132 results about "Sea surface wind" patented technology

The invention belongs to the technical field of ocean data processing, and discloses a GNSS-R and scatterometer combined same-orbit time sequence sea surface wind speed inversion method and system, and the method comprises the following steps: obtaining historical satellite data of ocean surface wind, carrying out the preprocessing of the historical satellite data, and obtaining the preprocessed data, constructing a training sample set in combination with the reference wind speed and the surge auxiliary data; constructing an initial wind speed inversion model by using a plurality of single-moment multi-frequency information fusion modules and multi-moment interactive attention enhancement modules, and training the initial wind speed inversion model by using the training sample set to obtain a same-orbit time sequence sea surface wind speed inversion model; and obtaining a time delay-Doppler correlation power diagram, GNSS-R observation parameters, scatterometer parameters and effective wave height of storm and surge, and completing sea surface wind speed inversion by using the same-orbit time sequence sea surface wind speed inversion model to obtain sea surface wind speed prediction data.

The invention relates to the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

The invention discloses a synthetic aperture radar sea surface wind field inversion method considering Doppler centroid anomaly, and the method comprises the following steps: obtaining vertical polarization SAR image data, and extracting a radar backscattering coefficient NRCS and an original Doppler centroid anomaly DCA related image; based on Doppler frequency data of the VV polarized SAR image, through quintic polynomial fitting and land area correction processing, DCA information caused by geophysical factors is extracted; acquiring actually measured wind speed data of the buoy, matching the actually measured wind speed data with an external background wind field, and performing nonlinear fitting correction by adopting an exponential function to obtain a corrected background wind field speed; constructing a variational analysis model containing an NRCS and DCA observation field, taking the corrected external wind field as a background field, and obtaining a wind speed field and a wind direction field through minimum cost function inversion; performing verification analysis on an inversion result by adopting buoy observation data subjected to space-time matching; the precision and reliability of the vector parameters of the sea surface wind field can be improved.

The invention discloses a C-SAR satellite sea surface wind speed inversion method combining spatial features, and relates to the technical field of sea surface wind speed inversion, and the method comprises the steps: selecting C-SAR data and ERA-Interim wind field data; pre-processing the C-SAR data, and then carrying out time and space matching on the pre-processed C-SAR data and ERA-Interim wind field data; and inputting the matched C-SAR data and ERA-Interim wind field data into a double-branch feature fusion network model based on attention mechanism guidance, inverting the wind speed of the central pixel position of the sub-image, and then comparing the wind speed with the ASCAT wind field data and the ERA-Interim wind field data for verification. According to the method, a double-branch neural network structure is adopted, Branch1 is utilized to construct nonlinear features among a backscattering coefficient, an incident angle, a viewing angle, a sea surface wind speed and a wind direction, and Branch2 is utilized to excavate spatial features of C-SAR data and ERA-Interim wind field data; carrying out feature fusion on the nonlinear features and the spatial features by utilizing a channel attention mechanism module; the dynamic characteristics of C-SAR inversion of the sea surface wind speed are guaranteed, and the inversion precision is improved.

The invention discloses a sea surface wind speed inversion method and device based on marine environment noise, and a storage medium. The method comprises the following steps: S1, obtaining marine noise data; s2, acquiring wind speed data according to time and latitude and longitude information in the ocean noise data; s3, audio data in the ocean noise data are processed, and center frequency point noise spectrum level data are obtained; s4, summarizing the noise spectrum level data of the center frequency point and the corresponding wind speed data into a neural network data set through time and latitude and longitude information; step S5, constructing an initial Elman network; s6, performing iterative optimization on the network weight and bias of the Elman network by adopting a cuckoo search algorithm to generate optimized network parameters; step S7, initializing an Elman network by using the optimized network parameters, and generating a CS-Elman model; training a CS-Elman model by using the neural network data set; and step S8, carrying out quantitative evaluation on the CS-Elman model. The method can achieve the high-precision prediction of the sea surface wind speed.

