CN114758080A - Sea surface salinity gridding inversion method and device - Google Patents
Sea surface salinity gridding inversion method and device Download PDFInfo
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Abstract
The invention provides a method and a device for sea surface salinity gridding inversion, and relates to the technical field of ocean salinity monitoring, wherein the method comprises the following steps: acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality marker array; in each grid to be determined of the meshed SSS field, determining a scatter set meeting a preset longitude and latitude threshold in a first data set; based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scattered point in the scattered point set by taking the minimum cost function as a target; and determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point. According to the method, through the constructed double-weight model, the L2-level data of the SMAP are optimized, the distance characteristic and the quality characteristic of SSS sampling point data are considered, and the accuracy of a gridding SSS field is improved.
Description
Technical Field
The invention relates to the technical field of ocean salinity monitoring, in particular to a method and a device for sea surface salinity gridding inversion.
Background
Sea Surface Salinity (SSS) is one of the important parameters describing the basic properties of the ocean and plays a very important role in ocean-gas interaction, ocean circulation and ocean processes. The SMAP satellite is a soil humidity active and passive remote sensing satellite, is equipped with an L-band radar and an L-band radiometer, and can observe the SSS.
When the measurement is performed by the microwave radiometer corresponding to the SMAP to obtain the measurement data, the measurement data is likely to be influenced by the marine environmental factors, such as: wind speed, rainfall, land, sea ice, water temperature, etc., and different factors have different effects on the results. Therefore, the quality of the measured data changes with different external conditions, and in the related art, in the sea surface salinity gridding process, the influence of the distance factor on the gridding result is mainly considered, so that the accuracy of the final gridded sea surface salinity result is not high. Therefore, this problem has been called a technical problem to be solved in the industry.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a sea surface salinity gridding inversion method and device.
In a first aspect, the present invention provides a method for sea surface salinity gridding inversion, comprising:
Acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality marker array;
in each grid to be determined of the meshed SSS field, determining a scatter set meeting a preset longitude and latitude threshold in the first data set;
based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scatter point in the scatter point set by taking the minimum cost function as a target;
and determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point.
Optionally, the obtaining, based on a preset quality flag array, the sampling data of all SSS sampling points to form a first data set includes:
acquiring a second data set in SMAP gridded secondary SSS data;
sampling the second data set through a preset sampling window according to a preset quality marker array to obtain a third data set formed by the first quality marker array of the corresponding SSS sampling point; the quality flag array comprises 16 elements, wherein one or more specified elements are used for indicating whether the sampling data of the SSS sampling points have serious quality problems;
Deleting all SSS sampling points meeting a first screening rule in the third data set to obtain a first data set;
the first screening rule is that if any specified element in the first quality tag array of the SSS sampling point indicates that the sampling data of the SSS sampling point has a serious quality problem.
Optionally, the determining, in each grid to be determined of the meshed SSS field, a scatter set in the first data set that meets a preset longitude and latitude threshold includes:
and in each grid to be determined of the gridded SSS field, determining all SSS sampling points which meet a preset longitude and latitude threshold value with the geodetic coordinates of the central point of the grid to be determined as a scatter set corresponding to the grid to be determined.
Optionally, before determining the distance weight parameter and the quality weight parameter corresponding to each scatter in the scatter set with the objective of minimum cost function based on the constructed double-weight model, the method includes:
determining a distance weight parameter of each scatter point based on the Euclidean distance between each scatter point in the scatter point set and the central point of the grid to be determined and the distance weight coefficient of each scatter point;
Determining the data quality corresponding to each scatter point based on the mass mark array corresponding to each scatter point in the scatter point set and the weight coefficient corresponding to each mass mark;
determining a mass weight parameter of each scatter point based on the data mass corresponding to the scatter point and the corresponding mass weight coefficient;
determining an SSS estimation value of the grid to be determined based on a total weight parameter of each scattered point and an observed salinity value of each scattered point;
wherein the total weight parameter of each of the scatter points is determined based on the distance weight parameter and the quality weight parameter of each of the scatter points.
Optionally, the determining the SSS estimate for the grid to be determined based on the total weight parameter for each of the scatters and the observed salinity value for each of the scatters includes:
in the scattered point set, determining an SSS estimation value of the grid to be determined by adopting a weighted average algorithm based on the observed salinity value of each scattered point;
and the weight in the weighted average algorithm is a total weight parameter of each scattered point.
