CN117150437A - Multi-source satellite sea surface wind field data fusion method, device, equipment and medium - Google Patents
Multi-source satellite sea surface wind field data fusion method, device, equipment and medium Download PDFInfo
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Abstract
The invention provides a multi-source satellite sea surface wind field data fusion method, a device, equipment and a medium, relating to the technical field of microwave remote sensing, comprising the following steps: acquiring multi-source satellite sea surface wind field data of a research area, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data; preliminary screening is carried out on the multi-source satellite sea surface wind field data; under different conditions, carrying out space-time matching and secondary screening on the preliminarily screened multi-source satellite sea surface wind field data to obtain a grid data set to be fused and matched; and determining the space-time weight corresponding to the matched grid data set to be fused, and carrying out data fusion on the matched grid data set to be fused based on the space-time weight to obtain a sea surface wind field fusion product corresponding to the research area. The sea surface wind field fusion product with high precision and high space-time resolution can be obtained by utilizing the sea surface wind field data of the multi-source satellite.
Description
Technical Field
The invention relates to the technical field of microwave remote sensing, in particular to a multi-source satellite sea surface wind field data fusion method, device, equipment and medium.
Background
The sea surface wind field is used as one of the most important parameters of the sea, is widely applied and researched, and has higher and higher requirements on the precision of the sea surface wind field. At present, the observation data of a sea surface wind field mainly comprise two types of field measurement and satellite remote sensing observation, and the wind field data measured on the field are nonuniform in spatial distribution, limited in data quantity and high in observation cost; the sea surface wind field data based on remote sensing can provide measurement in a large area at the same time, but the data source is single, the time for acquiring the global sea surface wind field is long, and more accurate scientific research and application cannot be satisfied. Therefore, research on how to fuse the multi-source satellite observation data, fully utilizes the advantages of multi-source satellite sea surface wind field products, and has important significance in obtaining the fused wind field products with high space-time coverage rate.
Disclosure of Invention
In view of the above, the invention aims to provide a multi-source satellite sea surface wind field data fusion method, a device, equipment and a medium, which can obtain a sea surface wind field fusion product with high precision and high space-time resolution by utilizing multi-source satellite sea surface wind field data.
In a first aspect, an embodiment of the present invention provides a method for fusion of multi-source satellite sea surface wind field data, including:
Acquiring multi-source satellite sea surface wind field data of a research area, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data;
according to the buoy station site field observation data, preliminary screening is carried out on the multi-source satellite sea surface wind field data to obtain the multi-source satellite sea surface wind field data after preliminary screening;
under different conditions, carrying out space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data according to the buoy station site field observation data, the analyzed sea surface wind field data and the rainfall rate data to obtain a to-be-fused matching grid data set;
and determining the space-time weight corresponding to the matched grid data set to be fused, so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, and obtaining the sea surface wind field fusion product corresponding to the research area.
In one embodiment, the step of primarily screening the multi-source satellite sea surface wind field data according to the buoy site field observation data to obtain the primarily screened multi-source satellite sea surface wind field data includes:
performing space-time matching on the multi-source satellite sea surface wind field data and the buoy station site on-site observation data;
Respectively determining first evaluation indexes corresponding to the multi-source satellite sea surface wind field data by utilizing the buoy site in-situ observation data with space-time matching relation with the multi-source satellite sea surface wind field data; wherein the first evaluation index comprises one or more of an error mean, a root mean square error and a correlation coefficient;
and performing preliminary screening on the multi-source satellite sea surface wind field data based on the first evaluation index to obtain the preliminarily screened multi-source satellite sea surface wind field data.
In one embodiment, under different conditions, according to the buoy site in-situ observation data, the analysis sea surface wind field data and the rainfall rate data, performing space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data to obtain a grid data set to be fused and matched, including the steps of:
according to a preset space-time window, carrying out gridding treatment on the analyzed sea surface wind field data and the primarily screened multi-source satellite sea surface wind field data to obtain an initial grid data set;
for each grid unit in the initial grid data set, determining wind speed attribute information corresponding to the grid unit based on an average value of the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit; determining rainfall attribute information corresponding to the grid unit according to the rainfall rate data;
Determining a condition corresponding to the grid unit based on the wind speed attribute information and the rainfall attribute information, and performing secondary screening on the primary screened multi-source satellite sea surface wind field data corresponding to the grid unit according to buoy site on-site observation data under the condition to obtain a to-be-fused matching grid data set; the grid data set to be fused comprises two-screen multi-source satellite sea surface wind field data corresponding to each grid unit.