The invention discloses a typhoon center positioning method, device and equipment based on an SAR (Synthetic Aperture Radar) satellite, relates to the technical field of ocean microwave remote sensing, and is used for solving the problems that a typhoon positioning method in the prior art cannot accurately extract the position of a typhoon center and the positioning accuracy of the typhoon center is low. Comprising the following steps: acquiring basic data of an SAR satellite according to typhoon path information; performing sea surface wind speed and wind direction inversion based on the basic data to obtain inversion data; according to the inversion data, a gradient wind equation, a wind profile equation and the maximum wind speed are adopted to determine a high-wind-speed area of the typhoon; according to a cyclonic vortex structure of the typhoon, based on the vertical vortex field of the high-wind-speed area, the horizontal divergence field of the high-wind-speed area and the composite field of the high-wind-speed area, determining the position range of the center point of the typhoon; and positioning a typhoon center meeting preset precision according to the typhoon center point position range. According to the technical scheme, the positioning precision of the typhoon center can be improved.

The invention provides a simulation method for a GNSS scattering signal of a sea surface wind field. The simulation method comprises the following steps: establishing a simulation model of a delay-Doppler domain mapping waveform; a compact polarization model based on a microwave scattering model is established, a polarization component of a complete polarization scattering coefficient matrix is calculated by using the complete polarization microwave scattering model, and a scattering coefficient of a compact polarization signal is obtained according to the polarization component of the complete polarization scattering coefficient matrix and a Stokes sub-vector of the compact polarization signal. The coefficient is used as a bistatic radar scattering coefficient in a simulation model of a delay-Doppler domain mapping waveform; establishing a sea wave spectrum model for obtaining a sea wave spectrum density function; and simulating by using the model to obtain a delay-Doppler domain mapping waveform. According to the method provided by the invention, the full-polarization DDM waveform of the GNSS scattering signal can be generated in the compact polarization mode, so that the hardware complexity is reduced, and sufficient and comprehensive sea surface wind field inversion information is reserved.

The invention relates to the technical field of marine survey and big data processing, and provides a rolling sea wave forecast correction method and device, electronic equipment and a storage medium. Comprising the steps of training a sea wave rolling forecast correction model based on artificial intelligence and real-time observation; obtaining to-be-corrected numerical mode sea wave factor forecast data and real-time observation data; the numerical mode sea wave element forecast data comprises sea wave significant wave height data, and the real-time observation data comprises sea surface wind vectors and sea wave significant wave height real-time observation data; correcting the numerical mode sea wave element forecast data according to a sea wave rolling forecast correction model obtained through training, obtaining a group of new sea wave forecast data after new observation data are input, and obtaining numerical mode sea wave significant wave height data capable of being updated and corrected in a rolling manner; the method can effectively reduce the negative influence of the error on the subsequent application in the fields of marine meteorology and the like, and effectively improves the application effect of the sea wave forecast data.

The invention provides a method and a device for correcting a backscattering coefficient of a wide scanning synthetic aperture radar, and the method comprises the steps: determining a backscattering coefficient of each pixel in a to-be-processed image, and then determining a backscattering coefficient correction coefficient of the pixel through employing the data of a mode sea surface wind field, a geophysical model function and the like; and correcting the backscattering coefficient of the pixel by using the backscattering coefficient correction coefficient to generate a target backscattering coefficient corresponding to the pixel. According to the method and the device, the backscattering coefficients of the pixels are corrected, so that the amplitude band inconsistency between the SAR image strips in the wide-width scanning mode can be eliminated, the more uniform and higher-precision SAR image in the wide-width scanning mode can be obtained, and better data support is provided for subsequent processing.

The invention provides a multi-modal information fusion sea surface wind speed inversion method, device and system, and the method comprises the steps: obtaining satellite navigation data, wind speed reference data and significant wave height data, performing matching and quality control on the wind speed reference data and the significant wave height data with the satellite navigation data to obtain an initial data set; recombining the initial data set according to a track to obtain a target data set, and dividing the target data set into a training set, a verification set and a test set; and constructing a wind speed inversion model by using the spatial-temporal correlation of continuous time delay-Doppler diagram observation values in the same trajectory and combining with auxiliary feature information. According to the invention, the extracted information can be fused with various auxiliary feature information, the inversion precision is improved, and the influence characteristics of the ocean state on the wind speed inversion can be quantitatively analyzed.

