Hourly sea surface wind field fusion method and system based on optical flow time displacement correction

Abstract

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.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of ocean remote sensing and meteorological data assimilation, and particularly discloses a method and system for fusing hourly sea surface wind field based on optical flow time displacement correction by using multi-source satellite remote sensing data. BACKGROUND

[0002] The sea surface wind field is a key dynamic factor for driving ocean circulation, affecting sea-air exchange and regulating the global climate system. Satellite remote sensing, especially scatterometers and microwave radiometers, has become an irreplaceable means for obtaining global high-resolution sea surface wind fields. In order to overcome the limitations of single satellite observation in time and space coverage, the fusion of multi-source satellite observation data to generate high-quality wind field products with continuous time and space has become an important research direction in the field of ocean remote sensing.

[0003] The existing multi-source satellite wind field fusion methods, such as objective analysis (OA), optimal interpolation (OI) and variational assimilation technology, have been widely used in sea surface wind field data sets, such as the Objectively Analyzed Air-Sea Fluxes (OAFlux), the daily ASCAT (DASCAT), the National Centers for Environmental Information Blended Sea winds (NBS) and the Cross-Calibrated Multi-Platform (CCMP) wind field products. The above technical means can effectively improve the spatial coverage and overall accuracy of the fusion product through mathematical weighted average or constrained optimization. However, there is a common limitation in the above technical means: most of them regard the satellite wind field as a quasi-static field, and use static or quasi-static interpolation scheme within the time and space window, without fully considering and correcting the spatial displacement of the sea surface wind field structure caused by the wind field advection motion within the observation time interval.

[0004] The neglect of dynamic evolution in the prior art leads to errors in representing the instantaneous state of the wind field. When fusing satellite observations at different times, mesoscale features such as fronts and cyclones in the wind field have already moved significantly in space within the observation gap. The existing fusion algorithm cannot trace or predict this movement, and the boundaries of the dynamic features in the fused wind field product will become blurred, the position will deviate, and even false structures will be generated, resulting in time and space matching errors and distortion of dynamic features, which limits the application value of such data products in the study of mesoscale and sub-mesoscale ocean processes sensitive to time evolution.

[0005] An ideal wind field fusion algorithm should be able to correct the observation data at different times to a reference time, thereby generating a physically coordinated global or regional sea surface wind field instantaneous snapshot. Although this need is obvious, the technical solution that can effectively realize the time displacement correction of the wind field structure and successfully apply to the hourly scale fusion of global or regional business generation is still blank at present. SUMMARY

[0006] Therefore, the design purpose of the present application is to provide an hourly sea surface wind field fusion method and system based on optical flow time displacement correction, thereby fundamentally solving the registration problem of asynchronous observation time, generating a truly hourly, global or regional sea surface wind field product with high spatio-temporal consistency by dynamically correcting the advection displacement of satellite wind field observation data at different times, so as to realize the technical effects of correcting the spatial displacement of wind field structure caused by the difference in observation time, eliminating the spatio-temporal matching error and the distortion of dynamic characteristics.

[0007] To achieve the above-mentioned purpose, the hourly sea surface wind field fusion method based on optical flow time displacement correction is based on the acquisition of multi-source satellite sea surface wind field data and hourly background wind field data in global or regional range, standardization preprocessing and multi-source data consistency correction and weight combination; taking the hourly background wind field as a dynamic reference, a time displacement correction model based on optical flow algorithm is constructed to calculate the two-dimensional displacement vector field from the satellite observation time to the target time; using the displacement vector field, the multi-source satellite wind field is projected from its original observation time to the target hourly time to complete the time registration of the wind field structure; the multi-source satellite wind field after 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;

[0008] The fusion method comprises the following steps:

[0009] Step S101, data acquisition and preprocessing;

[0010] Global or regional multi-source satellite sea surface wind field data are acquired; background wind field data corresponding to the spatio-temporal range with a time resolution not lower than hourly and a spatial resolution of 0.25°×0.25° are acquired as a dynamic reference field for subsequent processing; all data are subjected to quality control and spatial resampling to 0.25°×0.25° standard grid, and the data are subjected to cross calibration and weight combination based on error analysis;