In one embodiment, determining a condition corresponding to the grid unit based on the wind speed attribute information and the rainfall attribute information, and performing secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to the buoy site on-site observation data under the condition to obtain a to-be-fused matching grid data set, where the method comprises the following steps:
determining a wind speed condition corresponding to the grid unit according to the wind speed attribute information; determining clear sky rainfall conditions corresponding to the grid cells according to the rainfall attribute information; wherein the wind speed conditions include a low wind speed section condition, a medium wind speed section condition, or a high wind speed section condition;
determining a second evaluation index corresponding to the grid unit according to the buoy station site in-situ observation data;
And under the wind speed condition and the clear sky rainfall condition, carrying out secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to the second evaluation index to obtain a to-be-fused matching grid data set.
In one embodiment, the step of determining the spatio-temporal weights corresponding to the matched grid data set to be fused includes:
for each grid cell in the matched grid data set to be fused, determining a target grid point in the grid cell;
and determining the space-time weight corresponding to each grid point in the grid unit according to the preset space influence radius, the time influence radius, the space-time coordinates of the target grid point and the space-time coordinates of each grid point in the grid unit.
In one embodiment, the step of performing data fusion on the to-be-fused matching grid data set based on the space-time weight to obtain a sea surface wind field fusion product corresponding to the research area includes:
determining a warp wind field component and a weft wind field component corresponding to the grid points in the grid unit based on the two-screened multi-source satellite sea surface wind field data or the analyzed sea surface wind field data corresponding to the grid points in the grid unit;
According to the space-time weight corresponding to the grid point, carrying out data fusion on the warp-direction wind field component corresponding to the grid point in the grid unit to obtain a fused warp-direction wind field component corresponding to the grid unit; based on the space-time weight corresponding to the grid point, carrying out data fusion on the weft wind field component corresponding to the grid point in the grid unit to obtain a fused weft wind field component corresponding to the grid unit;
and converting the fused warp wind field component and the fused weft wind field component into sea surface wind field fusion products corresponding to the grid units.
In one embodiment, the step of determining the warp wind field component and the weft wind field component corresponding to the grid point in the grid unit based on the two-screen multi-source satellite sea surface wind field data corresponding to the grid point in the grid unit or the analysis sea surface wind field data includes:
judging whether grid points in the grid unit correspond to two-screened multi-source satellite sea surface wind field data or not;
if so, converting the sea surface wind field data of the two-screened multi-source satellite corresponding to the grid point into a warp wind field component and a weft wind field component;
and if not, converting the analysis sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component.
In a second aspect, an embodiment of the present invention further provides a multi-source satellite sea surface wind field data fusion device, including:
the data acquisition module is used for acquiring multi-source satellite sea surface wind field data, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data of a research area;
the primary screening module is used for carrying out primary screening on the multi-source satellite sea surface wind field data according to the buoy station site field observation data to obtain the primary screened multi-source satellite sea surface wind field data;
the secondary screening module is used for carrying out space-time matching and secondary screening on the primary screened multi-source satellite sea surface wind field data according to the buoy station site field observation data, the analysis sea surface wind field data and the rainfall rate data under different conditions to obtain a to-be-fused matching grid data set;
and the data fusion module is used for determining the space-time weight corresponding to the matched grid data set to be fused so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, and obtain the sea surface wind field fusion product corresponding to the research area.
In a third aspect, an embodiment of the present invention further provides an electronic device comprising a processor and a memory storing computer-executable instructions executable by the processor to implement the method of any one of the first aspects.
In a fourth aspect, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of the first aspects.
The embodiment of the invention provides a multi-source satellite sea surface wind field data fusion method, device, equipment and medium, which comprises the steps of firstly acquiring multi-source satellite sea surface wind field data, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data of a research area; then, according to the site field observation data of the buoy station, preliminary screening is carried out on the multi-source satellite sea surface wind field data to obtain the multi-source satellite sea surface wind field data after preliminary screening; under different conditions, secondary screening is carried out on the primary screened multi-source satellite sea surface wind field data according to the buoy station site field observation data, the analysis sea surface wind field data and the rainfall rate data to obtain a to-be-fused matching grid data set; and finally, determining the space-time weight corresponding to the matched grid data set to be fused, and carrying out data fusion on the matched grid data set to be fused based on the space-time weight to obtain a sea surface wind field fusion product corresponding to the research area. According to the embodiment of the invention, the buoy station site in-situ observation data, the analysis sea surface wind field data and the rainfall rate data are utilized to perform wind field data fusion on the sea surface wind field data of the multi-source satellite, so that the advantage complementation of the multi-source satellite is realized, the primary screening and the secondary screening are performed on the sea surface wind field data of the multi-source satellite, and finally, the space-time weight is utilized to realize data fusion, so that the sea surface wind field fusion product with wide coverage range and high wind field precision is obtained.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a multi-source satellite sea surface wind field data fusion method provided by an embodiment of the invention;
fig. 2 is a flow chart of another method for fusion of multi-source satellite sea surface wind field data according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of a multi-source satellite sea surface wind field data fusion device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described in conjunction with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
At present, the observed data of sea surface wind fields mainly comprise two types of field measurement and satellite remote sensing observation, and the wind field data of the field measurement are nonuniform in spatial distribution, limited in data quantity and high in observation cost; the sea surface wind field data fusion method, device, equipment and medium based on remote sensing can provide large-area and same-time measurement, but the data source is single, the time for acquiring the global sea surface wind field is long, and more accurate scientific research and application cannot be satisfied.
