formulas, failing to adequately consider the modulating effect of the atmospheric boundary layer's vertical temperature lapse rate on momentum transport at the air-sea interface. This leads to discrepancies between simulation results and observed data under complex weather systems. Particularly during frontal processes or temperature inversions, existing models cannot accurately characterize the abrupt changes in wave growth rates caused by variations in stratification stability. Current technologies suffer from three core deficiencies: First, the wind energy input parameterization scheme lacks a dynamic coupling mechanism with atmospheric stratification stability, still employing fixed wind speed-wave height conversion coefficients. Second, the preprocessing of marine meteorological observation data lacks a standardized spatiotemporal matching process, resulting in a 15%-20% systematic error when processing data from different sources through gridding. Third, model validation methods lack a multi-indicator comprehensive evaluation system; relying solely on the root mean square error (RMSE) is insufficient to fully reflect the model's performance under extreme weather conditions.

[0004] Therefore, there is an urgent need to develop an atmospheric stratification stability correction technology with a solid physical foundation, adaptable to seasonal characteristics of different sea areas, and applicable to operational use, in order to improve the accuracy and reliability of numerical wave forecasts. Summary of the Invention

[0005] To address the aforementioned issues, this application provides an atmospheric stratification stability correction method, system, device, and medium. It employs a technical approach that combines physical constraints with multi-source data fusion. By establishing a dynamic correlation mechanism between atmospheric stratification stability and wave growth rate, it automatically adjusts empirical coefficients to form an optimized correction model adapted to different seasonal characteristics, thereby improving the reliability of numerical wave forecasts.

[0006] Firstly, this application provides an atmospheric stratification stability correction method, the method comprising, The relationship between atmospheric stratification stability and wind and wave growth rate was analyzed to determine the core evaluation parameters of atmospheric stratification stability. Collect a number of marine meteorological observation data and preprocess them to construct a standardized training dataset; An initial correction model for wind and wave growth rate is constructed based on physical constraints; The initial correction model is integrated into the wind energy input source term of the wave numerical model to correct atmospheric stratification stability in real time and obtain correction stability. The correction effect was verified through long-term numerical simulation, and the initial correction model was validated by multi-index statistical evaluation to obtain the model validation results. Based on the model validation results, the initial calibration model is subjected to parameter sensitivity analysis and optimization to obtain an optimized calibration model.

[0007] Furthermore, The relationship between atmospheric stratification stability and wind and wave growth rate was analyzed, specifically including: Based on the atmospheric boundary layer theory, a quantitative relationship between sea surface temperature difference and atmospheric stratification stability is established. Analyze the mechanism by which atmospheric stratification stability affects momentum flux at the air-sea interface; This study investigates the modulating effect of momentum flux changes on wave growth rate and establishes a complete correlation path from atmospheric stratification stability to wave growth rate.

[0008] Furthermore, Preprocessing of several marine meteorological observation data, specifically including: Outlier detection and removal were performed on several of the aforementioned marine meteorological observation data. Spatiotemporal interpolation is used to fill in missing data. Spatiotemporal matching and standardization processing are performed on data from different sources.

[0009] Furthermore, An initial correction model for wind and wave growth rate is constructed based on physical constraints, specifically including: Sea-air temperature difference was determined as the basic evaluation parameter; Determine atmospheric stratification stability parameters as dynamic evaluation parameters; Determine the parameters related to the wind and wave growth rate as a comprehensive evaluation value; The physical relationship between atmospheric stratification stability parameters and wind and wave growth rate parameters is used as a constraint. Combined with the quantitative correlation characteristics between sea surface temperature difference and wave growth rate; Construct an initial correction function for the wind and wave growth rate that includes empirical coefficients; The initial correction function is applied to the wind energy input term correction of the wave numerical model to characterize the regulatory effect of atmospheric stratification stability on the wind and wave growth rate.