[0011] Step S102, time displacement correction model construction;

[0012] With the hourly background wind field as the dynamic reference sequence, the wind vector field is regarded as a continuous image frame, the optical flow algorithm is applied to calculate the two-dimensional displacement vector field of the wind field characteristics from the observation time to the target reference time, and a time displacement correction model based on the wind field structure and approximately conserving the Lagrange in a short time scale is constructed. The model is a dynamic reference model capable of quantifying and correcting the wind field advection displacement;

[0013] Step S103, satellite wind field time displacement correction;

[0014] Using the displacement vector field constructed in step S102, each satellite combined wind field is corrected by spatial translation to project to the preset hourly reference time from its original observation time for time registration, so as to obtain the satellite wind vector at the target reference time; the displacement of all available satellite wind fields in the time window is corrected to generate a set of multi-source satellite wind field data accurately registered at the reference time;

[0015] Step S104, sea surface wind field fusion;

[0016] The satellite combined wind field data at the same reference time after time displacement correction is fused to generate a grid fusion product of the multi-source satellite wind field data combined with the hourly background field by using a spatial data fusion algorithm; the fusion algorithm is an optimal interpolation method or a variational assimilation method.

[0017] The step S101 uses coordinated universal time UTC as the time standard.

[0018] In the step S101, the weight combination is based on the accuracy index obtained by comparing each data source with the reference true value , the fusion weight is calculated , so as to realize the preliminary optimal combination of multi-source satellite data by weighted average, and form an initial fusion background field;

[0019] The weighted calculation formula is:

[0020] (1)

[0021] Wherein, X represents the comprehensive wind after weighted calculation, the unit is m / s; n represents the total number of satellite data sets participating in fusion; i represents the index of the satellite data set; represents the wind speed or wind vector component value of the i th satellite data set; represents the weight value of the i th satellite data set; represents the relative accuracy index of the i th satellite data, which is obtained by error measurement of different data comparison; j represents the virtual index used in summation calculation.

[0022] The step S102, the optical flow algorithm is used for solving the globally smooth displacement field by minimizing the optical flow constraint equation, including taking the background wind field corresponding to the satellite acquisition time as the reference field, applying the optical flow algorithm to the data of the corresponding observation time and the target time in the reference wind field sequence, estimating the globally smooth reference displacement vector field by minimizing the energy function containing the brightness constant constraint and the smoothing constraint, and the reference displacement vector field is used for quantifying the advection motion of the wind field in the observation gap.

[0023] The step S102, the optical flow algorithm is a multi-scale pyramid Horn-Schunck;

[0024] The U and V components of the wind field and the wind speed scalar field are regarded as continuous image frames, the optical flow constraint equation of the background wind field sequence is minimized, and a globally smooth displacement vector field from the observation time to the target time is solved , wherein the observation time , and the calculation formula is:

[0025] (2)

[0026] , wherein, : the east-west component of the displacement vector field; : the north-south component of the displacement vector field; : represents the optical flow calculation function, which uses the wind field data at two times to calculate the displacement vector field; : the background wind field at position p and time t; the optical flow field is estimated by minimizing the following energy functional:

[0027] (3)

[0028] , wherein, represents the optical flow energy functional, the objective function to be minimized; , , respectively represent the partial derivatives of the wind field intensity in the spatial east-west direction (x) and the north-south direction (y) and the time t; u and v are the east-west component and the north-south component of the optical flow field to be solved; is a weight coefficient for controlling the smoothness of the displacement field; and represent the gradient modulus square of the u component and the v component of the displacement field; is the spatial east-west and north-south direction integral unit.

[0029] Further, through the multi-scale pyramid solving strategy from coarse to fine, the time displacement correction model calculates the two-dimensional displacement vector field from any time in the window to the target time To quantify the trajectory of the advection motion of the wind field structure within the observation gap.