For the convenience of understanding the present embodiment, first, a detailed description will be given of a method for fusion of multi-source satellite sea surface wind field data disclosed in the present embodiment, referring to a flow chart of a method for fusion of multi-source satellite sea surface wind field data shown in fig. 1, the method mainly includes the following steps S102 to S108:
step S102, multi-source satellite sea surface wind field data, buoy station site on-site observation data, and analysis sea surface wind field data and rainfall rate data of a research area are obtained.
The buoy site on-site observation data can be on-site observation data of an NDBC buoy site and a domestic buoy site, the analysis sea surface wind field data can be analysis sea surface wind field data provided by ECMWF, and the rainfall rate data can be rainfall rate data provided by ERA 5.
And step S104, carrying out preliminary screening on the multi-source satellite sea surface wind field data according to the buoy station site field observation data to obtain the multi-source satellite sea surface wind field data after preliminary screening.
In one embodiment, space-time matching is firstly carried out between buoy site in-situ observation data and multi-source satellite sea surface wind field data, then a first evaluation index is calculated according to the buoy site in-situ observation data and the multi-source satellite sea surface wind field data, the first evaluation index comprises one or more of error mean value, root mean square error and correlation coefficient, and finally the multi-source satellite sea surface wind field data is subjected to primary screening by utilizing the first evaluation index to obtain primary screened multi-source satellite sea surface wind field data.
And step S106, carrying out space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data according to the buoy site on-site observation data, the analyzed sea surface wind field data and the rainfall rate data under different conditions to obtain a to-be-fused matching grid data set.
The conditions may include a wind speed condition, i.e., a low wind speed section condition, a medium wind speed section condition, or a high wind speed section condition, and a clear sky rainfall condition, i.e., a clear sky condition and a rainfall condition. In one embodiment, the buoy site on-site observation data and the analyzed sea surface wind field data can be subjected to gridding processing to obtain an initial grid data set; then, analyzing sea surface wind field data and rainfall rate data after gridding treatment to determine attribute information corresponding to each grid unit in the initial grid data set, wherein the attribute information is used for determining specific conditions corresponding to the grid units; and determining a second evaluation index corresponding to the primarily screened multi-source satellite sea surface wind field data in the grid unit according to the buoy site on-site observation data, wherein the second evaluation index comprises one or more of error mean value, root mean square error and correlation coefficient, and finally, performing secondary screening on the primarily screened multi-source satellite sea surface wind field data in the grid unit by using the second evaluation index under different conditions to obtain a to-be-fused matched grid data set, wherein the to-be-fused matched grid data set comprises a plurality of two-screened multi-source satellite sea surface wind field data corresponding to each grid unit.
And S108, determining the space-time weight corresponding to the matched grid data set to be fused, and carrying out data fusion on the matched grid data set to be fused based on the space-time weight to obtain a sea surface wind field fusion product corresponding to the research area.
In one embodiment, for each grid unit in the grid data set to be fused and matched, corresponding space-time weights can be determined according to space-time coordinates of each grid point in the grid unit, further, data fusion processing is carried out on the two-screen multi-source satellite sea surface wind field data corresponding to each grid point in the grid unit by utilizing the space-time weights, sea surface wind field fusion products corresponding to the grid unit are obtained, and the process is repeated, so that the sea surface wind field fusion products corresponding to all the grid units can be used as sea surface wind field fusion products of the whole research area.
According to the multi-source satellite sea surface wind field data fusion method provided by the embodiment of the invention, the buoy site on-site observation data, the sea surface wind field data and the rainfall rate data are analyzed, the wind field data fusion is carried out on the multi-source satellite sea surface wind field data, the advantage complementation of the multi-source satellite is realized, the primary screening and the secondary screening are carried out on the multi-source satellite sea surface wind field data, and finally the data fusion is realized by utilizing the space-time weight, so that the sea surface wind field fusion product with wide coverage range and high wind field precision is obtained.
For easy understanding, the embodiment of the invention provides a specific implementation method of the multi-source satellite sea surface wind field data fusion method.
