88 results about "Sea surface wind" patented technology

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 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 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 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 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 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 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.

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.

This invention discloses a joint inversion method for sea surface wind speed, comprising: jointly inverting observation data from a microwave scatterometer and a spaceborne GNSS-R over an overlapping observation area to obtain a first wind speed inversion result for that area; independently inverting the observation data from the microwave scatterometer to obtain a second wind speed inversion result; expanding the first wind speed inversion result to the second wind speed inversion result to obtain a third wind speed inversion result for the complete observation area of ​​the microwave scatterometer; independently inverting the observation data from a synthetic aperture radar to obtain a fourth wind speed inversion result; and expanding the fourth wind speed inversion result to the third wind speed inversion result to obtain a final joint inversion result. The technical solution provided by this invention performs joint inversion based on observation data from a microwave scatterometer, a spaceborne GNSS-R, and a synthetic aperture radar, thereby obtaining a final joint inversion result with a large observation area, high accuracy, and high resolution.

The application discloses a marine environment data processing method and device and a storage medium. The marine environment data processing method comprises the following steps: receiving marine environment data from a marine environment data acquisition device; determining first effective marine environment data of first marine environment data according to first attitude information at the same time, and determining second effective marine environment data of second marine environment data according to second attitude information at the same time; determining a sea surface wind direction according to the first effective marine environment data and the second effective marine environment data, and determining a marine environment data true value according to the sea surface wind direction; and removing outliers in the marine environment data true value by a preset method, and outputting marine environment data information.

The application discloses a submarine cable burying plough launching and recovering auxiliary device and method, which comprises a crossbeam, a telescopic mechanism, two telescopic cylinders composed of upper cylinders and lower cylinders vertically hinged on the crossbeam, a first automatic telescopic rod vertically hinged between the upper cylinder and the lower cylinder of each telescopic cylinder, a conical cylinder arranged at the lower end of each telescopic cylinder, a hook hinged in each conical cylinder, a cable connected to the hook, the cable drawn out from the telescopic cylinder or the conical cylinder, an adjusting mechanism comprising a second automatic telescopic rod obliquely hinged between the crossbeam and the upper cylinder, and a stabilizing rod, the lower end of which is used for fixing the burying plough, and the upper end of which is provided with a ring hole for locking or unlocking the hook. The application overcomes the problem that the burying plough cannot be safely launched and recovered in the environment with large waves on the sea.

The application relates to a GNSS-R and GNSS-S radar combined sea surface high wind speed inversion method and a satellite-borne receiving system, and the method comprises the following steps: S1, acquiring space-time reflection signals and scattering signals under high wind speed conditions, and performing full-link sampling on the reflection signals and the scattering signals; S2, performing delay Doppler mapping processing on the reflection signals to generate a sea surface delay Doppler map; performing double-station SAR imaging processing on the scattering signals to generate a Q-amplitude GNSS-S radar image with different double-station angles; S3, extracting statistical feature vectors of the sea surface delay Doppler map and the Q-amplitude GNSS-S radar image with different double-station angles, and forming a combined feature X; and S4, inputting the combined feature X into a pre-trained deep neural network to output a sea surface wind speed result. The application can fully utilize the characteristics of GNSS reflection signals and scattering signals, and effectively improve the precision of sea surface high wind speed inversion.

The present invention comprises a hull (110), a sensor unit (120) having a lidar sensor (121) for measuring wind conditions and an information processing module (122) for processing wind condition information of the sea measured by the lidar sensor (121), a communication unit (130), a first power generation unit (140) composed of a wind turbine (141), a solar panel (142), and a lead-acid battery (143), a second power generation unit (150) composed of a seawater secondary battery (153), a BMS power control unit (160) including a plurality of battery packs (161) for storing power generated from the seawater secondary battery (153), a first charge / discharge unit (170), a second charge / discharge unit (180), and power from the sensor unit (120) and the communication unit (130) for which a relatively rapid response is required through the first charge / discharge unit (170) or the second charge / discharge unit (180). A linear type lidar buoy for ocean observation is disclosed, equipped with a hybrid power supply system that implements redundancy for power storage and power supply, including a main control unit that applies power from a lead-acid battery (143) and, through a BMS power control unit (160), uses power generated by a seawater secondary battery (153) as power that requires relatively long-term storage.

