38 results about "Satellite altimeter" patented technology

The invention discloses an external calibration method of a satellite-borne interferometric synthetic aperture radar (InSAR) system. The external calibration method comprises that sea surface height data is measured by a satellite height gauge, and average sea surface height data is acquired after time varying influence factors are removed; sea surface height data measured by the satellite-borne InSAR system is acquired through sea images and uncalibrated interference parameters of preset time and space distribution acquired by the satellite-borne InSAR system; sea height variation brought about by the time varying influence factors except for the spreading effect under the condition of the preset time and space distribution is calculated in a simulation mode; time varying synthesized sea surface height data is acquired by the average sea surface height and the sea surface height variation brought about by time varying influence factors except for the spreading effect; and the sea surface height data measured by the satellite-borne InSAR system is corrected by taking the time varying synthesized sea surface height data as standard data, thereby realizing calibration of the satellite-borne InSAR system. According to the invention, an external calibration method based on a sea field is adopted, thereby being capable of carrying out long-term dynamic monitoring on slow variation elements in error of the satellite-borne InSAR system.

The invention discloses a high precision water level elevation extraction algorithm comprising simulation of a universal waveform; mutual correlation retracing algorithm of the universal waveform and a Level 1b interference waveform; atmosphere and geophysical correction; elevation computation and exclusion of abnormal values; and water level filtering and noise analysis based on SSA. Due to limitation of a satellite altimeter and complex geographical environment, a Cryosat-2 Level 2 altimeter data product has a lot of data losses and precision losses in an inland or nearshore region, the invention improves an altimeter echo waveform retracing algorithm to extract a high precision wave level sequence product, and thus the defect that inland water body or nearshore height measurement data is small in quantity and not high in precision is remedied.

The invention relates to a parallel-based global ocean mesoscale eddy rapid identification algorithm. The algorithm is based on day-average ocean dynamics field sea level anomaly (SLA) satellite altimeter data, firstly, the high-pass filtering processing of the data is carried out, then the world is divided into several regions, and each region has an overlapped region with a surrounding area. Eddy seed points in each area are identified and an SLA contour is generated, the SLA contour is iterated, and whether an eddy boundary condition is satisfied is judged and the eddy identification is carried out. Based on parallel computing, the eddy identification of multiple regions is carried out at the same time, after the eddy identification of all regions is completed, eddy identification results are merged, repeated eddies of overlapped regions of the adjacent regions are eliminated, and thus the identification of global ocean mesoscale eddies is realized.

Embodiments of the invention provide a satellite altimeter data-based sea level change trend calculation method and apparatus. The method comprises the steps of obtaining an altimeter data set formed by multiple pieces of altimeter data and obtained by multiple satellites in a specified time period; screening the data in the altimeter data set, and screening out effective data from the altimeter data, wherein the effective data includes sea level data; performing calculation according to the effective data to obtain corresponding abnormal sea level data; performing grid processing on the abnormal sea level data of different satellites based on spherical coordinates; fusing gridded data of the satellites; performing calculation according to the fused data to obtain a function of change, with time, of the sea level; and obtaining a calculation result of sea level change at a specified time in the future according to the change function and outputting the calculation result.

The present invention discloses a method for the production of mean sea height products based on satellite altimeter data network function interpolation, which comprises the steps of: data preprocessing, determination of grid size and central point, grid interpolation, and refined interpolation. The method according to the present invention facilitates automatic grid segmentation on the computer, has obvious statistical characteristics, reflect the spatial distribution of the marine gravity field on the physical properties, wherein the marine gravity field is in a positive proportion to mean sea surface height, gives full consideration to the physical characteristics of sea level, and can improve mean sea height products of high accuracy and spatial resolution.

The invention relates to an absolute calibration method for a satellite radar altimeter without a special calibration field and belongs to the technical field of calibration of satellite altimeters. According to the method, an absolute deviation of the satellite radar altimeter can be determined through processing tidal observation data and satellite altimetry data of a satisfactory oceanographic station, adding essential correction entries such as a geoid model, carrying out height datum unification, altimetry data space position interpolation and tidal observation data time interpolation, and finally, carrying out difference comparison on sea surface height of tidal observation and sea surface height of the satellite radar altimeter. Compared with the prior art, the method has the advantages that the special calibration field is not required, the existing oceanographic stations can have calibration capability by using existing business observation data of oceanographic stations of coasts or oceanic islands of our country and a regional accurate geoid, and thus, absolute measured deviations of the satellite altimeters at home and abroad can be acquired.