For the foregoing step S102, multisource satellite sea surface wind field data, NDBC buoy site and domestic buoy site in-situ observation data (for short, buoy site in-situ observation data), analysis sea surface wind field data provided by ECMWF, and rainfall rate data provided by ERA5 may be obtained.
For the step S104, the step A1 to the step A3 may be performed according to the buoy site in-situ observation data, and the preliminary screening is performed on the multi-source satellite sea surface wind field data to obtain the multi-source satellite sea surface wind field data after the preliminary screening:
and A1, performing space-time matching on the multi-source satellite sea surface wind field data and the buoy station site in-situ observation data.
And A2, respectively determining first evaluation indexes corresponding to the multi-source satellite sea surface wind field data by using buoy site in-situ observation data which have a space-time matching relationship with the multi-source satellite sea surface wind field data. The first evaluation index is used for evaluating the precision of each multi-source satellite sea surface wind field data, and comprises one or more of error mean value MBE, root mean square error RMSE and correlation coefficient CC.
In one example, the error mean MBE may be determined according to the following formula:
；
in one example, the root mean square error RMSE may be determined as follows:
；
in one example, the correlation coefficient CC may be determined according to the following formula:
；
wherein,and->Respectively representing the multi-source satellite sea surface wind field data at the observation grid point i and the buoy site in-situ observation data, wherein n represents the total number of matching points,/for>And->Respectively represent multi-source satellite seaSurface wind field data and buoy site in-situ observation data averages.
And step A3, primarily screening the multi-source satellite sea surface wind field data based on the first evaluation index to obtain the primarily screened multi-source satellite sea surface wind field data. In an alternative embodiment, the primary screening can be performed on the multi-source satellite sea surface wind field data according to the error mean value MBE, so as to obtain the multi-source satellite sea surface wind field data after primary screening.
For the step S106, the steps B1 to B3 may be performed under different conditions, and space-time matching and secondary screening are performed on the preliminarily screened multi-source satellite sea surface wind field data according to the buoy site field observation data, the re-analyzed sea surface wind field data and the rainfall rate data, so as to obtain a mesh data set to be fused and matched:
And B1, carrying out gridding treatment on the analyzed sea surface wind field data and the preliminarily screened multi-source satellite sea surface wind field data according to a preset space-time window to obtain an initial grid data set. In one embodiment, the size of the space-time window can be determined according to the time resolution and the spatial resolution requirements of the fusion wind field, and the analyzed sea surface wind field data and the primarily screened multi-source satellite sea surface wind field data are divided into corresponding grid data according to the size of the space-time window, so that an initial grid data set is obtained.
Step B2, for each grid unit in the initial grid data set, determining wind speed attribute information corresponding to the grid unit based on an average value of the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit; and determining rainfall attribute information corresponding to the grid unit according to the rainfall rate data.
In one embodiment, calculating the average value of all the primary screened multi-source satellite sea surface wind field data in the grid unit to obtain an average wind speed value corresponding to the grid unit, wherein the average wind speed value is also wind speed attribute information and is used for judging whether the grid unit belongs to a low wind speed section, a medium wind speed section or a high wind speed section under the wind speed condition; in addition, the rainfall rate data also carries time information and space information, so that the rainfall rate data corresponding to each grid unit can be determined, and the rainfall rate data, namely rainfall attribute information, is used for judging whether the grid unit belongs to a clear sky state or a rainfall state under clear sky rainfall conditions.
And B3, determining the condition corresponding to the grid unit based on the wind speed attribute information and the rainfall attribute information, and performing secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to the buoy site on-site observation data under the condition to obtain a grid data set to be fused and matched. The matched grid data set to be fused comprises two-screen multi-source satellite sea surface wind field data corresponding to each grid unit. Specifically, see the following steps B3-1 to B3-3:
step B3-1, determining a wind speed condition corresponding to the grid unit according to the wind speed attribute information; and determining clear sky rainfall conditions corresponding to the grid cells according to the rainfall attribute information.
And step B3-2, determining a second evaluation index corresponding to the grid unit according to the site field observation data of the buoy.
In one example, the grid cells are divided into low wind speed segments (v<5 m/s), medium wind speed section (5v15 m/s), high wind speed section (v->15 m/s), checking the data in different wind speed sections by using a calculation formula of an error mean value MBE, a root mean square error RMSE and a correlation coefficient CC, and obtaining the error mean value MBE, the root mean square error RMSE and the correlation coefficient CC of the primarily screened multi-source satellite sea surface wind field data in each wind speed section.