Optionally, the cost function is a square of a difference between the SSS estimation value of the grid to be determined and the SSS real value corresponding to the grid to be determined.
Optionally, the determining, based on the constructed double-weight model, a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set with a minimum cost function as a target includes:
iteratively determining a cost function corresponding to the grid to be determined based on a Levenberg-Marquardt algorithm;
and when the iteration condition meets that the difference between the values of the cost function of the two times before and after the iteration condition is smaller than a first threshold, or the iteration frequency is greater than or equal to a second threshold, or the descending gradient is smaller than any one of a third threshold, determining a distance weight parameter and a quality weight parameter of each scattered point in the scattered point set corresponding to the grid to be determined.
In a second aspect, the present invention further provides an apparatus for sea surface salinity gridding inversion, including:
the acquisition module is used for acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality mark array;
the gridding module is used for determining a scattered point set which meets a preset longitude and latitude threshold value in each grid to be determined of the gridded SSS field in the first data set;
the double-weight module is used for determining a distance weight parameter and a quality weight parameter corresponding to each scatter point in the scatter point set by taking the minimum cost function as a target based on the constructed double-weight model;
And the inversion module is used for determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scattered point, the distance weight parameter corresponding to each scattered point and the quality weight parameter of each scattered point.
In a third aspect, the present invention also provides an electronic device, including a memory, a transceiver, and a processor;
a memory for storing a computer program; a transceiver for transceiving data under the control of the processor; a processor for reading the computer program in the memory and implementing the method for sea surface salinity gridding inversion as described above in the first aspect.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of grid inversion of sea surface salinity as described above in the first aspect.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program which, when executed by a processor, implements a method of sea surface salinity gridding inversion as described above in relation to the first aspect.
According to the sea surface salinity gridding inversion method and device, the L2-level data of the SMAP are optimized through the constructed double-weight model, the distance characteristic of SSS sampling point data is considered, the quality characteristic of the SSS sampling point data is also considered, and the accuracy of a gridding SSS field is improved.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a method for sea surface salinity gridding inversion provided by the present invention;
FIG. 2 is a schematic diagram of a process for performing sea surface salinity gridding inversion according to the present invention;
FIG. 3 is a schematic structural diagram of an apparatus for sea surface salinity gridding inversion provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The method and apparatus for sea surface salinity gridding inversion provided by the present invention are described with reference to fig. 1 to 4.
Fig. 1 is a schematic flow chart of a method for sea surface salinity gridding inversion provided by the present invention, as shown in fig. 1, the method includes:
specifically, official release standard SMAP grid SSS 2-level (L2) rail-level sea surface salinity SSS data is normalized through a preset quality mark array on the sampling data of all SSS sampling points of a grid SSS field, and corresponding values in the quality mark array are determined, wherein each item in the quality mark array represents characteristics of different parameters of the sampling data of a single sampling point, such as whether wind direction influence exists on the data of the SSS sampling points, or whether wind speed influence exists, or whether sea surface billow height influence exists, or whether sea surface temperature influence exists.
The preset quality flag array can change the composition of the array and the capability or meaning of each element to be characterized according to requirements, and the data set of iqc _ flag in the L2 track-level SSS data of the SMAP is normalized through the preset quality flag array to obtain a corresponding first data set.
102, in each grid to be determined of a gridded SSS field, determining a scatter set meeting a preset longitude and latitude threshold in the first data set;
after the SSS field is uniformly gridded, an SSS value corresponding to each grid needs to be determined, and the first data set is screened according to the geographical position information, so that the longitude and latitude difference value between the first data set and the center of the grid meets a preset longitude and latitude threshold value, namely within the range of the preset longitude and latitude threshold value, the SSS data meeting the conditions are used as scatter sets corresponding to the grids to be determined.
103, based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scattered point in the scattered point set by taking the minimum cost function as a target;
after a scatter set corresponding to a grid to be determined is determined, determining a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set based on the constructed double-weight model; the quality weighting parameter of each scatter point is related to the data quality of the scatter point, and the data quality of a single scatter point is determined according to the quality tag array corresponding to each scatter point, for example, by setting a weighting coefficient corresponding to each dimension in the quality tag array, the data quality of a single scatter point is determined.