The application discloses a global average sea clutter near real-time estimation method, system, device and storage medium, including the following steps: obtaining multi-source satellite-borne radar historical backscattering coefficient data, and performing time and space matching and binning on the ERA5 wind field; an average sea clutter estimation model is established according to a geophysical model function fitting; the satellite-borne microwave scatterometer wind field is corrected in deviation and fused with a numerical weather prediction wind field, so that a high-coverage, continuous near real-time global sea surface wind field is obtained; the relative azimuth is calculated in combination with radar observation parameters and fused wind direction, and global average sea clutter data is obtained by using the model; the application improves the precision and application range of sea clutter estimation under the condition of medium and small incident angles, provides a high-quality input wind field through a multi-source wind field fusion technology, realizes rapid, stable and near real-time estimation of global average sea clutter, has high timeliness and business feasibility, and can meet the needs of near real-time sea clutter monitoring and ocean remote sensing application.

The invention relates to an SAR sea surface wind speed inversion method based on deep learning, and the method comprises the steps: carrying out the space-time matching of a preprocessed SAR image and reanalysis data ERA5, and extracting the sea surface wind speed data and significant wave height data of the reanalysis data ERA5; constructing a double-input wind speed inversion model based on a convolutional neural network CNN and a channel attention mechanism; the wind speed inversion model comprises an SAR image input branch and a significant wave height input branch which are parallel to each other; training the model and outputting a sea surface wind speed inversion result; the performance of the wind speed inversion model is evaluated by adopting the actually measured wind speed data observed by the NDBC buoy and comparing the error between the predicted wind speed and the actually measured wind speed of the buoy. According to the method, sea surface wind speed inversion can be realized through the SAR image and the corresponding ERA5 effective wave height, so that errors caused by inaccurate wind direction information are avoided, the operation process of wind speed inversion is simplified, the degree of dependence on external data is reduced, and the practicability and flexibility of the method are improved.

The application discloses a kind of anti-rainfall wind speed inversion methods based on same satellite platform GNSS-R and dual-frequency scatterometer, utilize same platform dual-frequency scatterometer data and synchronous precipitation data, construct dual-band rainfall detection model, identify the rainfall area in the observation width of scatterometer;In the identified rainfall area, the observation point that coincides with same platform GNSS-R space-time is screened, and the anti-rainfall wind speed inverted by GNSS-R is used as reference true value, trains dual-band wind speed inversion model specially used in rainfall condition;The trained model is applied to the entire scatterometer observation width, and the unit determined as rainfall is corrected for wind speed, and the non-rainfall unit is inverted using the standard model, so as to generate high-precision, full-coverage sea surface wind field product under rainfall condition.The application makes full use of the anti-interference characteristics of GNSS-R under rainfall condition, the wide-width detection capability of dual-frequency scatterometer and the advantages of synchronous observation time of same platform, and improves the precision, reliability and spatial integrity of wide-width sea surface wind field product under complex weather.

The invention discloses a floating type fan integrated coupling simulation method and system under the typhoon sea condition. The method comprises the steps that a Holland theoretical model wind field and an ECMWF sea surface wind field are adopted to be overlapped to construct a mixed wind field; a typhoon wave model is established by taking the mixed wind field as an input wind field of the SWAN model and combining actual measurement topographic data of the global topographic data set, so that typhoon wave spectrums in different time periods of the typhoon landing process are obtained; according to the typhoon sea wave spectrum, generating a typhoon sea wave time sequence by utilizing irregular wave linear theory superposition; simulating a three-dimensional time-domain turbulent wind field according to the maximum wind speeds of different time periods in the typhoon landing process; and inputting the turbulent wind field, the typhoon sea wave time sequence and the hydrodynamic coefficient into OpenFAST software, setting a simulation working condition to carry out wind and wave coupling integrated simulation, and obtaining structural response and motion response time history data. The method has the advantages of improving the calculation precision and efficiency of the floating type wind power load and the like.