In one example, rainfall rate identification and rainfall rate data in the primarily screened multi-source satellite sea surface wind field data are utilized to divide the primarily screened multi-source satellite sea surface wind field data into clear sky data and rainfall data, and the clear sky data and the rainfall data are respectively checked by utilizing calculation formulas of error mean MBE, root mean square error RMSE and correlation coefficient CC to obtain error mean MBE, root mean square error RMSE and correlation coefficient CC of the primarily screened multi-source satellite sea surface wind field data under clear sky and rainfall conditions.
And B3-3, performing secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to a second evaluation index under the condition of wind speed and clear sky rainfall to obtain a to-be-fused matching grid data set. The method comprises the steps of performing secondary screening on the primarily screened multi-source satellite sea surface wind field data contained in each grid unit by using error mean value MBEs under different wind speed conditions and clear sky rainfall conditions, wherein the screening standard is to reject discrete points with MAEs larger than 3 times in the grid units, and a grid data set to be fused and matched is obtained.
For the step S108, determining the space-time weight corresponding to the matched grid data set to be fused according to the following steps C1 to C5, so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, thereby obtaining a sea surface wind field fusion product corresponding to the research area:
Step C1, for each grid cell in the matched grid data set to be fused, determining a target grid point in the grid cell. Exemplary, the spatiotemporal sitting of target lattice point 0 is marked as。
And C2, determining the space-time weight corresponding to each grid point in the grid unit according to the preset space influence radius, the time influence radius, the space-time coordinates of the target grid point and the space-time coordinates of each grid point in the grid unit. Exemplary, the spatio-temporal sitting for any grid point k in the grid cell is labeledThe spatio-temporal weights of lattice point k can be calculated as follows:
；
wherein,and->The space-influencing radius and the time-influencing radius, respectively, are also the size of the real-time empty window.
And C3, determining a warp wind field component and a weft wind field component corresponding to the grid points in the grid units based on the two-screen multi-source satellite sea surface wind field data corresponding to the grid points in the grid units or the analyzed sea surface wind field data.
In one embodiment, it may be determined whether grid points in the grid cell correspond to two-screened multi-source satellite sea surface wind field data; if the judgment result is yes, converting the two-screen multi-source satellite sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component; and if the judgment result is negative, converting the analyzed sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component. In practical application, the sea surface wind field data of the multisource satellite without two screens can be supplemented by analyzing the sea surface wind field data.
Further, data conversion may be performed according to the following formula to obtain a warp wind field component and a weft wind field component:
；
；
u represents a weft wind field, v represents a warp wind field,representing the wind speed in the sea surface wind field data of the multisource satellite after two screens or analyzing the sea surface wind field data,/and>and expressing the sea surface wind field data of the multisource satellite after the two screens or analyzing the wind direction in the sea surface wind field data.
Step C4, according to the space-time weight corresponding to the grid point, carrying out data fusion on the warp-direction wind field component corresponding to the grid point in the grid unit to obtain the fused warp-direction wind field component corresponding to the grid unit; and based on the space-time weight corresponding to the grid point, carrying out data fusion on the weft-wise wind field component corresponding to the grid point in the grid unit to obtain the fused weft-wise wind field component corresponding to the grid unit.
In one embodiment, the data fusion may be performed for the warp or weft wind field components according to the following formula:
；
wherein,representing spatiotemporal weights, +.>Representing weft wind field component or warp wind field component, < ->Representing the fused trailing weft wind field component and the fused trailing warp wind field component.
And step C5, converting the fused warp wind field components and the fused weft wind field components into sea surface wind field fusion products corresponding to the grid units. In the Chinese implementation mode, the fused warp wind field component and the fused weft wind field component are required to be converted into wind speeds and wind directions, and then the sea surface wind field fusion product corresponding to the grid unit can be obtained.
In summary, a large amount of satellite remote sensing data lays a foundation for continuously observing wind fields on a global scale, but the number of times that a single satellite passes through a certain area is at most twice a day, wind field data in all time periods cannot be obtained, and space coverage is uneven. Aiming at the defects, the embodiment of the invention utilizes the multi-source satellite sea surface wind field data to perform wind field data fusion, realizes the advantage complementation of the multi-source satellite, and utilizes the authenticity inspection and the wind field precision results under different conditions to screen the multi-source satellite data so as to obtain the global sea surface wind field fusion product with wide coverage range and high wind field precision. The method has the advantages of simple algorithm, easy program implementation, rich existing resources and easy data acquisition, and can be used in various offshore scenes to realize multi-source satellite sea surface wind field fusion.
Further, another implementation manner of the multi-source satellite sea surface wind field data fusion method is provided in the embodiment of the present invention, referring to a flow chart of another multi-source satellite sea surface wind field data fusion method shown in fig. 2, including the following steps S202 to S210:
step S202, acquiring multi-source satellite sea surface wind field data;
step S204, wind field data precision inspection;
Step S206, wind field applicability analysis under different conditions;
step S208, a matching grid data set to be fused is established;
and S210, wind field data fusion.