The invention discloses a novel GNSS-R typhoon area sea surface high wind speed inversion method, and relates to the technical field of wind speed inversion, and the method comprises the steps: introducing a multi-source feature fusion structure, achieving the combined modeling of CYGNSS parameters and environment field data, enhancing the response capability of a model to high wind speed disturbance and a complex wind field structure, and improving the stability of the model; a KL divergence optimization strategy is adopted to carry out explicit alignment on feature representation of a CNN layer and a Transform layer in a training stage, so that the problem of inversion deviation of a traditional single-path model in a wind speed rapid change area is relieved, and the wind speed inversion precision and stability of a contrast learning model in extreme weather are remarkably improved; a physical constraint channel of a medium-low wind speed interval is constructed through a GMF model, so that the model is guided to output an inversion result more conforming to a physical mechanism in a non-high wind speed scene, and over-fitting of a high wind speed sample is avoided; meanwhile, an adaptive fusion layer is designed to dynamically adjust the fusion weight according to the output of each module so as to enhance the robustness of the model under the wind speed multi-scale distribution.

The invention relates to a sea surface wind wave field parameter integrated inversion method combining HFR and SAR, and the method mainly comprises the steps: extracting the positive and negative first-order echo spectrum peak values of HFR, carrying out the inversion of a sea surface wind direction, and carrying out the interpolation of an HFR wind direction result to an SAR data grid through bilinear interpolation; based on the registered HFR wind direction result, utilizing a CMOD5. N model and an SAR backscattering coefficient to invert the wind speed; constructing a wind wave spectrum based on the inverted wind speed and wind direction results; calculating a surge spectrum peak wave number, a surge spectrum peak amplitude and a surge propagation direction by using the SAR image information, and constructing a surge spectrum based on the surge spectrum peak wave number, the surge spectrum peak amplitude and the surge propagation direction; and superposing the obtained wind wave spectrum and the surge spectrum to obtain a sea wave spectrum result, and further inverting the significant wave height. According to the method, the defect of sea state parameter observation through a single means is overcome, integrated high-precision inversion of the wind wave parameters is achieved, and the method has important significance on marine environment monitoring, disaster forecasting, shipping safety and the like.

The invention relates to the technical field of global navigation satellite system reflection, in particular to a GNSS-R sea surface wind speed inversion method, which comprises the following steps of: firstly, comprehensively considering multiple constraint conditions such as inversion error distribution characteristics, interval quantity, sample distribution uniformity and interval minimum sample requirements by utilizing the global optimal search characteristic of an SA algorithm; a multi-objective optimization function is constructed to preliminarily divide a wind speed interval, and then a GD algorithm is adopted to accurately adjust the boundary of the preliminarily divided interval. And constructing a special XGBoost prediction model for each wind speed interval, and finally, fusing wind speed prediction results of a plurality of sub-interval models through a stacked integrated learning architecture to obtain a wind speed inversion value. According to the method provided by the invention, the problem that the inversion error of a traditional single model in different wind speed ranges is extremely unstable can be effectively relieved, the inversion precision of the whole wind speed range is improved, and meanwhile, the problem that a real-time observation sample cannot correspond to a sub-model under an interval model in practical application is solved; and the application conversion of the GNSS-R sea surface wind speed inversion technology is promoted.

The invention discloses an anti-storm liftable floating type photovoltaic system which comprises a floating body structure, a photovoltaic panel is installed above the floating body structure, and the lower portion of the floating body structure is connected with a gravity anchor located on the seabed through an anchor chain; the winch is connected with the floating body structure through a steel wire rope and can drive the floating body structure to descend to the position below the sea level. The device can adapt to the deep sea environment and can well cope with severe environments such as strong wind, and the winch can be used for driving the floating body structure to sink to the seabed so as to avoid sea surface storm waves; a traditional floating photovoltaic device depends on a weighted base or rigid anchoring, the wind resistance is limited, and the application sea area range (such as a typhoon high-incidence area) is remarkably expanded by dynamically adjusting the position of a floating body; by sinking to a safe depth, the floating body avoids a typhoon strong wind area, the wind load is reduced, and the photovoltaic panel is prevented from being turned over, torn or collided with the fan tower drum.

The invention belongs to the technical field of satellite-borne GNSS-R marine environment monitoring, and discloses a satellite-borne GNSS-R multi-system multi-polarization sea surface wind speed estimation method and system, and the method comprises the steps: extracting a satellite-borne GNSS-R feature value and ERA5 environment factor data; performing quality control on the multi-GNSS system and the multi-polarization mode data set, and performing grid matching interpolation with the ERA5 data; dividing a training set and a test set, and constructing a machine learning wind speed estimation model based on Optuna optimization; increasing the proportion of the high-wind-speed data set to determine an optimal high-wind-speed proportion and an optimal model; and performing multi-GNSS system fusion and multi-polarization mode fusion, comparing with pure data fusion to determine an optimal model, and finally outputting a sea surface wind speed estimation value of a track point. According to the method, accurate inversion under different wind speed conditions is realized, the industrial problem of wind speed underestimation under extreme weather is effectively solved, and the sea surface wind speed estimation capability and reliability of satellite-borne GNSS-R are improved.