The application provides an hourly sea surface wind field fusion method and system based on optical flow time displacement correction, and belongs to the technical field of marine remote sensing and meteorological data assimilation. The application can fundamentally solve the registration problem of asynchronous observation time, and on the basis of obtaining multi-source satellite sea surface wind field data and hourly background wind field data in a global or regional range, standardization preprocessing, multi-source data consistency correction and weight combination are performed. An hourly background wind field is taken as a dynamic reference to construct a time displacement correction model based on an optical flow algorithm, and a two-dimensional displacement vector field of each hour from a satellite observation time to a target time is calculated. The multi-source satellite wind field is projected from the original observation time to the target hourly time by using the displacement vector field, and time registration of the wind field structure is completed. The multi-source satellite wind field subjected to time displacement correction is fused with the hourly background wind field to generate a regular gridded hourly sea surface wind field product covering the global or regional range.

This invention provides a method and system for assimilating tropical cyclone wind speeds using spaceborne GNSS-R, comprising: partitioning the tropical cyclone (TC) structure based on a matching dataset of massive historical SAR and ERA5 reanalysis data, and statistically decoupling the radial and tangential correlation scales of the wind corona discharge (BEC) within each partition; establishing an empirical mapping model between BEC statistics and key TC parameters; and driving the empirical model to construct a flow-dependent anisotropic BEC matrix based on real-time TC parameters provided by the international best path archive, and introducing it into a variational assimilation system to assimilate multi-source spaceborne GNSS-R observation data, reconstructing a high-resolution TC sea surface wind field analysis field. This invention, starting from a physical mechanism, effectively suppresses the gradient smoothing effect of background errors in the eyewall region, reducing the root mean square error of the background field by 33% under extreme wind speeds, significantly improving the engineering and operational capabilities of spaceborne GNSS-R data in TC intensity and structure monitoring.

The application discloses a kind of physical guiding multi-source fusion's GNSS-R sea surface wind speed inversion method, comprising: obtaining time series DDM two-dimensional features and one-dimensional auxiliary features;The time series DDM two-dimensional features and auxiliary features are input to sea surface wind speed inversion model, obtain inversion result, the model includes input module, feature extraction module, feature fusion module and physical guiding output module;Feature extraction module utilizes multi-scale convolution to extract DDM scattering feature, and generates conditional parameters by auxiliary features through FiLM, channel level dynamic guidance is carried out to DDM feature extraction process, to enhance the adaptive representation capability of model;Feature fusion module is used to convert sea surface scattering structure feature and condition vector into uniform dimension feature sequence, obtain global fusion feature by global aggregation and cross-scale interaction;Physical guiding output module is used to obtain high-precision sea surface wind speed inversion result according to physical wind speed benchmark item and wind speed residual correction amount.

The application provides a kind of offshore wind farm hub height wind speed calculation method, it is related to offshore wind resource evaluation technical field. Including: obtaining the satellite SAR of target sea area is retrieved sea surface wind speed, and simultaneously in the wind farm area is arranged not less than three floating laser radar to measure high altitude three-dimensional wind speed profile, the two kinds of data sources are matched in time and space, to the data after time and space matching constructs training sample set, and with sea surface wind speed, atmospheric stability, sea surface roughness as input characteristics, hub height wind speed as output label, trains machine learning model, the global SAR sea surface wind speed data is batch calculated to hub height, finally generates the hub height wind resource atlas covering the whole target sea area;Data source level realizes the organic complement of planar sea surface wind field and point vertical profile, improves the accuracy of data, improves the calculation accuracy, improves the accuracy and reliability from sea surface to hub height wind speed calculation.

The invention provides an hour-by-hour sea surface wind field fusion method and system based on optical flow time displacement correction, and belongs to the technical field of ocean remote sensing and meteorological data assimilation. According to the method, the problem of asynchronous observation time registration can be fundamentally solved, and on the basis of obtaining global or regional multi-source satellite sea surface wind field data and hourly background wind field data, standardized preprocessing and multi-source data consistency correction and weight combination are performed; an hour-by-hour background wind field is used as a dynamic reference, a time displacement correction model based on an optical flow algorithm is constructed, and an hour-by-hour two-dimensional displacement vector field from a satellite observation moment to a target moment is calculated; projecting a multi-source satellite wind field from an original observation moment to a target hour-by-hour moment by using the displacement vector field to complete time registration of a wind field structure; and fusing the multi-source satellite wind field subjected to time displacement correction with the hourly background wind field to generate a regular gridding hourly sea surface wind field product covering the whole world or region.