[0010] Furthermore, Real-time correction of atmospheric stratification stability, specifically including: During the operation of the numerical wave model, real-time observational data on sea surface temperature, atmospheric temperature, wind speed, and wind zone length are acquired. Real-time values ​​of the correlation parameters between sea surface temperature difference, atmospheric stratification stability parameters, and wind and wave growth rate are dynamically calculated. The real-time values ​​are input into the initial correction model to generate dynamic adjustment coefficients for the wind energy input. Based on the aforementioned dynamic adjustment coefficient, the wind energy input source term value in the wave numerical model is corrected in real time.

[0011] Furthermore, The correction effect was verified through long-term numerical simulation, specifically including: Set up numerical simulation experiments that include at least a full year cycle; Design a comparative scheme for control experiments and calibration experiments; Select representative verification areas and typical wind and wave events; The simulation results of the system are obtained for evaluation of the correction effect.

[0012] Furthermore, The initial calibration model is validated using a multi-index statistical evaluation method, specifically including: Calculate the deviation, root mean square error, relative error, and correlation coefficient between the simulation results and the observed data before and after correction; Analyze the distribution pattern of the ratio of observed values ​​to simulated values ​​as a function of sea surface temperature difference; The correction results were verified by multi-source comparison with reanalysis data and field observation data. The performance of the initial calibration model is evaluated from multiple dimensions based on a joint analysis of statistical indicators and distribution characteristics.

[0013] Furthermore, Based on the model validation results, parameter sensitivity analysis and optimization are performed on the initial calibration model, specifically including: Perform parameter sensitivity analysis on the empirical coefficients in the initial calibration model to obtain the sensitivity analysis results; The impact of the accuracy of sea surface temperature difference calculation on the correction results was assessed, and multi-indicator verification feedback was obtained. Based on the sensitivity analysis results and multi-index verification feedback, the coefficients of the initial calibration model were adjusted using a parameter optimization algorithm to obtain an optimized calibration model.

[0014] Secondly, based on the same inventive concept, this application provides an atmospheric stratification stability correction system, the system comprising: The analysis module is used to analyze the correlation between atmospheric stratification stability and wind and wave growth rate, and to determine the core evaluation parameters of atmospheric stratification stability. The collection module is used to collect a number of marine meteorological observation data and preprocess the marine meteorological observation data to construct a standardized training dataset. A module is built to construct an initial correction model for the wind and wave growth rate based on physical constraints; The correction module is used to integrate the initial correction model into the wind energy input source term of the wave numerical model to correct the atmospheric stratification stability in real time and obtain the correction stability. The verification module is used to verify the correction effect through long-term numerical simulation and to perform model performance verification on the initial correction model using multi-index statistical evaluation to obtain model verification results. The optimization module is used to perform parameter sensitivity analysis and optimization on the initial calibration model based on the model validation results, so as to obtain an optimized calibration model.

[0015] Thirdly, this application also provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When the processor executes a program stored in memory, it implements any of the atmospheric stratification stability correction methods described above.

[0016] Fourthly, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements any of the atmospheric stratification stability correction methods described above.

[0017] Compared with the prior art, this application has the following advantages: 1. This application establishes a complete correlation path from atmospheric stratification stability to wave growth rate and constructs a correction model based on physical constraints, overcoming the limitation of traditional methods that ignore the influence of atmospheric stratification stability, and significantly improving the accuracy and physical consistency of wave growth rate simulation.

[0018] 2. This application adopts a real-time correction mechanism to dynamically integrate the initial correction model into the wind energy input source term of the wave numerical model, which can automatically adjust the wind energy input according to real-time marine meteorological data, thereby enhancing the adaptability and real-time forecasting capability of the wave numerical model.

[0019] 3. This application comprehensively evaluates the calibration effect through long-term numerical simulation verification and multi-index statistical evaluation, combined with the comparison of control experiments and calibration experiments, as well as multi-source data verification, ensuring the reliability and robustness of the model and reducing the uncertainty of simulation results.