According to the method, after the distance weight parameter and the quality weight parameter corresponding to each scattered point in the scattered point set are determined, the corresponding distance weight parameter and the corresponding quality weight parameter are determined when the value of the cost function is minimum according to a preset cost function. In determining the minimum value of the cost function, various ways may be specifically adopted, for example, the number of iterations is set, or the difference between the values of any two adjacent cost functions is smaller than a preset threshold value.
And 104, determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scattered point, the distance weight parameter corresponding to each scattered point and the quality weight parameter of each scattered point.
After determining a distance weight parameter and a quality weight parameter corresponding to each scattered point in the scattered point set, determining the total weight of the scattered point according to the distance weight parameter and the quality weight parameter;
and then determining an SSS estimation value of the grid to be determined according to the observed salinity value measured at each scattered point time in the scattered point set and the total weight corresponding to each scattered point, and taking the SSS estimation value as an inversion result of the SSS corresponding to the grid to be determined. And when the inversion result of the SSS of each grid to be determined in the uniform grid SSS field is determined, forming the L3-level uniform grid SSS.
According to the sea surface salinity gridding inversion method, the L2-level data of the SMAP are optimized through the constructed double-weight model, the distance characteristic of SSS sampling point data and the quality characteristic of the SSS sampling point data are considered, and the precision of a gridding SSS field is improved.
Optionally, the obtaining, based on the preset quality flag array, the sampling data of all SSS sampling points to form a first data set includes:
acquiring a second data set in SMAP gridded secondary SSS data;
sampling the second data set through a preset sampling window according to a preset quality tag array to obtain a third data set formed by the first quality tag array of the corresponding SSS sampling point; the quality tag array comprises 16 elements, wherein one or more specified elements are used for indicating whether serious quality problems exist in the sampling data of the SSS sampling points;
deleting all SSS sampling points meeting a first screening rule in the third data set to obtain a first data set;
the first screening rule is that if any specified element in the first quality tag array of the SSS sampling point indicates that the sampling data of the SSS sampling point has a serious quality problem.
Specifically, official release standard SMAP meshed SSS level 3 (L3) data was generated from level 2 (L2) orbital level sea salinity SSS data without any additional climate, parametric model output, or field data adjustments. The "iqc _ flag" data set is the data set with important quality indicators in the SMAP L2 level data, which indicates abnormal data conditions when measured by the sensor.
Acquiring an iqc _ flag data set in the L2 orbit and sea surface salinity SSS data through an SMAP satellite, taking the iqc _ flag data set as a second data set, namely, a part of original data acquired from the SMAP, sampling the second data set through a preset sampling window, and determining a quality mark array corresponding to each sampling point; the quality index array includes 16 elements, i.e., 16 indexes, and performs sampling analysis on each sampling point. If the value of the element is 0, the data information meeting the index does not exist under the corresponding data condition, and the data quality can be represented to be excellent on one hand; if the value of an element is 1, it means that there is data information satisfying the index under the corresponding data condition, which may indicate that the data quality is poor from one side. The data corresponding to each element of array A is shown in the following table:
TABLE 1 quality tag array data case for each array element
The numerical value of one or more specified indexes can indicate whether the sampling data of the SSS sampling points have serious quality problems or not; the method specifically includes the steps of indicating whether effective observation data exist in the preset sampling window, whether an Optimal Interpolation (OI) problem exists, whether severe land pollution exists and whether severe sea ice pollution exists, and of course, specific positions of each index in an array are not limited, table 1 is only schematic, and the sequence of elements can be changed at will.
If any one corresponding value of indexes 'no effective observation data in the sampling window', 'OI problem', 'heavy land pollution', 'heavy sea ice pollution' and 'Maximum Likelihood Estimation (MLE) unconvergence during SSS inversion' in the quality marker array corresponding to the SSS data of a certain sampling point is 1 in the preset sampling window, the sampling data of the SSS sampling point is deleted.
The indexes are all used for indicating that the sampling data of the SSS sampling point have serious quality problems, so that the sampling data need to be deleted, the effectiveness of the sampling data is improved, and the corresponding data quality is improved.