[0030] The application is a new hourly sea surface wind field fusion system based on optical flow time displacement correction, which comprises:

[0031] 1) Data acquisition and preprocessing module;

[0032] It is used to acquire multi-source satellite sea surface wind field data and hourly background wind field data of global or specified area, and to perform standardization preprocessing, multi-source data consistency correction and weight combination;

[0033] 2) Time displacement correction model construction module;

[0034] It is used to construct a time displacement correction model based on optical flow algorithm with the hourly background wind field as a dynamic reference, to eliminate the spatial displacement of satellite wind field structure caused by observation time difference;

[0035] 3) Satellite wind field time displacement correction module;

[0036] It is used to project multi-source satellite wind field from its original observation time to the target hourly time by using the displacement vector field, to realize accurate registration of wind field structure in time dimension;

[0037] 4) Sea surface wind field fusion module;

[0038] It is used to fuse the multi-source satellite wind field after time displacement correction with the hourly background wind field, to generate hourly sea surface wind field product with high spatiotemporal consistency.

[0039] The application proposes a new computer device comprising one or more processors and storage devices, the storage devices storing one or more programs, when the one or more programs are executed by the one or more processors, the above-mentioned sea surface wind field fusion method based on optical flow time displacement correction is implemented.

[0040] The application proposes a new computer readable storage medium, which stores one or more executable programs, when the programs are executed by the processor of the computer device, the above-mentioned sea surface wind field fusion method based on optical flow time displacement correction is implemented.

[0041] In summary, the application has the following beneficial effects and advantages compared with the prior art:

[0042] The application has high spatiotemporal consistency and physical authenticity. By introducing an optical flow algorithm and dynamically correcting the wind field advection displacement caused by observation time difference in the business process, the inherent defects of the existing static interpolation method can be effectively overcome. The generated hourly fusion wind field product can accurately reproduce the instantaneous structure and evolution process of mesoscale weather systems (such as tropical cyclones).

[0043] The application improves data coverage and accuracy. By setting a 7-hour time window and performing effective time registration, the effective utilization rate of satellite data can be improved, and the global or regional data coverage rate can reach more than 70%. At the same time, the weight combination based on error analysis and the optical flow dynamic correction together improve the accuracy of the final fusion product.

[0044] The application has good technical universality and practicality. The proposed fusion method and system have reliable algorithms, are easy to program, and can stably generate hourly sea surface wind field products with wide coverage, high spatiotemporal resolution, and accurate dynamic characteristics, providing key data support for higher requirements of marine meteorological research, weather forecasting, and climate monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0045] The drawings described herein are used to provide further understanding of the application, and form a part of the application. The schematic embodiments of the application and their descriptions are used to explain the application, and do not constitute an improper limitation on the application. Some specific embodiments of the application will be described in detail below with reference to the drawings in an exemplary and non-limiting manner. The same reference signs in the drawings indicate the same or similar components or parts. Those skilled in the art should understand that the drawings are not necessarily drawn to scale.

[0046] Figure 1 is a flow chart of the hourly sea surface wind field fusion method based on optical flow time displacement correction described in the application;

[0047] Figure 2 is an architecture diagram of the hourly multi-source satellite sea surface wind field fusion system based on optical flow time displacement correction described in the application. DETAILED DESCRIPTION

[0048] To make the purpose, technical solutions and advantages of the embodiments of the application clearer, the technical solutions of the application will be described clearly and completely below with reference to the drawings. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0049] The following embodiments of the present application aim to solve the technical bottleneck that the existing sea surface wind field fusion product cannot correct the wind field advection motion in the observation gap due to the use of static or quasi-static interpolation method, resulting in spatial displacement of wind field structure, increase of time and space matching error and distortion of dynamic characteristics. By introducing the optical flow time displacement correction, the present application realizes high-precision time registration of different time satellite observations, and finally generates hourly, global or regional coverage sea surface wind field fusion product with high time and space consistency.