The main technical flow of the embodiment of the invention is as follows: acquiring multi-source satellite sea surface wind field data, buoy station site field observation data, analyzing sea surface wind field data and rainfall rate data, firstly preprocessing the multi-source satellite sea surface wind field data, and evaluating the wind field precision of each multi-source satellite sea surface wind field data; secondly, analyzing the applicability of the multi-source satellite sea surface wind field data under different conditions, checking the wind field precision of each multi-source satellite sea surface wind field data under different wind speeds and different rainfall rates, and checking and eliminating discrete data in the multi-source satellite sea surface wind field data according to the wind field precision under different conditions; then determining the size of a space-time window, performing entry gridding treatment on the multi-source satellite sea surface wind field data, and establishing a matched data set to be fused; and finally, carrying out data fusion according to a space-time weighting algorithm to obtain a sea surface wind field fusion product.
According to the multi-source satellite sea surface wind field data fusion method provided by the embodiment of the invention, on one hand, a high-precision fusion wind field product is obtained by processing the multi-source satellite sea surface wind field data; on the other hand, the wind field product with high space-time resolution is obtained by utilizing the multi-source satellite sea surface wind field data fusion.
On the basis of the foregoing embodiments, the embodiment of the present invention provides a multi-source satellite sea surface wind field data fusion device, referring to a schematic structural diagram of the multi-source satellite sea surface wind field data fusion device shown in fig. 3, the device mainly includes the following parts:
the data acquisition module 302 is used for acquiring multi-source satellite sea surface wind field data, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data of a research area;
the preliminary screening module 304 is configured to perform preliminary screening on the multi-source satellite sea surface wind field data according to the buoy site on-site observation data, so as to obtain the preliminarily screened multi-source satellite sea surface wind field data;
the secondary screening module 306 is configured to perform space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data according to the buoy site on-site observation data, the re-analysis sea surface wind field data and the rainfall rate data under different conditions, so as to obtain a mesh data set to be fused and matched;
the data fusion module 308 is configured to determine a space-time weight corresponding to the matched grid data set to be fused, so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, and obtain a sea surface wind field fusion product corresponding to the research area.
According to the multi-source satellite sea surface wind field data fusion device provided by the embodiment of the invention, the buoy site on-site observation data, the sea surface wind field data and the rainfall rate data are analyzed, the wind field data fusion is carried out on the multi-source satellite sea surface wind field data, the advantage complementation of the multi-source satellite is realized, the primary screening and the secondary screening are carried out on the multi-source satellite sea surface wind field data, and finally the data fusion is realized by utilizing the space-time weight, so that the sea surface wind field fusion product with wide coverage range and high wind field precision is obtained.
In one embodiment, the preliminary screening module 304 is further configured to:
space-time matching is carried out on the multi-source satellite sea surface wind field data and buoy station site field observation data;
respectively determining first evaluation indexes corresponding to the multi-source satellite sea surface wind field data by using buoy site in-situ observation data with space-time matching relation with the multi-source satellite sea surface wind field data; wherein the first evaluation index comprises one or more of an error mean, a root mean square error and a correlation coefficient;
And performing primary screening on the multi-source satellite sea surface wind field data based on the first evaluation index to obtain the primary screened multi-source satellite sea surface wind field data.
In one embodiment, the secondary screening module 306 is further configured to:
according to a preset space-time window, performing gridding treatment on the analyzed sea surface wind field data and the primarily screened multi-source satellite sea surface wind field data to obtain an initial grid data set;
for each grid unit in the initial grid data set, determining wind speed attribute information corresponding to the grid unit based on an average value of the primary screened multi-source satellite sea surface wind field data corresponding to the grid unit; determining rainfall attribute information corresponding to the grid unit according to the rainfall rate data;
determining conditions corresponding to the grid unit based on wind speed attribute information and rainfall attribute information, and secondarily screening the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to buoy site on-site observation data under the conditions to obtain a to-be-fused matching grid data set; the matched grid data set to be fused comprises two-screen multi-source satellite sea surface wind field data corresponding to each grid unit.
In one embodiment, the secondary screening module 306 is further configured to:
Determining a wind speed condition corresponding to the grid unit according to the wind speed attribute information; determining clear sky rainfall conditions corresponding to the grid units according to rainfall attribute information; wherein the wind speed conditions include a low wind speed section condition, a medium wind speed section condition, or a high wind speed section condition;
determining a second evaluation index corresponding to the grid unit according to the buoy site in-situ observation data;
and under the condition of wind speed and clear sky rainfall, carrying out secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to a second evaluation index to obtain a to-be-fused matching grid data set.