[0020] 4. Based on parameter sensitivity analysis and optimization algorithms, this application finely adjusts the empirical coefficients in the initial calibration model, improving the model's accuracy and applicability, enabling it to adapt to different marine environmental conditions, and enhancing the method's universality and practicality.

[0021] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating an atmospheric stratification stability correction method according to an embodiment of this application is shown. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0025] Figure 1 A schematic flowchart of an atmospheric stratification stability correction method according to an embodiment of this application is shown. Figure 1 As shown in the embodiment of this application, an atmospheric stratification stability correction method includes: S1. Analyze the correlation between atmospheric stratification stability and wind and wave growth rate to determine the core evaluation parameters of atmospheric stratification stability. In this embodiment of the application, step S1 specifically includes: S11, based on atmospheric boundary layer theory, establishes a quantitative relationship between sea surface temperature difference and atmospheric stratification stability; S12, Analyze the mechanism by which atmospheric stratification stability affects momentum flux at the air-sea interface; S13, investigate the modulating effect of momentum flux changes on wave growth rate, and establish a complete correlation path from atmospheric stratification stability to wave growth rate.

[0026] In this embodiment of the application, the sea surface temperature difference is the primary factor determining the stability of the near-sea atmospheric stratification: Unstable stratification (ΔT<0): When the air temperature is lower than the sea surface temperature (i.e., "warm sea, cold air"), the sea surface heats the air above it. The heated air, due to its reduced density, experiences strong upward motion (thermal convection), resulting in intense turbulent activity in the atmospheric boundary layer.

[0027] Stable stratification (ΔT>0): When the air temperature is higher than the sea surface temperature (i.e., "cold sea surface, warm air"), the sea surface cools the lower air, forming an inversion layer, which inhibits the vertical movement of air, and the turbulent activity in the atmospheric boundary layer is weak.

[0028] Neutral stratification (ΔT≈0): The sea-temperature difference is minimal, the thermal effect is negligible, and turbulence is mainly generated by wind shear (dynamic factor). This is a simplified assumption commonly used in traditional wave models.

[0029] S2, collect several marine meteorological observation data, preprocess the several marine meteorological observation data to construct a standardized training dataset; In this embodiment of the application, step S2 specifically includes: S21, perform outlier detection and removal processing on several of the aforementioned marine meteorological observation data; S22, perform spatiotemporal interpolation to fill in missing data; S23 performs spatiotemporal matching and standardization on data from different sources.

[0030] In this embodiment of the application, outlier detection and removal of several marine meteorological observation data can be performed using physical thresholding, statistical methods, and spatiotemporal consistency testing. The physical threshold method, based on the fundamental principles of marine meteorology, sets reasonable upper and lower limits for each physical parameter. Any data point exceeding this range will be considered an outlier. For example: Wind speed (U10): 0 m / s ≤ U10 ≤ 80 m / s (above 80 m / s may indicate instrument error, unless at the center of an extreme typhoon). Significant wave height (Hs): 0 m ≤ Hs ≤ 25 m. Sea surface temperature (SST): -2°C ≤ SST ≤ 40°C (covering the possible temperatures of most oceans globally). Sea-air temperature difference (ΔT): |ΔT| ≤ 30°C (setting a very large reasonable range); Statistical methods (based on data distribution) use criteria such as the 3σ criterion (Laida criterion) for parameters that approximate a normal distribution. That is, the mean (μ) and standard deviation (σ) of the dataset are calculated, and data falling outside the interval [μ-3σ, μ+3σ] are considered low-probability events and thus outliers. The spatiotemporal consistency test includes temporal consistency and spatial consistency. Temporal consistency is determined by calculating the rate of change of adjacent time steps. If the change (such as a sudden increase in wind speed of 20 m / s in 1 hour) far exceeds the possible speed of the physical process, it is marked as an anomaly. Spatial consistency uses data from surrounding stations and spatial interpolation to obtain an expected value. If the observed value of a station deviates too much from the expected value (e.g., more than 3 times the standard deviation), the point is judged as an anomaly.