[0050] As shown in Figure 1 The hourly sea surface wind field fusion method based on optical flow time displacement correction proposed in the present application, on the basis of obtaining multi-source satellite sea surface wind field data and hourly background wind field data in global or regional range, carries out standardized preprocessing and multi-source data consistency correction and weight combination; taking the hourly background wind field as a dynamic reference, a time displacement correction model based on optical flow algorithm is constructed to calculate the two-dimensional displacement vector field from the satellite observation time to the target time; using the displacement vector field, the multi-source satellite wind field is projected from its original observation time to the target hourly time to complete the time registration of wind field structure; the multi-source satellite wind field after time displacement correction is fused with the hourly background wind field to generate regular gridded hourly sea surface wind field product covering global or regional range.

[0051] Specifically, the following steps are included:

[0052] Step S101, data acquisition and preprocessing;

[0053] Obtain multi-source satellite sea surface wind field data covering global or target regional marine range;

[0054] Obtain background wind field data corresponding to the time and space range, with time resolution not less than hourly and spatial resolution of 0.25°×0.25°, as a dynamic reference field for subsequent processing;

[0055] Perform quality control and spatial resampling to 0.25°×0.25° standard grid on all data, and perform cross calibration and weight combination based on error analysis on the data.

[0056] Specifically,

[0057] The sea surface wind field data of scatterometers of multiple satellites in the global range and the sea surface wind speed or wind field data retrieved by microwave radiometers and other remote sensing are obtained by taking coordinated universal time (UTC) as the time standard, wherein the sea surface wind field data of HSCAT scatterometers of HY-2B / C / D series satellites, the CSCAT scatterometer wind field of CFOSAT satellite, the ASCAT scatterometer wind field of MetOp-A / B / C series satellites, and the sea surface wind speed or wind field data retrieved by SSMIS radiometers from DMSP F16 / 17 / 18 satellites, AMSR2 radiometers of GCOM-W1 satellite, GMI radiometers of GPM satellite, MIRAS radiometers of SMOS satellite, and L-band radiometers of SMAP satellite are included but not limited to.

[0058] For data from different sources, in order to ensure consistency in the spatial reference, all satellite data and background data are uniformly resampled to a grid size of 0.25°×0.25° by using a bilinear interpolation method, the spatial range of the cell center is continuously extended from 0.125 degrees east to 0.125 degrees west (i.e. 0.125° to 359.875°) in longitude and from 89.875 degrees south to 89.875 degrees north (i.e. -89.875° to 89.875°) in latitude, forming a map of 1440 columns and 720 rows; strict quality control is performed on all satellite data, and abnormal values and invalid data points obviously beyond the reasonable range are removed.

[0059] Consistency correction of multi-source data is performed, that is, high-precision background wind field and global buoy observation data are used as reference true values to cross-calibrate different satellite loads and eliminate system bias.

[0060] The weight combination is based on the accuracy index obtained by comparing each data source with the reference true value , and the fusion weight is calculated to realize the preliminary optimal combination of multi-source satellite data by weighted average, and form an initial fusion background field;

[0061] The weighted calculation formula is:

[0062] (1)

[0063] Wherein, X represents the comprehensive wind after weighted calculation, the unit is m / s; n represents the total number of satellite data sets participating in fusion; i represents the index of satellite data set; represents the wind speed or wind vector component value of the i th satellite data set; represents the weight value of the i th satellite data set; represents the relative accuracy index of the i th satellite data, which is obtained by error measurement of different data comparison; j represents the virtual index used in summation calculation.

[0064] Step S102, time displacement correction model construction;

[0065] With the hourly background wind field as a dynamic reference sequence, the wind vector field is regarded as a continuous image frame, the optical flow algorithm is applied to calculate the two-dimensional displacement vector field of the wind field feature from the observation time to the target reference time, and a time displacement correction model based on the wind field structure and the approximate Lagrangian conservation in the short time scale is constructed. The model is a dynamic reference model capable of quantifying and correcting the wind field advection displacement;