The invention discloses a method for building a global ionospheric grid VTEC model by GNSS, HY-2 and COSMIC data fusion. The method comprises the following steps of determining time for building the global ionospheric grid VTEC model; determining a GNSS observation station participating in building the global ionospheric grid VTEC model; collecting data: collecting data of the GNSS observation station, ku-waveband ionospheric correction data of an HY-2 satellite altimeter and COSMIC electron density data; determining rights of VTEC G, VTEC H and VTEC C; after iteration is finished, taking an adjustment result X obtained by last iteration as a spherical harmonic function model coefficient of fusion; setting an interval of 5 degrees in a longitude direction [-180 degrees to +180 degrees]; setting an interval of 2.5 degrees in a latitude direction [-87.5 degrees to +87.5 degrees]; and substituting the intervals into a spherical harmonic function model to obtain a global ionospheric grid VTEC value of fusion.

The invention relates to a reanalyzing method of wave long-term data. The method comprises the following steps of constructing wave element (significant wave height and average period) long-term observation data based on a satellite altimeter and wave element long-term simulation hindcasting data based on a wave numerical mode, and conducting space-time registering on the wave element long-term observation data and the wave element long-term simulation hindcasting data; using space-time synchronizing data to analyze and correct the wave element long-term simulation hindcasting data to obtain reanalyzed data of wave elements. Compared with common data assimilation methods, the disclosed new reanalyzing method applied to the wave element long-term simulation hindcasting data is high in the processing efficiency, flexible inintegrated observation data, and is particularly applicable to processing the wave element long-term hindcasting simulation data, and strong support can be provided for study and analysis of the climatic variability and the interannual variation of the wave element and the oceanographic engineering environment.

The invention provides a data processing method and device for a multi-source satellite altimeter, equipment and a storage medium. The data processing method comprises the steps of performing data quality control on data of a to-be-processed satellite altimeter and a Jason-3 satellite altimeter; by taking the Jason-3 satellite altimeter as a reference, performing data unification on height measurement data of the to-be-processed satellite altimeter after data quality control; by taking the Jason-3 satellite altimeter as a reference, performing orbit error correction on the height measurement data of the to-be-processed satellite altimeter after data unification; and calculating sea surface height abnormal data of each satellite altimeter according to the height measurement data after orbit error correction. In the implementation process, before the sea surface height abnormal data of each satellite altimeter is calculated, data unification and orbit error correction of the height measurement data of the satellite altimeters are performed, and the sea surface height abnormal data is calculated according to the height measurement data after orbit error correction, so that the accuracy of a sea surface height abnormal result is greatly improved.

The invention discloses a method for recognizing tidal mixing frontal surface with satellite altimeter along-track data.A non-dimensional momentum equation is obtained through dimensional analysis according to satellite altimeter 20 Hz along-track data, on the sea surface, the second vertical gradient of tidal stress is neglected, the momentum equation obtained in step 1 is subjected to integral from the shore side A to the offshore side B of the frontal surface to obtain an sea surface height SSH, the obtained sea surface height is subjected to EMD decomposition to obtain a series of IMF components, an HHT method is applied to each IMF component, amplitude functions and phase functions of the IMF components are utilized to represent the obtained sea surface height, and therefore the sea surface height is converted to be an Hilbert spectrum equation, so that an Hilbert spectrum of the sea surface height is obtained.The HHT method is utilized to decompose the 20 Hz along-track data into the IMF components, and an SSH saltus step can be recognized clearly through the HHT method.

The invention provides a sea area gravity anomaly inversion method and a system based on satellite height measurement data, and the method comprises the steps: calculating the sea surface height gradient of a sub-satellite point according to the sea surface height data collected by a satellite altimeter at the sub-satellite point in a preset sea area, and calculating the model vertical line deviation gradient of the sub-satellite point according to the position of the sub-satellite point, subtracting the sea surface height gradient of the sub-satellite point from the model vertical line deviation gradient to obtain the residual gradient of the sub-satellite point; converting the residual gradient of the sub-satellite point into a vertical line deviation grid based on a weighted least square method, and obtaining the residual vertical line deviation of the grid; and calculating the residual gravity anomaly of the grid according to the residual vertical line deviation of the grid, calculating the model gravity anomaly of the grid based on the gravity field model, and adding the model gravity anomaly of the grid and the residual gravity anomaly to obtain the sea area gravity anomaly value of the grid. According to the method, the accurate sea area gravity abnormal value is obtained after strict theoretical derivation.

The invention aims to provide a synthesis method of vortex three-dimensional information, and the technical scheme adopted by the invention is as follows: basic data preparation is carried out, wherein the basic data comprises Argo data, climate state temperature salt field data, climate state data and data required by calculation of geostrophic flow and ocean current vertical velocity shearing; and vortex synthesis is carried out. In order to reveal the horizontal and vertical structure of the vortex, a vortex signal detected from satellite altimeter data needs to be combined with Argo temperature and salt data.