In one embodiment, the data fusion module 308 is further configured to:
for each grid cell in the matched grid data set to be fused, determining a target grid point in the grid cell;
and determining the space-time weight corresponding to each grid point in the grid unit according to the preset space influence radius, the time influence radius, the space-time coordinates of the target grid point and the space-time coordinates of each grid point in the grid unit.
In one embodiment, the data fusion module 308 is further configured to:
determining a warp wind field component and a weft wind field component corresponding to grid points in the grid units based on the two-screen multi-source satellite sea surface wind field data or the analysis sea surface wind field data corresponding to the grid points in the grid units;
According to the space-time weight corresponding to the grid point, carrying out data fusion on the warp wind field component corresponding to the grid point in the grid unit to obtain the fused warp wind field component corresponding to the grid unit; based on the space-time weight corresponding to the grid point, carrying out data fusion on the weft-wise wind field component corresponding to the grid point in the grid unit to obtain a fused weft-wise wind field component corresponding to the grid unit;
and converting the fused warp wind field component and the fused weft wind field component into sea surface wind field fusion products corresponding to the grid units.
In one embodiment, the data fusion module 308 is further configured to:
judging whether grid points in the grid unit correspond to two-screened multi-source satellite sea surface wind field data or not;
if so, converting the two-screen multi-source satellite sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component;
if not, converting the analyzed sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component.
The device provided by the embodiment of the present invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brevity, reference may be made to the corresponding content in the foregoing method embodiment where the device embodiment is not mentioned.
The embodiment of the invention provides electronic equipment, which comprises a processor and a storage device; the storage means has stored thereon a computer program which, when executed by the processor, performs the method of any of the embodiments described above.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, the processor 40, the communication interface 43 and the memory 41 being connected by the bus 42; the processor 40 is arranged to execute executable modules, such as computer programs, stored in the memory 41.
The memory 41 may include a high-speed random access memory (RAM, random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory. The communication connection between the system network element and the at least one other network element is achieved via at least one communication interface 43 (which may be wired or wireless), which may use the internet, a wide area network, a local network, a metropolitan area network, etc.
Bus 42 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be classified as address buses, data buses, control buses, etc. For ease of illustration, only one bi-directional arrow is shown in FIG. 4, but not only one bus or type of bus.
The memory 41 is configured to store a program, and the processor 40 executes the program after receiving an execution instruction, and the method executed by the apparatus for flow defining disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 40 or implemented by the processor 40.
The processor 40 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuitry in hardware or instructions in software in processor 40. The processor 40 may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a digital signal processor (Digital Signal Processing, DSP for short), application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA for short), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in a memory 41 and the processor 40 reads the information in the memory 41 and in combination with its hardware performs the steps of the method described above.
The computer program product of the readable storage medium provided by the embodiment of the present invention includes a computer readable storage medium storing a program code, where the program code includes instructions for executing the method described in the foregoing method embodiment, and the specific implementation may refer to the foregoing method embodiment and will not be described herein.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The multi-source satellite sea surface wind field data fusion method is characterized by comprising the following steps of:
acquiring multi-source satellite sea surface wind field data of a research area, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data;
according to the buoy station site field observation data, preliminary screening is carried out on the multi-source satellite sea surface wind field data to obtain the multi-source satellite sea surface wind field data after preliminary screening;
Under different conditions, carrying out space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data according to the buoy station site field observation data, the analyzed sea surface wind field data and the rainfall rate data to obtain a to-be-fused matching grid data set;
and determining the space-time weight corresponding to the matched grid data set to be fused, so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, and obtaining the sea surface wind field fusion product corresponding to the research area.
2. The method for merging the multi-source satellite sea surface wind field data according to claim 1, wherein the step of primarily screening the multi-source satellite sea surface wind field data according to the buoy site on-site observation data to obtain the primarily screened multi-source satellite sea surface wind field data comprises the following steps:
performing space-time matching on the multi-source satellite sea surface wind field data and the buoy station site on-site observation data;
respectively determining first evaluation indexes corresponding to the multi-source satellite sea surface wind field data by utilizing the buoy site in-situ observation data with space-time matching relation with the multi-source satellite sea surface wind field data; wherein the first evaluation index comprises one or more of an error mean, a root mean square error and a correlation coefficient;
And performing preliminary screening on the multi-source satellite sea surface wind field data based on the first evaluation index to obtain the preliminarily screened multi-source satellite sea surface wind field data.