[0066] Specifically, the optical flow algorithm solves the globally smooth displacement field by minimizing the optical flow constraint equation, including taking the background wind field corresponding to the satellite acquisition time as the reference field, applying the optical flow algorithm to the data of the corresponding observation time and target time in the reference wind field sequence, and estimating the globally smooth reference displacement vector field by minimizing the energy function containing the brightness constant constraint and the smoothing constraint. The reference displacement vector field is used to quantify the advection motion of the wind field in the observation gap;

[0067] For example, taking the generation of the fusion product of the target time T (for example, UTC 12:00) as an example, a sliding window with T as the center and a time span of 7 hours (i.e. from T-3 hours to T+3 hours) is automatically selected;

[0068] Read the hourly background wind field sequence of the seven whole point times (i.e. UTC 09:00, 10:00, 11:00, 12:00, 13:00, 14:00, 15:00) in the window;

[0069] The U and V components of the wind field and the wind speed scalar field are regarded as continuous image frames, and optical flow algorithms such as multi-scale pyramid Horn-Schunck are applied;

[0070] Minimize the optical flow constraint equation of the background wind field sequence to obtain the globally smooth displacement vector field from the observation time to the target time , the observation time , and the calculation formula is:

[0071] (2)

[0072] Wherein, : the east-west component of the displacement vector field; : the north-south component of the displacement vector field; : an optical flow calculation function that uses wind field data at two times to calculate the displacement vector field; : the background wind field at position p and time t;

[0073] The light flow algorithm is a multi-scale pyramid Horn-Schunck algorithm, and the light flow field is estimated by minimizing the following energy functional:

[0074] (3)

[0075] wherein, represents a light flow energy functional, a target function to be minimized; , , represents the partial derivative of the wind field intensity in the spatial east-west direction (x) and the north-south direction (y) and the time t; u and v are the east-west component and the north-south component of the light flow field to be solved; is a weight coefficient for controlling the smoothness of the displacement field; and represent the gradient modulus square of the displacement field u component and the v component; is the spatial east-west and north-south direction integral unit.

[0076] Through the multi-scale pyramid solving strategy from coarse to fine, the time displacement correction model calculates the two-dimensional displacement vector field from any time in the window to the target time , accurately quantifies the trajectory of the advection motion of the wind field structure in the observation gap;

[0077] Step S103, satellite wind field time displacement correction;

[0078] Using the displacement vector field constructed in step S102, each satellite combined wind field is corrected by spatial translation from its original observation time to project to the preset hourly reference time for time registration, so as to obtain the satellite wind vector at the target reference time after time registration, thereby eliminating the spatial misplacement of the satellite wind field structure caused by the observation time difference; the displacement correction is performed on all available satellite wind fields in the time window, and a group of multi-source satellite wind field data accurately registered at the reference time is generated;

[0079] Specifically, first, the original observation time of each satellite combined wind field is identified, and for the optimization processing flow, the satellite data acquired within UTC 12:00 ± 0.5 hours is preliminarily identified as the initial field representing the instantaneous state of UTC 12:00; for each satellite combined wind field with the observation time falling within the 7-hour window, the corresponding displacement vector field is called by the system;

[0080] Subsequently, the satellite wind vector field is implemented with reverse spatial transformation; for the satellite wind vector acquired at the observation time , utilize the displacement vector field , by inverse spatial transformation, it is along the trajectory of the reverse advection correction to the target time , thereby obtaining the time registration after the correction of the wind vector ; this process is represented as:

[0081] (4)

[0082] By repeating the above operation on all available satellite wind fields within the time window , a set of multi-source satellite wind field data accurately registered in space at the target reference time T is generated, fundamentally eliminating the spatial misplacement of wind field structure caused by observation time differences;

[0083] Step S104, sea surface wind field fusion;

[0084] The satellite combined wind field data at the same reference time after time displacement correction is fused to generate a grid fusion product of multi-source satellite wind field data combined with hourly background field by using a spatial data fusion algorithm; the fusion algorithm is an optimal interpolation method or a variational assimilation method.