The invention relates to a sea surface wind speed inversion method, system, equipment and medium in combination with advantages of a simulation model and satellite data, a simulated time delay Doppler (DDM) image is generated by inputting parameters such as positions and speeds of a satellite and a receiver, gain of a receiving antenna and the like, satellite actual measurement data is expanded in combination with the simulated image, an actual measurement-simulation combined data set is constructed, and a sea surface wind speed inversion result is obtained. Extracting a leading edge slope and a trailing edge slope, and calculating to obtain a leading edge-trailing edge composite characteristic value to invert a sea surface wind speed value; the system, the equipment and the medium are used for implementing the method. According to the invention, the simulation of the time delay Doppler diagram is carried out through the position and speed of the satellite and the receiver, the wind speed, the GNSS signal frequency and the receiving antenna gain parameter, and the data sample can be expanded under the condition that the actually measured data sample of a specific sea area or sea condition is scarce. The method has the advantages that the applicability is wide, and sea surface wind speed inversion can be accurately achieved under the condition that samples are scarce.

The invention discloses an enteromorpha green tide full life cycle wind action coefficient determination method, and belongs to the field of enteromorpha green tide drift prediction. The method comprises the following steps: S1, acquiring satellite remote sensing images of enteromorpha green tide in different development stages, and interpreting the satellite remote sensing images to obtain enteromorpha green tide distribution data; and / or acquiring sea drift tracking observation data of enteromorpha green tide at different development stages; s2, constructing an enteromorpha green tide short-term drift numerical model; s3, acquiring surface ocean current and sea surface wind data of the research sea area; s4, determining optimal wind action coefficients of the enteromorpha green tide in different development stages; and S5, performing quadratic polynomial fitting, establishing a wind action coefficient dynamic model suitable for the enteromorpha green tide full life cycle, and further determining an enteromorpha green tide full life cycle wind action coefficient value. According to the method, the prediction precision of the enteromorpha green tide drift path can be improved.

The invention discloses a sea surface wind speed inversion method and system based on a CYGNSS and Willoughby model, and belongs to the technical field of ocean remote sensing. The method comprises the following steps: firstly, extracting a cyclone parameter from IBTrACS data through a Willoughby model, and encrypting the cyclone parameter to 1 second for sampling by utilizing segmented three times of Hermite interpolation; screening CYGNSS mirror reflection points and constructing a data set in combination with a space matching algorithm; processing CYGNSS L1-level data and extracting observation parameters; establishing a basic GMF (Gaussian Mixture Function) according to incidence angle grouping, and optimizing the weight through nonlinear fitting; the Willoughby model wind speed and the basic GMF are fused to generate an enhanced GMF, the DDM signal-to-noise ratio is dynamically adjusted in combination with minimum variance estimation, and the precision is improved. According to the method, the GNSS-R technology is utilized to overcome the defects of high cost, limited coverage and the like of a traditional method, the sea surface wind speed monitoring reliability under the extreme weather is enhanced through collaborative optimization of a physical model and actually measured data, and the method has the advantages of low cost, wide coverage and interference resistance and is suitable for refined marine meteorological monitoring.

The invention discloses an oil spill rapid simulation method applied to a submarine pipeline oil leakage accident. The method comprises the following steps: firstly, adding a reading module for three-dimensional flow field data on the basis of a conventional ocean surface two-dimensional oil spill model; secondly, designing a stress driving algorithm of the sea surface wind field to oil particles at different water depth positions below the sea surface; thirdly, designing a floating speed algorithm of oil particles below the sea surface; fourthly, designing a sea surface vertical mixing layer height and vertical diffusion coefficient algorithm; and finally, driving a three-dimensional oil spill drifting and diffusing model to calculate in combination with flow field forecast data and wind field forecast data. According to the method, on the basis of a conventional two-dimensional oil spill model, a vertical physical change process of oil particles is added, and three-dimensional flow field data are loaded; simulation calculation of related physical processes such as floating, drifting and diffusion of oil particles in a three-dimensional water body space from the seabed to the sea surface is achieved, and reference values are provided for rapid simulation and emergency deduction decision making of petroleum pollutant leakage in marine pollution.