Optionally, the determining, in each grid to be determined of the meshed SSS field, a scatter set in the first data set that meets a preset longitude and latitude threshold includes:
and in each grid to be determined of the gridded SSS field, determining all SSS sampling points which meet a preset longitude and latitude threshold value with the geodetic coordinates of the central point of the grid to be determined, and taking the SSS sampling points as a scatter set corresponding to the grid to be determined.
In particular, for any mesh P to be solved, the geodetic coordinates of the center point of the mesh are determined and can be expressed as
；
Traversing all SSS sampling points in the first data set, and determining the latitude difference value between the geodetic coordinates corresponding to the SSS sampling points and the geodetic coordinates of the grid P points
And the difference in longitude
；
Including all the satisfaction
And is provided with
The sampled data of the SSS sampling points form a scatter set M, where K1 and K2 are preset latitude thresholds and preset longitude thresholds. K1, K2 is [0, 1 ]]And the values of K1 and K2 are generally the same. In practice, the value is usually 0.5.
Optionally, before determining the distance weight parameter and the quality weight parameter corresponding to each scatter in the scatter set with the objective of minimum cost function based on the constructed double-weight model, the method includes:
Determining a distance weight parameter of each scattered point based on the Euclidean distance between each scattered point in the scattered point set and the central point of the grid to be determined and the distance weight coefficient of each scattered point;
determining the data quality corresponding to each scatter point based on the mass mark array corresponding to each scatter point in the scatter point set and the weight coefficient corresponding to each mass mark;
determining a mass weight parameter of each scatter point based on the data mass corresponding to the scatter point and the corresponding mass weight coefficient;
determining an SSS estimation value of the grid to be determined based on a total weight parameter of each scattered point and an observed salinity value of each scattered point;
wherein the total weight parameter for each of the scatter points is determined based on the distance weight parameter and the quality weight parameter for each of the scatter points.
Specifically, after a scatter set corresponding to each grid P to be determined is determined, a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set of each grid P are determined based on a double-weight model with each grid P as a unit.
Before determining the distance weight parameter and the quality weight parameter corresponding to each scattered point, the geodetic coordinates of the sampling data of each SSS sampling point are calculated through Gaussian positive calculation 
Conversion to plane coordinates
And grid P point geodetic coordinates
Conversion to plane coordinates
。
The double-weight model specifically comprises:
determining a distance weight parameter of each scattered point in the scattered point set M, wherein a corresponding formula is as follows:
in the formula (I), the compound is shown in the specification,
in order to be a distance-weighting factor,
is the Euclidean distance of the scattering point from the central point of the grid, and the unit is kilometer. And the distance weighting coefficients of all the scattered points in the scattered point set M are the same.
Determining a quality weight parameter of each scattered point in the scattered point set M, wherein a corresponding formula is as follows:
in the formula (I), the compound is shown in the specification,
in order to be the mass weight coefficient,
is the data quality. And the quality weight coefficients of all the scattered points in the scattered point set M are the same.
Determining the quality of the scatter data according to the quality mark array corresponding to each scatter
The formula of (1) is as follows:
in the formula (I), the compound is shown in the specification,
the weight coefficient of the index corresponding to the quality mark data group corresponding to each scatter point,iindicating the number of indexes in the quality index array.
Further determining a total weight parameter for each scatter point
The corresponding formula is as follows:
and then determining the SSS estimation value of the grid P to be determined according to the total weight parameter corresponding to each scatter in the scatter set M and the observed salinity value corresponding to each scatter.
Optionally, the determining the SSS estimate for the grid to be determined based on the total weight parameter for each of the scatter points and the observed salinity value for each of the scatter points includes:
In the scattered point set, based on the observed salinity value of each scattered point, determining an SSS estimation value of the grid to be determined by adopting a weighted average algorithm;
and the weight in the weighted average algorithm is a total weight parameter of each scattered point.
Specifically, the observed salinity value corresponding to each scattered point in scattered point set is obtained
，
The number of scattered points in the scattered point set M, therefore, the SSS estimation value of the grid P
Can be calculated from the following formula:
in the formula (I), the compound is shown in the specification,
is the observed salinity value of any scatter N in the scatter set M,
is the total weight parameter corresponding to the scatter point N.