[0085] Specifically, under this framework, the observations of different satellites are regarded as independent samples with different observation errors, and the background field and its error covariance are regarded as prior information. By minimizing the difference between the analysis field and the background field and the observation field, the optimal wind field analysis solution is solved on the 0.25°×0.25° standard latitude-longitude grid.

[0086] The system standardizes and formats the fusion analysis results to generate the final product of the regular gridded hourly sea surface wind field covering the global or specified area. This product has unprecedented spatiotemporal consistency and physical authenticity, and can accurately capture the instantaneous fine structure and evolution process of mesoscale weather systems such as tropical cyclones.

[0087] In summary, the above-mentioned sea surface wind field fusion method based on optical flow time displacement correction proposed in the present application is significantly superior to the traditional static interpolation method in terms of maintaining the integrity of the wind field dynamics, and has obvious improvement in spatiotemporal consistency compared to the original background data, and the demand for computing resources is relatively low. It should be noted that although the present embodiment mainly takes sea surface wind field fusion as an example for illustration, the core idea of optical flow time displacement correction described in the present application is also applicable to the spatiotemporal fusion and reconstruction of other sea surface element fields with advection motion characteristics, such as sea wave field, sea surface temperature field, etc.

[0088] To realize the above-mentioned hourly sea surface wind field fusion method based on optical flow time displacement correction, the present application proposes the following hourly multi-source satellite sea surface wind field fusion system based on optical flow time displacement correction, comprising:

[0089] 1) data acquisition and preprocessing module;

[0090] The module is used for acquiring multi-source satellite sea surface wind field data and hourly background wind field data in a global or specified area, and performing standardization preprocessing and multi-source data consistency correction; it comprises:

[0091] The data acquisition submodule is configured to acquire original wind field data in a global or regional range from scatterometers, microwave radiometers of multiple satellites and background data sources; wherein, in order to meet the physical assumption that the wind field structure is approximately Lagrangian conservative in a short time scale and ensure data coverage, the interval between the observation time and the target time of the acquired satellite data is within a preset 7-hour time window;

[0092] The grid standardization submodule is configured to uniformly resample the original wind field data to a standard latitude and longitude grid of 0.25°x0.25°;

[0093] The consistency correction submodule is configured to cross-calibrate multi-source satellite data, and perform weight calculation and weighted fusion based on the relative accuracy index of each data source to form the satellite wind field after consistency correction.

[0094] 2) time displacement correction model construction module;

[0095] The module is used for constructing a time displacement correction model based on an optical flow algorithm with the hourly background wind field as a dynamic reference, to eliminate the spatial displacement of the satellite wind field structure caused by the difference in observation time; it comprises:

[0096] The reference field sequence acquisition submodule is configured to select a continuous hourly background wind field sequence in a sliding window with a time span of 7 hours centered on the target time;

[0097] The displacement field calculation submodule is configured to apply the optical flow algorithm to solve the reference displacement vector field from each observation time to the target time by minimizing the optical flow constraint equation of the background wind field sequence.

[0098] 3) satellite wind field time displacement correction module;

[0099] The module is used for projecting the multi-source satellite wind field from its original observation time to the target hourly time using the displacement vector field, to realize accurate registration of the wind field structure in the time dimension; it comprises:

[0100] The space-time mapping submodule is configured to identify the original observation time of the satellite wind field and determine its belonging to the 7-hour correction time window centered on the target time T;

[0101] a wind field projection sub-module configured to utilize the displacement vector field to correct satellite wind vectors from their original spatial positions by inverse spatial transformation, thereby obtaining satellite wind vectors time-registered at the target time T.

[0102] 4) a sea surface wind field fusion module;

[0103] The module is used to fuse the time-displacement-corrected multi-source satellite wind field with the hourly background wind field, thereby generating an hourly sea surface wind field product with high spatiotemporal consistency; the module includes:

[0104] a data assimilation sub-module configured to utilize an optimal interpolation method or a variational assimilation method to fuse and analyze the time-registered multi-source satellite wind field data and the background wind field on the 0.25°×0.25° standard latitude-longitude grid;

[0105] a product generation sub-module configured to format the fusion analysis result for output, thereby generating a regularly gridded hourly sea surface wind field fusion product covering the whole globe or a specified region.