The invention discloses a conditional diffusion method for reconstructing multi-source meteorological data for tropical cyclones. The conditional diffusion method comprises the following steps: acquiring an infrared radiation brightness set, a polarization correction temperature set and a sea surface wind speed of different tropical cyclones in a specified historical period; constructing a polarization correction temperature reconstruction model based on the conditional diffusion model; the input of the polarization correction temperature reconstruction model is an infrared radiation brightness temperature set, and the output target is a polarization correction temperature set; carrying out transfer learning on the trained polarization correction temperature reconstruction model to form a sea surface wind speed reconstruction model; the input of the sea surface wind speed reconstruction model is an infrared radiation brightness temperature set, and the output target is the sea surface wind speed. Therefore, the mapping relation from the infrared radiation brightness temperature to the multi-source meteorological data is established through generative deep learning of conditional diffusion, so that the multi-source meteorological data with the temporal-spatial resolution the same as that of geostationary orbit observation is reconstructed, and good performance is shown on various meteorological indexes and computer vision indexes.

This invention discloses a method for inverting sea surface wind speed from GNSS reflected signals based on mask perception and image-based Mamba-CNN. The method includes: performing spatiotemporal matching and interpolation processing on multi-source spaceborne GNSS-R observation data and ERA5 reanalysis data; gridding the processed observation parameters according to geographical location to obtain a multi-channel grid image and acquiring a binarized occupancy mask; inputting the multi-channel grid image and the binarized occupancy mask into a mask perception Mamba-CNN model; calibrating the grid features through a mask conditional encoder and a feature linear modulation layer, and performing spatial modeling using feature extraction blocks combining convolutional and Mamba state space paths to obtain the wind speed inversion result; aggregating the extracted features using center-perception pooling, and outputting the final sea surface wind speed inversion image through a regression network. This invention can significantly reduce computational complexity, effectively reconstruct the continuous wind field structure, and improve the inversion accuracy and robustness in high-wind-speed areas.

The present invention relates to a gridded intelligent fusion method and system for sea surface wind fields based on multi-source satellite payloads. The method comprises: constructing a deep neural network model and combining the advantages of the D-Matrix statistical algorithm for medium and low wind speed inversion to obtain MWRI sea surface wind speed products for the entire wind speed range; performing spatiotemporal matching processing between multi-source satellite data and an EC background field to obtain the data source for the fusion analysis period; combining the EC background field with the wind speed, divergence, vorticity, and second-order derivative terms of wind speed at each point in the fused wind field to obtain a cost function; obtaining the gradient terms of each cost function within the cost function based on the cost function and summing them to obtain the function gradient; and obtaining the final fused wind field based on the cost function and the function gradient. The present invention achieves efficient fusion of sea surface wind field data from multi-source satellite payloads, and can improve the accuracy and spatiotemporal resolution of sea surface wind field data.

This application provides a method and apparatus for reconstructing synthetic aperture radar (SAR) typhoon data. The method involves acquiring an initial SAR typhoon image to be reconstructed; preprocessing the initial SAR typhoon image to identify target typhoon regions, resulting in a typhoon image to be reconstructed; using pre-trained local and global branch networks, extracting features from the typhoon image to be reconstructed, resulting in an aggregated typhoon image; using a pre-constructed aggregation network, aggregating features from the aggregated typhoon image, resulting in an aggregated typhoon image; and adjusting the size of the aggregated typhoon image according to the size of the initial SAR typhoon image to obtain the reconstructed typhoon image. This allows for the reconstruction of typhoon data within a portion of the target typhoon region in a typhoon image, ensuring the quality of SAR typhoon data used for predicting sea surface wind speed, and indirectly improving the accuracy of typhoon intensity estimation and typhoon structure information.

The utility model provides a multifunctional navigation aid device which comprises a navigation aid assembly, a duckweed assembly and a power assembly. The duckweed assembly comprises a cabin body, a duckweed assembly and a balance weight part, the duckweed assembly is movably connected with the peripheral side of the cabin body, and the balance weight part is connected with the bottom of the cabin body; the navigation aiding assembly is arranged at the top of the cabin body; and the power assembly is arranged at the bottom of the cabin body. The multifunctional navigation aid device not only can resist sea surface storm waves, but also can automatically return to the original position when position deviation occurs.