Then, with the minimum cost function as a target, determining a distance weight coefficient of each scattered point in a scattered point set corresponding to the grid P
Quality weight coefficient
And the weight coefficient of the index corresponding to the quality mark data group corresponding to each scattered point
The numerical value of (c). The cost function can be formulated as follows:
in the formula (I), the compound is shown in the specification,
the real salinity value of the sea surface of the grid can be obtained from Argo SSS observation data in the related art.
Optionally, the determining, based on the constructed double-weight model, a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set with a minimum cost function as a target includes:
iteratively determining a cost function corresponding to the grid to be determined based on a Levenberg-Marquardt algorithm;
And when the iteration condition meets that the difference between the values of the cost function of the two times before and after the iteration condition is smaller than a first threshold, or the iteration frequency is greater than or equal to a second threshold, or the descending gradient is smaller than any one of a third threshold, determining a distance weight parameter and a quality weight parameter of each scattered point in the scattered point set corresponding to the grid to be determined.
Specifically, when the distance weight parameter and the quality weight parameter of the corresponding scatter point in the scatter set corresponding to each grid P to be determined are determined, a Levenberg-Marquardt (LM) algorithm is adopted to iteratively solve the cost function based on the nonlinear least square principle
Weight coefficient when minimum value is obtained
、
And
the numerical solution of (c). The iteration termination condition is that the descending gradient is smaller than a threshold value T1, or two times before and after
The difference in the values is less than a threshold T2,or the number of iterations reaches a threshold T3. If the iteration times reach a threshold value T3 when the iteration is terminated, the function is not converged and has no optimal solution, and the weight parameter is marked as an invalid value; otherwise, the function is explained to obtain the optimal solution, and the distance weight coefficient at the moment is recorded
Mass weight coefficient
Weight coefficient for each data case
And the value is the coefficient value of the weight model, and finally, the complete weight model is constructed. Therein, the falling gradient may be expressed as a derivative or partial derivative of the cost function.
According to the sea surface salinity gridding inversion method, the L2-level data of the SMAP is optimized through the constructed double-weight model, the distance characteristic of SSS sampling point data and the quality characteristic of the SSS sampling point data are considered, and the accuracy of a gridding SSS field is improved.
Fig. 2 is a schematic diagram of an implementation flow of the method for sea surface salinity gridding inversion provided by the present invention, as shown in fig. 2, specifically including:
2.1 traversing an iqc _ flag data set in L2 rail-level SSS data, and normalizing the data set according to a one-dimensional quality flag array containing 16 indexes for any SSS sampling point, wherein each index/element of the quality flag array corresponds to different data conditions, and if the value of the element is 0, the data quality is excellent under the corresponding data conditions; if the value of an element is 1, it means that the data quality is poor under the corresponding data conditions.
2.2 selecting indexes for data screening: effective observation data, OI problems, severe land pollution, severe sea ice pollution and Maximum Likelihood Estimation (MLE) unconvergence during SSS inversion do not exist in the sampling window, and when the value corresponding to any one of the indexes is 1, the track-level SSS data is deleted.
And uniformly meshing the SSS field, and determining a distance weight parameter and a quality weight parameter corresponding to each mesh P based on a double-weight model.
3.1 determining the scatter set corresponding to each grid P.
3.1.1, determining the geodetic coordinates of the central point of the grid for any grid P to be solved
；
3.1.2 geodetic coordinates traversal of SMAP L2 orbital SSS data
To find the geodetic coordinates of the point P
Geodetic coordinates with SMAP L2 orbital SSS data
Coordinate difference of (2)
、
；
3.1.3, Retention of all sations
And is
The orbit-level SSS data form a scatter set M;
3.2 determining a distance weight parameter of each scatter point in the scatter set M according to the distance weight coefficient of each scatter point N in the scatter set M and the distance between the scatter point and the grid center in combination with the formula (1);
determining a quality weight parameter of each scatter point in the scatter point set M according to the quality weight coefficient of each scatter point N in the scatter point set M and the weight coefficient corresponding to each index in the quality marker array corresponding to the scatter point, and combining the formulas (2) and (3);
Determining the total weight of each scattered point in the scattered point set M according to the distance weight parameter and the quality weight parameter of each scattered point N in the scattered point set M in combination with the formula (4)
。
And step 204, solving an SSS value corresponding to each grid.