[0106] The present application proposes a novel computer device including one or more processors and a storage device, the storage device storing one or more programs, when the one or more programs are executed by the one or more processors, the above-mentioned sea surface wind field fusion method based on optical flow time-displacement correction is implemented.

[0107] The present application proposes a novel computer-readable storage medium, which stores one or more executable programs, when the programs are executed by the processor of a computer device, the above-mentioned sea surface wind field fusion method based on optical flow time-displacement correction is implemented.

[0108] The above-mentioned embodiments are only used to help understand the method of the present application and its core idea; meanwhile, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range can be changed. The content of the present description should not be understood as a limitation of the present application.

Claims

1. An hourly sea surface wind field fusion method based on optical flow time displacement correction, characterized in that: On the basis of acquiring multi-source satellite sea surface wind field data and hourly background wind field data in a global or regional range, standardization preprocessing and consistency correction and weight combination of multi-source data are performed; taking the hourly background wind field as a dynamic reference, a time displacement correction model based on an optical flow algorithm is constructed, and a two-dimensional displacement vector field from a satellite observation time to a target time is calculated; Using the displacement vector field, the multi-source satellite wind field is projected from its original observation time to the target hourly time, and time registration of the wind field structure is completed; the multi-source satellite wind field after time displacement correction is fused with the hourly background wind field, and a regular gridded hourly sea surface wind field product covering the global or regional range is generated; The method comprises the following steps, Step S101, data acquisition and preprocessing; Multi-source satellite sea surface wind field data covering the global or target regional marine range are acquired; background wind field data corresponding to the space-time range, with a time resolution not lower than hourly and a spatial resolution of 0.25°*0.25°, are acquired as a dynamic reference field for subsequent processing; all data are subjected to quality control and spatial resampling to a standard grid of 0.25°*0.25°, and cross calibration and weight combination based on error analysis are performed on the data; Step S102, construction of a time displacement correction model; Taking the hourly background wind field as a dynamic reference sequence, the wind vector field is regarded as a continuous image frame, the optical flow algorithm is applied to calculate a two-dimensional displacement vector field of the wind field features from the observation time to the target reference time, a time displacement correction model based on the wind field structure and approximately conserving Lagrange in a short time scale is constructed, and the model is a dynamic reference model capable of quantifying and correcting the wind field advection displacement; Step S103, time displacement correction of satellite wind field; Using the displacement vector field constructed in step S102, each satellite combined wind field is time-registered by spatial translation correction from its original observation time to the preset hourly reference time, so as to obtain a satellite wind vector time-registered at the target reference time; all available satellite wind fields in a time window are subjected to displacement correction, and a group of multi-source satellite wind field data accurately registered at the reference time is generated; Step S104, sea surface wind field fusion; The satellite combined wind field data at the same reference time after time displacement correction is fused by using a spatial data fusion algorithm to generate a gridded fusion product combining the multi-source satellite wind field data and the hourly background field; the fusion algorithm is an optimal interpolation method or a variational assimilation method.

2. The hourly sea surface wind field fusion method based on the time displacement correction of optical flow according to claim 1, characterized in that: In step S101, coordinated universal time (UTC) is used as the time standard.

3. The hourly sea surface wind field fusion method based on the optical flow time displacement correction according to claim 1, characterized in that: The step S101 is that the weight combination is based on the accuracy index obtained by comparing each data source with the reference true value , and calculating the fusion weight to realize the preliminary optimal combination of the multi-source satellite data by weighted average to form an initial fusion background field. The weight calculation formula is: （1） Wherein, X represents the wind speed or wind vector component value after weight calculation, and the unit is m / s; n represents the total number of satellite data sets participating in fusion; i represents the main index of external summation of the satellite data set, which is used to indicate the i th satellite data set being calculated, and the value range is from 1 to the total number n of available satellite data sets; wind speed or wind vector component values for the i-th satellite data set; wi represents the weight value of the i-th satellite data set; Ri represents the relative accuracy index of the i-th satellite data, obtained by error measurement of different data comparison; j represents the summation traversal index in the denominator normalization term, which is used to traverse all n satellite data sets to calculate the sum of the accuracy inverses of all satellite data sets; represents the relative accuracy indicator of the jth satellite data in the normalization denominator, used to ensure that the sum of all weights is 1 as part of the total error contribution when calculating the weight of the ith satellite data set.