[0031] In this embodiment of the application, spatiotemporal interpolation for filling missing data includes time series interpolation and spatial field interpolation, wherein... Common methods for time series interpolation include: Linear interpolation: suitable for situations where data changes gradually and the missing period is short; Spline interpolation: suitable for situations where data changes smoothly and continuously, and can better fit the natural curve of the data; Regression interpolation based on neighboring stations (spatial aid): When there are many missing data for a single station, a regression equation is established by utilizing the strong correlation with neighboring stations, and the missing values ​​of the current station are estimated using the data from neighboring stations. Common methods for spatial field interpolation include Kriging interpolation and quality control.

[0032] In this embodiment of the application, spatiotemporal matching and standardization processing of data from different sources includes spatiotemporal matching and standardization processing. Spatiotemporal matching includes, Time matching: Data from different platforms (such as satellite transit time, buoy hourly observation, and model output time) are uniformly aligned to a set of standard timestamps (e.g., 00:00, 06:00, 12:00, 18:00) by time interpolation or taking the nearest time.

[0033] Spatial matching: Data with different spatial resolutions (such as high-resolution satellite data, sparse buoy data, and model grid data) are uniformly resampled onto a standard spatial grid using spatial interpolation methods.

[0034] Common standardization methods include Z-Score standardization. This involves applying Z-Score to each parameter (e.g., U10, SST, Hs).

[0035] S3, Constructing an initial correction model for wind and wave growth rate based on physical constraints; In this embodiment of the application, step S3 specifically includes: S31, Determine the sea-temperature difference as the basic evaluation parameter; S32, determine the atmospheric stratification stability parameters as dynamic evaluation parameters; S33, determine the parameters related to the wind and wave growth rate as a comprehensive evaluation value; S34, with the physical relationship between atmospheric stratification stability parameters and wind and wave growth rate correlation parameters as constraints; S35, combining the quantitative correlation characteristics between sea surface temperature difference and wave growth rate; S36, Construct an initial correction function for the wind and wave growth rate that includes empirical coefficients; S37, The initial correction function is applied to the wind energy input term correction of the wave numerical model to characterize the regulatory effect of atmospheric stratification stability on the wind and wave growth rate.

[0036] In this embodiment, parameter ξ is used as a parameter reflecting the instantaneous atmospheric stratification state, ξ=(τ-τ m ) / τ m In the formula, τ represents the actual wind stress calculated based on the actual (non-neutral) stratification conditions. Its calculation depends on stability parameters such as Rib or L. m This represents the baseline wind stress calculated under the same wind speed, assuming a neutral stratification. ξ>0 indicates an unstable stratification, where the actual wind stress is greater than the neutral value. ξ<0 indicates a stable stratification, where the actual wind stress is less than the neutral value. ξ=0 indicates a neutral stratification.

[0037] In this embodiment of the application, a formula is constructed to determine the parameters related to the wind and wave growth rate as a comprehensive evaluation value.

[0038] In the formula, R b Here, g is the acceleration due to gravity, and T is the wave growth rate. a For air temperature, T w Sea surface temperature, g(T) a -T w ) a Characterizing the buoyancy effect, directly related to sea-temperature difference (thermal factor), 'a' is the empirical index of the sea-temperature difference term, and 'z'... t Here, (u / z) represents sea surface roughness, which is related to sea surface conditions; u represents wind speed; and z represents the reference altitude. b : Characterizes wind speed shear (dynamic factor), b is the undetermined exponent of the wind speed shear term, D is the wind zone length, c is the undetermined exponent of the wind zone length term, and a, b, and c are determined through subsequent data training and model optimization.

[0039] S4. Integrate the initial correction model into the wind energy input source term of the wave numerical model to correct atmospheric stratification stability in real time and obtain correction stability. In this embodiment of the application, step S4 specifically includes: S41 acquires real-time observational data on sea surface temperature, atmospheric temperature, wind speed, and wind zone length during the operation of the wave numerical model. S42, dynamically calculates the real-time values ​​of the correlation parameters between sea surface temperature difference, atmospheric stratification stability parameters and wind and wave growth rate; S43, Input the real-time values ​​into the initial correction model to generate dynamic adjustment coefficients for the wind energy input items; S44, Based on the aforementioned dynamic adjustment coefficient, the wind energy input source term value in the wave numerical model is corrected in real time.