And (5) determining the SSS estimation value of the grid P according to the total weight of each scatter point N in the scatter point set M and the observed salinity value of each scatter point and by combining the formula (5).
And when the cost function is minimum, determining the distance weight coefficient and the quality weight coefficient of each scatter point in the scatter point set corresponding to the grid and the weight coefficient of each index of the quality mark array corresponding to each scatter point by using the cost function (refer to formula (6)).
And sequentially determining SSS estimated values corresponding to each grid in the SSS field until the SSS estimated values corresponding to all grids are determined, namely obtaining the corresponding L3-level uniform grid SSS field.
And step 206, SSS field precision evaluation.
After an L3-level uniform grid SSS field is determined according to the sea surface salinity gridding inversion method provided by the invention, the difference value of the L3-level uniform grid SSS and the Argo uniform grid SSS is compared with the difference value of the SMAP official L3-level uniform grid SSS and the Argo uniform grid SSS, and the difference value of the former is obviously smaller than that of the latter.
In addition, the difference between the L3-level uniform grid SSS determined by the sea surface salinity gridding inversion method provided by the present invention and the uniform grid SSS in the related art may also be determined in a mathematical calculation manner, for example, the SSS value of each sampling point in the determined uniform grid SSS field and the observation data of the Argo uniform grid SSS are determined, and the corresponding root mean square error is determined by using the following formula:
in the formula (I), the compound is shown in the specification,
for evaluating accuracy
The total number of points, i, ranges from 1 to n.
And with
The salinity data to be evaluated are in one-to-one correspondence,
and (4) obtaining observation data of sea salinity SSS of the Argo uniform grid.
The formula (7) is used for calculating to obtain the sea surface salinity gridding inversion method provided by the invention, the RMSE of the generated L3-grade uniform grid SSS is 0.16psu, and the RMSE of the SMAP official L3-grade uniform grid SSS is 0.29psu, which shows that the sea surface salinity gridding inversion method provided by the invention greatly improves the accuracy of the L3-grade uniform grid SSS.
Fig. 3 is a schematic structural diagram of an apparatus for sea surface salinity gridding inversion provided by the present invention, as shown in fig. 3, the apparatus comprises:
an obtaining module 301, configured to obtain, based on a preset quality flag array, sampling data of all SSS sampling points to form a first data set;
A gridding module 302, configured to determine, in each to-be-determined grid of the gridded SSS field, a scatter set in the first data set that meets a preset longitude and latitude threshold;
a double-weight module 303, configured to determine, based on the constructed double-weight model, a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set with a minimum cost function as a target;
an inversion module 304, configured to determine an inversion result of the SSS corresponding to the to-be-determined grid based on the observed salinity value of each scatter, the distance weight parameter corresponding to each scatter, and the quality weight parameter of each scatter.
Optionally, the obtaining module 301, in the process of obtaining the sampling data of all SSS sampling points to form the first data set based on the preset quality flag array, is further configured to:
acquiring a second data set in SMAP gridded secondary SSS data;
sampling the second data set through a preset sampling window according to a preset quality marker array to obtain a third data set formed by the first quality marker array of the corresponding SSS sampling point; the quality mark array comprises 16 indexes, wherein one or more specified indexes are used for indicating whether serious quality problems exist in the sampling data of the SSS sampling points;
Deleting all SSS sampling points meeting a first screening rule in the third data set to obtain a first data set;
the first screening rule is that if any specified element in the first quality tag array of the SSS sampling point indicates that the sampling data of the SSS sampling point has a serious quality problem.
Optionally, the gridding module 302, in the process of determining, in each grid to be determined of the gridded SSS field, a scatter set in the first data set that meets a preset longitude and latitude threshold, is further configured to:
and in each grid to be determined of the gridded SSS field, determining all SSS sampling points which meet a preset longitude and latitude threshold value with the geodetic coordinates of the central point of the grid to be determined, and taking the SSS sampling points as a scatter set corresponding to the grid to be determined.
Optionally, the double-weight module 303 is further configured to:
determining a distance weight parameter of each scatter point based on the Euclidean distance between each scatter point in the scatter point set and the central point of the grid to be determined and the distance weight coefficient of each scatter point;
determining the data quality corresponding to each scatter point based on the mass mark array corresponding to each scatter point in the scatter point set and the weight coefficient corresponding to each mass mark;
Determining a mass weight parameter of each scatter point based on the data mass corresponding to the scatter point and the corresponding mass weight coefficient;
determining an SSS estimation value of the grid to be determined based on a total weight parameter of each scattered point and an observed salinity value of each scattered point;
wherein the total weight parameter of each of the scatter points is determined based on the distance weight parameter and the quality weight parameter of each of the scatter points.