3. The method for merging the multi-source satellite sea surface wind field data according to claim 1, wherein under different conditions, the steps of performing space-time matching and secondary screening on the primarily screened multi-source satellite sea surface wind field data according to the buoy site in-situ observation data, the re-analyzed sea surface wind field data and the rainfall rate data to obtain a grid data set to be merged, comprise:
according to a preset space-time window, carrying out gridding treatment on the analyzed sea surface wind field data and the primarily screened multi-source satellite sea surface wind field data to obtain an initial grid data set;
for each grid unit in the initial grid data set, determining wind speed attribute information corresponding to the grid unit based on an average value of the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit; determining rainfall attribute information corresponding to the grid unit according to the rainfall rate data;
determining a condition corresponding to the grid unit based on the wind speed attribute information and the rainfall attribute information, and performing secondary screening on the primary screened multi-source satellite sea surface wind field data corresponding to the grid unit according to buoy site on-site observation data under the condition to obtain a to-be-fused matching grid data set; the grid data set to be fused comprises two-screen multi-source satellite sea surface wind field data corresponding to each grid unit.
4. The method for merging the multi-source satellite sea surface wind field data according to claim 3, wherein the step of determining the condition corresponding to the grid unit based on the wind speed attribute information and the rainfall attribute information, and performing secondary screening on the pre-screened multi-source satellite sea surface wind field data corresponding to the grid unit according to the buoy site in-situ observation data under the condition to obtain the matched grid data set to be merged comprises the following steps:
determining a wind speed condition corresponding to the grid unit according to the wind speed attribute information; determining clear sky rainfall conditions corresponding to the grid cells according to the rainfall attribute information; wherein the wind speed conditions include a low wind speed section condition, a medium wind speed section condition, or a high wind speed section condition;
determining a second evaluation index corresponding to the grid unit according to the buoy station site in-situ observation data;
and under the wind speed condition and the clear sky rainfall condition, carrying out secondary screening on the primarily screened multi-source satellite sea surface wind field data corresponding to the grid unit according to the second evaluation index to obtain a to-be-fused matching grid data set.
5. The method of claim 1, wherein the step of determining the spatio-temporal weights corresponding to the matched grid dataset to be fused comprises:
For each grid cell in the matched grid data set to be fused, determining a target grid point in the grid cell;
and determining the space-time weight corresponding to each grid point in the grid unit according to the preset space influence radius, the time influence radius, the space-time coordinates of the target grid point and the space-time coordinates of each grid point in the grid unit.
6. The method for fusion of multi-source satellite sea surface wind field data according to claim 5, wherein the step of obtaining the sea surface wind field fusion product corresponding to the research area by data fusion of the to-be-fused matching grid data set based on the space-time weight comprises the following steps:
determining a warp wind field component and a weft wind field component corresponding to the grid points in the grid unit based on the two-screened multi-source satellite sea surface wind field data or the analyzed sea surface wind field data corresponding to the grid points in the grid unit;
according to the space-time weight corresponding to the grid point, carrying out data fusion on the warp-direction wind field component corresponding to the grid point in the grid unit to obtain a fused warp-direction wind field component corresponding to the grid unit; based on the space-time weight corresponding to the grid point, carrying out data fusion on the weft wind field component corresponding to the grid point in the grid unit to obtain a fused weft wind field component corresponding to the grid unit;
And converting the fused warp wind field component and the fused weft wind field component into sea surface wind field fusion products corresponding to the grid units.
7. The method of claim 6, wherein determining the warp and weft wind field components corresponding to the grid points in the grid unit based on the two-screened multi-source satellite sea field data or the re-analyzed sea field data corresponding to the grid points in the grid unit comprises:
judging whether grid points in the grid unit correspond to two-screened multi-source satellite sea surface wind field data or not;
if so, converting the sea surface wind field data of the two-screened multi-source satellite corresponding to the grid point into a warp wind field component and a weft wind field component;
and if not, converting the analysis sea surface wind field data corresponding to the grid points into a warp wind field component and a weft wind field component.
8. A multi-source satellite sea surface wind field data fusion device, comprising:
the data acquisition module is used for acquiring multi-source satellite sea surface wind field data, buoy station site on-site observation data, and analyzing sea surface wind field data and rainfall rate data of a research area;
The primary screening module is used for carrying out primary screening on the multi-source satellite sea surface wind field data according to the buoy station site field observation data to obtain the primary screened multi-source satellite sea surface wind field data;
the secondary screening module is used for carrying out space-time matching and secondary screening on the primary screened multi-source satellite sea surface wind field data according to the buoy station site field observation data, the analysis sea surface wind field data and the rainfall rate data under different conditions to obtain a to-be-fused matching grid data set;
and the data fusion module is used for determining the space-time weight corresponding to the matched grid data set to be fused so as to perform data fusion on the matched grid data set to be fused based on the space-time weight, and obtain the sea surface wind field fusion product corresponding to the research area.
9. An electronic device comprising a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which, when invoked and executed by a processor, cause the processor to implement the method of any one of claims 1 to 7.
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