4. The hourly sea surface wind field fusion method based on the time displacement correction of optical flow according to claim 1, characterized in that: The step S102 is that the optical flow algorithm is used for solving a globally smooth displacement field by minimizing an optical flow constraint equation, including taking a background wind field corresponding to a satellite acquisition time as a reference field, applying the optical flow algorithm to data of a corresponding observation time and a target time in the reference wind field sequence, and estimating a globally smooth reference displacement vector field by minimizing an energy function containing a brightness constant constraint and a smooth constraint, and the reference displacement vector field is used for quantifying a horizontal movement of the wind field in an observation gap.

5. The hourly sea surface wind field fusion method based on the time displacement correction of optical flow according to claim 4, characterized in that: The step S102 is that the optical flow algorithm is a multi-scale pyramid Horn-Schunck. The U, V components and the wind speed scalar field of the reference wind field are regarded as continuous image frames, a flow constraint equation of the reference wind field sequence is minimized, and a globally smooth displacement vector field from an observation time to a target time is solved wherein the observation time The calculation formula is: （2） wherein : eastward component of displacement vector field; : North-south component of displacement vector field; : represents the optical flow computation function, which computes the displacement vector field using the wind field data at two time instants; : Reference wind field at position p and time t; T: represents a target time; The optical flow field is estimated by minimizing the following energy functional: （3） wherein, represents the optical flow energy functional, the objective function to be minimized; , , respectively represent the partial derivatives of the wind field intensity in the spatial east-west (x) and north-south (y) directions and time t; u and v are east-west and north-south components of the optical flow field to be solved; a weight coefficient for controlling the smoothness of the displacement field; and denotes the gradient modulus square of the displacement field u components and v components; Spatial east-west and north-south direction integration units.

6. The hourly sea surface wind field fusion method based on the time displacement correction of optical flow according to claim 5, characterized in that: Through a multi-scale pyramid solving strategy from coarse to fine, the time-lapse correction model calculates the two-dimensional displacement vector field from any time within the window to the target time T to quantify the trajectory of the wind field structure's advection motion within the observation gap. ​ 7. A system for fusing hourly sea surface wind fields based on optical flow time displacement correction according to any one of claims 1 to 6, characterized in that: The system comprises, 1) a data acquisition and preprocessing module; It is used for acquiring multi-source satellite sea surface wind field data and hourly background wind field data of the whole world or a specified area, and performing standardization preprocessing, multi-source data consistency correction and weight combination; 2) a time displacement correction model construction module; It is used for constructing a time displacement correction model based on the optical flow algorithm by taking the hourly background wind field as a dynamic reference, so as to eliminate the spatial displacement of the satellite wind field structure caused by the observation time difference; 3) a satellite wind field time displacement correction module; It is used for projecting the multi-source satellite wind field from its original observation time to the target hourly time by using the displacement vector field, so as to realize accurate registration of the wind field structure in the time dimension; 4) a sea surface wind field fusion module; It is used for fusing the multi-source satellite wind field after time displacement correction with the hourly background wind field, so as to generate an hourly sea surface wind field product with high spatiotemporal consistency.

8. A computer device, comprising: One or more processors and storage devices are included, and the storage devices store one or more programs, when the one or more programs are executed by the one or more processors, the hourly sea surface wind field fusion method based on optical flow time displacement correction in any one of claims 1 to 6 is implemented.

9. A computer readable storage medium, characterized in that: One or more executable programs are stored thereon, and when the programs are executed by a processor of a computer device, the hourly sea surface wind field fusion method based on optical flow time displacement correction in any one of claims 1 to 6 is implemented.

Images