[0040] In this embodiment of the application, in the operational forecasting system, wave models are typically coupled with atmospheric numerical forecasting models (such as WRF and ECMWF-IFS). The wave model receives gridded data from the atmospheric model in real time for the current forecast period via a model coupler (such as OASIS-MCT), including: Sea surface temperature (SST), atmospheric temperature at 10 meters or a specified altitude (T_a), wind speed at 10 meters altitude (U10) and its components, and wind field data can be used to diagnose or calculate wind zone length (D).

[0041] From the data assimilation system: For reanalysis or real-time forecasting, the system will access multi-source observation data (such as satellite remote sensing SST, buoy temperature, scatterometer wind field) and generate optimized, gridded initial fields and boundary conditions through the data assimilation system.

[0042] S5. The correction effect is verified through long-term numerical simulation, and the model performance of the initial correction model is verified by multi-index statistical evaluation to obtain the model verification results. In this embodiment of the application, step S5 specifically includes: S51, Set up numerical simulation experiments that include at least a complete annual cycle; S52, Design a comparison scheme for control tests and calibration tests; S53, Select representative verification areas and typical wind and wave events; S54, Obtain the simulation results of the system for evaluating the correction effect.

[0043] In this embodiment, numerical simulation experiments covering at least a full year are included to fully consider the impact of different seasons and weather systems on atmospheric stratification stability and wave growth rates, thereby comprehensively evaluating the performance of the initial calibration model under various conditions. A comparison scheme of control and calibration experiments is designed. The control experiment uses an uncalibrated wave numerical model for simulation, while the calibration experiment applies the initial calibration model constructed in this application. By comparing the results of the two sets of experiments, the degree of improvement of the calibration model on the simulation effect can be clearly seen. Representative verification areas and typical wave events are selected. The verification area should cover different marine environmental conditions, such as different latitudes and different ocean current influence areas; typical wave events can include extreme weather processes such as typhoons and storm surges, as well as daily wave variations. The simulation results of the system are obtained for calibration effect evaluation. The simulation results are statistically evaluated from multiple indicators, such as the error between the simulated wave growth rate and the actual observed value, the correlation of the simulation results, and the stability of the simulation. Through the comprehensive evaluation of these indicators, model validation results can be obtained, determining whether the initial calibration model has effectively improved the accuracy and reliability of the wave growth rate simulation.

[0044] In this embodiment of the application, step S5 further includes: S55, calculate the deviation, root mean square error, relative error and correlation coefficient between the simulation results and the observed data before and after correction; S56, Analyze the distribution pattern of the ratio of observed values ​​to simulated values ​​as a function of sea surface temperature difference; S57, The correction results are compared and verified with reanalysis data and field observation data from multiple sources; S58, based on the joint analysis of statistical indicators and distribution characteristics, evaluates the performance of the initial calibration model from multiple dimensions.

[0045] In this embodiment, when calculating the deviation between the simulation results and the observed data before and after correction, a combination of absolute and relative deviations can be used to comprehensively measure the degree to which the simulated values ​​deviate from the observed values. The root mean square error reflects the dispersion of the difference between the simulated and observed values. It is obtained by taking the square root of the average of the squares of the deviations at each data point, effectively avoiding the problem of positive and negative deviations canceling each other out. The relative error, based on the observed values, reflects the relative magnitude of the simulation error and more intuitively reflects the relative accuracy of the simulation. The correlation coefficient is used to measure the degree of linear correlation between the simulation results and the observed data. Its value ranges from -1 to 1; the closer it is to 1, the stronger the correlation and the better the simulation effect.