Optionally, the double-weight module 303, in determining the SSS estimate for the grid to be determined based on the total weight parameter of each of the scattered points and the observed salinity value of each of the scattered points, is further configured to:
in the scattered point set, based on the observed salinity value of each scattered point, determining an SSS estimation value of the grid to be determined by adopting a weighted average algorithm;
and the weight in the weighted average algorithm is a total weight parameter of each scattered point.
Optionally, the cost function is a square of a difference between the SSS estimation value of the grid to be determined and the SSS real value corresponding to the grid to be determined.
Optionally, the double-weight module 303 determines, based on the constructed double-weight model, a distance weight parameter and a quality weight parameter corresponding to each scatter in the scatter set with a minimum cost function as a target, and is further configured to:
Iteratively determining a cost function corresponding to the grid to be determined based on a Levenberg-Marquardt algorithm;
and when the iteration condition meets that the difference between the values of the cost function of the two times before and after the iteration condition is smaller than a first threshold, or the iteration times are greater than or equal to a second threshold, or the descending gradient is smaller than any one of a third threshold, determining a distance weight parameter and a quality weight parameter of each scattered point in the scattered point set corresponding to the grid to be determined.
Specifically, the apparatus for sea surface salinity gridding inversion provided by the present invention can implement all the method steps implemented by the method embodiment, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiment in this embodiment are omitted here.
FIG. 4 is a schematic structural diagram of an electronic device provided in the present invention; as shown in fig. 4, the electronic device includes a memory 420, a transceiver 410, and a processor 400; wherein the processor 400 and the memory 420 may also be physically separated.
A memory 420 for storing a computer program; a transceiver 410 for transceiving data under the control of the processor 400.
In particular, the transceiver 410 is used to receive and transmit data under the control of the processor 400.
Where, in fig. 4, the bus architecture may include any number of interconnected buses and bridges, in particular one or more processors, represented by processor 400, and various circuits of memory, represented by memory 420, linked together. The bus architecture may also link various other circuits such as peripherals, voltage regulators, power management circuits, etc., which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 410 may be a number of elements including a transmitter and receiver that provide a means for communicating with various other apparatus over a transmission medium including wireless channels, wired channels, fiber optic cables, and the like.
The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 400 in performing operations.
The processor 400 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and may also have a multi-core architecture.
The processor 400, by calling the computer program stored in the memory 420, is adapted to execute any of the methods provided by the present invention according to the obtained executable instructions, such as:
acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality mark array;
in each grid to be determined of the gridded SSS field, determining a scatter set which meets a preset longitude and latitude threshold value in the first data set;
based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scatter point in the scatter point set by taking the minimum cost function as a target;
and determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point.
It should be noted that, the electronic device provided in the present invention can implement all the method steps implemented by the method embodiments and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as the method embodiments in this embodiment are omitted here.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, which when executed by a computer, the computer is capable of executing the method of sea surface salinity gridding inversion provided by the above embodiments.
In another aspect, the present invention further provides a processor-readable storage medium storing a computer program, which is configured to cause the processor to execute the method for sea surface salinity gridding inversion provided in the above embodiments.
The processor-readable storage medium may be any available media or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for sea surface salinity gridding inversion is characterized by comprising the following steps:
acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality marker array;
in each grid to be determined of the gridded SSS field, determining a scatter set meeting a preset longitude and latitude threshold in the first data set;
based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scattered point in the scattered point set by taking the minimum cost function as a target;
and determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point.
2. The method for sea surface salinity gridding inversion according to claim 1, wherein the obtaining of the sample data of all SSS sample points based on a preset array of mass markers constitutes a first data set comprising:
acquiring a second data set in SMAP gridded secondary SSS data;
sampling the second data set through a preset sampling window according to a preset quality marker array to obtain a third data set formed by the first quality marker array of the corresponding SSS sampling point; the quality mark array comprises 16 indexes, wherein one or more specified indexes are used for indicating whether the sampling data of the SSS sampling points have serious quality problems;
Deleting all the SSS sampling points meeting a first screening rule in the third data set to obtain a first data set;
the first screening rule is that if any specified element in the first quality flag array of the SSS sampling point indicates that the sampling data of the SSS sampling point has a serious quality problem.