The invention discloses a synthetic aperture radar (SAR) sight line direction deformation and gradient direction sensitivity calculation method, which comprises the following steps of: acquiring SAR data and DEM data covering a slope body, and extracting an image local incident angle by utilizing a satellite side-looking imaging principle; carrying out geometric distortion judgment on the ascending rail and the descending rail of the slope body through the local incidence angle, and obtaining the specific positions of geometric distortion areas of orbit rising and orbit falling; according to the collected parameter information of SAR satellite orbit rising and the satellite height, caculating the detection sensitivity of orbit rising when the gradient and the slope direction change, and according to the collected parameter information of SAR satellite orbit falling and the satellite height, calculating the detection sensitivity of orbit falling when the gradient and the slope direction change; and dividing sensitivity distribution by combining the specific position and sensitivity of the geometric distortion area. The method can determine the sensitivity of different slope directions under different track InSAR detection in large-range landslide hidden danger identification in alpine and canyon areas, and has orbit rising and falling detection applicability when the gradient and the slope direction change.

The invention relates to a refined grid data partition construction method and system for mesoscale vortex recognition. The method comprises the following steps: acquiring the occurrence number of mesoscale vortexes of a research sea area in previous years; carrying out zoning processing on the research sea area according to the occurrence number of the mesoscale vortexes in the previous years to generate a plurality of divided areas, wherein the divided area comprises a vortex high-incidence area and a vortex low-incidence area; performing refined construction on the satellite altimeter grid data in each divided region by using a BP neural network to generate grid data subjected to partitioned refined construction; and identifying the mesoscale vortex by using the grid data which is finely constructed by the partitions. The mesoscale vortex recognition efficiency can be improved.

The invention discloses a sea level change prediction method based on satellite altimetry data, and relates to the technical field of satellite altimetry data application. The sea surface height datainterpolation result at the grid center point is calculated by searching the interpolation weight of each sea surface height data within the radius with the determined grid center point as the circlecenter by using the sea surface height long-time series data measured by the satellite altimeter. Then the parameters of seasonal difference autoregressive moving average prediction model are determined at the center of each grid by using the interpolation results of sea surface height data. Finally, the predicted sea surface height at the center of each grid in the study area is calculated basedon the predicted model. By combining the satellite altimetry data with the seasonal difference autoregressive moving average model, the invention can predict the large-scale sea level change.

The invention belongs to the fields of physical ocean, ocean engineering, underwater acoustic engineering and the like, and relates to a method for quickly estimating an underwater sound velocity field of a mesoscale vortex based on satellite altimeter data, which establishes a function relationship between underwater sound velocity disturbance caused by the mesoscale vortex and sea surface height anomaly, and further obtains a parameterized vortex normalized sound velocity disturbance model. On the basis of the parameterized model, by means of satellite altimeter data with high space-time coverage rate, the underwater sound velocity disturbance field of the mesoscale vortex can be quickly inferred, and after a climate state background sound velocity field is superposed, the underwater sound velocity field of the mesoscale vortex is finally obtained, so that quick estimation of the underwater sound velocity field of the mesoscale vortex by only using the satellite altimeter data is realized.

The invention discloses a novel multi-source satellite altimeter fusion method. The method comprises the steps of decomposing a background error covariance matrix B = sigma C sigma by adopting a diagonal matrix and a correlation coefficient matrix based on an optimal algorithm of two-dimensional variation, estimating and constructing background error correlation by utilizing a Gaussian function, fitting Gaussian distribution through a least square method, obtaining background error correlation coefficient scale, adopting a multi-time satellite altimeter to observe the density to determine the fusion duration, and fusing observation data to obtain a fusion product. According to the method, a two-dimensional variational method is taken as a fusion basis, evolution errors of same-orbit observation are considered, background error covariance correlation coefficient scales are selected according to the altimeter observation density, a more reasonable background error correlation coefficient representation function is constructed, changes of background error correlation coefficient scales of different areas are considered, and the fusion field of the previous day is used as the background field, so that more small-scale signals are reserved in the fusion product, and the effective resolution of the fusion product is remarkably improved.

The invention discloses a composite vortex recognition model construction method based on Helmholtz decomposition and deep learning, and the method comprises the following steps: obtaining data of a flow velocity U in the east-west direction and a flow velocity V in the south-north direction of seawater, and constructing a velocity vector field image; performing Helmholtz decomposition on the velocity vector field image to obtain an image with a potential field Fd and an image with a rotation field Fc; according to a potential function and UV data in the rotation field Fc, labeling a single-vortex air vortex, a single-vortex anti-cyclone vortex, a compound-vortex air vortex and a compound-vortex anti-cyclone vortex, and taking rotation field images before and after labeling as a deep learning data set; and training the deep learning data set by using a neural network to obtain a composite vortex recognition model. The Helmholtz decomposition method is used for processing derivative data of the satellite altimeter, the recognition accuracy of the compound vortex is improved, the deep learning method is adopted, the recognition efficiency of the compound vortex is improved, and finally application of the model to the whole sea area is achieved.