[0046] In this embodiment, when analyzing the distribution pattern of the ratio of observed to simulated values ​​as a function of sea-temperature difference, the ratios of observed to simulated values ​​within different sea-temperature difference intervals are statistically analyzed. Corresponding distribution curves or scatter plots are plotted to observe their trends. If the ratio exhibits a regular change as the sea-temperature difference increases or decreases, such as monotonically increasing, decreasing, or exhibiting specific peaks or troughs, the underlying physical mechanisms can be explored in depth to determine the applicability and accuracy of the initial calibration model under different sea-temperature difference conditions.

[0047] In this embodiment, when the correction results are compared and verified with reanalysis data and field observation data from multiple sources, the reanalysis data, with its long-term series and global coverage, can serve as a macroscopic reference for the simulation results. The field observation data, on the other hand, possesses high accuracy and real-time performance, providing detailed local marine environmental information. The corrected simulation results are then compared point-by-point with both the reanalysis data and the field observation data to analyze the magnitude and distribution characteristics of the differences. For areas with significant differences, the causes are further analyzed to determine whether the issue stems from problems with the model itself or errors introduced during data acquisition and processing.

[0048] In this embodiment, based on the joint analysis of statistical indicators and distribution characteristics, the performance of the initial calibration model is evaluated from multiple dimensions. This includes comprehensively considering previously calculated statistical indicators such as deviation, root mean square error, relative error, and correlation coefficient, as well as the distribution pattern of the ratio of observed to simulated values ​​with sea-temperature difference. If the statistical indicators show small simulation errors, strong correlations, and a distribution pattern consistent with the expected physical process, it indicates that the initial calibration model performs well and can effectively improve the accuracy and reliability of wind and wave growth rate simulation. Conversely, if the statistical indicators are unsatisfactory or the distribution pattern is abnormal, further optimization and improvement of the model are needed, adjusting empirical coefficients or refining physical constraints to enhance the model's applicability and accuracy.

[0049] S6. Based on the model validation results, perform parameter sensitivity analysis and optimization on the initial calibration model to obtain an optimized calibration model.

[0050] In this embodiment of the application, step S6 specifically includes: S61, Perform parameter sensitivity analysis on the empirical coefficients in the initial calibration model to obtain the sensitivity analysis results; S62, assess the impact of sea surface temperature difference calculation accuracy on correction results, and obtain multi-index verification feedback; S63. Based on the sensitivity analysis results and multi-index verification feedback, the coefficients of the initial calibration model are adjusted using a parameter optimization algorithm to obtain an optimized calibration model.

[0051] In this embodiment, when performing parameter sensitivity analysis on the empirical coefficients in the initial calibration model, the controlled variable method can be used. This involves changing only one empirical coefficient at a time while keeping other coefficients constant, and then observing the changes in the model output. By setting different coefficient variation ranges and step sizes, multiple sets of simulation experiments are conducted. Statistical indicators such as the deviation and root mean square error of the model output results are calculated for each set of experiments. This determines the degree of influence of each empirical coefficient on the model performance, obtaining the sensitivity analysis results. The sensitivity analysis results can intuitively show which empirical coefficients have a greater impact on the model output results and which have a smaller impact, providing an important basis for subsequent parameter optimization.

[0052] In this embodiment of the application, when evaluating the impact of sea-temperature difference (STD) calculation accuracy on the correction results, multiple experimental schemes with different STD calculation accuracies can be designed. For example, random errors of different magnitudes can be artificially introduced into the original observation data to simulate STD data with different accuracies. These data are then input into the initial correction model for simulation calculation. By comparing the correction results of the model under different accuracies, the changes in indicators such as the deviation of the simulation results from the observed data and the root mean square error before and after correction are calculated. The impact of STD calculation accuracy on the correction results is analyzed, and multi-indicator verification feedback is obtained. Multi-indicator verification feedback can comprehensively reflect the relationship between STD calculation accuracy and correction results, providing a reference for improving the accuracy and reliability of the model.