3. The method of sea surface salinity gridding inversion according to claim 1, wherein the determining, in each grid to be determined of the gridded SSS field, a set of scatters in the first data set that meet a preset latitude and longitude threshold comprises:
and in each grid to be determined of the gridded SSS field, determining all SSS sampling points which meet a preset longitude and latitude threshold value with the geodetic coordinates of the central point of the grid to be determined as a scatter set corresponding to the grid to be determined.
4. The method for sea surface salinity gridding inversion according to claim 1, wherein before determining the distance weight parameter and the quality weight parameter corresponding to each scatter point in the scatter set with the objective of minimizing the cost function based on the constructed double-weight model, the method comprises:
determining a distance weight parameter of each scatter point based on the Euclidean distance between each scatter point in the scatter point set and the central point of the grid to be determined and the distance weight coefficient of each scatter point;
Determining the data quality corresponding to each scatter point based on the mass mark array corresponding to each scatter point in the scatter point set and the weight coefficient corresponding to each mass mark;
determining a mass weight parameter of each scatter point based on the data mass corresponding to the scatter point and the corresponding mass weight coefficient;
determining an SSS estimation value of the grid to be determined based on a total weight parameter of each scatter point and an observed salinity value of each scatter point;
wherein the total weight parameter of each of the scatter points is determined based on the distance weight parameter and the quality weight parameter of each of the scatter points.
5. The method of sea surface salinity gridding inversion according to claim 4, wherein the determining the SSS estimate for the grid to be determined based on the total weight parameter for each of the scatter points and the observed salinity value for each of the scatter points comprises:
in the scattered point set, determining an SSS estimation value of the grid to be determined by adopting a weighted average algorithm based on the observed salinity value of each scattered point;
and the weight in the weighted average algorithm is a total weight parameter of each scattered point.
6. The method for sea surface salinity gridding inversion according to claim 1, wherein the cost function is the square of the difference between the SSS estimated value of the grid to be determined and the SSS real value corresponding to the grid to be determined.
7. The method for sea surface salinity gridding inversion according to claim 1, wherein the determining a distance weight parameter and a quality weight parameter corresponding to each scatter point in the scatter set with a minimum cost function as a target based on the constructed double-weight model comprises:
iteratively determining a cost function corresponding to the grid to be determined based on a Levenberg-Marquardt algorithm;
and when the iteration condition meets that the difference between the values of the cost function of the two times before and after the iteration condition is smaller than a first threshold, or the iteration times are greater than or equal to a second threshold, or the descending gradient is smaller than any one of a third threshold, determining a distance weight parameter and a quality weight parameter of each scattered point in the scattered point set corresponding to the grid to be determined.
8. An apparatus for sea surface salinity gridding inversion, the apparatus comprising:
the acquisition module is used for acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality mark array;
the gridding module is used for determining a scatter set meeting a preset longitude and latitude threshold in each grid to be determined of the gridded SSS field in the first data set;
the double-weight module is used for determining a distance weight parameter and a quality weight parameter corresponding to each scattered point in the scattered point set by taking the minimum cost function as a target based on the constructed double-weight model;
And the inversion module is used for determining an inversion result of the SSS corresponding to the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point.
9. An electronic device comprising a memory, a transceiver, a processor;
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
acquiring sampling data of all SSS sampling points to form a first data set based on a preset quality mark array;
in each grid to be determined of the meshed SSS field, determining a scatter set meeting a preset longitude and latitude threshold in the first data set;
based on the constructed double-weight model, determining a distance weight parameter and a quality weight parameter corresponding to each scatter point in the scatter point set by taking the minimum cost function as a target;
and determining an inversion result of the SSS value of the grid to be determined based on the observed salinity value of each scatter point, the distance weight parameter corresponding to each scatter point and the quality weight parameter of each scatter point.
10. A computer readable storage medium having stored thereon a computer program for causing a computer to perform the method of sea surface salinity gridded inversion of any one of claims 1 to 7.
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