The application provides a multifunctional navigation device, which comprises a navigation assembly, a duckweed assembly and a power assembly; the duckweed assembly comprises a cabin, a duckweed component and a counterweight, the duckweed component is movably connected with the periphery of the cabin, and the counterweight is connected with the bottom of the cabin; the navigation assembly is arranged on the top of the cabin; and the power assembly is arranged on the bottom of the cabin. The multifunctional navigation device can not only resist wind and waves on the sea surface, but also can automatically return to the original position when the position deviates.

The application belongs to the technical field of data analysis and processing, and specifically discloses a typhoon track trend change confirmation method, system, device and medium. The method obtains the sea surface pressure disturbance field, the sea surface wind field and the diagnostic field in the sea basin scale. Based on the multi-field joint determination, the typhoon track and the influence area in the climate model data are determined. Further iteration is performed to determine the typhoon track and the influence area data set. Based on the modal decomposition and by using the influence area data set and the TDF method, the typhoon trend change is determined. The method can comprehensively utilize the forecast model data wind pressure field data to jointly research and judge, effectively determine the typhoon center position and the typhoon track, and objectively and reliably determine the typhoon influence range by the sea surface pressure disturbance field, so that the influence of the artificial factor introduced by the artificial given area weight influence factor in the TDF method can be reduced. The typhoon change trend is determined by using the principal component analysis method on the objective typhoon influence range, and the operability is high.

The invention provides a sea surface wind speed deviation correction method and system based on statistical learning and physical constraint, and belongs to the field of satellite ocean remote sensing and meteorological data reanalysis. According to the method, the technical problems that the correction result of the existing multi-source satellite wind speed data is unstable in statistics and unreasonable in physics due to inherent system errors of a sensor and sparse samples in a high-wind-speed interval, and an accurate and reliable correction result cannot be generated in a full-wind-speed range (especially under a high-wind-speed condition) can be solved. Specifically, quality control and space-time matching are carried out by acquiring multi-source satellite wind speed data and high-precision reference wind field data, an initial deviation correction lookup table is constructed by adopting a double-reference partitioning strategy based on satellite observation wind speed and reference wind field wind speed, statistical stabilization processing is carried out on the lookup table by utilizing a hierarchical Bayesian contraction estimator, and the initial deviation correction lookup table is obtained. The physical continuity of a wind speed-deviation curve is improved through an adaptive LOESS smoothing algorithm, and an exponential decay function based on a turbulence energy spectrum theory is introduced.

The invention provides a wind field inversion method and system based on multi-source fusion and physical collaborative modeling, and belongs to the technical field of atmospheric science and remote sensing, and the method comprises the steps: obtaining and carrying out the time-space alignment of a GNSS radio occultation refractive index profile, a GNSS reflection measurement sea surface wind vector, a foundation GNSS zenith total delay, a radar wind profile and mobile platform observation data; constructing a standardized observation vector; the observation vector is input into a physical information neural network, a loss function comprises a data fitting item and a physical constraint item composed of a heat-to-wind relation, a mass continuity equation and a vortex equation, and observation data and an atmospheric physical rule are fitted at the same time; dynamically adjusting the fusion weight of each observation source based on the weather type identification result; and finally, outputting a three-dimensional wind field with uncertainty quantization and derivative products. According to the invention, high-precision, high-resolution and physically credible three-dimensional wind field inversion is realized, and the inversion robustness and application value under complex weather conditions are significantly improved.

The invention discloses an unstructured grid adaptive sub-mesoscale hybrid parameterization method and system, and the method comprises the steps: calculating a sea surface horizontal density gradient defined on an edge in a sea model of an unstructured grid, correcting the sea surface horizontal density gradient according to an effective resolution ratio and a frontal surface scale, and carrying out the calculation of the submesoscale hybrid parameterization of the submesoscale hybrid parameterization of the submesoscale hybrid parameterization of the submesoscale hybrid parameterization of the unstructured grid. Calculating a sea surface horizontal buoyancy gradient; calculating the sea surface level Ekman buoyancy flux according to the sea surface level buoyancy gradient and the sea surface wind stress; and if the sea surface level Ekman buoyancy flux is a negative number, further calculating a mixing coefficient in the boundary layer. According to the method, the influence of the symmetrical unstable process on boundary layer mixing can be effectively introduced into the global unstructured grid ocean mode, and the simulation effect of the depth of the mixing layer under coarse resolution is remarkably improved.