[0053] In this embodiment, based on sensitivity analysis results and multi-index verification feedback, a parameter optimization algorithm is used to adjust the coefficients of the initial calibration model. Commonly used parameter optimization algorithms include genetic algorithms and particle swarm optimization algorithms. Taking the genetic algorithm as an example, firstly, the value range and encoding method of the empirical coefficients are determined, and each empirical coefficient is encoded as a chromosome segment. Multiple empirical coefficients are combined into a complete chromosome, representing a possible solution. Then, a fitness function is set, using statistical indicators such as the deviation between the model output and the observed data, and the root mean square error, as the measurement standard. The smaller the deviation and the smaller the root mean square error, the higher the fitness. Through operations such as selection, crossover, and mutation, new chromosomes, i.e., new solutions, are continuously generated, and the optimal combination of empirical coefficients is gradually searched. After multiple generations of iteration, when the fitness function value reaches the preset convergence condition or the maximum number of iterations is reached, the search stops, and the optimized empirical coefficients are obtained, thus obtaining the optimized calibration model. The optimized calibration model can better adapt to different marine meteorological conditions and improve the accuracy and reliability of wind and wave growth rate simulation.

[0054] Based on the above method, this application also provides an atmospheric stratification stability correction system corresponding to the above method, the system comprising: The analysis module is used to analyze the correlation between atmospheric stratification stability and wind and wave growth rate, and to determine the core evaluation parameters of atmospheric stratification stability. The collection module is used to collect a number of marine meteorological observation data and preprocess the marine meteorological observation data to construct a standardized training dataset. A module is built to construct an initial correction model for the wind and wave growth rate based on physical constraints; The correction module is used to integrate the initial correction model into the wind energy input source term of the wave numerical model to correct the atmospheric stratification stability in real time and obtain the correction stability. The verification module is used to verify the correction effect through long-term numerical simulation and to perform model performance verification on the initial correction model using multi-index statistical evaluation to obtain model verification results. The optimization module is used to perform parameter sensitivity analysis and optimization on the initial calibration model based on the model validation results, so as to obtain an optimized calibration model.

[0055] Based on the same inventive concept disclosed above, this application also provides an electronic device. The electronic device of this application includes at least one processor and at least one memory electrically connected to the processor. The memory is electrically connected to the processor, wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described above.

[0056] It should be noted that the electrical connections between the above-mentioned units do not necessarily represent the connections between lines. Indirect connections are applicable to the embodiments of this application as long as they achieve the purpose of this application.

[0057] Based on the same inventive concept, this application also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the above method.

[0058] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for correcting atmospheric stratification stability, characterized in that, The method includes, The relationship between atmospheric stratification stability and wind and wave growth rate was analyzed to determine the core evaluation parameters of atmospheric stratification stability. Collect a number of marine meteorological observation data and preprocess them to construct a standardized training dataset; An initial correction model for wind and wave growth rate is constructed based on physical constraints; The initial correction model is integrated into the wind energy input source term of the wave numerical model to correct atmospheric stratification stability in real time and obtain correction stability. The correction effect was verified through long-term numerical simulation, and the initial correction model was validated by multi-index statistical evaluation to obtain the model validation results. Based on the model validation results, the initial calibration model is subjected to parameter sensitivity analysis and optimization to obtain an optimized calibration model.

2. The method according to claim 1, characterized in that, The relationship between atmospheric stratification stability and wind and wave growth rate was analyzed, specifically including: Based on the atmospheric boundary layer theory, a quantitative relationship between sea surface temperature difference and atmospheric stratification stability is established. Analyze the mechanism by which atmospheric stratification stability affects momentum flux at the air-sea interface; This study investigates the modulating effect of momentum flux changes on wave growth rate and establishes a complete correlation path from atmospheric stratification stability to wave growth rate.

3. The method according to claim 2, characterized in that, Preprocessing of several marine meteorological observation data, specifically including: Outlier detection and removal were performed on several of the aforementioned marine meteorological observation data. Spatiotemporal interpolation is used to fill in missing data. Spatiotemporal matching and standardization processing are performed on data from different sources.

4. The method according to claim 3, characterized in that, An initial correction model for wind and wave growth rate is constructed based on physical constraints, specifically including: Sea-air temperature difference was determined as the basic evaluation parameter; Determine atmospheric stratification stability parameters as dynamic evaluation parameters; Determine the parameters related to the wind and wave growth rate as a comprehensive evaluation value; The physical relationship between atmospheric stratification stability parameters and wind and wave growth rate parameters is used as a constraint. Combined with the quantitative correlation characteristics between sea surface temperature difference and wave growth rate; Construct an initial correction function for the wind and wave growth rate that includes empirical coefficients; The initial correction function is applied to the wind energy input term correction of the wave numerical model to characterize the regulatory effect of atmospheric stratification stability on the wind and wave growth rate.

5. The method according to claim 4, characterized in that, Real-time correction of atmospheric stratification stability, specifically including: During the operation of the numerical wave model, real-time observational data on sea surface temperature, atmospheric temperature, wind speed, and wind zone length are acquired. Real-time values ​​of the correlation parameters between sea surface temperature difference, atmospheric stratification stability parameters, and wind and wave growth rate are dynamically calculated. The real-time values ​​are input into the initial correction model to generate dynamic adjustment coefficients for the wind energy input. Based on the aforementioned dynamic adjustment coefficient, the wind energy input source term value in the wave numerical model is corrected in real time.

6. The method according to claim 5, characterized in that, The correction effect was verified through long-term numerical simulation, specifically including: Set up numerical simulation experiments that include at least a full year cycle; Design a comparative scheme for control experiments and calibration experiments; Select representative verification areas and typical wind and wave events; The simulation results of the system are obtained for evaluation of the correction effect.

7. The method according to claim 6, characterized in that, The initial calibration model is validated using a multi-index statistical evaluation method, specifically including: Calculate the deviation, root mean square error, relative error, and correlation coefficient between the simulation results and the observed data before and after correction; Analyze the distribution pattern of the ratio of observed values ​​to simulated values ​​as a function of sea surface temperature difference; The correction results were verified by multi-source comparison with reanalysis data and field observation data. The performance of the initial calibration model is evaluated from multiple dimensions based on a joint analysis of statistical indicators and distribution characteristics.

8. The method according to claim 7, characterized in that, Based on the model validation results, parameter sensitivity analysis and optimization are performed on the initial calibration model, specifically including: Perform parameter sensitivity analysis on the empirical coefficients in the initial calibration model to obtain the sensitivity analysis results; The impact of the accuracy of sea surface temperature difference calculation on the correction results was assessed, and multi-indicator verification feedback was obtained. Based on the sensitivity analysis results and multi-index verification feedback, the coefficients of the initial calibration model were adjusted using a parameter optimization algorithm to obtain an optimized calibration model.

9. An atmospheric stratification stability correction system, characterized in that, The system includes: The analysis module is used to analyze the correlation between atmospheric stratification stability and wind and wave growth rate, and to determine the core evaluation parameters of atmospheric stratification stability. The collection module is used to collect a number of marine meteorological observation data and preprocess the marine meteorological observation data to construct a standardized training dataset. A module is built to construct an initial correction model for the wind and wave growth rate based on physical constraints; The correction module is used to integrate the initial correction model into the wind energy input source term of the wave numerical model to correct the atmospheric stratification stability in real time and obtain the correction stability. The verification module is used to verify the correction effect through long-term numerical simulation and to perform model performance verification on the initial correction model using multi-index statistical evaluation to obtain model verification results. The optimization module is used to perform parameter sensitivity analysis and optimization on the initial calibration model based on the model validation results, so as to obtain an optimized calibration model.

10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the steps of the method according to any one of claims 1-8.

11. A computer storage medium, characterized in that, The computer storage medium stores a computer program, which, when executed by a processor, implements the steps of any one of the methods described in claims 1-8.

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